From 2283df7b5d5789c06c304a9fc763f159e48a8aae Mon Sep 17 00:00:00 2001 From: Victor Zhu Date: Mon, 24 Jun 2024 15:22:44 -0700 Subject: [PATCH 01/16] Update SMP v2 notebooks to use latest PyTorch 2.3.1, TSM 2.4.0 release (#4678) * Update SMP v2 notebooks to use latest PT2.3.1-TSM2.4.0 release. * Update SMP v2 shared_scripts * Update minimum sagemaker pysdk version to 2.224 --- .../smp-finetuning-gpt-neox-fsdp-tp.ipynb | 6 +-- .../gpt-neox/smp-train-gpt-neox-fsdp-tp.ipynb | 10 ++--- .../smp-finetuning-llama-fsdp-tp.ipynb | 6 +-- .../smp-train-llama-fsdp-tp-fp8.ipynb | 10 ++--- .../mixtral/smp-train-mixtral-fsdp-ep.ipynb | 6 +-- .../shared-scripts/requirements.txt | 6 +-- .../shared-scripts/train_lib.py | 2 +- .../shared-scripts/train_utils.py | 38 +++++++++++++++---- 8 files changed, 54 insertions(+), 30 deletions(-) diff --git a/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-finetuning-gpt-neox-fsdp-tp.ipynb b/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-finetuning-gpt-neox-fsdp-tp.ipynb index 0ebc523568..50fb20cf6f 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-finetuning-gpt-neox-fsdp-tp.ipynb +++ b/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-finetuning-gpt-neox-fsdp-tp.ipynb @@ -80,7 +80,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade \"sagemaker>=2.212\"\n", + "%pip install --upgrade \"sagemaker>=2.224\"\n", "%pip install sagemaker-experiments" ] }, @@ -882,8 +882,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", diff --git a/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-train-gpt-neox-fsdp-tp.ipynb b/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-train-gpt-neox-fsdp-tp.ipynb index 28638611cd..b8598276c5 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-train-gpt-neox-fsdp-tp.ipynb +++ b/training/distributed_training/pytorch/model_parallel_v2/gpt-neox/smp-train-gpt-neox-fsdp-tp.ipynb @@ -74,7 +74,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade \"sagemaker>=2.212\"\n", + "%pip install --upgrade \"sagemaker>=2.224\"\n", "%pip install sagemaker-experiments" ] }, @@ -873,8 +873,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", @@ -955,8 +955,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", diff --git a/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-finetuning-llama-fsdp-tp.ipynb b/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-finetuning-llama-fsdp-tp.ipynb index 46c5edbc42..c7c1b8bae1 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-finetuning-llama-fsdp-tp.ipynb +++ b/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-finetuning-llama-fsdp-tp.ipynb @@ -80,7 +80,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade \"sagemaker>=2.212\"\n", + "%pip install --upgrade \"sagemaker>=2.224\"\n", "%pip install sagemaker-experiments" ] }, @@ -867,8 +867,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", diff --git a/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-train-llama-fsdp-tp-fp8.ipynb b/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-train-llama-fsdp-tp-fp8.ipynb index 0a4c705b11..21d5c26c0d 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-train-llama-fsdp-tp-fp8.ipynb +++ b/training/distributed_training/pytorch/model_parallel_v2/llama_v2/smp-train-llama-fsdp-tp-fp8.ipynb @@ -74,7 +74,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade \"sagemaker>=2.212\"\n", + "%pip install --upgrade \"sagemaker>=2.224\"\n", "%pip install sagemaker-experiments" ] }, @@ -831,8 +831,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", @@ -913,8 +913,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", diff --git a/training/distributed_training/pytorch/model_parallel_v2/mixtral/smp-train-mixtral-fsdp-ep.ipynb b/training/distributed_training/pytorch/model_parallel_v2/mixtral/smp-train-mixtral-fsdp-ep.ipynb index c58b76c310..d9db6d36ff 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/mixtral/smp-train-mixtral-fsdp-ep.ipynb +++ b/training/distributed_training/pytorch/model_parallel_v2/mixtral/smp-train-mixtral-fsdp-ep.ipynb @@ -74,7 +74,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade \"sagemaker>=2.215\"\n", + "%pip install --upgrade \"sagemaker>=2.224\"\n", "%pip install sagemaker-experiments" ] }, @@ -916,8 +916,8 @@ " }\n", " },\n", " },\n", - " py_version=\"py310\",\n", - " framework_version=\"2.2.0\",\n", + " py_version=\"py311\",\n", + " framework_version=\"2.3.1\",\n", " # image_uri=$IMAGE, # Either provide `framework_version` or `image_uri`\n", " output_path=s3_output_bucket,\n", " max_run=86400,\n", diff --git a/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/requirements.txt b/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/requirements.txt index 8dd5fd9937..ed71162ed8 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/requirements.txt +++ b/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/requirements.txt @@ -1,9 +1,9 @@ accelerate>=0.12.0 -datasets>=2.16.1 +datasets>=2.19.1 einops evaluate expecttest -flash-attn>=2.3.6 +flash-attn>=2.3.6,<2.4 h5py humanize hypothesis @@ -14,4 +14,4 @@ protobuf scikit-learn sentencepiece!=0.1.92 tensorboard -transformers>=4.37.1 +transformers>=4.40.1 diff --git a/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_lib.py b/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_lib.py index b391dee3c2..188f199c1f 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_lib.py +++ b/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_lib.py @@ -397,7 +397,7 @@ def main(args): len(args.num_kept_checkpoints), ) if len(set(ckpt_lens)) != 1: - raise ValueError(f"Len mismtach for checkpoint dir, freq vs num to keep: {ckpt_lens}.") + raise ValueError(f"Len mismatch for checkpoint dir, freq vs num to keep: {ckpt_lens}.") if args.distributed_backend == "smddp": import smdistributed.dataparallel.torch.torch_smddp # pylint: disable=unused-import diff --git a/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_utils.py b/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_utils.py index 99c0264120..e5b73049c1 100644 --- a/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_utils.py +++ b/training/distributed_training/pytorch/model_parallel_v2/shared-scripts/train_utils.py @@ -34,11 +34,22 @@ def compute_num_params(model): def compute_tflops(args, global_batch_size, step_time, world_size): - # Based on + # Based on # https://github.com/NVIDIA/Megatron-LM/blob/ba773259dbe5735fbd91ca41e7f4ded60b335c52/megatron/training/training.py#L65 - num_experts_routed_to = 1 if args.moe > 1 else args.num_experts_per_tok - if args.num_key_value_heads is None: + # Attention projection size. + kv_channels = args.hidden_width // args.num_heads + query_projection_size = kv_channels * args.num_heads + query_projection_to_hidden_size_ratio = query_projection_size / args.hidden_width + + # Group Query Attention. + if not args.num_key_value_heads: args.num_key_value_heads = args.num_heads + + # MoE. + num_experts_routed_to = 1 if args.moe == 0 else args.num_experts_per_tok + gated_linear_multiplier = 3/2 if args.moe > 0 else 1 + + # Compute the number of floating point operations num_flops = ( 12 * global_batch_size @@ -47,13 +58,26 @@ def compute_tflops(args, global_batch_size, step_time, world_size): * args.hidden_width * args.hidden_width * ( - 1 - + ((args.intermediate_size / args.hidden_width) * num_experts_routed_to) - + (args.num_key_value_heads / args.num_heads) - + (args.max_context_width / args.hidden_width) + # Attention. + ( + ( + 1 + + (args.num_key_value_heads / args.num_heads) + + (args.max_context_width / args.hidden_width) + ) * query_projection_to_hidden_size_ratio + ) + # MLP. + + ( + (args.intermediate_size / args.hidden_width) + * num_experts_routed_to + * gated_linear_multiplier + ) + # Logit. + (args.vocab_size / (2 * args.num_layers * args.hidden_width)) ) ) + + # Convert to TFLOPs per GPU tflops_per_gpu = num_flops / ( step_time * 10**12 * world_size) return tflops_per_gpu From b997b4da198b4a837379b6bdad90324cd45e1b26 Mon Sep 17 00:00:00 2001 From: parsash2 <60193914+parsash2@users.noreply.github.com> Date: Thu, 27 Jun 2024 13:54:23 -0400 Subject: [PATCH 02/16] Parsash2 patch 1 (#4690) * tutorials-after-initial-feedback Added descriptive text to make the notebooks stand on their own. * move athena notebook into dedicated folder * renamed athena end2end notebooks * moved pyspark notebook into dedicated directory * minor change: consistent directory naming convention * Added overview, headers, and explantory text Tested the notebook end to end. Added more context for processing jobs and cleaning up. The output is visible in the cells. * Added overview, headers, explanatory text Also added troubleshooting note from further testing. * fix directory locations for new notebooks * clear notebook outputs * added integration for ci test results * updated formatting with black-nb * update athena notebook: fix parse predictions * fixed ci integration for pyspark-etl-training notebook --------- Co-authored-by: Janosch Woschitz --- .../athena_ml_workflow_end_to_end.ipynb | 1456 +++++++++++++++++ .../processing_data_split.py | 32 + .../pyspark-etl-training.ipynb | 734 +++++++++ 3 files changed, 2222 insertions(+) create mode 100644 use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb create mode 100644 use-cases/athena_ml_workflow_end_to_end/processing_data_split.py create mode 100644 use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb new file mode 100644 index 0000000000..e723f14c7e --- /dev/null +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -0,0 +1,1456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9fbac6ee", + "metadata": {}, + "source": [ + "# Create an end to end machine learning workflow using Amazon Athena\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \\n\",\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ece13bd7-19b2-47b3-976d-cf636fa68003", + "metadata": {}, + "source": [ + "Importing and transforming data can be one of the most challenging tasks in a machine learning workflow. We provide you with a Jupyter notebook that demonstrates a cost-effective strategy for an extract, transform, and load (ETL) workflow. Using Amazon Simple Storage Service (Amazon S3) and Amazon Athena, you learn how to query and transform data from a Jupyter notebook. Amazon S3 is an object storage service that allows you to store data and machine learning artifacts. Amazon Athena enables you to interactively query the data stored in those buckets, saving each query as a CSV file in an Amazon S3 location.\n", + "\n", + "The tutorial imports 16 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From these 16 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", + "\n", + "Amazon Athena stores the query results as a CSV file in the specified location. We provide the output to a SageMaker Processing Job to split the data into training, validation, and test sets. While data can be split using queries, a processing job ensures that the data is in a format that's parseable by the XGBoost algorithm.\n", + "\n", + "__Prerequisites:__\n", + "\n", + "The notebook must be run in the us-east-1 AWS Region. You also need your own Amazon S3 bucket and a database within Amazon Athena. You won't be able to access the data used in the tutorial otherwise.\n", + "\n", + "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database).\n", + "\n", + "Amazon Athena uses the AWS Glue Data Catalog to read the data from Amazon S3 into a database. You must have permissions to use Glue. To clean up, you also need permissions to delete the bucket you've created. For a quick guide to providing permissions, see [Setting up\n", + "](http://parsash-clouddesk-2024.aka.corp.amazon.com/sagemaker-dg/src/AWSIronmanApiDoc/build/server-root/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." + ] + }, + { + "cell_type": "markdown", + "id": "0b11693f-7c35-41cf-8e4b-4f86eea8f3b0", + "metadata": {}, + "source": [ + "## Solution overview\n", + "\n", + "To create the end to end workflow, we do the following:\n", + "\n", + "1. Create an Amazon Athena client within the us-east-1 AWS Region.\n", + "2. Define the run_athena_query function that runs queries and prints out the status in the following cell.\n", + "3. Create the `ride_fare` table within your database using all ride fare tables for the year 2019.\n", + "4. Create the `ride_info` table using ride info table for the year 2019.\n", + "5. Create the `ride_info_deduped` and `ride_fare_deduped` tables that have all duplicate values removed from the original tables.\n", + "6. Run test queries to get the first ten rows of each table to see whether they have data.\n", + "7. Define the `get_query_results` function that takes the query ID and returns comma separated values that can be stored as a dataframe.\n", + "8. View the results of the test queries within pandas dataframes.\n", + "9. Join the `ride_info_deduped` and `ride_fare_deduped` tables into the `combined_ride_data_deduped` table.\n", + "10. Select all values in the combined table.\n", + "11. Define the `get_csv_file_location` function to get the Amazon S3 location of the query results.\n", + "12. Download the CSV file to our environment.\n", + "13. Perform Exploratory Data Analysis (EDA) on the data.\n", + "14. Use the results of the EDA to select the relevant features in query.\n", + "15. Use the `get_csv_file_location` function to get the location of those query results.\n", + "16. Split the data into training, validation, and test sets using a processing job.\n", + "17. Download the test dataset.\n", + "18. Take a 20 row sample from the test dataset.\n", + "20. Create a dataframe with 20 rows of actual and predicted values.\n", + "21. Calculate the RMSE of the data.\n", + "22. Clean up the resources created within the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "54d7468c-c77b-4273-b02d-9e9c4e884d46", + "metadata": {}, + "source": [ + "### Define the run_athena_query function\n", + "\n", + "In the following cell, we define the `run_athena_query` function. It runs an Athena query and waits for its completion.\n", + "\n", + "It takes the following arguments:\n", + "\n", + "- query_string (str): The SQL query to be executed.\n", + "- database_name (str): The name of the Athena database.\n", + "- output_location (str): The S3 location where the query results are stored.\n", + "\n", + "\n", + "It returns the query execution ID string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ab1ff0e-fcde-4976-a1cd-51e75c18deb2", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required libraries\n", + "import time\n", + "import boto3\n", + "\n", + "\n", + "def run_athena_query(query_string, database_name, output_location):\n", + " # Create an Athena client\n", + " athena_client = boto3.client(\"athena\", region_name=\"us-east-1\")\n", + "\n", + " # Start the query execution\n", + " response = athena_client.start_query_execution(\n", + " QueryString=query_string,\n", + " QueryExecutionContext={\"Database\": database_name},\n", + " ResultConfiguration={\"OutputLocation\": output_location},\n", + " )\n", + "\n", + " query_execution_id = response[\"QueryExecutionId\"]\n", + " print(f\"Query execution ID: {query_execution_id}\")\n", + "\n", + " while True:\n", + " # Check the query execution status\n", + " query_status = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " state = query_status[\"QueryExecution\"][\"Status\"][\"State\"]\n", + "\n", + " if state == \"SUCCEEDED\":\n", + " print(\"Query executed successfully.\")\n", + " break\n", + " elif state == \"FAILED\":\n", + " print(\n", + " f\"Query failed with error: {query_status['QueryExecution']['Status']['StateChangeReason']}\"\n", + " )\n", + " break\n", + " else:\n", + " print(f\"Query is currently in {state} state. Waiting for completion...\")\n", + " time.sleep(5) # Wait for 5 seconds before checking again\n", + "\n", + " return query_execution_id" + ] + }, + { + "cell_type": "markdown", + "id": "8df0da48-89b3-45c2-a479-af422a51b962", + "metadata": {}, + "source": [ + "### Create the ride_fare table\n", + "\n", + "We've provided you with the query. You most provide the name of the database you created within Amazon Athena and the Amazon S3 output location. If you're not sure about how to specify the output location, provide the name of the S3 bucket. After running the query, you should get a message that says \"Query executed successfully.\" and a 36 character string in single quotes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64131b68-de28-4060-bb75-8148902846f7", + "metadata": {}, + "outputs": [], + "source": [ + "# SQL query to create the 'ride_fare' table\n", + "create_ride_fare_table = \"\"\"\n", + "CREATE EXTERNAL TABLE `ride_fare` (\n", + " `ride_id` bigint, \n", + " `payment_type` smallint, \n", + " `fare_amount` float, \n", + " `extra` float, \n", + " `mta_tax` float, \n", + " `tip_amount` float, \n", + " `tolls_amount` float, \n", + " `total_amount` float\n", + ")\n", + "ROW FORMAT DELIMITED \n", + " FIELDS TERMINATED BY ',' \n", + " LINES TERMINATED BY '\\n' \n", + "STORED AS INPUTFORMAT \n", + " 'org.apache.hadoop.mapred.TextInputFormat' \n", + "OUTPUTFORMAT \n", + " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", + "LOCATION\n", + " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-fare/year=2019'\n", + "TBLPROPERTIES (\n", + " 'skip.header.line.count'='1', \n", + " 'transient_lastDdlTime'='1716908234'\n", + ");\n", + "\"\"\"\n", + "\n", + "# Athena database name\n", + "database = \"example-database-name\"\n", + "\n", + "# S3 location for query results\n", + "s3_output_location = \"s3://example-s3-bucket/example-s3-prefix\"\n", + "\n", + "# Execute the query to create the 'ride_fare' table\n", + "run_athena_query(create_ride_fare_table, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "ebe5920a-4c36-48c0-9cb4-e418c738aa59", + "metadata": {}, + "source": [ + "### Create the ride fare table with the duplicates removed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d249cc5-2d53-4274-8f5e-6ab09ccd3ea6", + "metadata": {}, + "outputs": [], + "source": [ + "# SQL query to create a new table with duplicates removed\n", + "remove_duplicates_from_ride_fare = \"\"\"\n", + "CREATE TABLE ride_fare_deduped\n", + "AS\n", + "SELECT DISTINCT *\n", + "FROM ride_fare\n", + "\"\"\"\n", + "\n", + "# Run the preceding query\n", + "run_athena_query(remove_duplicates_from_ride_fare, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "2ac7fc34-37cb-4c46-993b-38f18576361c", + "metadata": {}, + "source": [ + "### Create the ride_info table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f9a68b9-bd11-49e9-ad72-b44b43d32e47", + "metadata": {}, + "outputs": [], + "source": [ + "# SQL query to create the ride_info table\n", + "create_ride_info_table_query = \"\"\"\n", + "CREATE EXTERNAL TABLE `ride_info` (\n", + " `ride_id` bigint, \n", + " `vendor_id` smallint, \n", + " `passenger_count` smallint, \n", + " `pickup_at` string, \n", + " `dropoff_at` string, \n", + " `trip_distance` float, \n", + " `rate_code_id` int, \n", + " `store_and_fwd_flag` string\n", + ")\n", + "ROW FORMAT DELIMITED \n", + " FIELDS TERMINATED BY ',' \n", + " LINES TERMINATED BY '\\n' \n", + "STORED AS INPUTFORMAT \n", + " 'org.apache.hadoop.mapred.TextInputFormat' \n", + "OUTPUTFORMAT \n", + " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", + "LOCATION\n", + " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-info/year=2019'\n", + "TBLPROPERTIES (\n", + " 'skip.header.line.count'='1', \n", + " 'transient_lastDdlTime'='1716907328'\n", + ");\n", + "\"\"\"\n", + "\n", + "# Run the query to create the ride_info table\n", + "run_athena_query(create_ride_info_table_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "4c17ea01-2c1e-4c10-a539-0d00e6e4bb1d", + "metadata": {}, + "source": [ + "### Create the ride info table with the duplicates removed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d883c-f189-43c0-9fbd-1a45093984e9", + "metadata": {}, + "outputs": [], + "source": [ + "# SQL query to create table with duplicates removed\n", + "remove_duplicates_from_ride_info = \"\"\"\n", + "CREATE TABLE ride_info_deduped\n", + "AS\n", + "SELECT DISTINCT *\n", + "FROM ride_info\n", + "\"\"\"\n", + "\n", + "# Run the query to create the table with the duplicates removed\n", + "run_athena_query(remove_duplicates_from_ride_info, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "a19f8e17-42c5-4412-96a8-b7bc1a74c73c", + "metadata": {}, + "source": [ + "### Run a test query on ride_info_deduped" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6db6bb67-44a9-4ff4-b662-ad969a84d3d8", + "metadata": {}, + "outputs": [], + "source": [ + "test_ride_info_query = \"\"\"\n", + "SELECT * FROM ride_info_deduped limit 10\n", + "\"\"\"\n", + "\n", + "run_athena_query(test_ride_info_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "b969d31f-e14a-473b-aefa-a1a19bc312f7", + "metadata": {}, + "source": [ + "### Run a test query on ride_fare_deduped" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92d8be21-3f20-453d-8b84-516571d9854d", + "metadata": {}, + "outputs": [], + "source": [ + "test_ride_fare_query = \"\"\"\n", + "SELECT * FROM ride_fare_deduped limit 10\n", + "\"\"\"\n", + "\n", + "run_athena_query(test_ride_fare_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "c86acade-c4b9-4918-860e-11ee5e386a44", + "metadata": {}, + "source": [ + "### Define the `get_query_results` function\n", + "\n", + "In the following cell, we define the `get_query_results` function to get the query results in CSV format. The function gets the 36 character query execution ID string. The end of the output of the preceding cell is an example of a query execution ID string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50e87ba6-42e9-4d99-862e-7eae16ad810e", + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "\n", + "\n", + "def get_query_results(query_execution_id):\n", + " athena_client = boto3.client(\"athena\", region_name=\"us-east-1\")\n", + " s3 = boto3.client(\"s3\")\n", + "\n", + " # Get the query execution details\n", + " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " s3_location = query_execution[\"QueryExecution\"][\"ResultConfiguration\"][\"OutputLocation\"]\n", + "\n", + " # Extract bucket and key from S3 output location\n", + " bucket_name, key = s3_location.split(\"/\", 2)[2].split(\"/\", 1)\n", + "\n", + " # Get the CSV file location\n", + " obj = s3.get_object(Bucket=bucket_name, Key=key)\n", + " csv_data = obj[\"Body\"].read().decode(\"utf-8\")\n", + " csv_buffer = io.StringIO(csv_data)\n", + "\n", + " return csv_buffer" + ] + }, + { + "cell_type": "markdown", + "id": "d3d2ed4f-d7e6-49dc-9ea1-0dc66f252c76", + "metadata": {}, + "source": [ + "### Read `ride_info_deduped` test query into a dataframe\n", + "\n", + "Specify the query execution ID string in the `get_query_results` function. The output is the head of the dataframe. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b04abae5-936b-4d96-98e8-d2e2b6a17b9c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Provide the query execution id of the test_ride_info query to get the query results\n", + "ride_info_sample = get_query_results(\"test_ride_info_query_execution_id\")\n", + "\n", + "df_ride_info_sample = pd.read_csv(ride_info_sample)\n", + "\n", + "df_ride_info_sample.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6d10ebe2-8c17-4f2b-97fe-a5f339cd89d7", + "metadata": {}, + "source": [ + "### Read `ride_fare_deduped` test query into a dataframe\n", + "\n", + "Specify the query execution ID string in the `get_query_results` function. The output is the head of the resulting dataframe. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be89957f-31b1-4710-bfc2-178d6db18592", + "metadata": {}, + "outputs": [], + "source": [ + "# Provide the query execution id of the test_ride_fare query to get the query results\n", + "\n", + "ride_fare_sample = get_query_results(\"test_ride_fare_query_execution_id\")\n", + "\n", + "df_ride_fare_sample = pd.read_csv(ride_fare_sample)\n", + "\n", + "df_ride_fare_sample.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3867e94a-7c89-48ed-86aa-92b09d47740d", + "metadata": {}, + "source": [ + "### Join the deduplicated tables together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8a76635-3c09-4cbc-b1b4-9318dc611250", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# SQL query to join the tables into a single table containing all the data.\n", + "create_ride_joined_deduped = \"\"\"\n", + "CREATE TABLE combined_ride_data_deduped AS\n", + "SELECT \n", + " rfs.ride_id, \n", + " rfs.payment_type, \n", + " rfs.fare_amount, \n", + " rfs.extra, \n", + " rfs.mta_tax, \n", + " rfs.tip_amount, \n", + " rfs.tolls_amount, \n", + " rfs.total_amount,\n", + " ris.vendor_id, \n", + " ris.passenger_count, \n", + " ris.pickup_at, \n", + " ris.dropoff_at, \n", + " ris.trip_distance, \n", + " ris.rate_code_id, \n", + " ris.store_and_fwd_flag\n", + "FROM \n", + " ride_fare_deduped rfs\n", + "JOIN \n", + " ride_info_deduped ris\n", + "ON \n", + " rfs.ride_id = ris.ride_id;\n", + ";\n", + "\"\"\"\n", + "\n", + "# Run the query to create the ride_data_deduped table\n", + "run_athena_query(create_ride_joined_deduped, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "b2f9f6ca-f668-42ab-ac4a-371a82e1786d", + "metadata": {}, + "source": [ + "### Select all values from the deduplicated table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0791e57-4351-4f27-a8f9-ad741441d214", + "metadata": {}, + "outputs": [], + "source": [ + "# SQL query to select all values from the table and create the dataset that we're using for our analysis\n", + "ride_combined_full_table_query = \"\"\"\n", + "SELECT * FROM combined_ride_data_deduped\n", + "\"\"\"\n", + "\n", + "# Run the query to select all values from the combined_ride_data_deduped table\n", + "run_athena_query(ride_combined_full_table_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "4492eaa8-b0cc-4a4d-9810-e9f1a39f21c7", + "metadata": {}, + "source": [ + "### Define get_csv_file_location function and get Amazon S3 location of query results\n", + "\n", + "Specify the query ID from the preceding cell in the function call. The output is the Amazon S3 URI of the dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97373c52-882b-4e44-8d75-a80d8d8c58df", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to get the Amazon S3 URI location of Amazon Athena select statements\n", + "def get_csv_file_location(query_execution_id):\n", + " athena_client = boto3.client(\"athena\", region_name=\"us-east-1\")\n", + " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " s3_location = query_execution[\"QueryExecution\"][\"ResultConfiguration\"][\"OutputLocation\"]\n", + "\n", + " return s3_location\n", + "\n", + "\n", + "# Provide the 36 character string at the end of the output of the preceding cell as the query.\n", + "get_csv_file_location(\"ride_combined_full_table_query_execution_id\")" + ] + }, + { + "cell_type": "markdown", + "id": "c7bf4f25-dc86-4f1f-95de-967c20c5a7af", + "metadata": {}, + "source": [ + "### Download the dataset and rename it\n", + "\n", + "Replace the example S3 path in the following cell with the output of the preceding cell. The second command renames the CSV file it downloads to `nyc-taxi-whole-dataset.csv`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "954022d5-bdf9-4dbd-be2e-66d0009ce522", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the S3 URI location returned from the preceding cell to download the dataset and rename it.\n", + "!aws s3 cp s3://example-s3-bucket/ride_combined_full_table_query_execution_id.csv .\n", + "!mv ride_combined_full_table_query_execution_id.csv nyc-taxi-whole-dataset.csv" + ] + }, + { + "cell_type": "markdown", + "id": "4d34ca22-8417-46f5-982f-dd22816f1d93", + "metadata": {}, + "source": [ + "### Get a 20,000 row sample and some information about it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79d2f2a5-5111-4fb8-90f3-67474f1072c1", + "metadata": {}, + "outputs": [], + "source": [ + "sample_nyc_taxi_combined = pd.read_csv(\"nyc-taxi-whole-dataset.csv\", nrows=20000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9dececa-272d-458c-9f64-baa13eca0832", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Dataset shape: \", sample_nyc_taxi_combined.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c117a0f-429e-4913-aded-c839675f9e17", + "metadata": {}, + "outputs": [], + "source": [ + "df = sample_nyc_taxi_combined\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3c56da9-0a1c-4c58-93e3-77260dfff40b", + "metadata": {}, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc25bcd9-a4b1-4491-867f-7534336d1ecd", + "metadata": {}, + "outputs": [], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18bd92b1-962a-40f2-b15f-7351d869f390", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"vendor_id\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4c4997f-85d8-4f57-a60c-51e3568cfe2e", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"passenger_count\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "ae527104-9312-498c-b0ee-d1e2303bf500", + "metadata": {}, + "source": [ + "### View the distribution of fare amount values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "641c278d-8fed-42b8-98d1-becba90d6259", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot to find the distribution of ride fare values\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(df[\"fare_amount\"], edgecolor=\"black\", bins=30, range=(0, 100))\n", + "plt.xlabel(\"Fare Amount\")\n", + "plt.ylabel(\"Count\")\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "id": "65d141c4-95ba-4176-8794-1475cb8f2a62", + "metadata": {}, + "source": [ + "### Make sure that all rows are unique" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d484f57-f150-45b5-9cc5-cc10a6e8e9f1", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"ride_id\"].nunique()" + ] + }, + { + "cell_type": "markdown", + "id": "abc60782-4411-46e0-9d31-55adaa4dd1f5", + "metadata": {}, + "source": [ + "### Drop the store_and_fwd flag\n", + "\n", + "Determining its relevance isn't in scope for this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f627790e-8aed-48e3-9c5d-52775bbb124d", + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(\"store_and_fwd_flag\", axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "96fc51be-6a0f-44e6-abb8-2a6bf9188367", + "metadata": {}, + "source": [ + "### Drop the time series columns\n", + "\n", + "Analyzing the time series data also isn't in scope for this analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c359f4db-b503-4d80-bb4c-55dc411f9b5e", + "metadata": {}, + "outputs": [], + "source": [ + "# We're dropping the time series columns to streamline the analysis.\n", + "time_series_columns_to_drop = [\"pickup_at\", \"dropoff_at\"]\n", + "df.drop(columns=time_series_columns_to_drop, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ad5d1df6-d418-483a-b06d-848205f3f8ed", + "metadata": {}, + "source": [ + "### Install seaborn and create scatterplots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05abe8af-bf44-471b-b130-19cee0dd822f", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6a10b9b-e916-48a9-88f5-ae94db2f6576", + "metadata": {}, + "outputs": [], + "source": [ + "# Create visualizations showing correlations between variables.\n", + "import seaborn as sns\n", + "\n", + "target = \"fare_amount\"\n", + "features = [col for col in df.columns if col != target]\n", + "\n", + "# Create a figure with subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=len(features), figsize=(50, 10))\n", + "\n", + "# Create scatter plots\n", + "for i, feature in enumerate(features):\n", + " sns.scatterplot(x=df[feature], y=df[target], ax=axes[i])\n", + " axes[i].set_title(f\"{feature} vs {target}\")\n", + " axes[i].set_xlabel(feature)\n", + " axes[i].set_ylabel(target)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "11c33316-1502-46b1-b265-6cf43d0d8f1d", + "metadata": {}, + "source": [ + "## Calculate the correlation coefficient between each feature and fare amount" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8dff114-adb5-4b34-a788-b93e42a2fee4", + "metadata": {}, + "outputs": [], + "source": [ + "# extra and mta_tax seem weakly correlated\n", + "# total_amount is almost perfectly correlated, indicating target leakage.\n", + "continuous_features = [\n", + " \"tip_amount\",\n", + " \"tolls_amount\",\n", + " \"extra\",\n", + " \"mta_tax\",\n", + " \"total_amount\",\n", + " \"trip_distance\",\n", + "]\n", + "\n", + "for i in continuous_features:\n", + " correlation = df[\"fare_amount\"].corr(df[i])\n", + " print(i, correlation)" + ] + }, + { + "cell_type": "markdown", + "id": "7ea2dc4f-c366-43f0-8a81-44ecd8289a3d", + "metadata": {}, + "source": [ + "### Calculate a one way ANOVA between the groups\n", + "\n", + "From running the ANOVA, `mta_tax` and `extra` have the most variance between the groups. We're using them as features to train our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e083025-3312-4fd9-8cd2-4c8e37db5859", + "metadata": {}, + "outputs": [], + "source": [ + "# The mta tax and extra have the most variance between the groups\n", + "from scipy.stats import f_oneway\n", + "\n", + "# Separate features and target variable\n", + "X = df[[\"payment_type\", \"extra\", \"mta_tax\", \"vendor_id\", \"passenger_count\"]]\n", + "y = df[\"fare_amount\"]\n", + "\n", + "# Perform one-way ANOVA for each feature\n", + "for feature in X.columns:\n", + " groups = [y[X[feature] == group] for group in X[feature].unique()]\n", + " if len(groups) > 1:\n", + " f_statistic, p_value = f_oneway(*groups)\n", + " print(f\"Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b2f3d07-8010-43c4-873e-f462fd0bd94e", + "metadata": {}, + "source": [ + "### Run a query to get the dataset we're using for ML workflow\n", + "\n", + "The XGBoost algorithm on Amazon SageMaker uses the first column as the target column. `fare_amount` must be the first column in our query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dbcf599-076c-468e-9e9b-2e0bd53c3fa7", + "metadata": {}, + "outputs": [], + "source": [ + "# Final select statement has tip_amount, tolls_amount, extra, mta_tax, trip_distance\n", + "ride_combined_notebook_relevant_features_query = \"\"\"\n", + "SELECT fare_amount, tip_amount, tolls_amount, extra, mta_tax, trip_distance FROM combined_ride_data_deduped\n", + "\"\"\"\n", + "\n", + "run_athena_query(ride_combined_notebook_relevant_features_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "4bbfeb06-e0e2-4ce0-9e73-98894053592d", + "metadata": {}, + "source": [ + "### Get the Amazon S3 URI of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "624a7833-c815-480e-b1da-c29da3d02c76", + "metadata": {}, + "outputs": [], + "source": [ + "get_csv_file_location(\"ride_combined_notebook_relevant_features_query_execution_id\")" + ] + }, + { + "cell_type": "markdown", + "id": "4632047c-eabc-495a-9758-b55b78937f73", + "metadata": {}, + "source": [ + "### Run a SageMaker processing job to split the data\n", + "\n", + "The code in `processing_data_split.py` splits the dataset into training, validation, and test sets. We use a SageMaker processing job to provide the compute needed to transform large volumes of data. For more information about processing jobs, see [Use processing jobs to run data transformation workloads](https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html). For more information about running sci-kit scripts, see [Data Processing with scikit-learn](https://docs.aws.amazon.com/sagemaker/latest/dg/use-scikit-learn-processing-container.html). \n", + "\n", + "For faster processing, we recommend using an `instance_count` of `2`, but you can use whatever value you prefer.\n", + "\n", + "For `source` within the `ProcessingInput` function, replace `'s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv'` with the output of the preceding cell. Within `processing_data_split.py`, you specify `/opt/ml/processing/input/query-id` as the `input_path`. The processing job is copying the query results to a location within its own container.\n", + "\n", + "For `Destination` under `ProcessingOutput`, replace `example-s3-bucket` with the Amazon S3 bucket that you've created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "788cae3c-a34b-4ee0-899e-0a461e21b210", + "metadata": {}, + "outputs": [], + "source": [ + "import sagemaker\n", + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", + "\n", + "\n", + "# Define the SageMaker execution role\n", + "role = sagemaker.get_execution_role()\n", + "\n", + "# Define the SKLearnProcessor\n", + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=\"0.20.0\", role=role, instance_type=\"ml.m5.4xlarge\", instance_count=2\n", + ")\n", + "\n", + "# Run the processing job\n", + "sklearn_processor.run(\n", + " code=\"processing_data_split.py\",\n", + " inputs=[\n", + " ProcessingInput(\n", + " source=\"s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv\",\n", + " destination=\"/opt/ml/processing/input\",\n", + " )\n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(\n", + " source=\"/opt/ml/processing/output/train\",\n", + " destination=\"s3://ux360-nyc-taxi-dogfooding/output/train\",\n", + " ),\n", + " ProcessingOutput(\n", + " source=\"/opt/ml/processing/output/validation\",\n", + " destination=\"s3://ux360-nyc-taxi-dogfooding/output/validation\",\n", + " ),\n", + " ProcessingOutput(\n", + " source=\"/opt/ml/processing/output/test\",\n", + " destination=\"s3://ux360-nyc-taxi-dogfooding/output/test\",\n", + " ),\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bc164657-fd8f-4f96-89ff-23e991945ea4", + "metadata": {}, + "source": [ + "### Verify that train.csv is in the location that you've specified" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41cb0fb0-079d-421d-a4b8-005ee38fc472", + "metadata": {}, + "outputs": [], + "source": [ + "# Verify that train.csv is in the location that you've specified\n", + "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" + ] + }, + { + "cell_type": "markdown", + "id": "d0d2ba3c-fd6d-4aa0-b75b-92ba5a70ad00", + "metadata": {}, + "source": [ + "### Verify that val.csv is in the location that you've specified" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee3f29f1-a135-4bf6-bba5-595fb80c471d", + "metadata": {}, + "outputs": [], + "source": [ + "# Verify that val.csv is in the location that you've specified\n", + "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" + ] + }, + { + "cell_type": "markdown", + "id": "c92d4b89-65a5-474b-aa22-dcb442c344b9", + "metadata": {}, + "source": [ + "### Specify `train.csv` and `val.csv` as the input for the training job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e4e4113-b76c-49d5-a3b0-2327eb174fdf", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.session import TrainingInput\n", + "\n", + "bucket = \"example-s3-bucket\"\n", + "\n", + "train_input = TrainingInput(f\"s3://{bucket}/output/train/train.csv\", content_type=\"csv\")\n", + "validation_input = TrainingInput(f\"s3://{bucket}/output/validation/val.csv\", content_type=\"csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "866262fe-5737-49af-9cde-af55575e07d1", + "metadata": {}, + "source": [ + "### Specify the model container and output location of the model artifact\n", + "\n", + "Specify the S3 location of the trained model artifact. You can access it later.\n", + "\n", + "It also gets the URI of the container image. We used version `1.2-2` of the XGBoost container image, but you can specify a different version. For more information about XGBoost container images, see [Use the XGBoost algorithm with Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5b6a9b2-54e5-4dfd-9a5e-3c7442f6d5af", + "metadata": {}, + "outputs": [], + "source": [ + "# Getting the XGBoost container that's in us-east-1\n", + "prefix = \"training-output-data\"\n", + "region = \"us-east-1\"\n", + "\n", + "from sagemaker.debugger import Rule, ProfilerRule, rule_configs\n", + "from sagemaker.session import TrainingInput\n", + "\n", + "s3_output_location = f\"s3://{bucket}/{prefix}/xgboost_model\"\n", + "\n", + "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-2\")\n", + "print(container)" + ] + }, + { + "cell_type": "markdown", + "id": "d04e189b-6f38-44cf-a046-6791abd32c00", + "metadata": {}, + "source": [ + "### Define the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44efb3a1-acf0-4193-987f-85025c7c3894", + "metadata": {}, + "outputs": [], + "source": [ + "xgb_model = sagemaker.estimator.Estimator(\n", + " image_uri=container,\n", + " role=role,\n", + " instance_count=2,\n", + " region=region,\n", + " instance_type=\"ml.m5.4xlarge\",\n", + " volume_size=5,\n", + " output_path=s3_output_location,\n", + " sagemaker_session=sagemaker.Session(),\n", + " rules=[\n", + " Rule.sagemaker(rule_configs.create_xgboost_report()),\n", + " ProfilerRule.sagemaker(rule_configs.ProfilerReport()),\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "44f1c8b1-7bf0-4381-9128-b00c2bfcf9f1", + "metadata": {}, + "source": [ + "### Set the model hyperparameters\n", + "\n", + "For the purposes of running the training job more quickly, we set the number of training rounds to 10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e28512bf-d246-4a46-a0c8-24d1a8ad65a8", + "metadata": {}, + "outputs": [], + "source": [ + "xgb_model.set_hyperparameters(\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + " objective=\"reg:squarederror\",\n", + " num_round=10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e5b6ed18-990f-4ec7-9d42-6965ec67e2ce", + "metadata": {}, + "source": [ + "### Train the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58b77fc0-407d-4743-ae35-7bc7b04478e6", + "metadata": {}, + "outputs": [], + "source": [ + "xgb_model.fit({\"train\": train_input, \"validation\": validation_input}, wait=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f0f8be08-10a5-4204-8f8b-60235d4b1f04", + "metadata": {}, + "source": [ + "### Deploy the model\n", + "\n", + "Copy the name of the model endpoint. We use it for our model evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1aa7bc3-feee-4602-a64c-8c1e08526d03", + "metadata": {}, + "outputs": [], + "source": [ + "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type=\"ml.m4.xlarge\")" + ] + }, + { + "cell_type": "markdown", + "id": "ddcf330c-8add-437d-af1f-687ed3ebc78d", + "metadata": {}, + "source": [ + "### Download the test.csv file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9cc4eea-a6d0-418f-ab35-db437ce2a99d", + "metadata": {}, + "outputs": [], + "source": [ + "!aws s3 cp s3://example-s3-bucket/output/test/test.csv ." + ] + }, + { + "cell_type": "markdown", + "id": "27b6cc9e-cb1c-43f6-99b8-fc26b38934c3", + "metadata": {}, + "source": [ + "### Create a 20 row test dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "953f9d9b-04d0-4398-8620-8f9ab4eb407b", + "metadata": {}, + "outputs": [], + "source": [ + "import boto3\n", + "import json\n", + "\n", + "test_df = pd.read_csv(\"test.csv\", nrows=20)\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "a27e6c58-1abb-41db-ab45-263b97ee01ed", + "metadata": {}, + "source": [ + "### Get predictions from the test dataframe\n", + "\n", + "Define the `get_predictions` function to convert the 20 row dataframe to a CSV string and get predictions from the model endpoint. Provide the `get_predictions` function with the name of the model and the model endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "218e7887-f37d-42e1-8f6a-9ee97d3c75c4", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "\n", + "# Initialize the SageMaker runtime client\n", + "runtime = boto3.client(\"runtime.sagemaker\")\n", + "\n", + "# Define the endpoint name\n", + "endpoint_name = \"sagemaker-xgboost-timestamp\"\n", + "\n", + "\n", + "# Function to make predictions\n", + "def get_predictions(data, endpoint_name):\n", + " # Convert the DataFrame to a CSV string and encode it to bytes\n", + " csv_data = data.to_csv(header=False, index=False).encode(\"utf-8\")\n", + "\n", + " response = runtime.invoke_endpoint(\n", + " EndpointName=endpoint_name, ContentType=\"text/csv\", Body=csv_data\n", + " )\n", + "\n", + " # Read the response body\n", + " response_body = response[\"Body\"].read().decode(\"utf-8\")\n", + "\n", + " try:\n", + " # Try to parse the response as JSON\n", + " result = json.loads(response_body)\n", + " except json.JSONDecodeError:\n", + " # If response is not JSON, just return the raw response\n", + " result = response_body\n", + "\n", + " return result\n", + "\n", + "\n", + "# Drop the target column from the test dataframe\n", + "test_df = test_df.drop(test_df.columns[0], axis=1)\n", + "\n", + "# Get predictions\n", + "predictions = get_predictions(test_df, endpoint_name)\n", + "print(predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "a136ae86-efd3-4d4f-9966-6610f445d84c", + "metadata": {}, + "source": [ + "### Create an array from the string of predictions\n", + "\n", + "The notebook uses the newline character as the separator, so we use the following code to create an array of predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58b45ac2-8a18-4d27-8aff-57370696d58f", + "metadata": {}, + "outputs": [], + "source": [ + "predictions_array = predictions.split(\"\\n\")\n", + "predictions_array = predictions_array[:-1]\n", + "predictions_array" + ] + }, + { + "cell_type": "markdown", + "id": "20097b4e-d515-45cf-9677-bd12953b6912", + "metadata": {}, + "source": [ + "### Get the 20 row sample of the test dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5b69119-c58d-401d-a683-345a21451090", + "metadata": {}, + "outputs": [], + "source": [ + "df_with_target_column_values = pd.read_csv(\"test.csv\", nrows=20)\n", + "df_with_target_column_values.head()" + ] + }, + { + "cell_type": "markdown", + "id": "85cd39f3-5f12-4cb1-aab2-6ca658e9d16e", + "metadata": {}, + "source": [ + "### Convert the values of the predictions array from strings to floats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75353856-df2f-4c45-9a9b-11e16a856aa6", + "metadata": {}, + "outputs": [], + "source": [ + "predictions_array = [float(x) for x in predictions_array]" + ] + }, + { + "cell_type": "markdown", + "id": "408a6da9-9a0c-4307-8966-acbcc11beacc", + "metadata": {}, + "source": [ + "### Create a dataframe to store the predicted versus actual values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9589000e-1ce0-4a08-9d9c-055d29e13639", + "metadata": {}, + "outputs": [], + "source": [ + "comparison_df = pd.DataFrame(predictions_array, columns=[\"predicted_values\"])\n", + "comparison_df" + ] + }, + { + "cell_type": "markdown", + "id": "e0652e07-1677-4fd4-b099-ccc2b1029cfd", + "metadata": {}, + "source": [ + "### Add the actual values to the comparison dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adf4f58c-f21c-4abf-b14c-2802cbd399b3", + "metadata": {}, + "outputs": [], + "source": [ + "column_to_add = df_with_target_column_values.iloc[:, 0]\n", + "\n", + "comparison_df[\"actual_values\"] = column_to_add\n", + "\n", + "comparison_df" + ] + }, + { + "cell_type": "markdown", + "id": "a1ee137e-2706-4972-b70a-4d908bb0cb0a", + "metadata": {}, + "source": [ + "### Verify that the datatypes of both columns are floats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48f6f988-0de8-4c44-8c10-9845ef4d476d", + "metadata": {}, + "outputs": [], + "source": [ + "comparison_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "8c7cce0b-ce8b-4320-b9a4-9a50b2c732b3", + "metadata": {}, + "source": [ + "### Compute the RMSE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "781fe125-4a2e-4527-8c45-fcd20558f4bb", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Calculate the squared differences between the predicted and actual values\n", + "comparison_df[\"squared_diff\"] = (\n", + " comparison_df[\"actual_values\"] - comparison_df[\"predicted_values\"]\n", + ") ** 2\n", + "\n", + "# Calculate the mean of the squared differences\n", + "mean_squared_diff = comparison_df[\"squared_diff\"].mean()\n", + "\n", + "# Take the square root of the mean to get the RMSE\n", + "rmse = np.sqrt(mean_squared_diff)\n", + "\n", + "print(f\"RMSE: {rmse}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4a21cb4e-d9be-466c-869d-ac0be688700c", + "metadata": {}, + "source": [ + "### Clean up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the S3 bucket\n", + "!aws s3 rb s3://example-s3-bucket --force" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c883864-e707-46d2-a183-76e5f2090368", + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the endpoint\n", + "xgb_predictor.delete_endpoint()" + ] + }, + { + "cell_type": "markdown", + "id": "cd9140e5", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + " \n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/use-cases/athena_ml_workflow_end_to_end/processing_data_split.py b/use-cases/athena_ml_workflow_end_to_end/processing_data_split.py new file mode 100644 index 0000000000..fb8472d011 --- /dev/null +++ b/use-cases/athena_ml_workflow_end_to_end/processing_data_split.py @@ -0,0 +1,32 @@ +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +import os + +# Define the input and output paths +input_path = '/opt/ml/processing/input/feature-selection-query-id.csv' +train_output_path = '/opt/ml/processing/output/train/train.csv' +val_output_path = '/opt/ml/processing/output/validation/val.csv' +test_output_path = '/opt/ml/processing/output/test/test.csv' + +# Read the input data +df = pd.read_csv(input_path, header=None) + +# Split the data into training, validation, and test sets +train, temp = train_test_split(df, test_size=0.3, random_state=42) +val, test = train_test_split(temp, test_size=0.5, random_state=42) + +# Save the splits to the output paths +os.makedirs(os.path.dirname(train_output_path), exist_ok=True) +train.to_csv(train_output_path, index=False) + +os.makedirs(os.path.dirname(val_output_path), exist_ok=True) +val.to_csv(val_output_path, index=False) + +os.makedirs(os.path.dirname(test_output_path), exist_ok=True) +test.to_csv(test_output_path, index=False) + +# Print the sizes of the splits +print(f"Training set: {len(train)} samples") +print(f"Validation set: {len(val)} samples") +print(f"Test set: {len(test)} samples") diff --git a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb new file mode 100644 index 0000000000..d441ff4ac6 --- /dev/null +++ b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb @@ -0,0 +1,734 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3ff2d442", + "metadata": {}, + "source": [ + "# Perform ETL and train a model using PySpark\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0a1828f9-efdc-4d12-a676-a2f3432e9ab0", + "metadata": {}, + "source": [ + "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the `Glue PySpark and Ray` kernel. The kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", + "\n", + "Within the notebook you can use Spark commands to join and transform your data. Writing Spark commands is both faster and easier than writing SQL queries. For example, you can use the join command to join two tables. Instead of writing a query that can sometimes take minutes to complete, you can join a table within seconds.\n", + "\n", + "To show the utility of using the PySpark kernel for your ETL and model training worklows, we're predicting the fare amount of the NYC taxi dataset. It imports data from 47 files across 2 different Amazon Simple Storage Service (Amazon S3) locations. Amazon S3 is an object storage service that you can use to save and access data and machine learning artifacts for your models. For more information about Amazon S3, see [What is Amazon S3?](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html).\n", + "\n", + "The notebook is not meant to be a comprehensive analysis. Instead, it's meant to be a proof of concept to help you quickly get started.\n", + "\n", + "__Prerequisites:__\n", + "\n", + "This tutorial assumes that you've in the us-east-1 AWS Region. It also assumes that you've provided the IAM role you're using to run the notebook with permissions to use Glue. For more information, see [Providing AWS Glue permissions\n", + "](docs.aws.amazon.com/sagemaker/latest/dg/perform-etl-and-train-model-pyspark.html#providing-aws-glue-permissions)." + ] + }, + { + "cell_type": "markdown", + "id": "dffc1f72-88d2-442d-97ee-0d1c4e095ffb", + "metadata": {}, + "source": [ + "## Solution overview \n", + "\n", + "To perform ETL on the NYC taxi data and train a model, we do the following\n", + "\n", + "1. Start a Glue Session and load the SageMaker Python SDK\n", + "2. Set up the utilities needed to work with AWS Glue.\n", + "3. Load the data from the Amazon S3 into Spark dataframes.\n", + "4. Verify that we've loaded the data successfully.\n", + "5. Save a 20000 row sample of the Spark dataframe as a pandas dataframe.\n", + "6. Create a correlation matrix as an example of the types of analyses we can perform.\n", + "7. Split the Spark dataframe into training, validation, and test datasets.\n", + "8. Write the datasets to Amazon S3 locations that can be accessed by an Amazon SageMaker training job.\n", + "9. Use the training and validation datasets to train a model." + ] + }, + { + "cell_type": "markdown", + "id": "e472c953-1625-49df-8df9-9529344783ab", + "metadata": {}, + "source": [ + "### Start a Glue Session and load the SageMaker Python SDK" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94172c75-f8a9-4590-a443-c872fb5c5d6e", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "%additional_python_modules sagemaker" + ] + }, + { + "cell_type": "markdown", + "id": "725bd4b6-82a0-4f02-95b9-261ce62c71b0", + "metadata": {}, + "source": [ + "### Set up the utilities needed to work with AWS Glue\n", + "\n", + "We're importing `Join` to join our Spark dataframes. `GlueContext` provides methods for transforming our dataframes. In the context of the notebook, it reads the data from the Amazon S3 locations and uses the Spark cluster to transform the data. `SparkContext` represents the connection to the Spark cluster. `GlueContext` uses `SparkContext` to transform the data. `getResolvedOptions` lets you resolve configuration options within the Glue interactive session." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ea1c3a4-8881-48b0-8888-9319812750e7", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "import sys\n", + "from awsglue.transforms import Join\n", + "from awsglue.utils import getResolvedOptions\n", + "from pyspark.context import SparkContext\n", + "from awsglue.context import GlueContext\n", + "from awsglue.job import Job\n", + "\n", + "glueContext = GlueContext(SparkContext.getOrCreate())" + ] + }, + { + "cell_type": "markdown", + "id": "e03664e5-89a2-4296-ba83-3518df4a58f0", + "metadata": {}, + "source": [ + "### Create the `df_ride_info` dataframe\n", + "\n", + "Create a single dataframe from all the ride_info Parquet files for 2019." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba577de7-9ffe-4bae-b4c0-b225181306d9", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", + " connection_type=\"s3\",\n", + " format=\"parquet\",\n", + " connection_options={\n", + " \"paths\": [\n", + " \"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"\n", + " ],\n", + " \"recurse\": True,\n", + " },\n", + ").toDF()" + ] + }, + { + "cell_type": "markdown", + "id": "b04ce553-bf3d-4922-bbb1-4aa264447276", + "metadata": {}, + "source": [ + "### Create the `df_ride_info` dataframe\n", + "\n", + "Create a single dataframe from all the ride_fare Parquet files for 2019." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6efc3d4a-81d7-40f5-bb62-cd206924a0c9", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", + " connection_type=\"s3\",\n", + " format=\"parquet\",\n", + " connection_options={\n", + " \"paths\": [\n", + " \"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"\n", + " ],\n", + " \"recurse\": True,\n", + " },\n", + ").toDF()" + ] + }, + { + "cell_type": "markdown", + "id": "6c8664da-2105-4ada-b480-06d50c59e878", + "metadata": {}, + "source": [ + "### Show the first five rows of `dr_ride_fare`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d63af3a3-358f-4c6e-97d4-97a1f1a552de", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_ride_fare.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "688a17e8-0c83-485d-a328-e89344a0e8bf", + "metadata": {}, + "source": [ + "### Join df_ride_fare and df_ride_info on the `ride_id` column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07a3baab-44b0-416a-b12e-049a270af8bd", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_joined = df_ride_info.join(df_ride_fare, [\"ride_id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "236c2efc-85f8-43f8-b6d3-7f0e61ccefb0", + "metadata": {}, + "source": [ + "### Show the first five rows of the joined dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a456733-4533-4688-8174-368e50f4dd66", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_joined.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "1396f6ee-c581-4274-baf8-243d38ec000b", + "metadata": {}, + "source": [ + "### Show the data types of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a52a903-f394-4d00-a216-6af8c2132d83", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_joined.printSchema()" + ] + }, + { + "cell_type": "markdown", + "id": "18bb75a2-eba5-4d06-8a26-f30e31776a02", + "metadata": {}, + "source": [ + "### Count the number of rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6bcc15f-8d41-4def-ae49-edaef4105343", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_joined.count()" + ] + }, + { + "cell_type": "markdown", + "id": "d2daa67c-4b21-433a-b46e-eed518ba9ce7", + "metadata": {}, + "source": [ + "### Drop duplicates if there are any" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d13d8d9-7eed-4efb-b972-601baf291842", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_no_dups = df_joined.dropDuplicates([\"ride_id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "657e48dc-1f4a-4550-afe1-d9754e6d0e1e", + "metadata": {}, + "source": [ + "### Count the number of rows after dropping the duplicates\n", + "\n", + "In this case, there were no duplicates in the original dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e3e82a3-e3db-4752-8bab-f42cbbae4928", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_no_dups.count()" + ] + }, + { + "cell_type": "markdown", + "id": "ae4c0fc4-7cb5-4b70-8430-965b5fe4506e", + "metadata": {}, + "source": [ + "### Drop columns\n", + "Time series data and categorical data is outside of the scope of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dc1d15f-53f6-404d-86fd-5a28f3792db8", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_cleaned = df_joined.drop(\n", + " \"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "081c81f9-f052-4ddb-b769-4d41b6138f6a", + "metadata": {}, + "source": [ + "### Take a sample from the notebook and convert it to a pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48382726-c767-4b0e-9336-decbf8184938", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_sample = df_cleaned.sample(False, 0.1, seed=0).limit(20000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bf2f181-0096-4044-8210-7d9de299d966", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_sample.count()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8b2f670-c5f9-4a01-8d9f-6a29a3dae660", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_pandas = df_sample.toPandas()\n", + "df_pandas.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "246c98e9-64bd-4644-a163-b86a943d6a09", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "print(\"Dataset shape: \", df_pandas.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5b2727c-de75-4cc0-94e9-d254e235d003", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_pandas.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d69b48b6-98c2-4851-9c7a-f24f092bae41", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_pandas.info()" + ] + }, + { + "cell_type": "markdown", + "id": "34222bea-8864-4934-8c93-a71a7e72325b", + "metadata": {}, + "source": [ + "### Create a correlation matrix of the features\n", + "\n", + "We're creating a correlation matrix to see which features are the most predictive. This is an example of an analysis that you can use for your own use case." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7f3e4f7-e04e-41e1-b94b-b32eb3bc3bbf", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "from pyspark.ml.stat import Correlation\n", + "from pyspark.ml.feature import VectorAssembler\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd # not sure how the kernel runs, but it looks like I have import pandas again after going back to the notebook after a while\n", + "\n", + "vector_col = \"corr_features\"\n", + "assembler = VectorAssembler(inputCols=df_sample.columns, outputCol=vector_col)\n", + "df_vector = assembler.transform(df_sample).select(vector_col)\n", + "\n", + "matrix = Correlation.corr(df_vector, vector_col).collect()[0][0]\n", + "corr_matrix = matrix.toArray().tolist()\n", + "corr_matrix_df = pd.DataFrame(data=corr_matrix, columns=df_sample.columns, index=df_sample.columns)\n", + "\n", + "plt.figure(figsize=(16, 10))\n", + "sns.heatmap(\n", + " corr_matrix_df,\n", + " xticklabels=corr_matrix_df.columns.values,\n", + " yticklabels=corr_matrix_df.columns.values,\n", + " cmap=\"Greens\",\n", + " annot=True,\n", + ")\n", + "\n", + "%matplot plt" + ] + }, + { + "cell_type": "markdown", + "id": "cbde3b29-d37d-485a-a114-5313c5a702c7", + "metadata": {}, + "source": [ + "### Split the dataset into train, validation, and test sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e207c64-2e22-468f-a0c7-948090bcfce2", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_train, df_val, df_test = df_cleaned.randomSplit([0.7, 0.15, 0.15])" + ] + }, + { + "cell_type": "markdown", + "id": "01a4d181-e2f0-4743-ab35-dd1f68b0fd31", + "metadata": {}, + "source": [ + "### Define the Amazon S3 locations that store the datasets\n", + "\n", + "If you're getting a module not found error, restart the kernel and run all the cells again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f16ea3a1-6d6d-4755-94ad-c743298bd130", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "# Define the S3 locations to store the datasets\n", + "import boto3\n", + "import sagemaker\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "s3_bucket = sagemaker_session.default_bucket()\n", + "train_data_prefix = \"sandbox/glue-demo/train\"\n", + "validation_data_prefix = \"sandbox/glue-demo/validation\"\n", + "test_data_prefix = \"sandbox/glue-demo/test\"\n", + "region = boto3.Session().region_name" + ] + }, + { + "cell_type": "markdown", + "id": "8899a159-700c-403a-b4f5-a00c62b06e5a", + "metadata": {}, + "source": [ + "### Write the files to the locations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64d7ae48-6158-4273-8bb3-2f00abb1c20c", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_train.write.parquet(f\"s3://{s3_bucket}/{train_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de3d1190-4717-4944-846d-0169c093cb90", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_val.write.parquet(f\"s3://{s3_bucket}/{validation_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d18ef1c-fc2f-4e34-a692-4a6c48be7cba", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "df_test.write.parquet(f\"s3://{s3_bucket}/{test_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "markdown", + "id": "73c947e4-b4a9-4cc4-aefe-755aa0a713c8", + "metadata": {}, + "source": [ + "### Train a model\n", + "\n", + "The following code uses the `df_train` and `df_val` datasets to train an XGBoost model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a31b7742-93df-44c5-8674-b6355032c508", + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, + "outputs": [], + "source": [ + "from sagemaker import image_uris\n", + "from sagemaker.inputs import TrainingInput\n", + "\n", + "hyperparameters = {\n", + " \"max_depth\": \"5\",\n", + " \"eta\": \"0.2\",\n", + " \"gamma\": \"4\",\n", + " \"min_child_weight\": \"6\",\n", + " \"subsample\": \"0.7\",\n", + " \"objective\": \"reg:squarederror\",\n", + " \"num_round\": \"50\",\n", + "}\n", + "\n", + "# Set an output path to save the trained model.\n", + "prefix = \"sandbox/glue-demo\"\n", + "output_path = f\"s3://{s3_bucket}/{prefix}/xgb-built-in-algo/output\"\n", + "\n", + "# The following line looks for the XGBoost image URI and builds an XGBoost container.\n", + "# We use version 1.7-1 of the image URI, you can specify a version that you prefer.\n", + "xgboost_container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.7-1\")\n", + "\n", + "# Construct a SageMaker estimator that calls the xgboost-container\n", + "estimator = sagemaker.estimator.Estimator(\n", + " image_uri=xgboost_container,\n", + " hyperparameters=hyperparameters,\n", + " role=sagemaker.get_execution_role(),\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.4xlarge\",\n", + " output_path=output_path,\n", + ")\n", + "\n", + "content_type = \"application/x-parquet\"\n", + "train_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/train/\", content_type=content_type)\n", + "validation_input = TrainingInput(\n", + " f\"s3://{s3_bucket}/{prefix}/validation/\", content_type=content_type\n", + ")\n", + "\n", + "# Run the XGBoost training job\n", + "estimator.fit({\"train\": train_input, \"validation\": validation_input})" + ] + }, + { + "cell_type": "markdown", + "id": "b1b1d546-1c7e-48f5-9262-939289ada936", + "metadata": {}, + "source": [ + "### Clean up\n", + "\n", + "To clean up, shut down the kernel. Shutting down the kernel, stops the Glue cluster. You won't be charged for any more compute other than what you used to run the tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "99668011", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + " \n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Glue PySpark and Ray", + "language": "python", + "name": "glue_pyspark" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "Python_Glue_Session", + "pygments_lexer": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9278731ea480e085cd1f063f710810093985574e Mon Sep 17 00:00:00 2001 From: Bobby Lindsey Date: Tue, 2 Jul 2024 10:40:33 -0600 Subject: [PATCH 03/16] MLflow setup (#4689) * Add SageMaker MLflow examples * Add badges * Add MLflow setup notebook; upgrade SageMaker Python SDK for deployment notebook * Linting * More linting changes --------- Co-authored-by: Bobby Lindsey --- .../sagemaker_deployment_mlflow.ipynb | 55 ++- sagemaker-mlflow/sagemaker_hpo_mlflow.ipynb | 30 +- sagemaker-mlflow/sagemaker_mlflow_setup.ipynb | 415 ++++++++++++++++++ .../sagemaker_pipelines_mlflow.ipynb | 166 ++++--- .../sagemaker_training_mlflow.ipynb | 30 +- 5 files changed, 553 insertions(+), 143 deletions(-) create mode 100644 sagemaker-mlflow/sagemaker_mlflow_setup.ipynb diff --git a/sagemaker-mlflow/sagemaker_deployment_mlflow.ipynb b/sagemaker-mlflow/sagemaker_deployment_mlflow.ipynb index cd866fa3ff..296ab997ea 100644 --- a/sagemaker-mlflow/sagemaker_deployment_mlflow.ipynb +++ b/sagemaker-mlflow/sagemaker_deployment_mlflow.ipynb @@ -23,6 +23,22 @@ "## Setup environment" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upgrade SageMaker Python SDK" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade --quiet sagemaker>=2.215.0" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -86,10 +102,10 @@ "region = sagemaker_session.boto_region_name\n", "\n", "# S3 prefix for the training dataset to be uploaded to\n", - "prefix = 'DEMO-scikit-iris'\n", + "prefix = \"DEMO-scikit-iris\"\n", "\n", "# Provide the ARN of the Tracking Server that you want to track your training job with\n", - "tracking_server_arn = 'your tracking server arn here'" + "tracking_server_arn = \"your tracking server arn here\"" ] }, { @@ -125,13 +141,13 @@ "\n", "s3_client = boto3.client(\"s3\")\n", "s3_client.download_file(\n", - " f\"sagemaker-example-files-prod-{region}\", 'datasets/tabular/iris/iris.data', './data/iris.csv'\n", + " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/iris/iris.data\", \"./data/iris.csv\"\n", ")\n", "\n", - "df_iris = pd.read_csv('./data/iris.csv', header=None)\n", - "df_iris[4] = df_iris[4].map({\"Iris-setosa\": 0, 'Iris-versicolor': 1, 'Iris-virginica': 2})\n", + "df_iris = pd.read_csv(\"./data/iris.csv\", header=None)\n", + "df_iris[4] = df_iris[4].map({\"Iris-setosa\": 0, \"Iris-versicolor\": 1, \"Iris-virginica\": 2})\n", "iris = df_iris[[4, 0, 1, 2, 3]].to_numpy()\n", - "np.savetxt('./data/iris.csv', iris, delimiter=',', fmt='%1.1f, %1.3f, %1.3f, %1.3f, %1.3f')" + "np.savetxt(\"./data/iris.csv\", iris, delimiter=\",\", fmt=\"%1.1f, %1.3f, %1.3f, %1.3f, %1.3f\")" ] }, { @@ -147,10 +163,10 @@ "metadata": {}, "outputs": [], "source": [ - "WORK_DIRECTORY = 'data'\n", + "WORK_DIRECTORY = \"data\"\n", "\n", "train_input = sagemaker_session.upload_data(\n", - " WORK_DIRECTORY, key_prefix='{}/{}'.format(prefix, WORK_DIRECTORY)\n", + " WORK_DIRECTORY, key_prefix=\"{}/{}\".format(prefix, WORK_DIRECTORY)\n", ")" ] }, @@ -278,17 +294,15 @@ "outputs": [], "source": [ "sklearn = SKLearn(\n", - " entry_point='train.py',\n", - " source_dir='training_code',\n", - " framework_version='1.2-1',\n", - " instance_type='ml.c4.xlarge',\n", + " entry_point=\"train.py\",\n", + " source_dir=\"training_code\",\n", + " framework_version=\"1.2-1\",\n", + " instance_type=\"ml.c4.xlarge\",\n", " role=role,\n", " sagemaker_session=sagemaker_session,\n", - " hyperparameters={'max_leaf_nodes': 30},\n", + " hyperparameters={\"max_leaf_nodes\": 30},\n", " keep_alive_period_in_seconds=3600,\n", - " environment={\n", - " 'MLFLOW_TRACKING_ARN': tracking_server_arn\n", - " }\n", + " environment={\"MLFLOW_TRACKING_ARN\": tracking_server_arn},\n", ")" ] }, @@ -394,9 +408,7 @@ " mode=Mode.SAGEMAKER_ENDPOINT,\n", " schema_builder=sklearn_schema_builder,\n", " role_arn=role,\n", - " model_metadata={\n", - " \"MLFLOW_MODEL_PATH\": source_path\n", - " }\n", + " model_metadata={\"MLFLOW_MODEL_PATH\": source_path},\n", ")" ] }, @@ -415,10 +427,7 @@ "metadata": {}, "outputs": [], "source": [ - "predictor = built_model.deploy(\n", - " initial_instance_count=1,\n", - " instance_type=\"ml.m5.large\"\n", - ")" + "predictor = built_model.deploy(initial_instance_count=1, instance_type=\"ml.m5.large\")" ] }, { diff --git a/sagemaker-mlflow/sagemaker_hpo_mlflow.ipynb b/sagemaker-mlflow/sagemaker_hpo_mlflow.ipynb index d5d1f03a1f..4b6853403a 100644 --- a/sagemaker-mlflow/sagemaker_hpo_mlflow.ipynb +++ b/sagemaker-mlflow/sagemaker_hpo_mlflow.ipynb @@ -109,11 +109,11 @@ "bucket = sagemaker_session.default_bucket()\n", "\n", "# S3 prefix for the training dataset to be uploaded to\n", - "prefix = 'DEMO-pytorch-mnist'\n", + "prefix = \"DEMO-pytorch-mnist\"\n", "\n", "# MLflow (replace these values with your own)\n", - "tracking_server_arn = 'your tracking server arn'\n", - "experiment_name = 'MNIST'" + "tracking_server_arn = \"your tracking server arn\"\n", + "experiment_name = \"MNIST\"" ] }, { @@ -149,9 +149,9 @@ "metadata": {}, "outputs": [], "source": [ - "local_dir = 'data'\n", + "local_dir = \"data\"\n", "MNIST.mirrors = [\n", - " f'https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/'\n", + " f\"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/\"\n", "]\n", "MNIST(\n", " local_dir,\n", @@ -177,7 +177,7 @@ "metadata": {}, "outputs": [], "source": [ - "train_input = sagemaker_session.upload_data(path='data', bucket=bucket, key_prefix=prefix)" + "train_input = sagemaker_session.upload_data(path=\"data\", bucket=bucket, key_prefix=prefix)" ] }, { @@ -577,10 +577,7 @@ "\n", "objective_metric_name = \"average test loss\"\n", "objective_type = \"Minimize\"\n", - "metric_definitions = [\n", - " {\"Name\": \"average test loss\",\n", - " \"Regex\": \"Test set: Average loss: ([0-9\\\\.]+)\"}\n", - "]" + "metric_definitions = [{\"Name\": \"average test loss\", \"Regex\": \"Test set: Average loss: ([0-9\\\\.]+)\"}]" ] }, { @@ -612,17 +609,14 @@ " framework_version=\"1.13\",\n", " instance_count=1,\n", " instance_type=\"ml.c5.2xlarge\",\n", - " hyperparameters={\n", - " \"epochs\": 5,\n", - " \"backend\": \"gloo\"\n", - " },\n", + " hyperparameters={\"epochs\": 5, \"backend\": \"gloo\"},\n", " environment={\n", - " 'MLFLOW_TRACKING_URI':tracking_server_arn,\n", - " 'MLFLOW_EXPERIMENT_NAME':experiment.name,\n", - " 'MLFLOW_PARENT_RUN_ID':run.info.run_id\n", + " \"MLFLOW_TRACKING_URI\": tracking_server_arn,\n", + " \"MLFLOW_EXPERIMENT_NAME\": experiment.name,\n", + " \"MLFLOW_PARENT_RUN_ID\": run.info.run_id,\n", " },\n", " )\n", - " \n", + "\n", " tuner = HyperparameterTuner(\n", " estimator,\n", " objective_metric_name,\n", diff --git a/sagemaker-mlflow/sagemaker_mlflow_setup.ipynb b/sagemaker-mlflow/sagemaker_mlflow_setup.ipynb new file mode 100644 index 0000000000..3ee5907980 --- /dev/null +++ b/sagemaker-mlflow/sagemaker_mlflow_setup.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "02127090-ee33-4005-b5af-5f4e386ed1a6", + "metadata": {}, + "source": [ + "# How to Setup Amazon SageMaker with MLflow" + ] + }, + { + "cell_type": "markdown", + "id": "557f10ee-714a-4378-9493-abe2cd010754", + "metadata": {}, + "source": [ + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "9f09f362-71a7-409c-a4c7-0ee5e59c1581", + "metadata": {}, + "source": [ + "## Updates and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd83cc42-fc1e-49cd-88e3-7a685add2404", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U --quiet boto3" + ] + }, + { + "cell_type": "markdown", + "id": "f932a722-a2cd-4aca-bdc0-d00553439966", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "329cf85a-352c-4f55-8e2a-4771a26fbe70", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import sagemaker\n", + "import boto3" + ] + }, + { + "cell_type": "markdown", + "id": "95c2869e-1845-4534-bf97-d530b5c27c48", + "metadata": {}, + "source": [ + "Session variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ece22344-f747-4fb2-9051-3640dd95dd6b", + "metadata": {}, + "outputs": [], + "source": [ + "sess = sagemaker.Session()\n", + "bucket_name = sess.default_bucket()\n", + "role = sagemaker.get_execution_role()\n", + "region = sess.boto_region_name\n", + "\n", + "iam_client = boto3.client(\"iam\")\n", + "sts_client = boto3.client(\"sts\")\n", + "sm_client = boto3.client(\"sagemaker\")\n", + "account_id = sts_client.get_caller_identity()[\"Account\"]\n", + "tracking_server_name = \"my-setup-test3\"\n", + "mlflow_role_name = \"mlflow-test3\"" + ] + }, + { + "cell_type": "markdown", + "id": "6c292837-353c-4c3c-91b9-3088e8d5a02b", + "metadata": {}, + "source": [ + "## MLflow Permissions" + ] + }, + { + "cell_type": "markdown", + "id": "e6bae350-030f-4ecf-8380-5b11b73b5806", + "metadata": {}, + "source": [ + "### IAM Role for the MLflow Tracking Server\n", + "\n", + "To run the next cell, make sure the IAM role used while running this notebook has permission to create an IAM Role. \n", + "The `iam:CreateRole`, `iam:CreatePolicy`, `iam:ListPolicies`, and `iam:AttachRolePolicy` action must be allowed by the notebook execution role's policy.\n", + "\n", + "If you are running this notebook from SageMaker Studio, you can update your notebook execution role through the following steps: \n", + "\n", + "1. Navigate to the AWS Console and select the Domain you are using\n", + "2. Under the Domain, select the User Profile you are using. You will see the Execution Role listed there.\n", + "3. Navigate to the IAM Console, search for the Execution Role under \"Roles\", and update your role with a policy that allows the `iam:CreateRole`, `iam:CreatePolicy`, `iam:ListPolicies`, and `iam:AttachRolePolicy` actions. \n", + "\n", + "If you are not using a SageMaker Studio Notebook, confirm that the role you have used to configure your AWS CLI has appropriate permissions to create an IAM role and attach a policy to it. \n", + "\n", + "Here is an example of an inline policy you can add to your role - \n", + "\n", + "```json\n", + "{\n", + " \"Version\": \"2012-10-17\",\n", + " \"Statement\": [\n", + " {\n", + " \"Sid\": \"Statement1\",\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": [\n", + " \"iam:ListPolicies\",\n", + " \"iam:CreatePolicy\",\n", + " \"iam:CreateRole\",\n", + " \"iam:AttachRolePolicy\"\n", + " ],\n", + " \"Resource\": [\n", + " \"*\"\n", + " ]\n", + " }\n", + " ]\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96c0ad98-f237-4bfd-b134-40b46ebfa81d", + "metadata": {}, + "outputs": [], + "source": [ + "mlflow_trust_policy = {\n", + " \"Version\": \"2012-10-17\",\n", + " \"Statement\": [\n", + " {\n", + " \"Effect\": \"Allow\",\n", + " \"Principal\": {\"Service\": [\"sagemaker.amazonaws.com\"]},\n", + " \"Action\": \"sts:AssumeRole\",\n", + " }\n", + " ],\n", + "}\n", + "\n", + "# Create role for MLflow\n", + "mlflow_role = iam_client.create_role(\n", + " RoleName=mlflow_role_name, AssumeRolePolicyDocument=json.dumps(mlflow_trust_policy)\n", + ")\n", + "mlflow_role_arn = mlflow_role[\"Role\"][\"Arn\"]\n", + "\n", + "# Create policy for S3 and SageMaker Model Registry\n", + "sm_s3_model_registry_policy = {\n", + " \"Version\": \"2012-10-17\",\n", + " \"Statement\": [\n", + " {\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": [\n", + " \"s3:Get*\",\n", + " \"s3:Put*\",\n", + " \"s3:List*\",\n", + " \"sagemaker:AddTags\",\n", + " \"sagemaker:CreateModelPackageGroup\",\n", + " \"sagemaker:CreateModelPackage\",\n", + " \"sagemaker:UpdateModelPackage\",\n", + " \"sagemaker:DescribeModelPackageGroup\",\n", + " ],\n", + " \"Resource\": \"*\",\n", + " }\n", + " ],\n", + "}\n", + "\n", + "mlflow_s3_sm_model_registry_iam_policy = iam_client.create_policy(\n", + " PolicyName=\"mlflow-s3-sm-model-registry\", PolicyDocument=json.dumps(sm_s3_model_registry_policy)\n", + ")\n", + "mlflow_s3_sm_model_registry_iam_policy_arn = mlflow_s3_sm_model_registry_iam_policy[\"Policy\"][\"Arn\"]\n", + "\n", + "# Attach the policy to the MLflow role\n", + "iam_client.attach_role_policy(\n", + " RoleName=mlflow_role_name, PolicyArn=mlflow_s3_sm_model_registry_iam_policy_arn\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "65e2744c-c1b4-4444-9e8f-fbf1315a71a4", + "metadata": {}, + "source": [ + "Note that your SageMaker execution role should have the following permissions to call Mlflow REST APIs:\n", + "\n", + "```json\n", + "{\n", + " \"Version\": \"2012-10-17\", \n", + " \"Statement\": [ \n", + " { \n", + " \"Effect\": \"Allow\", \n", + " \"Action\": [\n", + " \"sagemaker-mlflow:*\",\n", + " \"sagemaker:CreateMlflowTrackingServer\",\n", + " \"sagemaker:UpdateMlflowTrackingServer\",\n", + " \"sagemaker:DeleteMlflowTrackingServer\",\n", + " \"sagemaker:StartMlflowTrackingServer\",\n", + " \"sagemaker:StopMlflowTrackingServer\",\n", + " \"sagemaker:CreatePresignedMlflowTrackingServerUrl\"\n", + " ], \n", + " \"Resource\": \"*\" \n", + " } \n", + " ]\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "ade88b9a-961a-4ced-9320-e56d7e9cf3eb", + "metadata": {}, + "source": [ + "## Create MLflow Tracking Server" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d496f9b-4493-4ab2-9d35-8d4ec0f79620", + "metadata": {}, + "outputs": [], + "source": [ + "sm_client.create_mlflow_tracking_server(\n", + " TrackingServerName=tracking_server_name,\n", + " ArtifactStoreUri=f\"s3://{bucket_name}/{tracking_server_name}\",\n", + " TrackingServerSize=\"Small\",\n", + " MlflowVersion=\"2.13.2\",\n", + " RoleArn=mlflow_role_arn,\n", + " AutomaticModelRegistration=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20d535f6-6dd2-4c5c-99e3-8b428c052c70", + "metadata": {}, + "outputs": [], + "source": [ + "tracking_server_arn = (\n", + " f\"arn:aws:sagemaker:{region}:{account_id}:mlflow-tracking-server/{tracking_server_name}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddff09d7-73aa-4f77-b437-1e8c05c59ea2", + "metadata": {}, + "outputs": [], + "source": [ + "sm_client.describe_mlflow_tracking_server(TrackingServerName=tracking_server_name)" + ] + }, + { + "cell_type": "markdown", + "id": "e6c50a30-89e4-4ea9-8fe8-df15a2f7726e", + "metadata": {}, + "source": [ + "Install the MLflow SDK and our MLflow AWS Plugin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2232f516-f23c-4c0d-ada2-933a45fea6e9", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --quiet mlflow==2.13.2 sagemaker-mlflow==0.1.0" + ] + }, + { + "cell_type": "markdown", + "id": "073d12e9-b91e-4c0c-93d1-8cae66648e49", + "metadata": {}, + "source": [ + "## MLflow tracking test" + ] + }, + { + "cell_type": "markdown", + "id": "ad90cde7-9de2-4df7-80a5-010165edafce", + "metadata": {}, + "source": [ + "Connect to tracking server" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7a43ce7-3e9a-4b47-b051-9f59522ee43f", + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(tracking_server_arn)" + ] + }, + { + "cell_type": "markdown", + "id": "c9197fca-6370-4f91-a52f-440ef5b22484", + "metadata": {}, + "source": [ + "Log a metric" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bab5d5df-c1a8-4a2b-89e1-52d36d630f3d", + "metadata": {}, + "outputs": [], + "source": [ + "with mlflow.start_run():\n", + " mlflow.log_metric(\"foo\", 1)" + ] + }, + { + "cell_type": "markdown", + "id": "d603ef2f-9c42-4ef2-896e-73ab1eaa6ace", + "metadata": {}, + "source": [ + "See results in MLflow UI. You can either launch the MLflow UI from within SageMaker Studio, or generate a pre-signed URL like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0690708f-976c-472e-8e4d-281aa163e9aa", + "metadata": {}, + "outputs": [], + "source": [ + "sm_client.create_presigned_mlflow_tracking_server_url(TrackingServerName=tracking_server_name)" + ] + }, + { + "cell_type": "markdown", + "id": "0f739f1a-2a97-4cc5-bb6b-bc59e4111d0f", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-mlflow|sagemaker_mlflow_setup.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker-mlflow/sagemaker_pipelines_mlflow.ipynb b/sagemaker-mlflow/sagemaker_pipelines_mlflow.ipynb index 9b145f3257..4ad94a49f3 100644 --- a/sagemaker-mlflow/sagemaker_pipelines_mlflow.ipynb +++ b/sagemaker-mlflow/sagemaker_pipelines_mlflow.ipynb @@ -76,12 +76,10 @@ "region = sagemaker_session.boto_region_name\n", "\n", "pipeline_name = \"breast-cancer-xgb\"\n", - "instance_type = ParameterString(\n", - " name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\"\n", - ")\n", + "instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n", "\n", "# Mlflow (replace these values with your own)\n", - "tracking_server_arn = 'your tracking server arn'\n", + "tracking_server_arn = \"your tracking server arn\"\n", "experiment_name = \"sm-pipelines-experiment\"" ] }, @@ -129,7 +127,7 @@ "outputs": [], "source": [ "# Set path to config file\n", - "os.environ['SAGEMAKER_USER_CONFIG_OVERRIDE'] = os.getcwd()" + "os.environ[\"SAGEMAKER_USER_CONFIG_OVERRIDE\"] = os.getcwd()" ] }, { @@ -205,45 +203,46 @@ "outputs": [], "source": [ "random_state = 2023\n", - "label_column = 'diagnosis'\n", + "label_column = \"diagnosis\"\n", "\n", "feature_names = [\n", - " 'id',\n", - " 'diagnosis',\n", - " 'radius_mean',\n", - " 'texture_mean',\n", - " 'perimeter_mean',\n", - " 'area_mean',\n", - " 'smoothness_mean',\n", - " 'compactness_mean',\n", - " 'concavity_mean',\n", - " 'concave points_mean',\n", - " 'symmetry_mean',\n", - " 'fractal_dimension_mean',\n", - " 'radius_se',\n", - " 'texture_se',\n", - " 'perimeter_se',\n", - " 'area_se',\n", - " 'smoothness_se',\n", - " 'compactness_se',\n", - " 'concavity_se',\n", - " 'concave points_se',\n", - " 'symmetry_se',\n", - " 'fractal_dimension_se',\n", - " 'radius_worst',\n", - " 'texture_worst',\n", - " 'perimeter_worst',\n", - " 'area_worst',\n", - " 'smoothness_worst',\n", - " 'compactness_worst',\n", - " 'concavity_worst',\n", - " 'concave points_worst',\n", - " 'symmetry_worst',\n", - " 'fractal_dimension_worst',\n", + " \"id\",\n", + " \"diagnosis\",\n", + " \"radius_mean\",\n", + " \"texture_mean\",\n", + " \"perimeter_mean\",\n", + " \"area_mean\",\n", + " \"smoothness_mean\",\n", + " \"compactness_mean\",\n", + " \"concavity_mean\",\n", + " \"concave points_mean\",\n", + " \"symmetry_mean\",\n", + " \"fractal_dimension_mean\",\n", + " \"radius_se\",\n", + " \"texture_se\",\n", + " \"perimeter_se\",\n", + " \"area_se\",\n", + " \"smoothness_se\",\n", + " \"compactness_se\",\n", + " \"concavity_se\",\n", + " \"concave points_se\",\n", + " \"symmetry_se\",\n", + " \"fractal_dimension_se\",\n", + " \"radius_worst\",\n", + " \"texture_worst\",\n", + " \"perimeter_worst\",\n", + " \"area_worst\",\n", + " \"smoothness_worst\",\n", + " \"compactness_worst\",\n", + " \"concavity_worst\",\n", + " \"concave points_worst\",\n", + " \"symmetry_worst\",\n", + " \"fractal_dimension_worst\",\n", "]\n", "\n", + "\n", "@step(\n", - " name='DataPreprocessing',\n", + " name=\"DataPreprocessing\",\n", " instance_type=instance_type,\n", ")\n", "def preprocess(\n", @@ -261,28 +260,26 @@ " mlflow.set_experiment(experiment_name)\n", " with mlflow.start_run(run_name=run_name) as run:\n", " run_id = run.info.run_id\n", - " with mlflow.start_run(run_name='DataPreprocessing', nested=True):\n", + " with mlflow.start_run(run_name=\"DataPreprocessing\", nested=True):\n", " df = pd.read_csv(raw_data_s3_path, header=None, names=feature_names)\n", - " df.drop(columns='id', inplace=True)\n", + " df.drop(columns=\"id\", inplace=True)\n", " mlflow.log_input(\n", " mlflow.data.from_pandas(df, raw_data_s3_path, targets=label_column),\n", - " context='DataPreprocessing',\n", - " )\n", - " \n", - " train_df, test_df = train_test_split(\n", - " df, test_size=0.2, stratify=df[label_column]\n", + " context=\"DataPreprocessing\",\n", " )\n", + "\n", + " train_df, test_df = train_test_split(df, test_size=0.2, stratify=df[label_column])\n", " validation_df, test_df = train_test_split(\n", " test_df, test_size=0.5, stratify=test_df[label_column]\n", " )\n", " train_df.reset_index(inplace=True, drop=True)\n", " validation_df.reset_index(inplace=True, drop=True)\n", " test_df.reset_index(inplace=True, drop=True)\n", - " \n", - " train_s3_path = f's3://{bucket}/{output_prefix}/train.csv'\n", - " val_s3_path = f's3://{bucket}/{output_prefix}/val.csv'\n", - " test_s3_path = f's3://{bucket}/{output_prefix}/test.csv'\n", - " \n", + "\n", + " train_s3_path = f\"s3://{bucket}/{output_prefix}/train.csv\"\n", + " val_s3_path = f\"s3://{bucket}/{output_prefix}/val.csv\"\n", + " test_s3_path = f\"s3://{bucket}/{output_prefix}/test.csv\"\n", + "\n", " train_df.to_csv(train_s3_path, index=False)\n", " validation_df.to_csv(val_s3_path, index=False)\n", " test_df.to_csv(test_s3_path, index=False)\n", @@ -317,19 +314,19 @@ "source": [ "use_gpu = False\n", "param = dict(\n", - " objective='binary:logistic',\n", + " objective=\"binary:logistic\",\n", " max_depth=5,\n", " eta=0.2,\n", " gamma=4,\n", " min_child_weight=6,\n", " subsample=0.7,\n", - " tree_method='gpu_hist' if use_gpu else 'hist', # Use GPU accelerated algorithm\n", + " tree_method=\"gpu_hist\" if use_gpu else \"hist\", # Use GPU accelerated algorithm\n", ")\n", "num_round = 50\n", "\n", "\n", "@step(\n", - " name='ModelTraining',\n", + " name=\"ModelTraining\",\n", " instance_type=instance_type,\n", ")\n", "def train(\n", @@ -348,24 +345,24 @@ " mlflow.set_experiment(experiment_name)\n", "\n", " with mlflow.start_run(run_id=run_id):\n", - " with mlflow.start_run(run_name='ModelTraining', nested=True) as training_run:\n", + " with mlflow.start_run(run_name=\"ModelTraining\", nested=True) as training_run:\n", " training_run_id = training_run.info.run_id\n", " mlflow.xgboost.autolog(\n", " log_input_examples=True,\n", " log_model_signatures=True,\n", " log_models=True,\n", " log_datasets=True,\n", - " model_format='xgb',\n", + " model_format=\"xgb\",\n", " )\n", - " \n", + "\n", " # read data files from S3\n", " train_df = pd.read_csv(train_s3_path)\n", " validation_df = pd.read_csv(validation_s3_path)\n", - " \n", + "\n", " # create dataframe and label series\n", - " y_train = (train_df.pop(label_column) == 'M').astype('int')\n", - " y_validation = (validation_df.pop(label_column) == 'M').astype('int')\n", - " \n", + " y_train = (train_df.pop(label_column) == \"M\").astype(\"int\")\n", + " y_validation = (validation_df.pop(label_column) == \"M\").astype(\"int\")\n", + "\n", " xgb = XGBClassifier(n_estimators=num_round, **param)\n", " xgb.fit(\n", " train_df,\n", @@ -404,7 +401,7 @@ "outputs": [], "source": [ "@step(\n", - " name='ModelEvaluation',\n", + " name=\"ModelEvaluation\",\n", " instance_type=instance_type,\n", ")\n", "def evaluate(\n", @@ -420,19 +417,19 @@ " mlflow.set_experiment(experiment_name)\n", "\n", " with mlflow.start_run(run_id=run_id):\n", - " with mlflow.start_run(run_name='ModelEvaluation', nested=True):\n", + " with mlflow.start_run(run_name=\"ModelEvaluation\", nested=True):\n", " test_df = pd.read_csv(test_s3_path)\n", - " test_df[label_column] = (test_df[label_column] == 'M').astype('int')\n", - " model = mlflow.pyfunc.load_model(f'runs:/{training_run_id}/model')\n", - " \n", + " test_df[label_column] = (test_df[label_column] == \"M\").astype(\"int\")\n", + " model = mlflow.pyfunc.load_model(f\"runs:/{training_run_id}/model\")\n", + "\n", " results = mlflow.evaluate(\n", " model=model,\n", " data=test_df,\n", " targets=label_column,\n", - " model_type='classifier',\n", - " evaluators=['default'],\n", + " model_type=\"classifier\",\n", + " evaluators=[\"default\"],\n", " )\n", - " return {'f1_score': results.metrics['f1_score']}" + " return {\"f1_score\": results.metrics[\"f1_score\"]}" ] }, { @@ -459,7 +456,7 @@ "outputs": [], "source": [ "@step(\n", - " name='ModelRegistration',\n", + " name=\"ModelRegistration\",\n", " instance_type=instance_type,\n", ")\n", "def register(\n", @@ -474,8 +471,8 @@ " mlflow.set_experiment(experiment_name)\n", "\n", " with mlflow.start_run(run_id=run_id):\n", - " with mlflow.start_run(run_name='ModelRegistration', nested=True):\n", - " mlflow.register_model(f'runs:/{training_run_id}/model', pipeline_name)" + " with mlflow.start_run(run_name=\"ModelRegistration\", nested=True):\n", + " mlflow.register_model(f\"runs:/{training_run_id}/model\", pipeline_name)" ] }, { @@ -499,7 +496,7 @@ "source": [ "preprocessing_step = preprocess(\n", " raw_data_s3_path=input_path,\n", - " output_prefix=f'{pipeline_name}/dataset',\n", + " output_prefix=f\"{pipeline_name}/dataset\",\n", " experiment_name=experiment_name,\n", " run_name=ExecutionVariables.PIPELINE_EXECUTION_ID,\n", ")\n", @@ -512,7 +509,7 @@ ")\n", "\n", "conditional_register_step = ConditionStep(\n", - " name='ConditionalRegister',\n", + " name=\"ConditionalRegister\",\n", " conditions=[\n", " ConditionGreaterThanOrEqualTo(\n", " left=evaluate(\n", @@ -520,16 +517,17 @@ " experiment_name=preprocessing_step[3],\n", " run_id=preprocessing_step[4],\n", " training_run_id=training_step[2],\n", - " )['f1_score'],\n", + " )[\"f1_score\"],\n", " right=0.8,\n", " )\n", " ],\n", - " if_steps=[register(\n", - " pipeline_name=pipeline_name,\n", - " experiment_name=preprocessing_step[3],\n", - " run_id=preprocessing_step[4],\n", - " training_run_id=training_step[2],\n", - " )\n", + " if_steps=[\n", + " register(\n", + " pipeline_name=pipeline_name,\n", + " experiment_name=preprocessing_step[3],\n", + " run_id=preprocessing_step[4],\n", + " training_run_id=training_step[2],\n", + " )\n", " ],\n", " else_steps=[FailStep(name=\"Fail\", error_message=\"Model performance is not good enough\")],\n", ")\n", @@ -539,11 +537,7 @@ " parameters=[\n", " instance_type,\n", " ],\n", - " steps=[\n", - " preprocessing_step,\n", - " training_step,\n", - " conditional_register_step\n", - " ],\n", + " steps=[preprocessing_step, training_step, conditional_register_step],\n", ")" ] }, diff --git a/sagemaker-mlflow/sagemaker_training_mlflow.ipynb b/sagemaker-mlflow/sagemaker_training_mlflow.ipynb index a178ffec67..21bdcc7d7a 100644 --- a/sagemaker-mlflow/sagemaker_training_mlflow.ipynb +++ b/sagemaker-mlflow/sagemaker_training_mlflow.ipynb @@ -66,10 +66,10 @@ "region = sagemaker_session.boto_region_name\n", "\n", "# S3 prefix for the training dataset to be uploaded to\n", - "prefix = 'DEMO-scikit-iris'\n", + "prefix = \"DEMO-scikit-iris\"\n", "\n", "# MLflow (replace these values with your own)\n", - "tracking_server_arn = 'your tracking server arn'" + "tracking_server_arn = \"your tracking server arn\"" ] }, { @@ -105,13 +105,13 @@ "\n", "s3_client = boto3.client(\"s3\")\n", "s3_client.download_file(\n", - " f\"sagemaker-example-files-prod-{region}\", 'datasets/tabular/iris/iris.data', './data/iris.csv'\n", + " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/iris/iris.data\", \"./data/iris.csv\"\n", ")\n", "\n", - "df_iris = pd.read_csv('./data/iris.csv', header=None)\n", - "df_iris[4] = df_iris[4].map({\"Iris-setosa\": 0, 'Iris-versicolor': 1, 'Iris-virginica': 2})\n", + "df_iris = pd.read_csv(\"./data/iris.csv\", header=None)\n", + "df_iris[4] = df_iris[4].map({\"Iris-setosa\": 0, \"Iris-versicolor\": 1, \"Iris-virginica\": 2})\n", "iris = df_iris[[4, 0, 1, 2, 3]].to_numpy()\n", - "np.savetxt('./data/iris.csv', iris, delimiter=',', fmt='%1.1f, %1.3f, %1.3f, %1.3f, %1.3f')" + "np.savetxt(\"./data/iris.csv\", iris, delimiter=\",\", fmt=\"%1.1f, %1.3f, %1.3f, %1.3f, %1.3f\")" ] }, { @@ -127,10 +127,10 @@ "metadata": {}, "outputs": [], "source": [ - "WORK_DIRECTORY = 'data'\n", + "WORK_DIRECTORY = \"data\"\n", "\n", "train_input = sagemaker_session.upload_data(\n", - " WORK_DIRECTORY, key_prefix='{}/{}'.format(prefix, WORK_DIRECTORY)\n", + " WORK_DIRECTORY, key_prefix=\"{}/{}\".format(prefix, WORK_DIRECTORY)\n", ")" ] }, @@ -251,17 +251,15 @@ "outputs": [], "source": [ "sklearn = SKLearn(\n", - " entry_point='train.py',\n", - " source_dir='training_code',\n", - " framework_version='1.2-1',\n", - " instance_type='ml.c4.xlarge',\n", + " entry_point=\"train.py\",\n", + " source_dir=\"training_code\",\n", + " framework_version=\"1.2-1\",\n", + " instance_type=\"ml.c4.xlarge\",\n", " role=role,\n", " sagemaker_session=sagemaker_session,\n", - " hyperparameters={'max_leaf_nodes': 30},\n", + " hyperparameters={\"max_leaf_nodes\": 30},\n", " keep_alive_period_in_seconds=3600,\n", - " environment={\n", - " 'MLFLOW_TRACKING_ARN': tracking_server_arn\n", - " }\n", + " environment={\"MLFLOW_TRACKING_ARN\": tracking_server_arn},\n", ")" ] }, From e5635dac4ed161426671411a5544919758cca07a Mon Sep 17 00:00:00 2001 From: zicanl-amazon <115581573+zicanl-amazon@users.noreply.github.com> Date: Fri, 5 Jul 2024 12:41:33 -0700 Subject: [PATCH 04/16] feat: Model monitor json support for Explainability and Bias (#4696) --- ...oring-Bias-Drift-for-Batch-Transform.ipynb | 2539 +++++++++++++++ ...r-Monitoring-Bias-Drift-for-Endpoint.ipynb | 2816 +++++++++++++++++ ...ttribution-Drift-for-Batch-Transform.ipynb | 2132 +++++++++++++ ...ature-Attribution-Drift-for-Endpoint.ipynb | 2128 +++++++++++++ .../model/ll-adult-prediction-model.tar.gz | Bin 0 -> 950 bytes .../test_data/test-dataset.json | 1 + .../test_data/validation-dataset.json | 1 + sagemaker_model_monitor/index.rst | 4 + 8 files changed, 9621 insertions(+) create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/model/ll-adult-prediction-model.tar.gz create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/test_data/test-dataset.json create mode 100644 sagemaker_model_monitor/fairness_and_explainability_json/test_data/validation-dataset.json diff --git a/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb new file mode 100644 index 0000000000..600bb55b60 --- /dev/null +++ b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb @@ -0,0 +1,2539 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8fed5e9-c09d-4b46-97fb-a4c02a0406f1", + "metadata": {}, + "source": [ + "# Amazon SageMaker Clarify Model Bias Monitor for Batch Transform - JSON Format" + ] + }, + { + "cell_type": "markdown", + "id": "2809bd54-e139-4b94-ae51-fb11c3b2fd94", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b34e2a6c-85b0-4057-8dc7-e23b7d872f79", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 60 minutes to run." + ] + }, + { + "cell_type": "markdown", + "id": "6441b171-4921-4f22-9104-92d59a73da31", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "* [Introduction](#Introduction)\n", + "* [General Setup](#General-Setup)\n", + " * [Imports](#Imports)\n", + " * [Handful of configuration](#Handful-of-configuration)\n", + " * [Data files](#Data-files)\n", + " * [SageMaker model](#SageMaker-model)\n", + "* [Batch Transform Job](#Batch-Transform-Job)\n", + " * [Captured data](#Captured-data)\n", + " * [Transform output](#Transform-output)\n", + "* [Ground Truth Data](#Ground-Truth-Data)\n", + "* [Model Bias Monitor](#Model-Bias-Monitor)\n", + " * [Baselining job](#Baselining-job)\n", + " * [Configurations](#Configurations)\n", + " * [Kick off baselining job](#Kick-off-baselining-job)\n", + " * [Monitoring Schedule](#Monitoring-Schedule)\n", + " * [Wait for the first execution](#Wait-for-the-first-execution)\n", + " * [Wait for the execution to finish](#Wait-for-the-execution-to-finish)\n", + " * [Merged data](#Merged-data)\n", + " * [Inspect execution results](#Inspect-execution-results)\n", + "* [Cleanup](#Cleanup)" + ] + }, + { + "cell_type": "markdown", + "id": "1ea61cef-1e40-4d2f-ba58-7f8bbd55d258", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "b8fd9f7a-f943-4db4-ac5c-22a928312593", + "metadata": {}, + "source": [ + "[Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) continuously monitors the quality of Amazon SageMaker machine learning models in production. It enables developers to set alerts for when there are deviations in the model quality. Early and pro-active detection of these deviations enables corrective actions, such as retraining models, auditing upstream systems, or fixing data quality issues without having to monitor models manually or build additional tooling. \n", + "\n", + "[Amazon SageMaker Clarify Model Bias Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift.html) is a model monitor that helps data scientists and ML engineers monitor predictions for bias on a regular basis. Bias can be introduced or exacerbated in deployed ML models when the training data differs from the data that the model sees during deployment (that is, the live data). These kinds of changes in the live data distribution might be temporary (for example, due to some short-lived, real-world events) or permanent. In either case, it might be important to detect these changes. For example, the outputs of a model for predicting home prices can become biased if the mortgage rates used to train the model differ from current, real-world mortgage rates. With bias drift detection capabilities in model monitor, when SageMaker detects bias beyond a certain threshold, it automatically generates metrics that you can view in SageMaker Studio and through Amazon CloudWatch alerts. \n", + "\n", + "This notebook demonstrates the process for setting up a model monitor for continuous monitoring of bias drift of the data and model used by a regularly running [SageMaker Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html) job. The model input and output are in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats).\n", + "\n", + "In general, you can use the model bias monitor for batch transform in this way,\n", + "\n", + "1. Schedule a model bias monitor to monitor a data capture S3 location and a ground truth S3 location\n", + "1. Regularly run transform jobs with data capture enabled, the jobs save captured data to the data capture S3 URI\n", + "1. Regularly label the captured data, and then upload the ground truth labels to the ground truth S3 URI\n", + "\n", + "The monitor executes processing jobs regularly to merge the captured data and ground truth data, do bias analysis for the merged data, and then generate analysis reports and publish metrics to CloudWatch." + ] + }, + { + "cell_type": "markdown", + "id": "1a7fd45c-f969-4153-b8d0-c66bbdfc5d2c", + "metadata": {}, + "source": [ + "## General Setup" + ] + }, + { + "cell_type": "markdown", + "id": "c982d121-c590-42f9-9e8b-8864e08d4dd6", + "metadata": {}, + "source": [ + "The notebook uses the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk). The following cell upgrades the SDK and its dependencies. Then you may need to restart the kernel and rerun the notebook to pick up the up-to-date APIs, if the notebook is executed in the SageMaker Studio." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ec6b97c9-aedd-43f7-9bca-b2dfe9fb0e96", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: sagemaker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (2.203.1)\n", + "Requirement already satisfied: attrs<24,>=23.1.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (23.1.0)\n", + "Requirement already satisfied: 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\u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: boto3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (1.34.22)\n", + "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3) (1.0.1)\n", + "Requirement already satisfied: botocore<1.35.0,>=1.34.22 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3) 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"!pip install -U sagemaker\n", + "!pip install -U boto3\n", + "!pip install -U botocore" + ] + }, + { + "cell_type": "markdown", + "id": "e9ed2158-2412-4c5d-b78e-afbd44b24970", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "The following cell imports the APIs to be used by the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c417c278-ea72-4718-8b4f-79488a2b3c08", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/zicanl/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "import sagemaker\n", + "import pandas as pd\n", + "import datetime\n", + "import json\n", + "import os\n", + "import pprint\n", + "import random\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "4b25ca1f-4fbe-4615-a4a6-7ade8cf7840a", + "metadata": {}, + "source": [ + "### Handful of configuration\n", + "\n", + "To begin, ensure that these prerequisites have been completed.\n", + "\n", + "* Specify an AWS Region to host the model.\n", + "* Specify an IAM role to execute jobs.\n", + "* Define the S3 URIs that stores the model file, input data and output data. For demonstration purposes, this notebook uses the same bucket for them. In reality, they could be separated with different security policies." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cdbbc40f-958b-4f14-9340-e746e9cb5b67", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AWS region: us-west-2\n", + "RoleArn: arn:aws:iam::678264136642:role/Admin\n", + "Demo Bucket: sagemaker-us-west-2-678264136642\n", + "Demo Prefix: sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224\n", + "Demo S3 key: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224\n", + "The transform job will save the results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/transform-output\n", + "The transform job will save the captured data to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/data-capture\n", + "You should upload the ground truth data to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/ground-truth\n", + "The baselining job will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/baselining-output\n", + "The monitor will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output\n" + ] + } + ], + "source": [ + "sagemaker_session = sagemaker.Session()\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "print(f\"AWS region: {region}\")\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "print(f\"RoleArn: {role}\")\n", + "\n", + "# A different bucket can be used, but make sure the role for this notebook has\n", + "# the s3:PutObject permissions. This is the bucket into which the data is captured\n", + "bucket = sagemaker_session.default_bucket()\n", + "print(f\"Demo Bucket: {bucket}\")\n", + "prefix = sagemaker.utils.unique_name_from_base(\"sagemaker/DEMO-ClarifyModelMonitor\")\n", + "print(f\"Demo Prefix: {prefix}\")\n", + "s3_key = f\"s3://{bucket}/{prefix}\"\n", + "print(f\"Demo S3 key: {s3_key}\")\n", + "\n", + "data_capture_s3_uri = f\"{s3_key}/data-capture\"\n", + "ground_truth_s3_uri = f\"{s3_key}/ground-truth\"\n", + "transform_output_s3_uri = f\"{s3_key}/transform-output\"\n", + "baselining_output_s3_uri = f\"{s3_key}/baselining-output\"\n", + "monitor_output_s3_uri = f\"{s3_key}/monitor-output\"\n", + "\n", + "print(f\"The transform job will save the results to: {transform_output_s3_uri}\")\n", + "print(f\"The transform job will save the captured data to: {data_capture_s3_uri}\")\n", + "print(f\"You should upload the ground truth data to: {ground_truth_s3_uri}\")\n", + "print(f\"The baselining job will save the analysis results to: {baselining_output_s3_uri}\")\n", + "print(f\"The monitor will save the analysis results to: {monitor_output_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5941e5e7-c736-42ed-a62f-5753dc2de9a9", + "metadata": {}, + "source": [ + "### Data files\n", + "\n", + "This example includes two dataset files, both in the JSON format." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0febbeca-5f6d-45c2-a22a-d20fd4421987", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "train_dataset_path = \"test_data/validation-dataset.json\"\n", + "test_dataset_path = \"test_data/test-dataset.json\"\n", + "dataset_type = \"application/json\"" + ] + }, + { + "cell_type": "markdown", + "id": "4bb4a5b0-dc84-4b31-aa35-53f4ad8be13f", + "metadata": {}, + "source": [ + "The train dataset has the features and the ground truth label (pointed to by the key \"label\")," + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5680108b-3476-43e6-a83d-b2e1b8e5f012", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "!head -n 5 $train_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "5a838e40-d2e9-4dd8-907b-76c3220ea7d9", + "metadata": {}, + "source": [ + "The test dataset only has features." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b45b03e8-f5c5-4dbd-b1cf-5be4a1c43639", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "!head -n 5 $test_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "df3caae2-4d31-4ee1-b4ce-0eb7d1c811f8", + "metadata": {}, + "source": [ + "Here are the headers of the train dataset. \"Target\" is the header of the ground truth label, and the others are the feature headers. They will be used to beautify the analysis report." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5266e5e7-eb58-4dd7-8fa6-c385acf6b3a6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_headers = [\n", + " \"Age\",\n", + " \"Workclass\",\n", + " \"fnlwgt\",\n", + " \"Education\",\n", + " \"Education-Num\",\n", + " \"Marital Status\",\n", + " \"Occupation\",\n", + " \"Relationship\",\n", + " \"Ethnic group\",\n", + " \"Sex\",\n", + " \"Capital Gain\",\n", + " \"Capital Loss\",\n", + " \"Hours per week\",\n", + " \"Country\",\n", + " \"Target\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "386e0d4b-4597-466a-bce1-e18278cf93a7", + "metadata": {}, + "source": [ + "To verify that the execution role for this notebook has the necessary permissions to proceed, put a simple test object into the S3 bucket specified above. If this command fails, update the role to have `s3:PutObject` permission on the bucket and try again." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1537eaae-d22f-4a5b-bcc6-0b31f323e831", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! We are all set to proceed with uploading to S3.\n" + ] + } + ], + "source": [ + "sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=\"hello\",\n", + " desired_s3_uri=f\"{s3_key}/upload-test-file.txt\",\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(\"Success! We are all set to proceed with uploading to S3.\")" + ] + }, + { + "cell_type": "markdown", + "id": "abce5c2a-6b46-489b-aab2-6f26dc185c8e", + "metadata": {}, + "source": [ + "Then upload the data files to S3 so that they can be used by SageMaker." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "48a03cdf-6353-4b25-9807-e3bdd3957f6f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data is uploaded to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/validation-dataset.json\n", + "Test data is uploaded to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/test-dataset.json\n" + ] + } + ], + "source": [ + "train_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=train_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Train data is uploaded to: {train_data_s3_uri}\")\n", + "test_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=test_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Test data is uploaded to: {test_data_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "74ca9cc1-8eb9-491f-a210-b4b383f8b00a", + "metadata": {}, + "source": [ + "### SageMaker model\n", + "\n", + "This example includes a prebuilt [SageMaker Linear Learner](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html) model trained by [a SageMaker Clarify offline processing example notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb). The model supports [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats) (MIME type `\"application/jsonlines\"`).\n", + "\n", + "* The model input can one or more lines, each line is a JSON object that has a \"features\" key pointing to a list of feature values concerning demographic characteristics of individuals. For example,\n", + "\n", + "```\n", + "{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}\n", + "{\"features\":[43,2,72338,12,14,2,12,0,1,1,0,0,40,37]}\n", + "```\n", + "\n", + "* The model output has the predictions of whether a person has a yearly income that is more than $50,000. Each prediction is a JSON object that has a \"predicted_label\" key pointing to the predicted label, and the \"score\" key pointing to the confidence score. For example,\n", + "\n", + "```\n", + "{\"predicted_label\":1,\"score\":0.989977359771728}\n", + "{\"predicted_label\":1,\"score\":0.504138827323913}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "27f3be02-d05a-4083-aa2a-5828f42e495e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model file has been uploaded to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/ll-adult-prediction-model.tar.gz\n", + "SageMaker model name: DEMO-xgb-churn-pred-model-monitor-1705692245-0c05\n", + "SageMaker Linear Learner image: 174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:1\n", + "SageMaker model created\n" + ] + } + ], + "source": [ + "model_file = \"model/ll-adult-prediction-model.tar.gz\"\n", + "model_url = sagemaker.s3.S3Uploader.upload(\n", + " local_path=model_file,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Model file has been uploaded to {model_url}\")\n", + "\n", + "model_name = sagemaker.utils.unique_name_from_base(\"DEMO-xgb-churn-pred-model-monitor\")\n", + "print(f\"SageMaker model name: {model_name}\")\n", + "\n", + "image_uri = sagemaker.image_uris.retrieve(\"linear-learner\", region, \"1\")\n", + "print(f\"SageMaker Linear Learner image: {image_uri}\")\n", + "\n", + "model = sagemaker.model.Model(image_uri=image_uri, model_data=model_url, role=role)\n", + "container_def = model.prepare_container_def()\n", + "sagemaker_session.create_model(model_name, role, container_def)\n", + "print(\"SageMaker model created\")" + ] + }, + { + "cell_type": "markdown", + "id": "3e472892-aa3c-4d49-b2f1-079cc743a51d", + "metadata": {}, + "source": [ + "## Batch Transform Job\n", + "\n", + "For continuous monitoring, batch transform jobs should be executed regularly with the latest data. But for demonstration purpose, the following cell only executes the job once before the monitor is scheduled, so that the first monitoring execution has captured data to process. \n", + "\n", + "See [Transformer](https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform) for the API reference. Highlights,\n", + "\n", + "* `destination_s3_uri` is used to specify the data capture S3 URI which is a key connection between the job and the monitor.\n", + "* `join_source` must be set to \"Input\" for the transform output to include predictions (model output) as well as features (model input), because model bias monitor requires both.\n", + "* `generate_inference_id` must be set to True for the transform output to include a unique ID for each record. Model bias monitor requires both predicted labels and ground truth labels, so it needs the ID to join the captured data and the ground truth data.\n", + "\n", + "**NOTE**: The following cell takes about 5 minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1550a84a-cc26-4e47-a169-217194896302", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating transform job with name: linear-learner-2024-01-19-19-24-07-189\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "................................................................!\n" + ] + } + ], + "source": [ + "transfomer = model.transformer(\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " accept=dataset_type, # The transform output data format\n", + " assemble_with=\"None\", # JSON records are under a single JSON structure, but this is required if join_source is set\n", + " output_path=transform_output_s3_uri,\n", + ")\n", + "\n", + "transfomer.transform(\n", + " data=test_data_s3_uri,\n", + " content_type=dataset_type, # The transform input format\n", + " split_type=\"None\", # JSON records are under a single JSON structure, but this is required if join_source is set\n", + " join_source=\"Input\", # Include model input (features) in transform output\n", + " batch_data_capture_config=sagemaker.inputs.BatchDataCaptureConfig(\n", + " destination_s3_uri=data_capture_s3_uri,\n", + " generate_inference_id=True, # Inference ID is mandatory to join the captured data and the ground truth data\n", + " ),\n", + " wait=True, # In real world you don't have to wait, but for demo purpose we wait for the output\n", + " logs=False, # You can change it to True to view job logs inline\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3de796d1-3d2d-4224-83a8-4f954572a502", + "metadata": {}, + "source": [ + "### Captured data" + ] + }, + { + "cell_type": "markdown", + "id": "cb76e4e6-ef53-4a64-865a-470b25e1b700", + "metadata": {}, + "source": [ + "Once the transform job completed, an \"output\" folders is created under `data_capture_s3_uri`, to includes the captured data files of transform output. Note that, batch transform data capture is unlike endpoint data capture, it does not capture the data for real as it will create tremendous amount of duplications. Instead, it generates [manifest](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_S3DataSource.html#sagemaker-Type-S3DataSource-S3Uri) files which refer to the transform output S3 location." + ] + }, + { + "cell_type": "markdown", + "id": "a5aa5261-8e4a-4c46-bf35-fa1b27e19f6e", + "metadata": {}, + "source": [ + "Now list the captured data files stored in Amazon S3. There should be different files from different time periods organized based on the hour in which the batch transformation occurred. The format of the Amazon S3 path is:\n", + "\n", + "`s3://{data_capture_s3_uri}/output/yyyy/mm/dd/hh/filename.jsonl`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b525daed-2a5c-4808-bcfc-0fb33f193e1c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found capture data files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/data-capture/output/2024/01/19/19/5ef12a7a-2c09-4d6f-817b-84823cee935f.json\n" + ] + } + ], + "source": [ + "data_capture_output = f\"{data_capture_s3_uri}/output\"\n", + "captured_data_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=data_capture_output,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")\n", + "print(\"Found capture data files:\")\n", + "print(\"\\n \".join(captured_data_files[-5:]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cc046d8a-976c-4f45-bb3b-73c33ed3857f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"prefix\": \"s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/transform-output/\"\n", + " },\n", + " \"test-dataset.json.out\"\n", + "]\n" + ] + } + ], + "source": [ + "data_capture_output_dict = json.loads(\n", + " sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=captured_data_files[-1],\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")\n", + "print(json.dumps(data_capture_output_dict, indent=4))" + ] + }, + { + "cell_type": "markdown", + "id": "9d8848e2-18f8-4155-bdac-ffadb6625265", + "metadata": {}, + "source": [ + "### Transform output\n", + "\n", + "The captured data file refers to the transform output `.out` file." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9d87d37f-4429-4ff8-bf5a-b59dba99e29e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/transform-output/test-dataset.json.out'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transform_output = os.path.join(data_capture_output_dict[0][\"prefix\"], data_capture_output_dict[1])\n", + "transform_output" + ] + }, + { + "cell_type": "markdown", + "id": "d67fd60d-8d94-47f3-85cd-90b3fab1d39c", + "metadata": {}, + "source": [ + "View the content of the capture file." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1b4317f7-14b9-42d3-89a4-53a052dd89e2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "transform_output_content = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=transform_output,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(transform_output_content, sep=\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "d5af2f1d-f52a-4267-9019-81c62545d722", + "metadata": {}, + "source": [ + "The contents of a single line is present below in formatted JSON to observe a little better.\n", + "\n", + "* The features are captured because the `join_source` parameter is set to \"Input\".\n", + "* The predictions are captured into the `\"SageMakerOutput\"` field.\n", + "* The inference ID and inference time (the start time of the transform job) are also captured because the `generate_inference_id` parameter is set to True." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b18ee7c8-b430-4653-b4a4-c5e29c8d2d7d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# To pretty print the Transform output, uncomment below. Warning: this could result in a very long log!\n", + "# print(json.dumps(json.loads(transform_output_content), indent=4))" + ] + }, + { + "cell_type": "markdown", + "id": "a71d2671-f7eb-468d-aeb7-dd0af89f1edd", + "metadata": {}, + "source": [ + "## Ground Truth Data\n", + "\n", + "Besides captured data, bias drift monitoring execution also requires ground truth data. In real use cases, you should regularly label the captured data, then upload the ground truth data (labels) to designated S3 location. For demonstration purpose, this example notebook generates fake ground truth data following [this schema](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-merge.html), and then uploads it to `ground_truth_s3_uri` which is another key input to the monitor. The bias drift monitoring execution will first merge the captured data and the ground truth data, and then do bias analysis for the merged data.\n", + "\n", + "Notice the value of the `data` field in `groundTruthData` **must be in the same format as how the ground truth labels are stored in the input dataset**." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2884c0f9-6ccf-4555-8132-438d66619975", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def ground_truth_with_id(seeds, inference_id):\n", + " instances = []\n", + " for seed in seeds:\n", + " random.seed(seed) # to get consistent results\n", + " label = (\n", + " 1 if random.random() < 0.7 else 0\n", + " ) # randomly generate positive labels 70% of the time\n", + " instances.append(\n", + " {\"label\": label}\n", + " ) # Also use the \"label\" key, the same as in the input dataset.\n", + " # format required by the merge job and bias monitoring job\n", + " return {\n", + " \"groundTruthData\": {\n", + " \"data\": json.dumps({\"instances\": instances}),\n", + " \"encoding\": \"JSON\",\n", + " },\n", + " \"eventMetadata\": {\n", + " \"eventId\": str(inference_id),\n", + " },\n", + " \"eventVersion\": \"0\",\n", + " }\n", + "\n", + "\n", + "def upload_ground_truth(upload_time, upload_path, seeds, inference_id):\n", + " # Single JSON object, containing all records\n", + " fake_data = [ground_truth_with_id(seeds, inference_id)]\n", + " data_to_upload = json.dumps(fake_data)\n", + " target_s3_uri = f\"{upload_path}/{upload_time:%Y/%m/%d/%H/%M%S}.jsonl\"\n", + " print(f\"Uploading {len(seeds)} records to\", target_s3_uri)\n", + " sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=data_to_upload,\n", + " desired_s3_uri=target_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b405cafc-3300-4291-923c-78800630c505", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "94bcc22a-0462-4bd1-92c9-b46a5aaec1aa\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333]\n", + "Uploading 334 records to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/ground-truth/2024/01/19/18/2934.jsonl\n", + "Uploading 334 records to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/ground-truth/2024/01/19/19/2934.jsonl\n" + ] + } + ], + "source": [ + "now = datetime.datetime.utcnow()\n", + "# Use unique IDs for each record. JSON differs from JSONLines in that a single InferenceId can have multiple records,\n", + "# so we use arbitrary IDs so we can generate the ground truth labels consistently for each inference ID.\n", + "inference_id = json.loads(transform_output_content)[\"SageMakerInferenceId\"]\n", + "seeds = [i for i, _record in enumerate(json.loads(transform_output_content)[\"instances\"])]\n", + "print(inference_id)\n", + "print(seeds)\n", + "# Generate data for the last hour, in case the first monitoring execution is in this hour\n", + "upload_ground_truth(\n", + " upload_time=now - datetime.timedelta(hours=1),\n", + " upload_path=ground_truth_s3_uri,\n", + " seeds=seeds,\n", + " inference_id=inference_id,\n", + ")\n", + "# Generate data for this hour, in case the first monitoring execution will be in the next hour\n", + "upload_ground_truth(\n", + " upload_time=now,\n", + " upload_path=ground_truth_s3_uri,\n", + " seeds=seeds,\n", + " inference_id=inference_id,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fad6a33c-3a10-4550-86df-dd8fc2f61099", + "metadata": {}, + "source": [ + "## Model Bias Monitor\n", + "\n", + "Similar to the other monitoring types, the standard procedure of creating a [bias drift monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift.html) is first run a baselining job, and then schedule the monitor.\n", + "\n", + "A bias drift monitoring execution starts a merge job that joins the captured data and ground truth data together using the inference ID. Then a SageMaker Clarify bias analysis job is started to compute all the [pre-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html) and [post-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-post-training-bias.html). on the merged data. The max execution time is divided equally between two jobs, the notebook is scheduling an hourly model bias monitor, so the `max_runtime_in_seconds` parameter should not exceed 1800 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a0241193-a6ae-4156-b6df-abfa032f46dd", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "model_bias_monitor = sagemaker.model_monitor.ModelBiasMonitor(\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " max_runtime_in_seconds=1800,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c0983548-2835-404d-bd79-c3bb90e426e4", + "metadata": {}, + "source": [ + "### Baselining job\n", + "\n", + "A baselining job runs predictions on training dataset and suggests constraints. The `suggest_baseline()` method of `ModelBiasMonitor` starts a SageMaker Clarify processing job to generate the constraints.\n", + "\n", + "The step is not mandatory, but providing constraints file to the monitor can enable violations file generation." + ] + }, + { + "cell_type": "markdown", + "id": "5334db36-ac02-464f-9d10-f1fd8ce62b19", + "metadata": {}, + "source": [ + "#### Configurations\n", + "\n", + "Information about the input data need to be provided to the processor." + ] + }, + { + "cell_type": "markdown", + "id": "b763238a-8ce1-4619-9721-8f1b1b9ac804", + "metadata": {}, + "source": [ + "`DataConfig` stores information about the dataset to be analyzed. For example, the dataset file and its format (like JSON Lines), where to store the analysis results. Some special things to note about this configuration for the JSON Lines dataset,\n", + "\n", + "* The parameter value `\"features\"` or `\"label\"` is **NOT** a header string. Instead, it is a `JMESPath` expression ([refer to its specification](https://jmespath.org/specification.html)) that is used to locate the features list or the ground truth label in the dataset. In this example notebook they happen to be the same as the keys in the dataset. But for example, if the dataset has records like below, then the `features` parameter should use value `\"data.features.values\"`, and the `label` parameter should use value `\"data.label\"`.\n", + "\n", + " ```\n", + " {\"data\": {\"features\": {\"values\": [25, 2, 226802, 1, 7, 4, 6, 3, 2, 1, 0, 0, 40, 37]}, \"label\": 0}}\n", + " ```\n", + "\n", + "* SageMaker Clarify processing job will load the JSON Lines dataset into tabular representation for further analysis, and the parameter `headers` is the list of column names. **The label header shall be the last one in the headers list**, and the order of feature headers shall be the same as the order of features in a record." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6f97cdf8-53ec-4f9b-9168-69279ab4a80b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "features_jmespath = \"instances[*].features\"\n", + "ground_truth_label_jmespath = \"instances[*].label\"\n", + "data_config = sagemaker.clarify.DataConfig(\n", + " s3_data_input_path=train_data_s3_uri,\n", + " s3_output_path=baselining_output_s3_uri,\n", + " features=features_jmespath,\n", + " label=ground_truth_label_jmespath,\n", + " headers=all_headers,\n", + " dataset_type=dataset_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d43b2ecd-5c9a-40cc-9b4f-3699976d4cdd", + "metadata": {}, + "source": [ + "`ModelConfig` is configuration related to model to be used for inferencing. In order to compute post-training bias metrics, the computation needs to get inferences for the SageMaker model. To accomplish this, the processing job will use the model to create an ephemeral endpoint (also known as \"shadow endpoint\"). The processing job will delete the shadow endpoint after the computations are completed. One special thing to note about this configuration for the JSON Lines model input and output,\n", + "\n", + "* `content_template` is used by SageMaker Clarify processing job to convert the tabular data to the request payload acceptable to the shadow endpoint. To be more specific, the placeholder `$features` will be replaced by **the features list** from records. The request payload of a record from the testing dataset happens to be similar to the record itself, like `{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}`, because both the dataset and the model input conform to the same format." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "167fa7ab-d910-47da-8d3a-85a5e12860ef", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_config = sagemaker.clarify.ModelConfig(\n", + " model_name=model_name, # The name of the SageMaker model\n", + " instance_type=\"ml.m5.xlarge\", # The instance type of the shadow endpoint\n", + " instance_count=1, # The instance count of the shadow endpoint\n", + " content_type=dataset_type, # The data format of the model input\n", + " accept_type=dataset_type, # The data format of the model output\n", + " content_template='{\"instances\":$records}',\n", + " record_template='{\"features\":$features}',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "108cd499-e794-4357-ade8-072af816ff60", + "metadata": {}, + "source": [ + "`ModelPredictedLabelConfig` specifies how to extract predicted label from the model output. The example model returns the predicted label as well as the confidence score, so there are two ways to define this configuration,\n", + "\n", + "* Set the `label` parameter to \"predicted_label\" which is the `JMESPath` expression to locate the predicted label in the model output. This is the way used in this example.\n", + "* Alternatively, you can set the `probability` parameter to \"score\" which is the `JMESPath` expression to locate the confidence score in the model output. And set the `probability_threshold` parameter to a floating number in between 0 and 1. The post-training analysis will use it to convert a score to binary predicted label (`0` or `1`). The default value is 0.5, which means a probability value > 0.5 indicates predicted label `1`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fdf0b399-785c-4982-92a8-5c84d481bc5f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "predicted_label_jmespath = \"predictions[*].predicted_label\"\n", + "probability_jmespath = \"predictions[*].score\"\n", + "model_predicted_label_config = sagemaker.clarify.ModelPredictedLabelConfig(\n", + " label=predicted_label_jmespath,\n", + " probability=probability_jmespath,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "91c30fa7-7d07-4273-a6e3-3a3761f3861f", + "metadata": {}, + "source": [ + "`BiasConfig` is the configuration of the sensitive groups in the dataset. Typically, bias is measured by computing a metric and comparing it across groups.\n", + "\n", + " * The group of interest is specified using the facet parameters. With the following configuration, the baselining job will check for bias in the model's predictions with respect to gender and income. Specifically, it is checking if the model is more likely to predict that males have an annual income of over $50,000 compared to females. Although not demonstrated in this example, a bias monitor can measure bias against multiple sensitive attributes, if you provide a list of facets.\n", + " * The `group_name` parameter is used to form subgroups for the measurement of [Conditional Demographic Disparity in Labels](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-metric-cddl.html) (CDDL) and [Conditional Demographic Disparity in Predicted Labels](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cddpl.html) (CDDPL) with regard to [Simpson’s paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3a5da68b-bda0-44f1-8f20-7c68dbced478", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "bias_config = sagemaker.clarify.BiasConfig(\n", + " label_values_or_threshold=[1], # the positive outcome is earning >$50,000\n", + " facet_name=\"Sex\", # the sensitive attribute is the gender\n", + " facet_values_or_threshold=[0], # the disadvantaged group is female\n", + " group_name=\"Age\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cb9c591f-8c18-4979-8991-15e5ca43c803", + "metadata": {}, + "source": [ + "#### Kick off baselining job\n", + "\n", + "Call the `suggest_baseline()` method to start the baselining job. The job computes all the [pre-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html) and [post-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-post-training-bias.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "97b89496-d6c3-44a6-b590-79368253d7dc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.clarify:Analysis Config: {'dataset_type': 'application/json', 'features': 'instances[*].features', 'headers': ['Age', 'Workclass', 'fnlwgt', 'Education', 'Education-Num', 'Marital Status', 'Occupation', 'Relationship', 'Ethnic group', 'Sex', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Country', 'Target'], 'label': 'instances[*].label', 'label_values_or_threshold': [1], 'facet': [{'name_or_index': 'Sex', 'value_or_threshold': [0]}], 'group_variable': 'Age', 'methods': {'report': {'name': 'report', 'title': 'Analysis Report'}, 'pre_training_bias': {'methods': 'all'}, 'post_training_bias': {'methods': 'all'}}, 'predictor': {'model_name': 'DEMO-xgb-churn-pred-model-monitor-1705692245-0c05', 'instance_type': 'ml.m5.xlarge', 'initial_instance_count': 1, 'accept_type': 'application/json', 'content_type': 'application/json', 'content_template': '{\"instances\":$records}', 'record_template': '{\"features\":$features}', 'label': 'predictions[*].predicted_label', 'probability': 'predictions[*].score'}}\n", + "INFO:sagemaker:Creating processing-job with name baseline-suggestion-job-2024-01-19-19-29-35-080\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_bias_monitor.suggest_baseline(\n", + " bias_config=bias_config,\n", + " data_config=data_config,\n", + " model_config=model_config,\n", + " model_predicted_label_config=model_predicted_label_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6260e81a-a1b9-4370-a270-50aea5a795e1", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the baselining job is completed (in about 10 minutes). It then inspects the suggested constraints. This step can be skipped, because the monitor to be scheduled will automatically pick up baselining job name and wait for it before monitoring execution." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c274495b-c8ef-4f09-bc69-e7634279b6c1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................................................................................!\n", + "Suggested constraints: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/baselining-output/analysis.json\n", + "{\n", + " \"version\": \"1.0\",\n", + " \"post_training_bias_metrics\": {\n", + " \"label\": \"Target\",\n", + " \"facets\": {\n", + " \"Sex\": [\n", + " {\n", + " \"value_or_threshold\": \"0\",\n", + " \"metrics\": [\n", + " {\n", + " \"name\": \"AD\",\n", + " \"description\": \"Accuracy Difference (AD)\",\n", + " \"value\": -0.15156641604010024\n", + " },\n", + " {\n", + " \"name\": \"CDDPL\",\n", + " \"description\": \"Conditional Demographic Disparity in Predicted Labels (CDDPL)\",\n", + " \"value\": 0.28176563733194276\n", + " },\n", + " {\n", + " \"name\": \"DAR\",\n", + " \"description\": \"Difference in Acceptance Rates (DAR)\",\n", + " \"value\": -0.09508196721311479\n", + " },\n", + " {\n", + " \"name\": \"DCA\",\n", + " \"description\": \"Difference in Conditional Acceptance (DCA)\",\n", + " \"value\": -0.5278688524590163\n", + " },\n", + " {\n", + " \"name\": \"DCR\",\n", + " \"description\": \"Difference in Conditional Rejection (DCR)\",\n", + " \"value\": 0.027874251497005953\n", + " },\n", + " {\n", + " \"name\": \"DI\",\n", + " \"description\": \"Disparate Impact (DI)\",\n", + " \"value\": 0.17798594847775176\n", + " },\n", + " {\n", + " \"name\": \"DPPL\",\n", + " \"description\": \"Difference in Positive Proportions in Predicted Labels (DPPL)\",\n", + " \"value\": 0.2199248120300752\n", + " },\n", + " {\n", + " \"name\": \"DRR\",\n", + " \"description\": \"Difference in Rejection Rates (DRR)\",\n", + " \"value\": 0.12565868263473046\n", + " },\n", + " {\n", + " \"name\": \"FT\",\n", + " \"description\": \"Flip Test (FT)\",\n", + " \"value\": -0.03333333333333333\n", + " },\n", + " {\n", + " \"name\": \"GE\",\n", + " \"description\": \"Generalized Entropy (GE)\",\n", + " \"value\": 0.0841186702174704\n", + " },\n", + " {\n", + " \"name\": \"RD\",\n", + " \"description\": \"Recall Difference (RD)\",\n", + " \"value\": 0.1308103661044837\n", + " },\n", + " {\n", + " \"name\": \"SD\",\n", + " \"description\": \"Specificity Difference (SD)\",\n", + " \"value\": 0.10465328014037645\n", + " },\n", + " {\n", + " \"name\": \"TE\",\n", + " \"description\": \"Treatment Equality (TE)\",\n", + " \"value\": 2.916666666666667\n", + " }\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " \"label_value_or_threshold\": \"1\"\n", + " },\n", + " \"pre_training_bias_metrics\": {\n", + " \"label\": \"Target\",\n", + " \"facets\": {\n", + " \"Sex\": [\n", + " {\n", + " \"value_or_threshold\": \"0\",\n", + " \"metrics\": [\n", + " {\n", + " \"name\": \"CDDL\",\n", + " \"description\": \"Conditional Demographic Disparity in Labels (CDDL)\",\n", + " \"value\": 0.27459074287718793\n", + " },\n", + " {\n", + " \"name\": \"CI\",\n", + " \"description\": \"Class Imbalance (CI)\",\n", + " \"value\": 0.36936936936936937\n", + " },\n", + " {\n", + " \"name\": \"DPL\",\n", + " \"description\": \"Difference in Positive Proportions in Labels (DPL)\",\n", + " \"value\": 0.2326441102756892\n", + " },\n", + " {\n", + " \"name\": \"JS\",\n", + " \"description\": \"Jensen-Shannon Divergence (JS)\",\n", + " \"value\": 0.04508199943437752\n", + " },\n", + " {\n", + " \"name\": \"KL\",\n", + " \"description\": \"Kullback-Liebler Divergence (KL)\",\n", + " \"value\": 0.22434464102537785\n", + " },\n", + " {\n", + " \"name\": \"KS\",\n", + " \"description\": \"Kolmogorov-Smirnov Distance (KS)\",\n", + " \"value\": 0.2326441102756892\n", + " },\n", + " {\n", + " \"name\": \"LP\",\n", + " \"description\": \"L-p Norm (LP)\",\n", + " \"value\": 0.32900845595810163\n", + " },\n", + " {\n", + " \"name\": \"TVD\",\n", + " \"description\": \"Total Variation Distance (TVD)\",\n", + " \"value\": 0.2326441102756892\n", + " }\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " \"label_value_or_threshold\": \"1\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "model_bias_monitor.latest_baselining_job.wait(logs=False)\n", + "print()\n", + "model_bias_constraints = model_bias_monitor.suggested_constraints()\n", + "print(f\"Suggested constraints: {model_bias_constraints.file_s3_uri}\")\n", + "print(\n", + " sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=model_bias_constraints.file_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "02ec0da8-68db-4a08-a0f2-2c31b931112d", + "metadata": {}, + "source": [ + "### Monitoring Schedule\n", + "\n", + "With above constraints collected, now call `create_monitoring_schedule()` method to schedule an hourly model bias monitor." + ] + }, + { + "cell_type": "markdown", + "id": "c73e6f33-6c9b-4317-9c44-265e0efdcd9b", + "metadata": {}, + "source": [ + "If a baselining job has been submitted, then the monitor object will automatically pick up the analysis configuration from the baselining job. But if the baselining step is skipped, or if the capture dataset has different nature than the training dataset, then analysis configuration has to be provided.\n", + "\n", + "`BiasAnalysisConfig` is a subset of the configuration of the baselining job, many options are not needed because,\n", + "\n", + "* Model bias monitor will merge the captured data and the ground truth data, and then use the merged data as the input dataset.\n", + "* Capture data already includes predictions, so there is no need to create shadow endpoint.\n", + "* Attributes like probability threshold are provided as part of `BatchTransformInput`.\n", + "\n", + "Highlights,\n", + "\n", + "* `data_capture_s3_uri` is the location of data captured by the batch transform job\n", + "* `ground_truth_s3_uri` is the location of ground truth data\n", + "* `features_attribute` is the `JMESPath` expression to locate the features in model input, similar to the `features` parameter of `DataConfig`.\n", + "* `inference_attribute` is the `JMESPath` expression to locate the predicted label in model output, similar to the `label` parameter of `ModelPredictedLabelConfig`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2574e21f-02ee-40b4-8015-625a7e7bc403", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "schedule_expression = sagemaker.model_monitor.CronExpressionGenerator.hourly()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0cd1eec4-0e46-4b78-9b9c-1e0c6700e329", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.model_monitor.model_monitoring:Creating Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-39-971\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model bias monitoring schedule: monitoring-schedule-2024-01-19-19-39-39-971\n" + ] + } + ], + "source": [ + "model_bias_analysis_config = None\n", + "\n", + "model_bias_analysis_config = sagemaker.model_monitor.BiasAnalysisConfig(\n", + " bias_config,\n", + " headers=all_headers,\n", + " label=ground_truth_label_jmespath,\n", + ")\n", + "model_bias_monitor.create_monitoring_schedule(\n", + " analysis_config=model_bias_analysis_config,\n", + " batch_transform_input=sagemaker.model_monitor.BatchTransformInput(\n", + " data_captured_destination_s3_uri=data_capture_s3_uri,\n", + " destination=\"/opt/ml/processing/transform\",\n", + " dataset_format=sagemaker.model_monitor.MonitoringDatasetFormat.json(lines=False),\n", + " features_attribute=features_jmespath, # mandatory if no baselining job\n", + " inference_attribute=predicted_label_jmespath, # mandatory if no baselining job\n", + " # look back 6 hour for transform job output.\n", + " start_time_offset=\"-PT6H\",\n", + " end_time_offset=\"-PT0H\",\n", + " ),\n", + " ground_truth_input=ground_truth_s3_uri,\n", + " output_s3_uri=monitor_output_s3_uri,\n", + " schedule_cron_expression=schedule_expression,\n", + ")\n", + "print(f\"Model bias monitoring schedule: {model_bias_monitor.monitoring_schedule_name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c194c1c2-ef1e-4592-98af-ccd8a55b111f", + "metadata": {}, + "source": [ + "#### Wait for the first execution\n", + "\n", + "The schedule starts jobs at the previously specified intervals. Code below waits until time crosses the hour boundary (in UTC) to see executions kick off.\n", + "\n", + "Note: Even for an hourly schedule, Amazon SageMaker has a buffer period of 20 minutes to schedule executions. The execution might start in anywhere from zero to ~20 minutes from the hour boundary. This is expected and done for load balancing in the backend." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "bbec575c-acde-436f-a74e-dd3013ff4f2b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def wait_for_execution_to_start(model_monitor):\n", + " print(\n", + " \"An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\"\n", + " )\n", + "\n", + " print(\"Waiting for the first execution to happen\", end=\"\")\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " while \"LastMonitoringExecutionSummary\" not in schedule_desc:\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " print()\n", + " print(\"Done! Execution has been created\")\n", + "\n", + " print(\"Now waiting for execution to start\", end=\"\")\n", + " while schedule_desc[\"LastMonitoringExecutionSummary\"][\"MonitoringExecutionStatus\"] in \"Pending\":\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(10)\n", + "\n", + " print()\n", + " print(\"Done! Execution has started\")" + ] + }, + { + "cell_type": "markdown", + "id": "944a9edf-05c1-4ff7-b817-7c95baff3206", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the first monitoring execution is started. As explained above, the wait could take more than 60 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d7332096-d521-4caa-9b11-307252a0d857", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\n", + "Waiting for the first execution to happen............................\n", + "Done! Execution has been created\n", + "Now waiting for execution to start....\n", + "Done! Execution has started\n" + ] + } + ], + "source": [ + "wait_for_execution_to_start(model_bias_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "3f7665fd-098a-46ef-b32a-07c098d5a362", + "metadata": {}, + "source": [ + "In real world, a monitoring schedule is supposed to be active all the time. But in this example, it can be stopped to avoid incurring extra charges. A stopped schedule will not trigger further executions, but the ongoing execution will continue. And if needed, the schedule can be restarted by `start_monitoring_schedule()`." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6eee4204-b662-4a85-ad53-cb945d287a15", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Stopping Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-39-971\n" + ] + } + ], + "source": [ + "model_bias_monitor.stop_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "id": "3fa797ec-938e-4f7b-a028-3414384ef774", + "metadata": {}, + "source": [ + "#### Wait for the execution to finish\n", + "\n", + "In the previous cell, the first execution has started. This section waits for the execution to finish so that its analysis results are available. Here are the possible terminal states and what each of them mean:\n", + "\n", + "* `Completed` - This means the monitoring execution completed, and no issues were found in the violations report.\n", + "* `CompletedWithViolations` - This means the execution completed, but constraint violations were detected.\n", + "* `Failed` - The monitoring execution failed, maybe due to client error (perhaps incorrect role permissions) or infrastructure issues. Further examination of `FailureReason` and `ExitMessage` is necessary to identify what exactly happened.\n", + "* `Stopped` - job exceeded max runtime or was manually stopped." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "35cacb92-8337-4bbf-b84a-cbd2fe52b188", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Waits for the schedule to have last execution in a terminal status.\n", + "def wait_for_execution_to_finish(model_monitor):\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + " if execution_summary is not None:\n", + " print(\"Waiting for execution to finish\", end=\"\")\n", + " while execution_summary[\"MonitoringExecutionStatus\"] not in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + " \"Failed\",\n", + " \"Stopped\",\n", + " ]:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc[\"LastMonitoringExecutionSummary\"]\n", + " print()\n", + " print(f\"Done! Execution Status: {execution_summary['MonitoringExecutionStatus']}\")\n", + " else:\n", + " print(\"Last execution not found\")" + ] + }, + { + "cell_type": "markdown", + "id": "2fa71d56-71d6-4a98-b86c-ca1ec3d2cc36", + "metadata": {}, + "source": [ + "**NOTE**: The following cell takes about 10 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "58a6d1e1-ca5a-4da3-a32b-60af41f73253", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish...........\n", + "Done! Execution Status: CompletedWithViolations\n" + ] + } + ], + "source": [ + "wait_for_execution_to_finish(model_bias_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "95a0e9a8-7ea9-4d4e-a599-ffbc5da194a8", + "metadata": {}, + "source": [ + "#### Merged data\n", + "\n", + "Merged data is the intermediate results of bias drift monitoring execution. It is saved to JSON Lines files under the \"merge\" folder of `monitor_output_s3_uri`. Each line is a valid JSON object which combines the captured data and the ground truth data." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "7ac45f26-e538-4a27-b626-cb882699fd39", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/merge\n", + "Found merged data files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/merge/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/19/part-00000-8cf95ad5-2469-48d9-a3dc-dd5a17fc8823.c000.jsonl\n" + ] + } + ], + "source": [ + "merged_data_s3_uri = f\"{monitor_output_s3_uri}/merge\"\n", + "print(merged_data_s3_uri)\n", + "merged_data_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=merged_data_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")\n", + "print(\"Found merged data files:\")\n", + "print(\"\\n \".join(merged_data_files[-5:]))" + ] + }, + { + "cell_type": "markdown", + "id": "2eab9902-d42f-4d7c-ae4e-502f6fe99e86", + "metadata": {}, + "source": [ + "The following cell prints a single line of a merged data file.\n", + "\n", + "* `eventId` is the inference ID from the captured data and the ground truth data\n", + "* `groundTruthData` is from the ground truth data\n", + "* `captureData` is from the captured data. In this case, the `data` of `batchTransformOutput` is from the transform output." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d999738d-0dcc-4275-8e0e-18330f11ee9f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"eventMetadata\": {\n", + " \"eventId\": \"94bcc22a-0462-4bd1-92c9-b46a5aaec1aa\"\n", + " },\n", + " \"eventVersion\": \"0\",\n", + " \"groundTruthData\": {\n", + " \"data\": \"{\\\"instances\\\": [{\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 0}, 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,0,21,37]},{\\\"features\\\":[49,2,96854,11,9,0,7,4,4,1,0,0,40,37]},{\\\"features\\\":[45,2,293628,15,10,2,9,0,4,1,0,0,50,28]},{\\\"features\\\":[67,2,192995,11,9,6,0,4,4,0,6723,0,40,37]},{\\\"features\\\":[30,2,235847,9,13,4,7,3,4,0,0,0,24,37]}]}\",\n", + " \"encoding\": \"JSON\"\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "merged_record = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=merged_data_files[-1],\n", + " sagemaker_session=sagemaker_session,\n", + ").splitlines()[0]\n", + "print(json.dumps(json.loads(merged_record), indent=4))" + ] + }, + { + "cell_type": "markdown", + "id": "fd5e4c67-c0a4-44bf-95df-0e7260300295", + "metadata": {}, + "source": [ + "#### Inspect execution results\n", + "\n", + "List the generated reports,\n", + "\n", + "* analysis.json includes all the bias metrics.\n", + "* report.* files are static report files to visualize the bias metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "52e49e74-70fa-48b6-b38b-497116c0b8d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Report URI: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/20\n", + "Found Report Files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/20/analysis.json\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/20/constraint_violations.json\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/20/report.html\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/20/report.ipynb\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692244-1224/monitor-output/monitoring-schedule-2024-01-19-19-39-39-971/2024/01/19/20/report.pdf\n" + ] + } + ], + "source": [ + "schedule_desc = model_bias_monitor.describe_schedule()\n", + "execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + "if execution_summary and execution_summary[\"MonitoringExecutionStatus\"] in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + "]:\n", + " last_model_bias_monitor_execution = model_bias_monitor.list_executions()[-1]\n", + " last_model_bias_monitor_execution_report_uri = (\n", + " last_model_bias_monitor_execution.output.destination\n", + " )\n", + " print(f\"Report URI: {last_model_bias_monitor_execution_report_uri}\")\n", + " last_model_bias_monitor_execution_report_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=last_model_bias_monitor_execution_report_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " )\n", + " print(\"Found Report Files:\")\n", + " print(\"\\n \".join(last_model_bias_monitor_execution_report_files))\n", + "else:\n", + " last_model_bias_monitor_execution = None\n", + " print(\n", + " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", + " )\n", + " print(schedule_desc)" + ] + }, + { + "cell_type": "markdown", + "id": "c1204d4b-ab2f-4a7c-b3c1-78682fefe54a", + "metadata": {}, + "source": [ + "If there are any violations compared to the baseline, they are listed here. See [Bias Drift Violations](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift-violations.html) for the schema of the file, and how violations are detected." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a4bf40f9-42dc-4090-b64c-26f6936a9d49", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{ 'version': '1.0',\n", + " 'violations': [ { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.374894782529513 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.28176563733194276',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'CDDPL'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -0.34693877551020413 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement -0.09508196721311479',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'DAR'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -36.69387755102041 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement -0.5278688524590163',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'DCA'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -0.07650793650793647 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.027874251497005953',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'DCR'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.9454985573866695 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.0841186702174704',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'GE'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.17253086419753086 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.1308103661044837',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'RD'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.27419354838709675 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.10465328014037645',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'SD'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': \"Metric value Infinity doesn't meet \"\n", + " 'the baseline constraint requirement '\n", + " '2.916666666666667',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'TE'}]}\n" + ] + } + ], + "source": [ + "violations = model_bias_monitor.latest_monitoring_constraint_violations()\n", + "if violations is not None:\n", + " pprint.PrettyPrinter(indent=4).pprint(violations.body_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "86787189-5189-4acc-b254-e5e75b2b67d0", + "metadata": {}, + "source": [ + "By default, the analysis results are also published to CloudWatch, see [CloudWatch Metrics for Bias Drift Analysis](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift-cw.html)." + ] + }, + { + "cell_type": "markdown", + "id": "ad2f1a28-ae35-4a41-a24d-74a63310c431", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "If there is no plan to collect more data for bias drift monitoring, then the monitor should be stopped (and deleted) to avoid incurring additional charges. Note that deleting the monitor does not delete the data in S3." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8f47aa2a-5f16-4be7-9b92-a0a6a68c6d05", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Stopping Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-39-971\n", + "INFO:sagemaker:Deleting Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-39-971\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish\n", + "Done! Execution Status: CompletedWithViolations\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Deleting Model Bias Job Definition with name: model-bias-job-definition-2024-01-19-19-39-39-971\n" + ] + } + ], + "source": [ + "model_bias_monitor.stop_monitoring_schedule()\n", + "wait_for_execution_to_finish(model_bias_monitor)\n", + "model_bias_monitor.delete_monitoring_schedule()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "dba6514b-b9e3-4924-8c75-e3ec7a1d687f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting model with name: DEMO-xgb-churn-pred-model-monitor-1705692245-0c05\n" + ] + } + ], + "source": [ + "sagemaker_session.delete_model(model_name)" + ] + }, + { + "cell_type": "markdown", + "id": "0340d10c-1d01-48b4-9270-0c411f729e93", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Batch-Transform.ipynb)\n" + ] + } + ], + "metadata": { + "availableInstances": [ + { + "_defaultOrder": 0, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.t3.medium", + "vcpuNum": 2 + }, + { + "_defaultOrder": 1, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.t3.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 2, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.t3.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 3, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.t3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 4, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 5, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 6, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 7, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 8, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 9, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 10, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 11, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 12, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5d.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 13, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + 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"name": "ml.g5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 48, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 49, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 50, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 51, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 52, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + } + ], + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb new file mode 100644 index 0000000000..9ea09c3c5e --- /dev/null +++ b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb @@ -0,0 +1,2816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a524a4c-5a39-4b6b-abb1-1c8e1b2de84c", + "metadata": {}, + "source": [ + "# Amazon SageMaker Clarify Model Bias Monitor - JSON Format" + ] + }, + { + "cell_type": "markdown", + "id": "0a951176-6357-4afb-9cbc-c12c203d7a4e", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4eaae7a8-2ab1-4f7c-8cb2-6b23606c58c1", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 60 minutes to run." + ] + }, + { + "cell_type": "markdown", + "id": "8ea223b4-8caa-47a6-a65d-d4e21c9d72e5", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "* [Introduction](#Introduction)\n", + "* [General Setup](#General-Setup)\n", + " * [Imports](#Imports)\n", + " * [Handful of configuration](#Handful-of-configuration)\n", + " * [Model file and data files](#Model-file-and-data-files)\n", + "* [Real-time Inference Endpoint](#Real-time-Inference-Endpoint)\n", + " * [Deploy the model to an endpoint](#Deploy-the-model-to-an-endpoint)\n", + " * [Invoke the endpoint](#Invoke-the-endpoint)\n", + " * [Example: Single record](#Example:-Single-record)\n", + " * [Example: Two records](#Example:-Two-records)\n", + " * [View captured data](#View-captured-data)\n", + " * [Start generating some artificial traffic](#Start-generating-some-artificial-traffic)\n", + "* [Ground Truth Data](#Ground-Truth-Data)\n", + "* [Model Bias Monitor](#Model-Bias-Monitor)\n", + " * [Baselining job](#Baselining-job)\n", + " * [Configurations](#Configurations)\n", + " * [Kick off baselining job](#Kick-off-baselining-job)\n", + " * [Monitoring Schedule](#Monitoring-Schedule)\n", + " * [Wait for the first execution](#Wait-for-the-first-execution)\n", + " * [Wait for the execution to finish](#Wait-for-the-execution-to-finish)\n", + " * [Merged data](#Merged-data)\n", + " * [Inspect execution results](#Inspect-execution-results)\n", + "* [Cleanup](#Cleanup)" + ] + }, + { + "cell_type": "markdown", + "id": "a0a2c6a4-a249-40bf-adbc-8bd00fb06cfe", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "1879bacd-fedd-434a-8094-40cd48f5f140", + "metadata": {}, + "source": [ + "[Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) continuously monitors the quality of Amazon SageMaker machine learning models in production. It enables developers to set alerts for when there are deviations in the model quality. Early and pro-active detection of these deviations enables corrective actions, such as retraining models, auditing upstream systems, or fixing data quality issues without having to monitor models manually or build additional tooling. \n", + "\n", + "[Amazon SageMaker Clarify Model Bias Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift.html) is a model monitor that helps data scientists and ML engineers monitor predictions for bias on a regular basis. Bias can be introduced or exacerbated in deployed ML models when the training data differs from the data that the model sees during deployment (that is, the live data). These kinds of changes in the live data distribution might be temporary (for example, due to some short-lived, real-world events) or permanent. In either case, it might be important to detect these changes. For example, the outputs of a model for predicting home prices can become biased if the mortgage rates used to train the model differ from current, real-world mortgage rates. With bias drift detection capabilities in model monitor, when SageMaker detects bias beyond a certain threshold, it automatically generates metrics that you can view in SageMaker Studio and through Amazon CloudWatch alerts. \n", + "\n", + "This notebook demonstrates the process for setting up a model monitor for continuous monitoring of bias drift of the data and model of a [SageMaker real-time inference endpoint](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html). The model input and output are in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats). SageMaker Clarify model monitor also supports analyzing CSV data, which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_model_monitor/fairness_and_explainability/SageMaker-Model-Monitor-Fairness-and-Explainability.ipynb).\n", + "\n", + "In general, you can use the model bias monitor for real-time inference endpoint in this way,\n", + "\n", + "1. Enable the endpoint for data capture. Then, when the customer invokes the endpoint, the endpoint saves the invocations to a data capture S3 location. \n", + "1. Schedule a model bias monitor to monitor the endpoint (to be more specific, the data capture S3 location) and a ground truth S3 location.\n", + "1. You need to regularly fetch the captured data, label it, and then upload the ground truth labels to the ground truth S3 URI.\n", + "\n", + "The monitor executes processing jobs regularly to merge the captured data and ground truth data, do bias analysis for the merged data, and then generate analysis reports and publish metrics to CloudWatch." + ] + }, + { + "cell_type": "markdown", + "id": "a4eed2c2-4e67-49cd-8b16-01d10c0acdb0", + "metadata": {}, + "source": [ + "## General Setup" + ] + }, + { + "cell_type": "markdown", + "id": "56e754c8-d82a-49a3-9967-d7a487a42549", + "metadata": {}, + "source": [ + "The notebook uses the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk). The following cell upgrades the SDK and its dependencies. Then you may need to restart the kernel and rerun the notebook to pick up the up-to-date APIs, if the notebook is executed in the SageMaker Studio." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e815029f-6166-40f6-a5dd-da2358f8b7fa", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: sagemaker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (2.203.1)\n", + "Requirement already satisfied: jsonschema in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.19.0)\n", + "Requirement already satisfied: requests 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(/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install -U sagemaker\n", + "!pip install -U boto3\n", + "!pip install -U botocore" + ] + }, + { + "cell_type": "markdown", + "id": "43f20cf6-1672-45ab-966b-5db2d51aad53", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "The following cell imports the APIs to be used by the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "21f01570-2eee-46ef-b044-8b65569c26b7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/zicanl/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "import sagemaker\n", + "import pandas as pd\n", + "import datetime\n", + "import json\n", + "import random\n", + "import threading\n", + "import time\n", + "import pprint" + ] + }, + { + "cell_type": "markdown", + "id": "5baa9278-a1c9-427c-a9d9-5ddab19bcd49", + "metadata": {}, + "source": [ + "### Handful of configuration\n", + "\n", + "To begin, ensure that these prerequisites have been completed.\n", + "\n", + "* Specify an AWS Region to host the model.\n", + "* Specify an IAM role to execute jobs.\n", + "* Define the S3 URIs that stores the model file, input data and output data. For demonstration purposes, this notebook uses the same bucket for them. In reality, they could be separated with different security policies." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "74b11f7c-e9cd-4321-8de5-27ca6dd85d01", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AWS region: us-west-2\n", + "RoleArn: arn:aws:iam::678264136642:role/Admin\n", + "Demo Bucket: sagemaker-us-west-2-678264136642\n", + "Demo Prefix: sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a\n", + "Demo S3 key: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a\n", + "The endpoint will save the captured data to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/data-capture\n", + "You should upload the ground truth data to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/ground-truth\n", + "The baselining job will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/baselining-output\n", + "The monitor will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output\n" + ] + } + ], + "source": [ + "sagemaker_session = sagemaker.Session()\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "print(f\"AWS region: {region}\")\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "print(f\"RoleArn: {role}\")\n", + "\n", + "# A different bucket can be used, but make sure the role for this notebook has\n", + "# the s3:PutObject permissions. This is the bucket into which the data is captured\n", + "bucket = sagemaker_session.default_bucket()\n", + "print(f\"Demo Bucket: {bucket}\")\n", + "prefix = sagemaker.utils.unique_name_from_base(\"sagemaker/DEMO-ClarifyModelMonitor\")\n", + "print(f\"Demo Prefix: {prefix}\")\n", + "s3_key = f\"s3://{bucket}/{prefix}\"\n", + "print(f\"Demo S3 key: {s3_key}\")\n", + "\n", + "data_capture_s3_uri = f\"{s3_key}/data-capture\"\n", + "ground_truth_s3_uri = f\"{s3_key}/ground-truth\"\n", + "baselining_output_s3_uri = f\"{s3_key}/baselining-output\"\n", + "monitor_output_s3_uri = f\"{s3_key}/monitor-output\"\n", + "\n", + "print(f\"The endpoint will save the captured data to: {data_capture_s3_uri}\")\n", + "print(f\"You should upload the ground truth data to: {ground_truth_s3_uri}\")\n", + "print(f\"The baselining job will save the analysis results to: {baselining_output_s3_uri}\")\n", + "print(f\"The monitor will save the analysis results to: {monitor_output_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d7da5265-858f-4478-978b-ad592464b61d", + "metadata": {}, + "source": [ + "### Model file and data files\n", + "\n", + "This example includes a prebuilt [SageMaker Linear Learner](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html) model trained by [a SageMaker Clarify offline processing example notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb). The model supports [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats) (MIME type `\"application/jsonlines\"`).\n", + "\n", + "* The model input can one or more lines, each line is a JSON object that has a \"features\" key pointing to a list of feature values concerning demographic characteristics of individuals. For example,\n", + "\n", + "```\n", + "{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}\n", + "{\"features\":[43,2,72338,12,14,2,12,0,1,1,0,0,40,37]}\n", + "```\n", + "\n", + "* The model output has the predictions of whether a person has a yearly income that is more than $50,000. Each prediction is a JSON object that has a \"predicted_label\" key pointing to the predicted label, and the \"score\" key pointing to the confidence score. For example,\n", + "\n", + "```\n", + "{\"predicted_label\":1,\"score\":0.989977359771728}\n", + "{\"predicted_label\":1,\"score\":0.504138827323913}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f75d26c9-0f0b-422d-97cb-b74efd5eacd6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_file = \"model/ll-adult-prediction-model.tar.gz\"" + ] + }, + { + "cell_type": "markdown", + "id": "dc4d1d6a-c75c-4563-9699-33de88469093", + "metadata": {}, + "source": [ + "This example includes two dataset files, both in the JSON format. The data also originates from [the SageMaker Clarify offline processing example notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f1eaa4fe-622f-4745-a3cc-52d40db8ce9f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "train_dataset_path = \"test_data/validation-dataset.json\"\n", + "test_dataset_path = \"test_data/test-dataset.json\"\n", + "dataset_type = \"application/json\"" + ] + }, + { + "cell_type": "markdown", + "id": "5ca1001e-0b91-4133-8bce-6710aaa33270", + "metadata": {}, + "source": [ + "The train dataset has the features and the ground truth label (pointed to by the key \"label\")," + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "06c22c10-7ba8-417a-a0dc-1e152a0a3287", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + 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+ ] + } + ], + "source": [ + "!head -n 5 $train_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "ddebb1fd-d480-4700-8dd8-3143205331a6", + "metadata": {}, + "source": [ + "The test dataset only has features." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f78d463-f1ff-4483-8cf3-562bccb98a2b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "!head -n 5 $test_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "a7b89b8d-5036-4bd9-8aa5-f5d638617aba", + "metadata": {}, + "source": [ + "Here are the headers of the train dataset. \"Target\" is the header of the ground truth label, and the others are the feature headers. They will be used to beautify the analysis report." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a843093-0548-48dd-9f82-e80af07c357e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_headers = [\n", + " \"Age\",\n", + " \"Workclass\",\n", + " \"fnlwgt\",\n", + " \"Education\",\n", + " \"Education-Num\",\n", + " \"Marital Status\",\n", + " \"Occupation\",\n", + " \"Relationship\",\n", + " \"Ethnic group\",\n", + " \"Sex\",\n", + " \"Capital Gain\",\n", + " \"Capital Loss\",\n", + " \"Hours per week\",\n", + " \"Country\",\n", + " \"Target\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "2441fc17-0299-4b11-afe7-efdb167263ad", + "metadata": {}, + "source": [ + "To verify that the execution role for this notebook has the necessary permissions to proceed, put a simple test object into the S3 bucket specified above. If this command fails, update the role to have `s3:PutObject` permission on the bucket and try again." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dfe69a8c-9bf6-47c4-bb59-a775fd3b6934", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! We are all set to proceed with uploading to S3.\n" + ] + } + ], + "source": [ + "sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=\"hello\",\n", + " desired_s3_uri=f\"{s3_key}/upload-test-file.txt\",\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(\"Success! We are all set to proceed with uploading to S3.\")" + ] + }, + { + "cell_type": "markdown", + "id": "7a099ef6-8d09-478d-854c-989758bad1c5", + "metadata": {}, + "source": [ + "Then upload the files to S3 so that they can be used by SageMaker." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0f0fe183-4c83-4d22-bce5-65eba6a351e2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model file has been uploaded to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/ll-adult-prediction-model.tar.gz\n", + "Train data is uploaded to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/validation-dataset.json\n", + "Test data is uploaded to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/test-dataset.json\n" + ] + } + ], + "source": [ + "model_url = sagemaker.s3.S3Uploader.upload(\n", + " local_path=model_file,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Model file has been uploaded to {model_url}\")\n", + "\n", + "train_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=train_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Train data is uploaded to: {train_data_s3_uri}\")\n", + "test_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=test_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Test data is uploaded to: {test_data_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2d11cc57-8ab4-422e-9492-4126f34ef4c5", + "metadata": {}, + "source": [ + "## Real-time Inference Endpoint\n", + "\n", + "This section creates a SageMaker real-time inference endpoint to showcase the data capture capability in action. The model monitor will be scheduled for the endpoint and process the captured data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d295bc3-3a82-4f22-9768-29572c0ae4f3", + "metadata": { + "tags": [] + }, + "source": [ + "### Deploy the model to an endpoint\n", + "\n", + "Start with deploying the pre-trained model. Here, create a SageMaker `Model` object with the inference image and model file. Then deploy the model with the data capture configuration and wait until the endpoint is ready to serve traffic.\n", + "\n", + "[DataCaptureConfig](https://sagemaker.readthedocs.io/en/stable/api/inference/model_monitor.html#sagemaker.model_monitor.data_capture_config.DataCaptureConfig) enables capturing the request payload and the response payload of the endpoint. Payloads are typically treated as binary data and encoded in BASE64 by default, allowing them to be stored in capture data files. However, by specifying the data format in the `json_content_types` parameter as shown below, the payloads can be captured as plain text instead." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d0c565e0-051a-4f6c-bcb6-3dca8f4ec592", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SageMaker model name: DEMO-ll-adult-pred-model-monitor-1705692264-e088\n", + "SageMaker endpoint name: DEMO-ll-adult-pred-model-monitor-1705692264-e088\n", + "SageMaker Linear Learner image: 174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:1\n" + ] + } + ], + "source": [ + "model_name = sagemaker.utils.unique_name_from_base(\"DEMO-ll-adult-pred-model-monitor\")\n", + "endpoint_name = model_name\n", + "print(f\"SageMaker model name: {model_name}\")\n", + "print(f\"SageMaker endpoint name: {endpoint_name}\")\n", + "\n", + "image_uri = sagemaker.image_uris.retrieve(\"linear-learner\", region, \"1\")\n", + "print(f\"SageMaker Linear Learner image: {image_uri}\")\n", + "\n", + "model = sagemaker.model.Model(\n", + " role=role,\n", + " name=model_name,\n", + " image_uri=image_uri,\n", + " model_data=model_url,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "\n", + "data_capture_config = sagemaker.model_monitor.DataCaptureConfig(\n", + " enable_capture=True,\n", + " sampling_percentage=100, # Capture 100% of the traffic\n", + " destination_s3_uri=data_capture_s3_uri,\n", + " json_content_types=[dataset_type],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c86306f2-8f15-4d39-9cbb-2f6c0e7ee978", + "metadata": {}, + "source": [ + "**NOTE**: The following cell takes about 10 minutes to deploy the model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "77330b34-0640-4b00-b3bb-4a8ea6e9a223", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deploying model DEMO-ll-adult-pred-model-monitor-1705692264-e088 to endpoint DEMO-ll-adult-pred-model-monitor-1705692264-e088\n", + "------!" + ] + } + ], + "source": [ + "print(f\"Deploying model {model_name} to endpoint {endpoint_name}\")\n", + "model.deploy(\n", + " initial_instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " endpoint_name=endpoint_name,\n", + " data_capture_config=data_capture_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "14bf8504-bca2-4948-867a-cab4ca349bd9", + "metadata": {}, + "source": [ + "### Invoke the endpoint\n", + "\n", + "Now send data to this endpoint to get inferences in real time. The model supports mini-batch predictions, so you can put one or more records to a single request." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "44a908e5-c16f-41dc-b718-323ab5ed4268", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with open(test_dataset_path, \"r\") as f:\n", + " test_data = json.load(f)" + ] + }, + { + "cell_type": "markdown", + "id": "2ccc2ed6-355a-4cdb-a44e-1463c0d9ef9f", + "metadata": {}, + "source": [ + "#### Example: Single record" + ] + }, + { + "cell_type": "markdown", + "id": "ea0e8368-37b1-41d2-b0da-0f22fee2b87e", + "metadata": {}, + "source": [ + "Request payload:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "52fbb63a-e1d8-414e-968a-20822305f23c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"instances\": [{\"features\": [28, 2, 133937, 9, 13, 2, 0, 0, 4, 1, 15024, 0, 55, 37]}]}\n" + ] + } + ], + "source": [ + "request_payload = {\"instances\": [test_data[\"instances\"][0]]}\n", + "print(json.dumps(request_payload))" + ] + }, + { + "cell_type": "markdown", + "id": "f880886a-38cc-44c1-acc4-f3876956e2a8", + "metadata": {}, + "source": [ + "Response payload:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "87531e43-c9d1-4d9b-8019-19bec1a832eb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"predictions\": [{\"score\": 0.9899773597717285, \"predicted_label\": 1}]}'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=dataset_type,\n", + " Accept=dataset_type,\n", + " Body=json.dumps(request_payload),\n", + ")\n", + "response_payload = response[\"Body\"].read().decode(\"utf-8\")\n", + "response_payload" + ] + }, + { + "cell_type": "markdown", + "id": "22fe887e-ec0d-4b2a-9c32-28d93c2e25be", + "metadata": {}, + "source": [ + "#### Example: Two records" + ] + }, + { + "cell_type": "markdown", + "id": "6094ad1c-55dd-40d1-b31f-8d47f21814c3", + "metadata": {}, + "source": [ + "Request payload:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2cd41694-9e20-461f-ae85-5f792a521753", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'instances': [{'features': [28,\n", + " 2,\n", + " 133937,\n", + " 9,\n", + " 13,\n", + " 2,\n", + " 0,\n", + " 0,\n", + " 4,\n", + " 1,\n", + " 15024,\n", + " 0,\n", + " 55,\n", + " 37]},\n", + " {'features': [43, 2, 72338, 12, 14, 2, 12, 0, 1, 1, 0, 0, 40, 37]}]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request_payload[\"instances\"] = test_data[\"instances\"][:2]\n", + "request_payload" + ] + }, + { + "cell_type": "markdown", + "id": "3ab91982-67b4-4293-86cb-bb61be2f67aa", + "metadata": {}, + "source": [ + "Response payload:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fece49e7-38b9-4b33-91ca-f23fcd06dcbb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"predictions\": [{\"score\": 0.9899773597717285, \"predicted_label\": 1}, {\"score\": 0.5041388273239136, \"predicted_label\": 1}]}'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=dataset_type,\n", + " Accept=dataset_type,\n", + " Body=json.dumps(request_payload),\n", + ")\n", + "response_payload = response[\"Body\"].read().decode(\"utf-8\")\n", + "response_payload" + ] + }, + { + "cell_type": "markdown", + "id": "243eac0c-a697-42b6-a56f-c0279cc7cd57", + "metadata": {}, + "source": [ + "### View captured data\n", + "\n", + "Because data capture is enabled in the previous steps, the request and response payload, along with some additional metadata, are saved in the Amazon S3 location specified in the [DataCaptureConfig](https://sagemaker.readthedocs.io/en/stable/api/inference/model_monitor.html#sagemaker.model_monitor.data_capture_config.DataCaptureConfig).\n", + "\n", + "Now list the captured data files stored in Amazon S3. There should be different files from different time periods organized based on the hour in which the invocation occurred. The format of the Amazon S3 path is:\n", + "\n", + "`s3://{destination-bucket-prefix}/{endpoint-name}/{variant-name}/yyyy/mm/dd/hh/filename.jsonl`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "18c649dd-40ef-4260-b499-0f3c371f970f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for captured data to show up...............................................................\n", + "Found capture data files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/data-capture/DEMO-ll-adult-pred-model-monitor-1705692264-e088/AllTraffic/2024/01/19/19/27-57-062-fb33b08e-de02-414b-ba16-969c14d7e0f1.jsonl\n" + ] + } + ], + "source": [ + "print(\"Waiting for captured data to show up\", end=\"\")\n", + "for _ in range(120):\n", + " captured_data_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=f\"{data_capture_s3_uri}/{endpoint_name}\",\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " )\n", + " if captured_data_files:\n", + " break\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(1)\n", + "print()\n", + "print(\"Found capture data files:\")\n", + "print(\"\\n \".join(captured_data_files[-5:]))" + ] + }, + { + "cell_type": "markdown", + "id": "0b4b01fd-4df2-42ff-935e-8843f1bc568f", + "metadata": {}, + "source": [ + "Next, view the content of a single capture file." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e4ad7021-4bcc-4fe1-880e-11a872941ff1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"captureData\":{\"endpointInput\":{\"observedContentType\":\"application/json\",\"mode\":\"INPUT\",\"data\":\"{\\\"instances\\\": [{\\\"features\\\": [28, 2, 133937, 9, 13, 2, 0, 0, 4, 1, 15024, 0, 55, 37]}]}\",\"encoding\":\"JSON\"},\"endpointOutput\":{\"observedContentType\":\"application/json\",\"mode\":\"OUTPUT\",\"data\":\"{\\\"predictions\\\": [{\\\"score\\\": 0.9899773597717285, \\\"predicted_label\\\": 1}]}\",\"encoding\":\"JSON\"}},\"eventMetadata\":{\"eventId\":\"7ddb2d7c-4d6a-4e67-a68e-23870399829d\",\"inferenceTime\":\"2024-01-19T19:27:57Z\"},\"eventVersion\":\"0\"}\n", + "{\"captureData\":{\"endpointInput\":{\"observedContentType\":\"application/json\",\"mode\":\"INPUT\",\"data\":\"{\\\"instances\\\": [{\\\"features\\\": [28, 2, 133937, 9, 13, 2, 0, 0, 4, 1, 15024, 0, 55, 37]}, {\\\"features\\\": [43, 2, 72338, 12, 14, 2, 12, 0, 1, 1, 0, 0, 40, 37]}]}\",\"encoding\":\"JSON\"},\"endpointOutput\":{\"observedContentType\":\"application/json\",\"mode\":\"OUTPUT\",\"data\":\"{\\\"predictions\\\": [{\\\"score\\\": 0.9899773597717285, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.5041388273239136, \\\"predicted_label\\\": 1}]}\",\"encoding\":\"JSON\"}},\"eventMetadata\":{\"eventId\":\"9066ac80-dde1-4370-a73e-ab997e2544f1\",\"inferenceTime\":\"2024-01-19T19:27:57Z\"},\"eventVersion\":\"0\"}\n", + "\n" + ] + } + ], + "source": [ + "captured_data = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=captured_data_files[-1],\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(captured_data)" + ] + }, + { + "cell_type": "markdown", + "id": "6e09cffd-111a-43a1-8429-2fa3fbce9d2e", + "metadata": {}, + "source": [ + "Finally, the contents of a single line is present below in formatted JSON to observe a little better.\n", + "\n", + "* `captureData` has two fields, `endpointInput` has the captured invocation request, and `endpointOutput` has the response.\n", + "* `eventMetadata` has the inference ID and event ID." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "14611944-0ae1-4f9f-ab6e-4b5c74ee7f3f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"captureData\": {\n", + " \"endpointInput\": {\n", + " \"observedContentType\": \"application/json\",\n", + " \"mode\": \"INPUT\",\n", + " \"data\": \"{\\\"instances\\\": [{\\\"features\\\": [28, 2, 133937, 9, 13, 2, 0, 0, 4, 1, 15024, 0, 55, 37]}, {\\\"features\\\": [43, 2, 72338, 12, 14, 2, 12, 0, 1, 1, 0, 0, 40, 37]}]}\",\n", + " \"encoding\": \"JSON\"\n", + " },\n", + " \"endpointOutput\": {\n", + " \"observedContentType\": \"application/json\",\n", + " \"mode\": \"OUTPUT\",\n", + " \"data\": \"{\\\"predictions\\\": [{\\\"score\\\": 0.9899773597717285, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.5041388273239136, \\\"predicted_label\\\": 1}]}\",\n", + " \"encoding\": \"JSON\"\n", + " }\n", + " },\n", + " \"eventMetadata\": {\n", + " \"eventId\": \"9066ac80-dde1-4370-a73e-ab997e2544f1\",\n", + " \"inferenceTime\": \"2024-01-19T19:27:57Z\"\n", + " },\n", + " \"eventVersion\": \"0\"\n", + "}\n" + ] + } + ], + "source": [ + "print(json.dumps(json.loads(captured_data.splitlines()[-1]), indent=4))" + ] + }, + { + "cell_type": "markdown", + "id": "4b473f92-7142-4f79-8a27-86672682a5b2", + "metadata": {}, + "source": [ + "### Start generating some artificial traffic\n", + "The cell below starts a thread to send some traffic to the endpoint. If there is no traffic, the monitoring jobs are marked as `Failed` since there is no data to process.\n", + "\n", + "Notice the `InferenceId` attribute used to invoke, in this example, it will be used to join the captured data with the ground truth data. If it is not available, then the `eventId` will be used for the join operation." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0af95cc5-9e1d-46fd-b373-16015c87be58", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class WorkerThread(threading.Thread):\n", + " def __init__(self, do_run, *args, **kwargs):\n", + " super(WorkerThread, self).__init__(*args, **kwargs)\n", + " self.__do_run = do_run\n", + " self.__terminate_event = threading.Event()\n", + "\n", + " def terminate(self):\n", + " self.__terminate_event.set()\n", + "\n", + " def run(self):\n", + " while not self.__terminate_event.is_set():\n", + " self.__do_run(self.__terminate_event)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "00e832f7-8cc7-4044-b2aa-f22c93d2078d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def invoke_endpoint(terminate_event):\n", + " # We'll send 10 invocations to our endpoint with the same data\n", + " for index in range(10):\n", + " response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=dataset_type,\n", + " Accept=dataset_type,\n", + " # Sending the whole test_data as one JSON object containing multiple records\n", + " Body=json.dumps(test_data),\n", + " InferenceId=str(index), # unique ID per inference, which contains the whole JSON object\n", + " )\n", + " response[\"Body\"].read()\n", + " time.sleep(1)\n", + " if terminate_event.is_set():\n", + " break\n", + "\n", + "\n", + "# Keep invoking the endpoint with test data\n", + "invoke_endpoint_thread = WorkerThread(do_run=invoke_endpoint)\n", + "invoke_endpoint_thread.start()" + ] + }, + { + "cell_type": "markdown", + "id": "c61c772d-0628-4b9f-843d-1cd631cbf99f", + "metadata": { + "tags": [] + }, + "source": [ + "## Ground Truth Data\n", + "\n", + "Besides captured data, bias drift monitoring execution also requires ground truth data. In real use cases, you should regularly label the captured data, then upload the ground truth data (labels) to designated S3 location. For demonstration purpose, this example notebook generates fake ground truth data following [this schema](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-merge.html), and then uploads it to `ground_truth_s3_uri` which is another key input to the monitor. The bias drift monitoring execution will first merge the captured data and the ground truth data, and then do bias analysis for the merged data.\n", + "\n", + "Notice the value of the `data` field in `groundTruthData` **must be in the same format as how the ground truth labels are stored in the input dataset**." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d43e06d4-32d8-451c-81f2-be1f131a5ec0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def ground_truth_with_id(seeds, inference_id):\n", + " instances = []\n", + " for seed in seeds:\n", + " random.seed(seed) # to get consistent results\n", + " label = (\n", + " 1 if random.random() < 0.7 else 0\n", + " ) # randomly generate positive labels 70% of the time\n", + " instances.append(\n", + " {\"label\": label}\n", + " ) # Also use the \"label\" key, the same as in the input dataset.\n", + " # format required by the merge job and bias monitoring job\n", + " return {\n", + " \"groundTruthData\": {\n", + " \"data\": json.dumps({\"instances\": instances}),\n", + " \"encoding\": \"JSON\",\n", + " },\n", + " \"eventMetadata\": {\n", + " \"eventId\": str(inference_id),\n", + " },\n", + " \"eventVersion\": \"0\",\n", + " }\n", + "\n", + "\n", + "def upload_ground_truth(upload_time):\n", + " seeds = [i for i in range(len(test_data[\"instances\"]))]\n", + " fake_ground_truth_requests = [json.dumps(ground_truth_with_id(seeds, i)) for i in range(10)]\n", + " data_to_upload = \"\\n\".join(fake_ground_truth_requests)\n", + " target_s3_uri = f\"{ground_truth_s3_uri}/{upload_time:%Y/%m/%d/%H/%M%S}.jsonl\"\n", + " print(\n", + " f\"Uploading {len(fake_ground_truth_requests)} requests of {len(seeds)} records to\",\n", + " target_s3_uri,\n", + " )\n", + " sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=data_to_upload,\n", + " desired_s3_uri=target_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "49137517-172a-45ea-b139-ae78555b47e6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uploading 10 requests of 334 records to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/ground-truth/2024/01/19/18/2901.jsonl\n" + ] + } + ], + "source": [ + "# Generate data for the last hour, in case the first monitoring execution is in this hour\n", + "upload_ground_truth(datetime.datetime.utcnow() - datetime.timedelta(hours=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "573901f2-fbba-4bf0-b73c-807c44fe709b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uploading 10 requests of 334 records to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/ground-truth/2024/01/19/19/2901.jsonl\n" + ] + } + ], + "source": [ + "# Generate data once an hour\n", + "def generate_fake_ground_truth(terminate_event):\n", + " upload_ground_truth(datetime.datetime.utcnow())\n", + " for _ in range(0, 60):\n", + " time.sleep(60)\n", + " if terminate_event.is_set():\n", + " break\n", + "\n", + "\n", + "ground_truth_thread = WorkerThread(do_run=generate_fake_ground_truth)\n", + "ground_truth_thread.start()" + ] + }, + { + "cell_type": "markdown", + "id": "f8d87f96-1ab6-4ad9-bd0d-f21b18ebcded", + "metadata": {}, + "source": [ + "## Model Bias Monitor\n", + "\n", + "Similar to the other monitoring types, the standard procedure of creating a [bias drift monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift.html) is first run a baselining job, and then schedule the monitor.\n", + "\n", + "A bias drift monitoring execution starts a merge job that joins the captured data and ground truth data together using the inference ID. Then a SageMaker Clarify bias analysis job is started to compute all the [pre-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html) and [post-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-post-training-bias.html). on the merged data. The max execution time is divided equally between two jobs, the notebook is scheduling an hourly model bias monitor, so the `max_runtime_in_seconds` parameter should not exceed 1800 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "273af941-56ff-4a08-a1e1-023e2d4ec090", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_bias_monitor = sagemaker.model_monitor.ModelBiasMonitor(\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " max_runtime_in_seconds=1800,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c47a6f66-bdd8-4815-b3ed-286035f6e4ce", + "metadata": {}, + "source": [ + "### Baselining job\n", + "\n", + "A baselining job runs predictions on training dataset and suggests constraints. The `suggest_baseline()` method of `ModelBiasMonitor` starts a SageMaker Clarify processing job to generate the constraints.\n", + "\n", + "The step is not mandatory, but providing constraints file to the monitor can enable violations file generation." + ] + }, + { + "cell_type": "markdown", + "id": "b7bd931a-bacc-480b-8d2d-c363abe9943f", + "metadata": {}, + "source": [ + "#### Configurations\n", + "\n", + "Information about the input data need to be provided to the processor." + ] + }, + { + "cell_type": "markdown", + "id": "6398d447-0ccf-4c79-a29d-8d6a54e1c034", + "metadata": {}, + "source": [ + "`DataConfig` stores information about the dataset to be analyzed. For example, the dataset file and its format (like JSON Lines), where to store the analysis results. Some special things to note about this configuration for the JSON Lines dataset,\n", + "\n", + "* The parameter value `\"features\"` or `\"label\"` is **NOT** a header string. Instead, it is a `JMESPath` expression ([refer to its specification](https://jmespath.org/specification.html)) that is used to locate the features list or the ground truth label in the dataset. In this example notebook they happen to be the same as the keys in the dataset. But for example, if the dataset has records like below, then the `features` parameter should use value `\"data.features.values\"`, and the `label` parameter should use value `\"data.label\"`.\n", + "\n", + " ```\n", + " {\"data\": {\"features\": {\"values\": [25, 2, 226802, 1, 7, 4, 6, 3, 2, 1, 0, 0, 40, 37]}, \"label\": 0}}\n", + " ```\n", + "\n", + "* SageMaker Clarify processing job will load the JSON Lines dataset into tabular representation for further analysis, and the parameter `headers` is the list of column names. **The label header shall be the last one in the headers list**, and the order of feature headers shall be the same as the order of features in a record." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "fd146e26-a54c-4a31-acc9-5a406ddf8680", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "features_jmespath = \"instances[*].features\"\n", + "ground_truth_label_jmespath = \"instances[*].label\"\n", + "data_config = sagemaker.clarify.DataConfig(\n", + " s3_data_input_path=train_data_s3_uri,\n", + " s3_output_path=baselining_output_s3_uri,\n", + " features=features_jmespath,\n", + " label=ground_truth_label_jmespath,\n", + " headers=all_headers,\n", + " dataset_type=dataset_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "93c9c98b-67a5-45e0-8aa5-a488e25a6de8", + "metadata": {}, + "source": [ + "`ModelConfig` is configuration related to model to be used for inferencing. In order to compute post-training bias metrics, the computation needs to get inferences for the SageMaker model. To accomplish this, the processing job will use the model to create an ephemeral endpoint (also known as \"shadow endpoint\"). The processing job will delete the shadow endpoint after the computations are completed. One special thing to note about this configuration for the JSON Lines model input and output,\n", + "\n", + "* `content_template` is used by SageMaker Clarify processing job to convert the tabular data to the request payload acceptable to the shadow endpoint. To be more specific, the placeholder `$features` will be replaced by **the features list** from records. The request payload of a record from the testing dataset happens to be similar to the record itself, like `{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}`, because both the dataset and the model input conform to the same format." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3a49acc6-c6a9-46fa-aed7-e93e67fae373", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_config = sagemaker.clarify.ModelConfig(\n", + " model_name=model_name, # The name of the SageMaker model\n", + " instance_type=\"ml.m5.xlarge\", # The instance type of the shadow endpoint\n", + " instance_count=1, # The instance count of the shadow endpoint\n", + " content_type=dataset_type, # The data format of the model input\n", + " accept_type=dataset_type, # The data format of the model output\n", + " content_template='{\"instances\":$records}',\n", + " record_template='{\"features\":$features}',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ca3c02c3-0238-48c9-8f21-73ddb317c506", + "metadata": {}, + "source": [ + "`ModelPredictedLabelConfig` specifies how to extract predicted label from the model output. The example model returns the predicted label as well as the confidence score, so there are two ways to define this configuration,\n", + "\n", + "* Set the `label` parameter to \"predicted_label\" which is the `JMESPath` expression to locate the predicted label in the model output. This is the way used in this example.\n", + "* Alternatively, you can set the `probability` parameter to \"score\" which is the `JMESPath` expression to locate the confidence score in the model output. And set the `probability_threshold` parameter to a floating number in between 0 and 1. The post-training analysis will use it to convert a score to binary predicted label (`0` or `1`). The default value is 0.5, which means a probability value > 0.5 indicates predicted label `1`." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c6dc6502-8a28-4cda-a135-2c687e9097b6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "predicted_label_jmespath = \"predictions[*].predicted_label\"\n", + "model_predicted_label_config = sagemaker.clarify.ModelPredictedLabelConfig(\n", + " label=predicted_label_jmespath,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "506b583a-f643-45dc-bdd3-ae29120734fa", + "metadata": {}, + "source": [ + "`BiasConfig` is the configuration of the sensitive groups in the dataset. Typically, bias is measured by computing a metric and comparing it across groups. \n", + "\n", + " * The group of interest is specified using the facet parameters. With the following configuration, the baselining job will check for bias in the model's predictions with respect to gender and income. Specifically, it is checking if the model is more likely to predict that males have an annual income of over $50,000 compared to females. Although not demonstrated in this example, a bias monitor can measure bias against multiple sensitive attributes, if you provide a list of facets.\n", + " * The `group_name` parameter is used to form subgroups for the measurement of [Conditional Demographic Disparity in Labels](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-metric-cddl.html) (CDDL) and [Conditional Demographic Disparity in Predicted Labels](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cddpl.html) (CDDPL) with regard to [Simpson’s paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "0ead08ae-1867-41b9-8c0e-6202760c4175", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "bias_config = sagemaker.clarify.BiasConfig(\n", + " label_values_or_threshold=[1], # the positive outcome is earning >$50,000\n", + " facet_name=\"Sex\", # the sensitive attribute is the gender\n", + " facet_values_or_threshold=[0], # the disadvantaged group is female\n", + " group_name=\"Age\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3c9417f1-b2b2-4c23-81ba-256ff4616c5c", + "metadata": {}, + "source": [ + "#### Kick off baselining job\n", + "\n", + "Call the `suggest_baseline()` method to start the baselining job. The job computes all the [pre-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html) and [post-training bias metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-post-training-bias.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "9c27e74b-31f6-435a-a0d4-bef52a4cdcdb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating processing-job with name baseline-suggestion-job-2024-01-19-19-29-01-894\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_bias_monitor.suggest_baseline(\n", + " bias_config=bias_config,\n", + " data_config=data_config,\n", + " model_config=model_config,\n", + " model_predicted_label_config=model_predicted_label_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9cf396d3-c7ab-4041-8820-64c5ebd15d46", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the baselining job is completed (in about 10 minutes). It then inspects the suggested constraints. This step can be skipped, because the monitor to be scheduled will automatically pick up baselining job name and wait for it before monitoring execution." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ad0ece68-f130-4b66-b8ab-36d2916502c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................................................................................!\n", + "Suggested constraints: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/baselining-output/analysis.json\n", + "{\n", + " \"version\": \"1.0\",\n", + " \"post_training_bias_metrics\": {\n", + " \"label\": \"Target\",\n", + " \"facets\": {\n", + " \"Sex\": [\n", + " {\n", + " \"value_or_threshold\": \"0\",\n", + " \"metrics\": [\n", + " {\n", + " \"name\": \"AD\",\n", + " \"description\": \"Accuracy Difference (AD)\",\n", + " \"value\": -0.15156641604010024\n", + " },\n", + " {\n", + " \"name\": \"CDDPL\",\n", + " \"description\": \"Conditional Demographic Disparity in Predicted Labels (CDDPL)\",\n", + " \"value\": 0.28176563733194276\n", + " },\n", + " {\n", + " \"name\": \"DAR\",\n", + " \"description\": \"Difference in Acceptance Rates (DAR)\",\n", + " \"value\": -0.09508196721311479\n", + " },\n", + " {\n", + " \"name\": \"DCA\",\n", + " \"description\": \"Difference in Conditional Acceptance (DCA)\",\n", + " \"value\": -0.5278688524590163\n", + " },\n", + " {\n", + " \"name\": \"DCR\",\n", + " \"description\": \"Difference in Conditional Rejection (DCR)\",\n", + " \"value\": 0.027874251497005953\n", + " },\n", + " {\n", + " \"name\": \"DI\",\n", + " \"description\": \"Disparate Impact (DI)\",\n", + " \"value\": 0.17798594847775176\n", + " },\n", + " {\n", + " \"name\": \"DPPL\",\n", + " \"description\": \"Difference in Positive Proportions in Predicted Labels (DPPL)\",\n", + " \"value\": 0.2199248120300752\n", + " },\n", + " {\n", + " \"name\": \"DRR\",\n", + " \"description\": \"Difference in Rejection Rates (DRR)\",\n", + " \"value\": 0.12565868263473046\n", + " },\n", + " {\n", + " \"name\": \"FT\",\n", + " \"description\": \"Flip Test (FT)\",\n", + " \"value\": -0.03333333333333333\n", + " },\n", + " {\n", + " \"name\": \"GE\",\n", + " \"description\": \"Generalized Entropy (GE)\",\n", + " \"value\": 0.0841186702174704\n", + " },\n", + " {\n", + " \"name\": \"RD\",\n", + " \"description\": \"Recall Difference (RD)\",\n", + " \"value\": 0.1308103661044837\n", + " },\n", + " {\n", + " \"name\": \"SD\",\n", + " \"description\": \"Specificity Difference (SD)\",\n", + " \"value\": 0.10465328014037645\n", + " },\n", + " {\n", + " \"name\": \"TE\",\n", + " \"description\": \"Treatment Equality (TE)\",\n", + " \"value\": 2.916666666666667\n", + " }\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " \"label_value_or_threshold\": \"1\"\n", + " },\n", + " \"pre_training_bias_metrics\": {\n", + " \"label\": \"Target\",\n", + " \"facets\": {\n", + " \"Sex\": [\n", + " {\n", + " \"value_or_threshold\": \"0\",\n", + " \"metrics\": [\n", + " {\n", + " \"name\": \"CDDL\",\n", + " \"description\": \"Conditional Demographic Disparity in Labels (CDDL)\",\n", + " \"value\": 0.27459074287718793\n", + " },\n", + " {\n", + " \"name\": \"CI\",\n", + " \"description\": \"Class Imbalance (CI)\",\n", + " \"value\": 0.36936936936936937\n", + " },\n", + " {\n", + " \"name\": \"DPL\",\n", + " \"description\": \"Difference in Positive Proportions in Labels (DPL)\",\n", + " \"value\": 0.2326441102756892\n", + " },\n", + " {\n", + " \"name\": \"JS\",\n", + " \"description\": \"Jensen-Shannon Divergence (JS)\",\n", + " \"value\": 0.04508199943437752\n", + " },\n", + " {\n", + " \"name\": \"KL\",\n", + " \"description\": \"Kullback-Liebler Divergence (KL)\",\n", + " \"value\": 0.22434464102537785\n", + " },\n", + " {\n", + " \"name\": \"KS\",\n", + " \"description\": \"Kolmogorov-Smirnov Distance (KS)\",\n", + " \"value\": 0.2326441102756892\n", + " },\n", + " {\n", + " \"name\": \"LP\",\n", + " \"description\": \"L-p Norm (LP)\",\n", + " \"value\": 0.32900845595810163\n", + " },\n", + " {\n", + " \"name\": \"TVD\",\n", + " \"description\": \"Total Variation Distance (TVD)\",\n", + " \"value\": 0.2326441102756892\n", + " }\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " \"label_value_or_threshold\": \"1\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "model_bias_monitor.latest_baselining_job.wait(logs=False)\n", + "print()\n", + "model_bias_constraints = model_bias_monitor.suggested_constraints()\n", + "print(f\"Suggested constraints: {model_bias_constraints.file_s3_uri}\")\n", + "print(\n", + " sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=model_bias_constraints.file_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5545f7e0-8256-4b33-8385-741c23b9acc6", + "metadata": {}, + "source": [ + "### Monitoring Schedule\n", + "\n", + "With above constraints collected, now call `create_monitoring_schedule()` method to schedule an hourly model bias monitor." + ] + }, + { + "cell_type": "markdown", + "id": "b99f1d50-d9ce-42c6-84da-a710bfb7b47a", + "metadata": {}, + "source": [ + "If a baselining job has been submitted, then the monitor object will automatically pick up the analysis configuration from the baselining job. But if the baselining step is skipped, or if the capture dataset has different nature than the training dataset, then analysis configuration has to be provided.\n", + "\n", + "`BiasAnalysisConfig` is a subset of the configuration of the baselining job, many options are not needed because,\n", + "\n", + "* Model bias monitor will merge the captured data and the ground truth data, and then use the merged data as the dataset.\n", + "* Capture data already includes predictions, so there is no need to create shadow endpoint.\n", + "* Attributes like predicted label are provided as part of EndpointInput.\n", + "\n", + "Highlights,\n", + "\n", + "* From `endpoint_name` the monitor can figure out the location of data captured by the endpoint.\n", + "* `ground_truth_s3_uri` is the location of ground truth data\n", + "* `features_attribute` is the `JMESPath` expression to locate the features in model input, similar to the `features` parameter of `DataConfig`.\n", + "* `inference_attribute` is the `JMESPath` expression to locate the predicted label in model output, similar to the `label` parameter of `ModelPredictedLabelConfig`." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "8d160d3e-0482-4c4b-a171-e62eddb38b87", + "metadata": {}, + "outputs": [], + "source": [ + "schedule_expression = sagemaker.model_monitor.CronExpressionGenerator.hourly()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "1c7a1355-2997-46f2-ae02-cb00063e3661", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.model_monitor.model_monitoring:Creating Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-38-53-206\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model bias monitoring schedule: monitoring-schedule-2024-01-19-19-38-53-206\n" + ] + } + ], + "source": [ + "model_bias_analysis_config = None\n", + "if not model_bias_monitor.latest_baselining_job:\n", + " model_bias_analysis_config = sagemaker.model_monitor.BiasAnalysisConfig(\n", + " bias_config,\n", + " headers=all_headers,\n", + " label=ground_truth_label_jmespath,\n", + " )\n", + "model_bias_monitor.create_monitoring_schedule(\n", + " analysis_config=model_bias_analysis_config,\n", + " endpoint_input=sagemaker.model_monitor.EndpointInput(\n", + " endpoint_name=endpoint_name,\n", + " destination=\"/opt/ml/processing/input/endpoint\",\n", + " features_attribute=features_jmespath, # mandatory if no baselining job\n", + " inference_attribute=predicted_label_jmespath, # mandatory if no baselining job\n", + " # look back 6 hour for captured data\n", + " start_time_offset=\"-PT6H\",\n", + " end_time_offset=\"-PT0H\",\n", + " ),\n", + " ground_truth_input=ground_truth_s3_uri,\n", + " output_s3_uri=monitor_output_s3_uri,\n", + " schedule_cron_expression=schedule_expression,\n", + ")\n", + "print(f\"Model bias monitoring schedule: {model_bias_monitor.monitoring_schedule_name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "bf22401a-4662-4063-b47f-5be6becf3c3b", + "metadata": {}, + "source": [ + "#### Wait for the first execution\n", + "\n", + "The schedule starts jobs at the previously specified intervals. Code below waits until time crosses the hour boundary (in UTC) to see executions kick off.\n", + "\n", + "Note: Even for an hourly schedule, Amazon SageMaker has a buffer period of 20 minutes to schedule executions. The execution might start in anywhere from zero to ~20 minutes from the hour boundary. This is expected and done for load balancing in the backend." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ae00eb31-bbc7-4cf9-9fae-b323b4d380b2", + "metadata": {}, + "outputs": [], + "source": [ + "def wait_for_execution_to_start(model_monitor):\n", + " print(\n", + " \"An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\"\n", + " )\n", + "\n", + " print(\"Waiting for the first execution to happen\", end=\"\")\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " while \"LastMonitoringExecutionSummary\" not in schedule_desc:\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " print()\n", + " print(\"Done! Execution has been created\")\n", + "\n", + " print(\"Now waiting for execution to start\", end=\"\")\n", + " while schedule_desc[\"LastMonitoringExecutionSummary\"][\"MonitoringExecutionStatus\"] in \"Pending\":\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(10)\n", + "\n", + " print()\n", + " print(\"Done! Execution has started\")" + ] + }, + { + "cell_type": "markdown", + "id": "16fabf1c-8458-4186-9fb2-7bfa2462b705", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the first monitoring execution is started. As explained above, the wait could take more than 60 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b512df1e-57cf-4ba3-9262-0c325c4a600e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\n", + "Waiting for the first execution to happen.............................\n", + "Done! Execution has been created\n", + "Now waiting for execution to start......\n", + "Done! Execution has started\n" + ] + } + ], + "source": [ + "wait_for_execution_to_start(model_bias_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "210955ae-1709-423f-98c0-ca93476eebde", + "metadata": {}, + "source": [ + "In real world, a monitoring schedule is supposed to be active all the time. But in this example, it can be stopped to avoid incurring extra charges. A stopped schedule will not trigger further executions, but the ongoing execution will continue. And if needed, the schedule can be restarted by `start_monitoring_schedule()`." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "a6980d31-c96d-4850-a7fb-c8583eeac54e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Stopping Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-38-53-206\n" + ] + } + ], + "source": [ + "model_bias_monitor.stop_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "id": "117a4a1d-4410-4f60-b859-762f18f7370b", + "metadata": {}, + "source": [ + "#### Wait for the execution to finish\n", + "\n", + "In the previous cell, the first execution has started. This section waits for the execution to finish so that its analysis results are available. Here are the possible terminal states and what each of them mean:\n", + "\n", + "* `Completed` - This means the monitoring execution completed, and no issues were found in the violations report.\n", + "* `CompletedWithViolations` - This means the execution completed, but constraint violations were detected.\n", + "* `Failed` - The monitoring execution failed, maybe due to client error (perhaps incorrect role permissions) or infrastructure issues. Further examination of `FailureReason` and `ExitMessage` is necessary to identify what exactly happened.\n", + "* `Stopped` - job exceeded max runtime or was manually stopped." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2b07426d-f805-4527-9863-1d3d664734fa", + "metadata": {}, + "outputs": [], + "source": [ + "# Waits for the schedule to have last execution in a terminal status.\n", + "def wait_for_execution_to_finish(model_monitor):\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + " if execution_summary is not None:\n", + " print(\"Waiting for execution to finish\", end=\"\")\n", + " while execution_summary[\"MonitoringExecutionStatus\"] not in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + " \"Failed\",\n", + " \"Stopped\",\n", + " ]:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc[\"LastMonitoringExecutionSummary\"]\n", + " print()\n", + " print(f\"Done! Execution Status: {execution_summary['MonitoringExecutionStatus']}\")\n", + " else:\n", + " print(\"Last execution not found\")" + ] + }, + { + "cell_type": "markdown", + "id": "01434010-3c04-4ef5-acd2-21a3a0035fc8", + "metadata": {}, + "source": [ + "**NOTE**: The following cell takes about 10 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "25e36f00-f488-4a16-867f-92c53d819782", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish.............\n", + "Done! Execution Status: CompletedWithViolations\n" + ] + } + ], + "source": [ + "wait_for_execution_to_finish(model_bias_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "442c7bbd-0af7-44a1-bec9-a94f180f6892", + "metadata": {}, + "source": [ + "#### Merged data\n", + "\n", + "Merged data is the intermediate results of bias drift monitoring execution. It is saved to JSON Lines files under the \"merge\" folder of `monitor_output_s3_uri`. Each line is a valid JSON object which combines the captured data and the ground truth data." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "b6df9816-63ad-4e44-b26d-b79fba785307", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found merged files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/merge/DEMO-ll-adult-pred-model-monitor-1705692264-e088/AllTraffic/2024/01/19/19/part-00000-f3e4dbf9-81d4-4bbe-b6b9-528f652c3785.c000.jsonl\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/merge/DEMO-ll-adult-pred-model-monitor-1705692264-e088/AllTraffic/2024/01/19/19/part-00001-b083912a-a5ad-47d1-9c78-6a29f696cde4.c000.jsonl\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/merge/DEMO-ll-adult-pred-model-monitor-1705692264-e088/AllTraffic/2024/01/19/19/part-00002-59af0c48-6306-473e-b279-49feedcec499.c000.jsonl\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/merge/DEMO-ll-adult-pred-model-monitor-1705692264-e088/AllTraffic/2024/01/19/20/part-00000-3bd89ad1-7cc0-4cfd-b69d-5c6fe39d454a.c000.jsonl\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/merge/DEMO-ll-adult-pred-model-monitor-1705692264-e088/AllTraffic/2024/01/19/20/part-00002-99446fe5-7aff-450c-84fa-010ce40bab93.c000.jsonl\n" + ] + } + ], + "source": [ + "merged_data_s3_uri = f\"{monitor_output_s3_uri}/merge\"\n", + "merged_data_files = sagemaker.s3.S3Downloader.list(\n", + " s3_uri=merged_data_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(\"Found merged files:\")\n", + "print(\"\\n \".join(merged_data_files[-5:]))" + ] + }, + { + "cell_type": "markdown", + "id": "9f71db78-5d65-4768-b5ff-461057c5f922", + "metadata": {}, + "source": [ + "The following cell prints a single line of a merged data file.\n", + "\n", + "* `eventId` is the inference ID from the captured data and the ground truth data\n", + "* `groundTruthData` is from the ground truth data\n", + "* `captureData` is from the captured data." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "6581b300-4ee0-4884-aef7-bf94577c07aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"eventVersion\": \"0\",\n", + " \"groundTruthData\": {\n", + " \"data\": \"{\\\"instances\\\": [{\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, 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0.1369217336177826, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.5481252670288086, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.10556997358798981, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.03860599175095558, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.015571567229926586, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.10935700684785843, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.18715748190879822, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.3657187819480896, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.033314306288957596, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.535107433795929, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.06323137134313583, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.047560691833496094, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.38858675956726074, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.09035445749759674, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.2984286844730377, \\\"predicted_label\\\": 0}, {\\\"score\\\": 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0.9961671233177185, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.2714757025241852, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.029732858762145042, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.1591436266899109, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.3971065878868103, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.17690302431583405, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.2896363139152527, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.6779072880744934, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.009807982482016087, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.636303186416626, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.6927167177200317, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.09142012149095535, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.46173176169395447, \\\"predicted_label\\\": 0}, {\\\"score\\\": 1.0, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.009480840526521206, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.2092321813106537, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.7035172581672668, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.12638318538665771, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.03508545458316803, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.5264816284179688, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.15869060158729553, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.7289481163024902, \\\"predicted_label\\\": 1}, {\\\"score\\\": 0.37320321798324585, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.3075198531150818, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.056538213044404984, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.29357296228408813, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.05370595306158066, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.1574016511440277, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.06716842204332352, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.06344348192214966, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.15472890436649323, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.019497334957122803, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.3168521225452423, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.01945059932768345, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.2948471009731293, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.02696368843317032, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.04764571785926819, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.23794148862361908, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.3331327736377716, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.3215182423591614, \\\"predicted_label\\\": 0}, {\\\"score\\\": 0.05063043162226677, \\\"predicted_label\\\": 0}]}\",\n", + " \"encoding\": \"JSON\",\n", + " \"mode\": \"OUTPUT\",\n", + " \"observedContentType\": \"application/json\"\n", + " }\n", + " },\n", + " \"eventMetadata\": {\n", + " \"eventId\": \"eed5a268-2703-4392-901f-70ffab9a7fd3\",\n", + " \"inferenceId\": \"7\",\n", + " \"inferenceTime\": \"2024-01-19T20:06:08Z\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "merged_data_file = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=merged_data_files[-1],\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "merged_record = merged_data_file.splitlines()[-1]\n", + "print(json.dumps(json.loads(merged_record), indent=4))" + ] + }, + { + "cell_type": "markdown", + "id": "27ecf876-5999-4c2a-adcd-0a8537f082e6", + "metadata": {}, + "source": [ + "#### Inspect execution results\n", + "\n", + "List the generated reports,\n", + "\n", + "* analysis.json includes all the bias metrics.\n", + "* report.* files are static report files to visualize the bias metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "3c767cbd-78c5-433d-a850-e230cb5a55dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Report URI: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/DEMO-ll-adult-pred-model-monitor-1705692264-e088/monitoring-schedule-2024-01-19-19-38-53-206/2024/01/19/20\n", + "Found Report Files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/DEMO-ll-adult-pred-model-monitor-1705692264-e088/monitoring-schedule-2024-01-19-19-38-53-206/2024/01/19/20/analysis.json\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/DEMO-ll-adult-pred-model-monitor-1705692264-e088/monitoring-schedule-2024-01-19-19-38-53-206/2024/01/19/20/constraint_violations.json\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/DEMO-ll-adult-pred-model-monitor-1705692264-e088/monitoring-schedule-2024-01-19-19-38-53-206/2024/01/19/20/report.html\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/DEMO-ll-adult-pred-model-monitor-1705692264-e088/monitoring-schedule-2024-01-19-19-38-53-206/2024/01/19/20/report.ipynb\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692264-8c4a/monitor-output/DEMO-ll-adult-pred-model-monitor-1705692264-e088/monitoring-schedule-2024-01-19-19-38-53-206/2024/01/19/20/report.pdf\n" + ] + } + ], + "source": [ + "schedule_desc = model_bias_monitor.describe_schedule()\n", + "execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + "if execution_summary and execution_summary[\"MonitoringExecutionStatus\"] in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + "]:\n", + " last_model_bias_monitor_execution = model_bias_monitor.list_executions()[-1]\n", + " last_model_bias_monitor_execution_report_uri = (\n", + " last_model_bias_monitor_execution.output.destination\n", + " )\n", + " print(f\"Report URI: {last_model_bias_monitor_execution_report_uri}\")\n", + " last_model_bias_monitor_execution_report_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=last_model_bias_monitor_execution_report_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " )\n", + " print(\"Found Report Files:\")\n", + " print(\"\\n \".join(last_model_bias_monitor_execution_report_files))\n", + "else:\n", + " last_model_bias_monitor_execution = None\n", + " print(\n", + " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", + " )\n", + " print(schedule_desc)" + ] + }, + { + "cell_type": "markdown", + "id": "602a2ef3-4d6c-4d93-974e-77a679fc4757", + "metadata": {}, + "source": [ + "If there are any violations compared to the baseline, they are listed here. See [Bias Drift Violations](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift-violations.html) for the schema of the file, and how violations are detected." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "a7174d2e-9ee4-437f-be9a-c9d984318b76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{ 'version': '1.0',\n", + " 'violations': [ { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.3748947825295131 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.28176563733194276',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'CDDPL'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -0.34693877551020413 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement -0.09508196721311479',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'DAR'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -36.69387755102041 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement -0.5278688524590163',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'DCA'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -0.07650793650793647 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.027874251497005953',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'DCR'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value -0.13636363636363635 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement -0.03333333333333333',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'FT'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.9454985573866702 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.0841186702174704',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'GE'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.17253086419753086 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.1308103661044837',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'RD'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': 'Metric value 0.27419354838709675 '\n", + " \"doesn't meet the baseline constraint \"\n", + " 'requirement 0.10465328014037645',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'SD'},\n", + " { 'constraint_check_type': 'bias_drift_check',\n", + " 'description': \"Metric value Infinity doesn't meet \"\n", + " 'the baseline constraint requirement '\n", + " '2.916666666666667',\n", + " 'facet': 'Sex',\n", + " 'facet_value': '0',\n", + " 'metric_name': 'TE'}]}\n" + ] + } + ], + "source": [ + "violations = model_bias_monitor.latest_monitoring_constraint_violations()\n", + "if violations is not None:\n", + " pprint.PrettyPrinter(indent=4).pprint(violations.body_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "1b2e3d97-27cc-4325-814d-04219d25ab76", + "metadata": {}, + "source": [ + "By default, the analysis results are also published to CloudWatch, see [CloudWatch Metrics for Bias Drift Analysis](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-bias-drift-cw.html)." + ] + }, + { + "cell_type": "markdown", + "id": "f6388287-b810-4522-bcc1-928228982388", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "The endpoint can keep running and capturing data, but if there is no plan to collect more data or use this endpoint further, it should be deleted to avoid incurring additional charges. Note that deleting endpoint does not delete the data that was captured during the model invocations." + ] + }, + { + "cell_type": "markdown", + "id": "554e8db8-4918-420c-9b4d-5c7263a402e7", + "metadata": {}, + "source": [ + "First stop the worker threads," + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "f813097c-00cc-4ee4-91cc-d03b72915c67", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "invoke_endpoint_thread.terminate()\n", + "ground_truth_thread.terminate()" + ] + }, + { + "cell_type": "markdown", + "id": "80f971c4-c1ae-4766-ab44-a30d361df523", + "metadata": {}, + "source": [ + "Then stop all monitors scheduled for the endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "e4b99289-3924-4d40-9860-75ccea76646b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Stopping Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-38-53-206\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish\n", + "Done! Execution Status: CompletedWithViolations\n" + ] + } + ], + "source": [ + "model_bias_monitor.stop_monitoring_schedule()\n", + "wait_for_execution_to_finish(model_bias_monitor)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "3067c79f-193c-460a-8679-e51389a5999d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-38-53-206\n", + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Deleting Model Bias Job Definition with name: model-bias-job-definition-2024-01-19-19-38-53-206\n" + ] + } + ], + "source": [ + "model_bias_monitor.delete_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "id": "f2442401-06c9-481a-a04c-e339d618af54", + "metadata": {}, + "source": [ + "Finally, delete the endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "d6dd0678-66d3-493d-bee4-7e2a9dab901e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting endpoint with name: DEMO-ll-adult-pred-model-monitor-1705692264-e088\n", + "INFO:sagemaker:Deleting model with name: DEMO-ll-adult-pred-model-monitor-1705692264-e088\n" + ] + } + ], + "source": [ + "sagemaker_session.delete_endpoint(endpoint_name=endpoint_name)\n", + "sagemaker_session.delete_model(model_name=model_name)" + ] + }, + { + "cell_type": "markdown", + "id": "3776a471-dcb8-43bb-8018-4f65bef2833a", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Bias-Drift-for-Endpoint.ipynb)\n" + ] + } + ], + "metadata": { + "availableInstances": [ + { + "_defaultOrder": 0, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.t3.medium", + "vcpuNum": 2 + }, + { + "_defaultOrder": 1, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.t3.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 2, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.t3.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 3, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.t3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 4, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 5, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 6, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 7, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 8, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + 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"hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5d.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 14, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5d.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 15, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5d.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 16, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5d.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 17, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5d.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 18, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5d.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 19, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5d.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 20, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": true, + "memoryGiB": 0, + "name": "ml.geospatial.interactive", + "supportedImageNames": [ + "sagemaker-geospatial-v1-0" + ], + "vcpuNum": 0 + }, + { + "_defaultOrder": 21, + "_isFastLaunch": true, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.c5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 22, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.c5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 23, + 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+ "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.c5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 29, + "_isFastLaunch": true, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g4dn.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 30, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g4dn.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 31, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g4dn.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 32, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g4dn.8xlarge", + "vcpuNum": 32 + }, + { 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"ml.p3.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 38, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.p3dn.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 39, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.r5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 40, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.r5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 41, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.r5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 42, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": 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"name": "ml.g5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 48, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 49, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 50, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 51, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 52, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + } + ], + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "toc-autonumbering": false, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb new file mode 100644 index 0000000000..90d3ea06f1 --- /dev/null +++ b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb @@ -0,0 +1,2132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d6bcf871-9f26-4238-9954-09d13dc8ed4d", + "metadata": {}, + "source": [ + "# Amazon SageMaker Clarify Model Explainability Monitor for Batch Transform - JSON Format" + ] + }, + { + "cell_type": "markdown", + "id": "4f56e0c9-4778-4b47-a03c-0be6935f8939", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "d5fae672-0f0d-4416-90c9-d5af21e9fec2", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 60 minutes to run." + ] + }, + { + "cell_type": "markdown", + "id": "759e2db0-3572-445f-9503-5456d3e5f87b", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "* [Introduction](#Introduction)\n", + "* [General Setup](#General-Setup)\n", + " * [Imports](#Imports)\n", + " * [Handful of configuration](#Handful-of-configuration)\n", + " * [Data files](#Data-files)\n", + " * [SageMaker model](#SageMaker-model)\n", + "* [Batch Transform Job](#Batch-Transform-Job)\n", + " * [Captured data](#Captured-data)\n", + " * [Transform input](#Transform-input)\n", + "* [Model Explainability Monitor](#Model-Explainability-Monitor)\n", + " * [Baselining job](#Baselining-job)\n", + " * [Configurations](#Configurations)\n", + " * [Kick off baselining job](#Kick-off-baselining-job)\n", + " * [Monitoring Schedule](#Monitoring-Schedule)\n", + " * [Wait for the first execution](#Wait-for-the-first-execution)\n", + " * [Wait for the execution to finish](#Wait-for-the-execution-to-finish)\n", + " * [Inspect execution results](#Inspect-execution-results)\n", + "* [Cleanup](#Cleanup)" + ] + }, + { + "cell_type": "markdown", + "id": "14d6fc14-9b15-447d-bdd1-408214b7e6a9", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "[Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) continuously monitors the quality of Amazon SageMaker machine learning models in production. It enables developers to set alerts for when there are deviations in the model quality. Early and pro-active detection of these deviations enables corrective actions, such as retraining models, auditing upstream systems, or fixing data quality issues without having to monitor models manually or build additional tooling. \n", + "\n", + "[Amazon SageMaker Clarify Model Explainability Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html) is a model monitor that helps data scientists and ML engineers monitor predictions for feature attribution drift on a regular basis. A drift in the distribution of live data for models in production can result in a corresponding drift in the feature attribution values. As the model is monitored, customers can view exportable reports and graphs detailing feature attributions in SageMaker Studio and configure alerts in Amazon CloudWatch to receive notifications if it is detected that the attribution values drift beyond a certain threshold. \n", + "\n", + "This notebook demonstrates the process for setting up a [SageMaker Clarify Feature Attribution Drift Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html) for continuous monitoring of feature attribution drift of the data and model used by a regularly running [SageMaker Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html) job. The model input and output are in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats).\n", + "\n", + "In general, you can use the model explainability monitor for batch transform in this way,\n", + "\n", + "1. Schedule a model explainability monitor to monitor a data capture S3 location\n", + "1. Regularly run transform jobs with data capture enabled, the jobs save captured data to the data capture S3 URI\n", + "\n", + "The monitor executes processing jobs regularly to do feature attribution analysis, and then generate analysis reports and publish metrics to CloudWatch." + ] + }, + { + "cell_type": "markdown", + "id": "e6b6b92b-92f4-46c9-ad91-61dd25c03fe4", + "metadata": {}, + "source": [ + "## General Setup" + ] + }, + { + "cell_type": "markdown", + "id": "88a6e4c6-ab3f-4c0b-86f0-19003bae248b", + "metadata": {}, + "source": [ + "The notebook uses the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk). The following cell upgrades the SDK and its dependencies. Then you may need to restart the kernel and rerun the notebook to pick up the up-to-date APIs, if the notebook is executed in the SageMaker Studio." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bf5a0ced-48c3-440f-b777-69771f9de74c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: sagemaker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (2.203.1)\n", + "Requirement already satisfied: fastapi==0.95.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.95.2)\n", + "Requirement already satisfied: 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\u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: boto3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (1.34.22)\n", + "Requirement already satisfied: botocore<1.35.0,>=1.34.22 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3) (1.34.22)\n", + "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3) 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"!pip install -U sagemaker\n", + "!pip install -U boto3\n", + "!pip install -U botocore" + ] + }, + { + "cell_type": "markdown", + "id": "3dee3e5c-2c32-4b72-8834-feb7ca57f07b", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "The following cell imports the APIs to be used by the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c8a3dca-5e39-4d7e-aaf7-f025fb57df0b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/zicanl/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "import sagemaker\n", + "import pandas as pd\n", + "import copy\n", + "import datetime\n", + "import json\n", + "import os\n", + "import pprint\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "dd71dd08-c4eb-4a1a-b383-df735686d842", + "metadata": {}, + "source": [ + "### Handful of configuration\n", + "\n", + "To begin, ensure that these prerequisites have been completed.\n", + "\n", + "* Specify an AWS Region to host the model.\n", + "* Specify an IAM role to execute jobs.\n", + "* Define the S3 URIs that stores the model file, input data and output data. For demonstration purposes, this notebook uses the same bucket for them. In reality, they could be separated with different security policies." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8b9057d5-162f-4fa7-8d2e-3274d7f9baee", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AWS region: us-west-2\n", + "RoleArn: arn:aws:iam::678264136642:role/Admin\n", + "Demo Bucket: sagemaker-us-west-2-678264136642\n", + "Demo Prefix: sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764\n", + "Demo S3 key: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764\n", + "The transform job will save the results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/transform-output\n", + "The transform job will save the captured data to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/data-capture\n", + "The baselining job will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/baselining-output\n", + "The monitor will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output\n" + ] + } + ], + "source": [ + "sagemaker_session = sagemaker.Session()\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "print(f\"AWS region: {region}\")\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "print(f\"RoleArn: {role}\")\n", + "\n", + "# A different bucket can be used, but make sure the role for this notebook has\n", + "# the s3:PutObject permissions. This is the bucket into which the data is captured\n", + "bucket = sagemaker_session.default_bucket()\n", + "print(f\"Demo Bucket: {bucket}\")\n", + "prefix = sagemaker.utils.unique_name_from_base(\"sagemaker/DEMO-ClarifyModelMonitor\")\n", + "print(f\"Demo Prefix: {prefix}\")\n", + "s3_key = f\"s3://{bucket}/{prefix}\"\n", + "print(f\"Demo S3 key: {s3_key}\")\n", + "\n", + "data_capture_s3_uri = f\"{s3_key}/data-capture\"\n", + "transform_output_s3_uri = f\"{s3_key}/transform-output\"\n", + "baselining_output_s3_uri = f\"{s3_key}/baselining-output\"\n", + "monitor_output_s3_uri = f\"{s3_key}/monitor-output\"\n", + "\n", + "print(f\"The transform job will save the results to: {transform_output_s3_uri}\")\n", + "print(f\"The transform job will save the captured data to: {data_capture_s3_uri}\")\n", + "print(f\"The baselining job will save the analysis results to: {baselining_output_s3_uri}\")\n", + "print(f\"The monitor will save the analysis results to: {monitor_output_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7af1bf1c-e60e-4a07-9cb0-dba16d3d0576", + "metadata": {}, + "source": [ + "### Data files\n", + "\n", + "This example includes two dataset files, both in the JSON format." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1311db6e-25e2-4d30-8ea9-12f81f759feb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "train_dataset_path = \"test_data/validation-dataset.json\"\n", + "test_dataset_path = \"test_data/test-dataset.json\"\n", + "dataset_type = \"application/json\"" + ] + }, + { + "cell_type": "markdown", + "id": "a5f6ce22-bde4-4fb8-be05-74605f2248a5", + "metadata": {}, + "source": [ + "The train dataset has the features and the ground truth label (pointed to by the key \"label\")," + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea97710e-a4cc-4c5f-bd5d-8657eb17dd80", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "!head -n 5 $train_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "e81923f6-224a-4bbf-aee3-00702864a865", + "metadata": {}, + "source": [ + "The test dataset only has features." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5fb9caa5-589c-4559-82fd-03ac259e0a6f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "!head -n 5 $test_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "8db74735-e307-46cd-b1ab-4469508033bf", + "metadata": {}, + "source": [ + "Here are the headers of the train dataset. \"Target\" is the header of the ground truth label, and the others are the feature headers. They will be used to beautify the analysis report." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9cf229d7-c727-4cca-9674-a036d955f868", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_headers = [\n", + " \"Age\",\n", + " \"Workclass\",\n", + " \"fnlwgt\",\n", + " \"Education\",\n", + " \"Education-Num\",\n", + " \"Marital Status\",\n", + " \"Occupation\",\n", + " \"Relationship\",\n", + " \"Ethnic group\",\n", + " \"Sex\",\n", + " \"Capital Gain\",\n", + " \"Capital Loss\",\n", + " \"Hours per week\",\n", + " \"Country\",\n", + " \"Target\",\n", + "]\n", + "label_header = all_headers[-1]" + ] + }, + { + "cell_type": "markdown", + "id": "4258450e-47fd-4613-a89d-c78bfb1a26ab", + "metadata": {}, + "source": [ + "To verify that the execution role for this notebook has the necessary permissions to proceed, put a simple test object into the S3 bucket specified above. If this command fails, update the role to have `s3:PutObject` permission on the bucket and try again." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5aedff42-c561-402f-ba79-b5eb7fbd2e15", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! We are all set to proceed with uploading to S3.\n" + ] + } + ], + "source": [ + "sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=\"hello\",\n", + " desired_s3_uri=f\"{s3_key}/upload-test-file.txt\",\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(\"Success! We are all set to proceed with uploading to S3.\")" + ] + }, + { + "cell_type": "markdown", + "id": "00e2985e-829c-44df-acfe-83f02c6eae51", + "metadata": {}, + "source": [ + "Then upload the data files to S3 so that they can be used by SageMaker jobs." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "997fdb72-a9ba-42e2-a205-58e9c8aaa1ca", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data is uploaded to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/validation-dataset.json\n", + "Test data is uploaded to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/test-dataset.json\n" + ] + } + ], + "source": [ + "train_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=train_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Train data is uploaded to: {train_data_s3_uri}\")\n", + "test_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=test_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Test data is uploaded to: {test_data_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0537ba9-363d-4649-940a-27091a474b8a", + "metadata": {}, + "source": [ + "### SageMaker model\n", + "\n", + "This example includes a prebuilt [SageMaker Linear Learner](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html) model trained by [a SageMaker Clarify offline processing example notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb). The model supports [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats) (MIME type `\"application/jsonlines\"`).\n", + "\n", + "* The model input can one or more lines, each line is a JSON object that has a \"features\" key pointing to a list of feature values concerning demographic characteristics of individuals. For example,\n", + "\n", + "```\n", + "{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}\n", + "{\"features\":[43,2,72338,12,14,2,12,0,1,1,0,0,40,37]}\n", + "```\n", + "\n", + "* The model output has the predictions of whether a person has a yearly income that is more than $50,000. Each prediction is a JSON object that has a \"predicted_label\" key pointing to the predicted label, and the \"score\" key pointing to the confidence score. For example,\n", + "\n", + "```\n", + "{\"predicted_label\":1,\"score\":0.989977359771728}\n", + "{\"predicted_label\":1,\"score\":0.504138827323913}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1b1691d1-35cb-4459-979f-f1b3890a0796", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model file has been uploaded to s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/ll-adult-prediction-model.tar.gz\n", + "SageMaker model name: DEMO-xgb-churn-pred-model-monitor-1705692267-227f\n", + "SageMaker Linear Learner image: 174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:1\n", + "SageMaker model created\n" + ] + } + ], + "source": [ + "model_file = \"model/ll-adult-prediction-model.tar.gz\"\n", + "model_url = sagemaker.s3.S3Uploader.upload(\n", + " local_path=model_file,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Model file has been uploaded to {model_url}\")\n", + "\n", + "model_name = sagemaker.utils.unique_name_from_base(\"DEMO-xgb-churn-pred-model-monitor\")\n", + "print(f\"SageMaker model name: {model_name}\")\n", + "\n", + "image_uri = sagemaker.image_uris.retrieve(\"linear-learner\", region, \"1\")\n", + "print(f\"SageMaker Linear Learner image: {image_uri}\")\n", + "\n", + "model = sagemaker.model.Model(image_uri=image_uri, model_data=model_url, role=role)\n", + "container_def = model.prepare_container_def()\n", + "sagemaker_session.create_model(model_name, role, container_def)\n", + "print(\"SageMaker model created\")" + ] + }, + { + "cell_type": "markdown", + "id": "7fc2dec3-ce5d-493c-92be-4d3cccc49a65", + "metadata": {}, + "source": [ + "## Batch Transform Job\n", + "\n", + "For continuous monitoring, batch transform jobs should be executed regularly with the latest data. But for demonstration purpose, the following cell only executes the job once before the monitor is scheduled, so that the first monitoring execution has captured data to process. \n", + "\n", + "See [Transformer](https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform) for the API reference. The `destination_s3_uri` is used to specify the data capture S3 URI which is a key connection between the job and the monitor.\n", + "\n", + "**NOTE**: The following cell takes about 5 minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "da953e25-883c-4afe-bf64-8895e665002d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating transform job with name: linear-learner-2024-01-19-19-24-28-808\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................................!\n" + ] + } + ], + "source": [ + "transfomer = model.transformer(\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " accept=dataset_type, # The transform output data format\n", + " assemble_with=None, # JSON records are under a single JSON structure\n", + " output_path=transform_output_s3_uri,\n", + ")\n", + "\n", + "transfomer.transform(\n", + " data=test_data_s3_uri,\n", + " content_type=dataset_type, # The transform input format\n", + " split_type=None, # JSON records are under a single JSON structure\n", + " batch_data_capture_config=sagemaker.inputs.BatchDataCaptureConfig(\n", + " destination_s3_uri=data_capture_s3_uri,\n", + " ),\n", + " wait=True, # In real world you don't have to wait, but for demo purpose we wait for the output\n", + " logs=False, # You can change it to True to view job logs inline\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3c512806-c51c-43b4-b043-9f81b70f2f42", + "metadata": {}, + "source": [ + "### Captured data" + ] + }, + { + "cell_type": "markdown", + "id": "e80b5231-2111-45cf-8e36-ecc1a3630f3f", + "metadata": {}, + "source": [ + "Once the transform job completed, an \"input\" folders is created under `data_capture_s3_uri`, to includes the captured data files of transform input. Note that, batch transform data capture is unlike endpoint data capture, it does not capture the data for real as it will create tremendous amount of duplications. Instead, it generates [manifest](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_S3DataSource.html#sagemaker-Type-S3DataSource-S3Uri) files which refer to the transform output S3 location." + ] + }, + { + "cell_type": "markdown", + "id": "fc1e1039-3922-4b36-889c-7c73523e5a48", + "metadata": {}, + "source": [ + "Now list the captured data files stored in Amazon S3. There should be different files from different time periods organized based on the hour in which the batch transformation occurred. The format of the Amazon S3 path is:\n", + "\n", + "`s3://{data_capture_s3_uri}/input/yyyy/mm/dd/hh/filename.jsonl`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8e3daf52-e42a-496a-bf87-463da78122f7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found capture data files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/data-capture/input/2024/01/19/19/1b4bda25-59a8-476c-9bb4-65495d56a050.json\n" + ] + } + ], + "source": [ + "data_capture_output = f\"{data_capture_s3_uri}/input\"\n", + "captured_data_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=data_capture_output,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")\n", + "print(\"Found capture data files:\")\n", + "print(\"\\n \".join(captured_data_files[-5:]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c88051d9-12d0-4ef1-bb82-e174d33d7082", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"prefix\": \"s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/test-dataset.json\"\n", + " },\n", + " \"\"\n", + "]\n" + ] + } + ], + "source": [ + "captured_data_file = captured_data_files[-1]\n", + "captured_data_file_content = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=captured_data_files[-1],\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "data_capture_input_dict = json.loads(captured_data_file_content)\n", + "print(json.dumps(data_capture_input_dict, indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6cc97568-4384-45a1-b278-44f220a5c586", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def upload_captured_data(offset):\n", + " yyyy_mm_dd_hh = \"%Y/%m/%d/%H\"\n", + " file_path, file_name = os.path.split(captured_data_file)\n", + " this_hour_str = file_path[len(data_capture_output) + 1 :] # like \"2023/01/18/22\"\n", + " this_hour = datetime.datetime.strptime(this_hour_str, yyyy_mm_dd_hh)\n", + " next_hour = this_hour + datetime.timedelta(hours=offset)\n", + " next_hour_str = next_hour.strftime(yyyy_mm_dd_hh) # like \"2023/01/18/23\"\n", + " sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=captured_data_file_content,\n", + " desired_s3_uri=f\"{data_capture_output}/{next_hour_str}/{file_name}\",\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + "\n", + "\n", + "# For demostration purpose, only needed for this example\n", + "# copy the captured file to the last hour's folder, just in case the first monitoring execution is started in this hour.\n", + "upload_captured_data(-1)\n", + "# copy the captured file to the next hour's folder, just in case the first monitoring execution is started after next hour.\n", + "upload_captured_data(1)" + ] + }, + { + "cell_type": "markdown", + "id": "ea870333-e59d-4d80-9d44-8dffd54044d0", + "metadata": {}, + "source": [ + "### Transform input\n", + "\n", + "The captured data file refers to the transform input file. The cell below shows the first few records of the file." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1c970061-3bf3-4fab-9201-43eead9c4ae1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "transform_input = data_capture_input_dict[0][\"prefix\"]\n", + "transform_output_content = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=transform_input,\n", + " sagemaker_session=sagemaker_session,\n", + ").splitlines()\n", + "print(*transform_output_content[-5:], sep=\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "f4b55f85-56fe-4b95-a691-46eebb3ae140", + "metadata": {}, + "source": [ + "## Model Explainability Monitor\n", + "\n", + "Similar to the other monitoring types, the standard procedure of creating a [feature attribution drift monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html) is first run a baselining job, and then schedule the monitor." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e97232ac-c328-454e-b999-3a489e1af479", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "model_explainability_monitor = sagemaker.model_monitor.ModelExplainabilityMonitor(\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " max_runtime_in_seconds=3600,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dffea0b8-3dc7-426d-a91c-7ddd09f6143d", + "metadata": {}, + "source": [ + "### Baselining job\n", + "\n", + "A baselining job runs predictions on training dataset and suggests constraints. The `suggest_baseline()` method of `ModelExplainabilityMonitor` starts a SageMaker Clarify processing job to generate the constraints.\n", + "\n", + "The step is not mandatory, but providing constraints file to the monitor can enable violations file generation." + ] + }, + { + "cell_type": "markdown", + "id": "87d3bc55-beb4-4060-ab8d-43b9d8fd9365", + "metadata": {}, + "source": [ + "#### Configurations\n", + "\n", + "Information about the input data need to be provided to the processor." + ] + }, + { + "cell_type": "markdown", + "id": "6097e9de-cd90-4029-933d-1e8f427038f4", + "metadata": {}, + "source": [ + "`DataConfig` stores information about the dataset to be analyzed. For example, the dataset file and its format (like JSON Lines), where to store the analysis results. Some special things to note about this configuration for the JSON Lines dataset,\n", + "\n", + "* The parameter value `\"features\"` or `\"label\"` is **NOT** a header string. Instead, it is a `JMESPath` expression ([refer to its specification](https://jmespath.org/specification.html)) that is used to locate the features list or the ground truth label in the dataset (the ground truth label is not needed for the explainability analysis, the parameter is specified so that the job knows it should be excluded from the dataset). In this example notebook they happen to be the same as the keys in the dataset. But for example, if the dataset has records like below, then the `features` parameter should use value `\"data.features.values\"`, and the `label` parameter should use value `\"data.label\"`.\n", + "\n", + " ```\n", + " {\"data\": {\"features\": {\"values\": [25, 2, 226802, 1, 7, 4, 6, 3, 2, 1, 0, 0, 40, 37]}, \"label\": 0}}\n", + " ```\n", + "\n", + "* SageMaker Clarify processing job will load the JSON Lines dataset into tabular representation for further analysis, and the parameter `headers` is the list of column names. **The label header shall be the last one in the headers list**, and the order of feature headers shall be the same as the order of features in a record." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e935fbb4-44ba-478f-b286-b108c6bbb933", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "features_jmespath = \"instances[*].features\"\n", + "ground_truth_label_jmespath = \"instances[*].label\"\n", + "data_config = sagemaker.clarify.DataConfig(\n", + " s3_data_input_path=train_data_s3_uri,\n", + " s3_output_path=baselining_output_s3_uri,\n", + " features=features_jmespath,\n", + " label=ground_truth_label_jmespath,\n", + " headers=all_headers,\n", + " dataset_type=dataset_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1502e2d3-d532-4c2b-9d62-ea95a39b7736", + "metadata": {}, + "source": [ + "`ModelConfig` is configuration related to model to be used for inferencing. In order to compute SHAP values, the SageMaker Clarify explainer generates synthetic dataset and then get its predictions for the SageMaker model. To accomplish this, the processing job will use the model to create an ephemeral endpoint (also known as \"shadow endpoint\"). The processing job will delete the shadow endpoint after the computations are completed. One special thing to note about this configuration for the JSON Lines model input and output,\n", + "\n", + "* `content_template` is used by SageMaker Clarify processing job to convert the tabular data to the request payload acceptable to the shadow endpoint. To be more specific, the placeholder `$features` will be replaced by **the features list** from records. The request payload of a record from the testing dataset happens to be similar to the record itself, like `{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}`, because both the dataset and the model input conform to the same format." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "29488add-859f-4582-b592-a2d6dbad417e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_config = sagemaker.clarify.ModelConfig(\n", + " model_name=model_name, # The name of the SageMaker model\n", + " instance_type=\"ml.m5.xlarge\", # The instance type of the shadow endpoint\n", + " instance_count=1, # The instance count of the shadow endpoint\n", + " content_type=dataset_type, # The data format of the model input\n", + " accept_type=dataset_type, # The data format of the model output\n", + " content_template='{\"instances\":$records}',\n", + " record_template='{\"features\":$features}',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fd8833d4-b2f2-4d83-a471-4ef296620354", + "metadata": {}, + "source": [ + "Currently, the SageMaker Clarify explainer offers a scalable and efficient implementation of SHAP, so the explainability config is `SHAPConfig`, including\n", + "\n", + "* `baseline`: A list of records (at least one) to be used as the baseline dataset in the Kernel SHAP algorithm, each record is JSON object that includes a list of features. It can also be a S3 object URI, the S3 file should be in the same format as dataset.\n", + "* `num_samples`: Number of samples to be used in the Kernel SHAP algorithm. This number determines the size of the generated synthetic dataset to compute the SHAP values.\n", + "* `agg_method`: Aggregation method for global SHAP values. Valid values are\n", + " * \"mean_abs\" (mean of absolute SHAP values for all instances),\n", + " * \"median\" (median of SHAP values for all instances) and\n", + " * \"mean_sq\" (mean of squared SHAP values for all instances).\n", + "* `use_logit`: Indicator of whether the logit function is to be applied to the model predictions. Default is False. If \"use_logit\" is true then the SHAP values will have log-odds units.\n", + "* `save_local_shap_values`: Indicator of whether to save the local SHAP values in the output location. Default is True." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "679d8b02-d15c-4a1d-adc2-e1ae107f0b6c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SHAP baseline: {'instances': [{'features': [39, 2, 184870, 10, 10, 3, 6, 1, 4, 1, 1597, 61, 41, 37]}]}\n" + ] + } + ], + "source": [ + "# Here use the mean value of train dataset as SHAP baseline\n", + "dataset = []\n", + "with open(train_dataset_path) as f:\n", + " instances = json.load(f)[\"instances\"]\n", + " for instance in instances:\n", + " dataset.append(instance[\"features\"])\n", + "mean_values = pd.DataFrame(dataset).mean().round().astype(int).to_list()\n", + "mean_record = {\"features\": mean_values}\n", + "shap_baseline = {\"instances\": [mean_record]}\n", + "print(f\"SHAP baseline: {shap_baseline}\")\n", + "\n", + "shap_config = sagemaker.clarify.SHAPConfig(\n", + " baseline=shap_baseline,\n", + " num_samples=100,\n", + " agg_method=\"mean_abs\",\n", + " save_local_shap_values=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "223c977b-d1fe-41b0-a9b3-05884fedea67", + "metadata": {}, + "source": [ + "#### Kick off baselining job\n", + "\n", + "Call the `suggest_baseline()` method to start the baselining job. The model output has a key \"score\" pointing to a confidence score value between `0` and `1`. So, the `model_scores` parameter is set to the `JMESPath` expression\"score\" which can locate the score in the model output." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5454eeea-1013-4722-8065-7dcc5e9b1b07", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.clarify:Analysis Config: {'dataset_type': 'application/json', 'features': 'instances[*].features', 'headers': ['Age', 'Workclass', 'fnlwgt', 'Education', 'Education-Num', 'Marital Status', 'Occupation', 'Relationship', 'Ethnic group', 'Sex', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Country', 'Target'], 'label': 'instances[*].label', 'predictor': {'model_name': 'DEMO-xgb-churn-pred-model-monitor-1705692267-227f', 'instance_type': 'ml.m5.xlarge', 'initial_instance_count': 1, 'accept_type': 'application/json', 'content_type': 'application/json', 'content_template': '{\"instances\":$records}', 'record_template': '{\"features\":$features}', 'label': 'predictions[*].score'}, 'methods': {'report': {'name': 'report', 'title': 'Analysis Report'}, 'shap': {'use_logit': False, 'save_local_shap_values': False, 'baseline': {'instances': [{'features': [39, 2, 184870, 10, 10, 3, 6, 1, 4, 1, 1597, 61, 41, 37]}]}, 'num_samples': 100, 'agg_method': 'mean_abs'}}}\n", + "INFO:sagemaker:Creating processing-job with name baseline-suggestion-job-2024-01-19-19-30-16-810\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confidence_score_jmespath = \"predictions[*].score\"\n", + "model_explainability_monitor.suggest_baseline(\n", + " explainability_config=shap_config,\n", + " data_config=data_config,\n", + " model_config=model_config,\n", + " model_scores=confidence_score_jmespath, # The JMESPath to locate the confidence score in model output\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ac063d72-2955-40c9-a82c-4b1d177ccfef", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the baselining job is completed (in about 10 minutes). It then inspects the suggested constraints. This step can be skipped, because the monitor to be scheduled will automatically pick up baselining job name and wait for it before monitoring execution." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b5a2c40f-fea1-4477-a155-c46acceb88d8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................................................................!\n", + "Suggested constraints: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/baselining-output/analysis.json\n", + "{\n", + " \"version\": \"1.0\",\n", + " \"explanations\": {\n", + " \"kernel_shap\": {\n", + " \"0.7956783771514893\": {\n", + " \"global_shap_values\": {\n", + " \"Age\": 0.05967323398925334,\n", + " \"Workclass\": 0.009303977202541277,\n", + " \"fnlwgt\": 0.0011532925777696535,\n", + " \"Education\": 0.014668402906540028,\n", + " \"Education-Num\": 0.09897500295961109,\n", + " \"Marital Status\": 0.05465943541380248,\n", + " \"Occupation\": 0.002534111174299059,\n", + " \"Relationship\": 0.018197139997990445,\n", + " \"Ethnic group\": 0.005443095081746528,\n", + " \"Sex\": 0.03218866814815311,\n", + " \"Capital Gain\": 0.09933718978948747,\n", + " \"Capital Loss\": 0.013533278372092259,\n", + " \"Hours per week\": 0.03648060306227074,\n", + " \"Country\": 0.004879998492050835\n", + " },\n", + " \"expected_value\": 0.2506232261657715\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "model_explainability_monitor.latest_baselining_job.wait(logs=False)\n", + "print()\n", + "model_explainability_constraints = model_explainability_monitor.suggested_constraints()\n", + "print(f\"Suggested constraints: {model_explainability_constraints.file_s3_uri}\")\n", + "print(\n", + " sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=model_explainability_constraints.file_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3a1f3cb9-acb9-4d39-9730-a1226146a3fa", + "metadata": {}, + "source": [ + "### Monitoring Schedule\n", + "\n", + "With above constraints collected, now call `create_monitoring_schedule()` method to schedule an hourly model explainability monitor." + ] + }, + { + "cell_type": "markdown", + "id": "94470149-a340-47ab-8c65-20f25c9d76c8", + "metadata": { + "tags": [] + }, + "source": [ + "If a baselining job has been submitted, then the monitor object will automatically pick up the analysis configuration from the baselining job. But if the baselining step is skipped, or if the capture dataset has different nature than the training dataset, then analysis configuration has to be provided.\n", + "\n", + "`ModelConfig` is required by `ExplainabilityAnalysisConfig` for the same reason as it is required by the baselining job. Note that only features are required for computing feature attribution, so ground truth label should be excluded.\n", + "\n", + "Highlights,\n", + "\n", + "* `data_capture_s3_uri` is the location of data captured by the batch transform job\n", + "* `features_attribute` is the `JMESPath` expression to locate the features in model input, similar to the `features` parameter of `DataConfig`.\n", + "* `inference_attribute` stores the `JMESPath` expression to locate the confidence score in model output, similar to the `model_scores` parameter of the `suggest_baseline()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "55a5fbb0-71a6-4cd5-83b9-a6ceb6b67d32", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "schedule_expression = sagemaker.model_monitor.CronExpressionGenerator.hourly()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "17317354-7991-4d57-87bd-a7c7ce051de6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.model_monitor.model_monitoring:Creating Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-01-339\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model explainability monitoring schedule: monitoring-schedule-2024-01-19-19-39-01-339\n" + ] + } + ], + "source": [ + "# Remove label because only features are required for the analysis\n", + "headers_without_label_header = copy.deepcopy(all_headers)\n", + "headers_without_label_header.remove(label_header)\n", + "model_explainability_analysis_config = sagemaker.model_monitor.ExplainabilityAnalysisConfig(\n", + " explainability_config=shap_config,\n", + " model_config=model_config,\n", + " headers=headers_without_label_header,\n", + ")\n", + "model_explainability_monitor.create_monitoring_schedule(\n", + " analysis_config=model_explainability_analysis_config,\n", + " batch_transform_input=sagemaker.model_monitor.BatchTransformInput(\n", + " data_captured_destination_s3_uri=data_capture_s3_uri,\n", + " destination=\"/opt/ml/processing/transform\",\n", + " dataset_format=sagemaker.model_monitor.MonitoringDatasetFormat.json(lines=False),\n", + " features_attribute=features_jmespath,\n", + " inference_attribute=confidence_score_jmespath,\n", + " ),\n", + " output_s3_uri=monitor_output_s3_uri,\n", + " schedule_cron_expression=schedule_expression,\n", + ")\n", + "print(\n", + " f\"Model explainability monitoring schedule: {model_explainability_monitor.monitoring_schedule_name}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a83ab8ff-9f40-4c77-93bf-8fa3dec8980b", + "metadata": {}, + "source": [ + "#### Wait for the first execution\n", + "\n", + "The schedule starts jobs at the previously specified intervals. Code below waits until time crosses the hour boundary (in UTC) to see executions kick off.\n", + "\n", + "Note: Even for an hourly schedule, Amazon SageMaker has a buffer period of 20 minutes to schedule executions. The execution might start in anywhere from zero to ~20 minutes from the hour boundary. This is expected and done for load balancing in the backend." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "12eccd87-e503-4f2a-ae7c-ff5c5c6b84c1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def wait_for_execution_to_start(model_monitor):\n", + " print(\n", + " \"An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\"\n", + " )\n", + "\n", + " print(\"Waiting for the first execution to happen\", end=\"\")\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " while \"LastMonitoringExecutionSummary\" not in schedule_desc:\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " print()\n", + " print(\"Done! Execution has been created\")\n", + "\n", + " print(\"Now waiting for execution to start\", end=\"\")\n", + " while schedule_desc[\"LastMonitoringExecutionSummary\"][\"MonitoringExecutionStatus\"] in \"Pending\":\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(10)\n", + "\n", + " print()\n", + " print(\"Done! Execution has started\")" + ] + }, + { + "cell_type": "markdown", + "id": "53c29159-4c63-4e84-8b08-5ab3ae95214e", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the first monitoring execution is started. As explained above, the wait could take more than 60 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e4c96d12-deb7-4724-a84d-0c5b5023faff", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\n", + "Waiting for the first execution to happen...............................\n", + "Done! Execution has been created\n", + "Now waiting for execution to start..........\n", + "Done! Execution has started\n" + ] + } + ], + "source": [ + "wait_for_execution_to_start(model_explainability_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "6672426d-d2fd-49d9-a851-4f94db156c62", + "metadata": {}, + "source": [ + "In real world, a monitoring schedule is supposed to be active all the time. But in this example, it can be stopped to avoid incurring extra charges. A stopped schedule will not trigger further executions, but the ongoing execution will continue. And if needed, the schedule can be restarted by `start_monitoring_schedule()`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "434799f5-cbc0-4c45-b63a-697be96ba05b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Stopping Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-01-339\n" + ] + } + ], + "source": [ + "model_explainability_monitor.stop_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "id": "42324b88-111b-485f-82f6-c00a12f17205", + "metadata": {}, + "source": [ + "#### Wait for the execution to finish\n", + "\n", + "In the previous cell, the first execution has started. This section waits for the execution to finish so that its analysis results are available. Here are the possible terminal states and what each of them mean:\n", + "\n", + "* `Completed` - This means the monitoring execution completed, and no issues were found in the violations report.\n", + "* `CompletedWithViolations` - This means the execution completed, but constraint violations were detected.\n", + "* `Failed` - The monitoring execution failed, maybe due to client error (perhaps incorrect role permissions) or infrastructure issues. Further examination of `FailureReason` and `ExitMessage` is necessary to identify what exactly happened.\n", + "* `Stopped` - job exceeded max runtime or was manually stopped." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7cb1249a-d49a-423a-83ea-cf142affc785", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Waits for the schedule to have last execution in a terminal status.\n", + "def wait_for_execution_to_finish(model_monitor):\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + " if execution_summary is not None:\n", + " print(\"Waiting for execution to finish\", end=\"\")\n", + " while execution_summary[\"MonitoringExecutionStatus\"] not in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + " \"Failed\",\n", + " \"Stopped\",\n", + " ]:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc[\"LastMonitoringExecutionSummary\"]\n", + " print()\n", + " print(f\"Done! Execution Status: {execution_summary['MonitoringExecutionStatus']}\")\n", + " else:\n", + " print(\"Last execution not found\")" + ] + }, + { + "cell_type": "markdown", + "id": "c1d93e4e-e0b0-48b8-b51b-cd13e43ec727", + "metadata": {}, + "source": [ + "**NOTE**: The following cell takes about 10 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "4128240b-a510-4321-b34f-7e81b70f2d6d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish..........\n", + "Done! Execution Status: Completed\n" + ] + } + ], + "source": [ + "wait_for_execution_to_finish(model_explainability_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "27232906-188f-4d6d-8425-419b33b345b4", + "metadata": {}, + "source": [ + "#### Inspect execution results\n", + "\n", + "List the generated reports,\n", + "\n", + "* analysis.json includes the global SHAP values.\n", + "* report.* files are static report files to visualize the SHAP values." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "36089938-1460-4a11-b40a-79ad320e9657", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Report URI: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output/monitoring-schedule-2024-01-19-19-39-01-339/2024/01/19/20\n", + "Found Report Files:\n", + "s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output/monitoring-schedule-2024-01-19-19-39-01-339/2024/01/19/20/analysis.json\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output/monitoring-schedule-2024-01-19-19-39-01-339/2024/01/19/20/report.html\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output/monitoring-schedule-2024-01-19-19-39-01-339/2024/01/19/20/report.ipynb\n", + " s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output/monitoring-schedule-2024-01-19-19-39-01-339/2024/01/19/20/report.pdf\n" + ] + } + ], + "source": [ + "schedule_desc = model_explainability_monitor.describe_schedule()\n", + "execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + "if execution_summary and execution_summary[\"MonitoringExecutionStatus\"] in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + "]:\n", + " last_model_explainability_monitor_execution = model_explainability_monitor.list_executions()[-1]\n", + " last_model_explainability_monitor_execution_report_uri = (\n", + " last_model_explainability_monitor_execution.output.destination\n", + " )\n", + " print(f\"Report URI: {last_model_explainability_monitor_execution_report_uri}\")\n", + " last_model_explainability_monitor_execution_report_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=last_model_explainability_monitor_execution_report_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " )\n", + " print(\"Found Report Files:\")\n", + " print(\"\\n \".join(last_model_explainability_monitor_execution_report_files))\n", + "else:\n", + " last_model_explainability_monitor_execution = None\n", + " print(\n", + " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", + " )\n", + " print(schedule_desc)" + ] + }, + { + "cell_type": "markdown", + "id": "9716a5f4-66b2-42be-abe3-97999cc4946f", + "metadata": {}, + "source": [ + "If there are any violations compared to the baseline, they are listed here. See [Feature Attribution Drift Violations](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-model-attribution-drift-violations.html) for the schema of the file, and how violations are detected." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "adbdbf7f-14a8-4c6d-b864-64473fc85e2c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Could not retrieve constraints file at location 's3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692266-3764/monitor-output/monitoring-schedule-2024-01-19-19-39-01-339/2024/01/19/20/constraint_violations.json'. To manually retrieve ConstraintViolations object from a given uri, use 'my_model_monitor.constraints(my_s3_uri)' or 'ConstraintViolations.from_s3_uri(my_s3_uri)'\n" + ] + } + ], + "source": [ + "violations = model_explainability_monitor.latest_monitoring_constraint_violations()\n", + "if violations is not None:\n", + " pprint.PrettyPrinter(indent=4).pprint(violations.body_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "da9cabfe-1c81-41c5-a1ec-cdad45113819", + "metadata": {}, + "source": [ + "By default, the analysis results are also published to CloudWatch, see [CloudWatch Metrics for Feature Attribution Drift Analysis](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-drift-cw.html)." + ] + }, + { + "cell_type": "markdown", + "id": "af941ee2-cb23-4389-8896-dda7488dad58", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "If there is no plan to collect more data for feature attribution drift monitoring, then the monitor should be stopped (and deleted) to avoid incurring additional charges. Note that deleting the monitor does not delete the data in S3." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1a06d224-40a5-4688-8bc4-613eb4cacd8d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Stopping Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-01-339\n", + "INFO:sagemaker:Deleting Monitoring Schedule with name: monitoring-schedule-2024-01-19-19-39-01-339\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish\n", + "Done! Execution Status: Completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Deleting Model Explainability Job Definition with name: model-explainability-job-definition-2024-01-19-19-39-01-339\n" + ] + } + ], + "source": [ + "model_explainability_monitor.stop_monitoring_schedule()\n", + "wait_for_execution_to_finish(model_explainability_monitor)\n", + "model_explainability_monitor.delete_monitoring_schedule()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5d5bd369-0d5e-44e9-9571-ef0ca94d49bb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting model with name: DEMO-xgb-churn-pred-model-monitor-1705692267-227f\n" + ] + } + ], + "source": [ + "sagemaker_session.delete_model(model_name)" + ] + }, + { + "cell_type": "markdown", + "id": "526d1f79-4c69-41b8-960b-e79c7036d817", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. 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"hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + } + ], + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb new file mode 100644 index 0000000000..570a437a08 --- /dev/null +++ b/sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb @@ -0,0 +1,2128 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a524a4c-5a39-4b6b-abb1-1c8e1b2de84c", + "metadata": {}, + "source": [ + "# Amazon SageMaker Clarify Model Explainability Monitor - JSON Format" + ] + }, + { + "cell_type": "markdown", + "id": "538beb37-d6ec-4cfc-ad4d-7d86a890e94b", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4eaae7a8-2ab1-4f7c-8cb2-6b23606c58c1", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 60 minutes to run." + ] + }, + { + "cell_type": "markdown", + "id": "5e939eb9-9189-48fe-ab44-ddc6f942f8e3", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "* [Introduction](#Introduction)\n", + "* [General Setup](#General-Setup)\n", + " * [Imports](#Imports)\n", + " * [Handful of configuration](#Handful-of-configuration)\n", + " * [Model file and data files](#Model-file-and-data-files)\n", + "* [Real-time Inference Endpoint](#Real-time-Inference-Endpoint)\n", + " * [Deploy the model to an endpoint](#Deploy-the-model-to-an-endpoint)\n", + " * [Invoke the endpoint](#Invoke-the-endpoint)\n", + " * [Example: Single record](#Example:-Single-record)\n", + " * [Example: Two records](#Example:-Two-records)\n", + " * [View captured data](#View-captured-data)\n", + " * [Start generating some artificial traffic](#Start-generating-some-artificial-traffic)\n", + "* [Model Explainability Monitor](#Model-Explainability-Monitor)\n", + " * [Baselining job](#Baselining-job)\n", + " * [Configurations](#Configurations)\n", + " * [Kick off baselining job](#Kick-off-baselining-job)\n", + " * [Monitoring Schedule](#Monitoring-Schedule)\n", + " * [Wait for the first execution](#Wait-for-the-first-execution)\n", + " * [Wait for the execution to finish](#Wait-for-the-execution-to-finish)\n", + " * [Inspect execution results](#Inspect-execution-results)\n", + "* [Cleanup](#Cleanup)" + ] + }, + { + "cell_type": "markdown", + "id": "a0a2c6a4-a249-40bf-adbc-8bd00fb06cfe", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "1879bacd-fedd-434a-8094-40cd48f5f140", + "metadata": {}, + "source": [ + "[Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) continuously monitors the quality of Amazon SageMaker machine learning models in production. It enables developers to set alerts for when there are deviations in the model quality. Early and pro-active detection of these deviations enables corrective actions, such as retraining models, auditing upstream systems, or fixing data quality issues without having to monitor models manually or build additional tooling. \n", + "\n", + "[Amazon SageMaker Clarify Model Explainability Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html) is a model monitor that helps data scientists and ML engineers monitor predictions for feature attribution drift on a regular basis. A drift in the distribution of live data for models in production can result in a corresponding drift in the feature attribution values. As the model is monitored, customers can view exportable reports and graphs detailing feature attributions in SageMaker Studio and configure alerts in Amazon CloudWatch to receive notifications if it is detected that the attribution values drift beyond a certain threshold. \n", + "\n", + "This notebook demonstrates the process for setting up a model monitor for continuous monitoring of feature attribution drift of a [SageMaker real-time inference endpoint](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html). The model input and output are in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats). SageMaker Clarify model monitor also supports analyzing CSV data, which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_model_monitor/fairness_and_explainability/SageMaker-Model-Monitor-Fairness-and-Explainability.ipynb).\n", + "\n", + "In general, you can use the model explainability monitor for real-time inference endpoint in this way,\n", + "\n", + "1. Enable the endpoint for data capture. Then, when the customer invokes the endpoint, the endpoint saves the invocations to a data capture S3 location. \n", + "1. Schedule a model explainability monitor to monitor the endpoint (to be more specific, the data capture S3 location) and a ground truth S3 location.\n", + "\n", + "The monitor executes processing jobs regularly to do feature attribution analysis, and then generate analysis reports and publish metrics to CloudWatch." + ] + }, + { + "cell_type": "markdown", + "id": "a4eed2c2-4e67-49cd-8b16-01d10c0acdb0", + "metadata": {}, + "source": [ + "## General Setup" + ] + }, + { + "cell_type": "markdown", + "id": "56e754c8-d82a-49a3-9967-d7a487a42549", + "metadata": {}, + "source": [ + "The notebook uses the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk). The following cell upgrades the SDK and its dependencies. Then you may need to restart the kernel and rerun the notebook to pick up the up-to-date APIs, if the notebook is executed in the SageMaker Studio." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e815029f-6166-40f6-a5dd-da2358f8b7fa", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: sagemaker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (2.203.1)\n", + "Requirement already satisfied: tblib<3,>=1.7.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.7.0)\n", + "Requirement already satisfied: 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"!pip install -U sagemaker\n", + "!pip install -U boto3\n", + "!pip install -U botocore" + ] + }, + { + "cell_type": "markdown", + "id": "43f20cf6-1672-45ab-966b-5db2d51aad53", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "The following cell imports the APIs to be used by the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "21f01570-2eee-46ef-b044-8b65569c26b7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/zicanl/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "import sagemaker\n", + "import pandas as pd\n", + "import copy\n", + "import datetime\n", + "import json\n", + "import random\n", + "import threading\n", + "import time\n", + "import pprint" + ] + }, + { + "cell_type": "markdown", + "id": "5baa9278-a1c9-427c-a9d9-5ddab19bcd49", + "metadata": {}, + "source": [ + "### Handful of configuration\n", + "\n", + "To begin, ensure that these prerequisites have been completed.\n", + "\n", + "* Specify an AWS Region to host the model.\n", + "* Specify an IAM role to execute jobs.\n", + "* Define the S3 URIs that stores the model file, input data and output data. For demonstration purposes, this notebook uses the same bucket for them. In reality, they could be separated with different security policies." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "74b11f7c-e9cd-4321-8de5-27ca6dd85d01", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AWS region: us-west-2\n", + "RoleArn: arn:aws:iam::678264136642:role/Admin\n", + "Demo Bucket: sagemaker-us-west-2-678264136642\n", + "Demo Prefix: sagemaker/DEMO-ClarifyModelMonitor-1705692269-8d04\n", + "Demo S3 key: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692269-8d04\n", + "The endpoint will save the captured data to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692269-8d04/data-capture\n", + "The baselining job will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692269-8d04/baselining-output\n", + "The monitor will save the analysis results to: s3://sagemaker-us-west-2-678264136642/sagemaker/DEMO-ClarifyModelMonitor-1705692269-8d04/monitor-output\n" + ] + } + ], + "source": [ + "sagemaker_session = sagemaker.Session()\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "print(f\"AWS region: {region}\")\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "print(f\"RoleArn: {role}\")\n", + "\n", + "# A different bucket can be used, but make sure the role for this notebook has\n", + "# the s3:PutObject permissions. This is the bucket into which the data is captured\n", + "bucket = sagemaker_session.default_bucket()\n", + "print(f\"Demo Bucket: {bucket}\")\n", + "prefix = sagemaker.utils.unique_name_from_base(\"sagemaker/DEMO-ClarifyModelMonitor\")\n", + "print(f\"Demo Prefix: {prefix}\")\n", + "s3_key = f\"s3://{bucket}/{prefix}\"\n", + "print(f\"Demo S3 key: {s3_key}\")\n", + "\n", + "data_capture_s3_uri = f\"{s3_key}/data-capture\"\n", + "baselining_output_s3_uri = f\"{s3_key}/baselining-output\"\n", + "monitor_output_s3_uri = f\"{s3_key}/monitor-output\"\n", + "\n", + "print(f\"The endpoint will save the captured data to: {data_capture_s3_uri}\")\n", + "print(f\"The baselining job will save the analysis results to: {baselining_output_s3_uri}\")\n", + "print(f\"The monitor will save the analysis results to: {monitor_output_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d7da5265-858f-4478-978b-ad592464b61d", + "metadata": {}, + "source": [ + "### Model file and data files\n", + "\n", + "This example includes a prebuilt [SageMaker Linear Learner](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html) model trained by [a SageMaker Clarify offline processing example notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb). The model supports [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats) (MIME type `\"application/jsonlines\"`).\n", + "\n", + "* The model input can one or more lines, each line is a JSON object that has a \"features\" key pointing to a list of feature values concerning demographic characteristics of individuals. For example,\n", + "\n", + "```\n", + "{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}\n", + "{\"features\":[43,2,72338,12,14,2,12,0,1,1,0,0,40,37]}\n", + "```\n", + "\n", + "* The model output has the predictions of whether a person has a yearly income that is more than $50,000. Each prediction is a JSON object that has a \"predicted_label\" key pointing to the predicted label, and the \"score\" key pointing to the confidence score. For example,\n", + "\n", + "```\n", + "{\"predicted_label\":1,\"score\":0.989977359771728}\n", + "{\"predicted_label\":1,\"score\":0.504138827323913}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f75d26c9-0f0b-422d-97cb-b74efd5eacd6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_file = \"model/ll-adult-prediction-model.tar.gz\"" + ] + }, + { + "cell_type": "markdown", + "id": "dc4d1d6a-c75c-4563-9699-33de88469093", + "metadata": {}, + "source": [ + "This example includes two dataset files, both in the JSON format. The data also originates from [the SageMaker Clarify offline processing example notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f1eaa4fe-622f-4745-a3cc-52d40db8ce9f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "train_dataset_path = \"test_data/validation-dataset.json\"\n", + "test_dataset_path = \"test_data/test-dataset.json\"\n", + "dataset_type = \"application/json\"" + ] + }, + { + "cell_type": "markdown", + "id": "5ca1001e-0b91-4133-8bce-6710aaa33270", + "metadata": {}, + "source": [ + "The train dataset has the features and the ground truth label (pointed to by the key \"label\")," + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "06c22c10-7ba8-417a-a0dc-1e152a0a3287", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], + "source": [ + "!head -n 5 $train_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "ddebb1fd-d480-4700-8dd8-3143205331a6", + "metadata": {}, + "source": [ + "The test dataset only has features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f78d463-f1ff-4483-8cf3-562bccb98a2b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!head -n 5 $test_dataset_path" + ] + }, + { + "cell_type": "markdown", + "id": "a7b89b8d-5036-4bd9-8aa5-f5d638617aba", + "metadata": {}, + "source": [ + "Here are the headers of the train dataset. \"Target\" is the header of the ground truth label, and the others are the feature headers. They will be used to beautify the analysis report." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a843093-0548-48dd-9f82-e80af07c357e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_headers = [\n", + " \"Age\",\n", + " \"Workclass\",\n", + " \"fnlwgt\",\n", + " \"Education\",\n", + " \"Education-Num\",\n", + " \"Marital Status\",\n", + " \"Occupation\",\n", + " \"Relationship\",\n", + " \"Ethnic group\",\n", + " \"Sex\",\n", + " \"Capital Gain\",\n", + " \"Capital Loss\",\n", + " \"Hours per week\",\n", + " \"Country\",\n", + " \"Target\",\n", + "]\n", + "label_header = all_headers[-1]" + ] + }, + { + "cell_type": "markdown", + "id": "2441fc17-0299-4b11-afe7-efdb167263ad", + "metadata": {}, + "source": [ + "To verify that the execution role for this notebook has the necessary permissions to proceed, put a simple test object into the S3 bucket specified above. If this command fails, update the role to have `s3:PutObject` permission on the bucket and try again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfe69a8c-9bf6-47c4-bb59-a775fd3b6934", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sagemaker.s3.S3Uploader.upload_string_as_file_body(\n", + " body=\"hello\",\n", + " desired_s3_uri=f\"{s3_key}/upload-test-file.txt\",\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(\"Success! We are all set to proceed with uploading to S3.\")" + ] + }, + { + "cell_type": "markdown", + "id": "7a099ef6-8d09-478d-854c-989758bad1c5", + "metadata": {}, + "source": [ + "Then upload the files to S3 so that they can be used by SageMaker." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f0fe183-4c83-4d22-bce5-65eba6a351e2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_url = sagemaker.s3.S3Uploader.upload(\n", + " local_path=model_file,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Model file has been uploaded to {model_url}\")\n", + "\n", + "train_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=train_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Train data is uploaded to: {train_data_s3_uri}\")\n", + "test_data_s3_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=test_dataset_path,\n", + " desired_s3_uri=s3_key,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(f\"Test data is uploaded to: {test_data_s3_uri}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2d11cc57-8ab4-422e-9492-4126f34ef4c5", + "metadata": {}, + "source": [ + "## Real-time Inference Endpoint\n", + "\n", + "This section creates a SageMaker real-time inference endpoint to showcase the data capture capability in action. The model monitor will be scheduled for the endpoint and process the captured data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d295bc3-3a82-4f22-9768-29572c0ae4f3", + "metadata": { + "tags": [] + }, + "source": [ + "### Deploy the model to an endpoint\n", + "\n", + "Start with deploying the pre-trained model. Here, create a SageMaker `Model` object with the inference image and model file. Then deploy the model with the data capture configuration and wait until the endpoint is ready to serve traffic.\n", + "\n", + "[DataCaptureConfig](https://sagemaker.readthedocs.io/en/stable/api/inference/model_monitor.html#sagemaker.model_monitor.data_capture_config.DataCaptureConfig) enables capturing the request payload and the response payload of the endpoint. Payloads are typically treated as binary data and encoded in BASE64 by default, allowing them to be stored in capture data files. However, by specifying the data format in the `json_content_types` parameter as shown below, the payloads can be captured as plain text instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0c565e0-051a-4f6c-bcb6-3dca8f4ec592", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_name = sagemaker.utils.unique_name_from_base(\"DEMO-ll-adult-pred-model-monitor\")\n", + "endpoint_name = model_name\n", + "print(f\"SageMaker model name: {model_name}\")\n", + "print(f\"SageMaker endpoint name: {endpoint_name}\")\n", + "\n", + "image_uri = sagemaker.image_uris.retrieve(\"linear-learner\", region, \"1\")\n", + "print(f\"SageMaker Linear Learner image: {image_uri}\")\n", + "\n", + "model = sagemaker.model.Model(\n", + " role=role,\n", + " name=model_name,\n", + " image_uri=image_uri,\n", + " model_data=model_url,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "\n", + "data_capture_config = sagemaker.model_monitor.DataCaptureConfig(\n", + " enable_capture=True,\n", + " sampling_percentage=100, # Capture 100% of the traffic\n", + " destination_s3_uri=data_capture_s3_uri,\n", + " json_content_types=[dataset_type],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c86306f2-8f15-4d39-9cbb-2f6c0e7ee978", + "metadata": {}, + "source": [ + "**NOTE**: The following cell takes about 10 minutes to deploy the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77330b34-0640-4b00-b3bb-4a8ea6e9a223", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(f\"Deploying model {model_name} to endpoint {endpoint_name}\")\n", + "model.deploy(\n", + " initial_instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " endpoint_name=endpoint_name,\n", + " data_capture_config=data_capture_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "14bf8504-bca2-4948-867a-cab4ca349bd9", + "metadata": {}, + "source": [ + "### Invoke the endpoint\n", + "\n", + "Now send data to this endpoint to get inferences in real time. The model supports mini-batch predictions, so you can put one or more records to a single request." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44a908e5-c16f-41dc-b718-323ab5ed4268", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with open(test_dataset_path, \"r\") as f:\n", + " test_data = json.load(f)" + ] + }, + { + "cell_type": "markdown", + "id": "2ccc2ed6-355a-4cdb-a44e-1463c0d9ef9f", + "metadata": {}, + "source": [ + "#### Example: Single record" + ] + }, + { + "cell_type": "markdown", + "id": "ea0e8368-37b1-41d2-b0da-0f22fee2b87e", + "metadata": {}, + "source": [ + "Request payload:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52fbb63a-e1d8-414e-968a-20822305f23c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "request_payload = {\"instances\": [test_data[\"instances\"][0]]}\n", + "print(json.dumps(request_payload))" + ] + }, + { + "cell_type": "markdown", + "id": "f880886a-38cc-44c1-acc4-f3876956e2a8", + "metadata": {}, + "source": [ + "Response payload:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87531e43-c9d1-4d9b-8019-19bec1a832eb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=dataset_type,\n", + " Accept=dataset_type,\n", + " Body=json.dumps(request_payload),\n", + ")\n", + "response_payload = response[\"Body\"].read().decode(\"utf-8\")\n", + "response_payload" + ] + }, + { + "cell_type": "markdown", + "id": "22fe887e-ec0d-4b2a-9c32-28d93c2e25be", + "metadata": {}, + "source": [ + "#### Example: Two records" + ] + }, + { + "cell_type": "markdown", + "id": "6094ad1c-55dd-40d1-b31f-8d47f21814c3", + "metadata": {}, + "source": [ + "Request payload:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cd41694-9e20-461f-ae85-5f792a521753", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "request_payload[\"instances\"] = test_data[\"instances\"][:2]\n", + "request_payload" + ] + }, + { + "cell_type": "markdown", + "id": "3ab91982-67b4-4293-86cb-bb61be2f67aa", + "metadata": {}, + "source": [ + "Response payload:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fece49e7-38b9-4b33-91ca-f23fcd06dcbb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=dataset_type,\n", + " Accept=dataset_type,\n", + " Body=json.dumps(request_payload),\n", + ")\n", + "response_payload = response[\"Body\"].read().decode(\"utf-8\")\n", + "response_payload" + ] + }, + { + "cell_type": "markdown", + "id": "243eac0c-a697-42b6-a56f-c0279cc7cd57", + "metadata": {}, + "source": [ + "### View captured data\n", + "\n", + "Because data capture is enabled in the previous steps, the request and response payload, along with some additional metadata, are saved in the Amazon S3 location specified in the [DataCaptureConfig](https://sagemaker.readthedocs.io/en/stable/api/inference/model_monitor.html#sagemaker.model_monitor.data_capture_config.DataCaptureConfig).\n", + "\n", + "Now list the captured data files stored in Amazon S3. There should be different files from different time periods organized based on the hour in which the invocation occurred. The format of the Amazon S3 path is:\n", + "\n", + "`s3://{data_capture_s3_uri}/{endpoint_name}/{variant-name}/yyyy/mm/dd/hh/filename.jsonl`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18c649dd-40ef-4260-b499-0f3c371f970f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(\"Waiting for captured data to show up\", end=\"\")\n", + "for _ in range(120):\n", + " captured_data_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=f\"{data_capture_s3_uri}/{endpoint_name}\",\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " )\n", + " if captured_data_files:\n", + " break\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(1)\n", + "print()\n", + "print(\"Found capture data files:\")\n", + "print(\"\\n \".join(captured_data_files[-5:]))" + ] + }, + { + "cell_type": "markdown", + "id": "0b4b01fd-4df2-42ff-935e-8843f1bc568f", + "metadata": {}, + "source": [ + "Next, view the content of a single capture file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4ad7021-4bcc-4fe1-880e-11a872941ff1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "captured_data = sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=captured_data_files[-1],\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "print(captured_data)" + ] + }, + { + "cell_type": "markdown", + "id": "6e09cffd-111a-43a1-8429-2fa3fbce9d2e", + "metadata": {}, + "source": [ + "Finally, the contents of a single line is present below in formatted JSON to observe a little better.\n", + "\n", + "* `captureData` has two fields, `endpointInput` has the captured invocation request, and `endpointOutput` has the response.\n", + "* `eventMetadata` has the inference ID and event ID." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14611944-0ae1-4f9f-ab6e-4b5c74ee7f3f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(json.dumps(json.loads(captured_data.splitlines()[-1]), indent=4))" + ] + }, + { + "cell_type": "markdown", + "id": "4b473f92-7142-4f79-8a27-86672682a5b2", + "metadata": {}, + "source": [ + "### Start generating some artificial traffic\n", + "The cell below starts a thread to send some traffic to the endpoint. If there is no traffic, the monitoring jobs are marked as `Failed` since there is no data to process.\n", + "\n", + "Notice the `InferenceId` attribute used to invoke, in this example, it will be used to join the captured data with the ground truth data. If it is not available, then the `eventId` will be used for the join operation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0af95cc5-9e1d-46fd-b373-16015c87be58", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class WorkerThread(threading.Thread):\n", + " def __init__(self, do_run, *args, **kwargs):\n", + " super(WorkerThread, self).__init__(*args, **kwargs)\n", + " self.__do_run = do_run\n", + " self.__terminate_event = threading.Event()\n", + "\n", + " def terminate(self):\n", + " self.__terminate_event.set()\n", + "\n", + " def run(self):\n", + " while not self.__terminate_event.is_set():\n", + " self.__do_run(self.__terminate_event)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00e832f7-8cc7-4044-b2aa-f22c93d2078d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def invoke_endpoint(terminate_event):\n", + " for index, record in enumerate(test_data[\"instances\"]):\n", + " response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=dataset_type,\n", + " Accept=dataset_type,\n", + " Body=json.dumps({\"instances\": [record]}),\n", + " InferenceId=str(index), # unique ID per row\n", + " )\n", + " response[\"Body\"].read()\n", + " time.sleep(1)\n", + " if terminate_event.is_set():\n", + " break\n", + "\n", + "\n", + "# Keep invoking the endpoint with test data\n", + "invoke_endpoint_thread = WorkerThread(do_run=invoke_endpoint)\n", + "invoke_endpoint_thread.start()" + ] + }, + { + "cell_type": "markdown", + "id": "f8d87f96-1ab6-4ad9-bd0d-f21b18ebcded", + "metadata": {}, + "source": [ + "## Model Explainability Monitor\n", + "\n", + "Similar to the other monitoring types, the standard procedure of creating a [feature attribution drift monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html) is first run a baselining job, and then schedule the monitor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "273af941-56ff-4a08-a1e1-023e2d4ec090", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_explainability_monitor = sagemaker.model_monitor.ModelExplainabilityMonitor(\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " max_runtime_in_seconds=3600,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c47a6f66-bdd8-4815-b3ed-286035f6e4ce", + "metadata": {}, + "source": [ + "### Baselining job\n", + "\n", + "A baselining job runs predictions on training dataset and suggests constraints. The `suggest_baseline()` method of `ModelExplainabilityMonitor` starts a SageMaker Clarify processing job to generate the constraints.\n", + "\n", + "The step is not mandatory, but providing constraints file to the monitor can enable violations file generation." + ] + }, + { + "cell_type": "markdown", + "id": "b7bd931a-bacc-480b-8d2d-c363abe9943f", + "metadata": {}, + "source": [ + "#### Configurations\n", + "\n", + "Information about the input data need to be provided to the processor." + ] + }, + { + "cell_type": "markdown", + "id": "6398d447-0ccf-4c79-a29d-8d6a54e1c034", + "metadata": {}, + "source": [ + "`DataConfig` stores information about the dataset to be analyzed. For example, the dataset file and its format (like JSON Lines), where to store the analysis results. Some special things to note about this configuration for the JSON Lines dataset,\n", + "\n", + "* The parameter value `\"features\"` or `\"label\"` is **NOT** a header string. Instead, it is a `JMESPath` expression ([refer to its specification](https://jmespath.org/specification.html)) that is used to locate the features list or the ground truth label in the dataset (the ground truth label is not needed for the explainability analysis, the parameter is specified so that the job knows it should be excluded from the dataset). In this example notebook they happen to be the same as the keys in the dataset. But for example, if the dataset has records like below, then the `features` parameter should use value `\"data.features.values\"`, and the `label` parameter should use value `\"data.label\"`.\n", + "\n", + " ```\n", + " {\"data\": {\"features\": {\"values\": [25, 2, 226802, 1, 7, 4, 6, 3, 2, 1, 0, 0, 40, 37]}, \"label\": 0}}\n", + " ```\n", + "\n", + "* SageMaker Clarify processing job will load the JSON Lines dataset into tabular representation for further analysis, and the parameter `headers` is the list of column names. **The label header shall be the last one in the headers list**, and the order of feature headers shall be the same as the order of features in a record." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd146e26-a54c-4a31-acc9-5a406ddf8680", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "features_jmespath = \"instances[*].features\"\n", + "ground_truth_label_jmespath = \"instances[*].label\"\n", + "data_config = sagemaker.clarify.DataConfig(\n", + " s3_data_input_path=train_data_s3_uri,\n", + " s3_output_path=baselining_output_s3_uri,\n", + " features=features_jmespath,\n", + " label=ground_truth_label_jmespath,\n", + " headers=all_headers,\n", + " dataset_type=dataset_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "93c9c98b-67a5-45e0-8aa5-a488e25a6de8", + "metadata": {}, + "source": [ + "`ModelConfig` is configuration related to model to be used for inferencing. In order to compute SHAP values, the SageMaker Clarify explainer generates synthetic dataset and then get its predictions for the SageMaker model. To accomplish this, the processing job will use the model to create an ephemeral endpoint (also known as \"shadow endpoint\"). The processing job will delete the shadow endpoint after the computations are completed. One special thing to note about this configuration for the JSON Lines model input and output,\n", + "\n", + "* `content_template` is used by SageMaker Clarify processing job to convert the tabular data to the request payload acceptable to the shadow endpoint. To be more specific, the placeholder `$features` will be replaced by **the features list** from records. The request payload of a record from the testing dataset happens to be similar to the record itself, like `{\"features\":[28,2,133937,9,13,2,0,0,4,1,15024,0,55,37]}`, because both the dataset and the model input conform to the same format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a49acc6-c6a9-46fa-aed7-e93e67fae373", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_config = sagemaker.clarify.ModelConfig(\n", + " model_name=model_name, # The name of the SageMaker model\n", + " instance_type=\"ml.m5.xlarge\", # The instance type of the shadow endpoint\n", + " instance_count=1, # The instance count of the shadow endpoint\n", + " content_type=dataset_type, # The data format of the model input\n", + " accept_type=dataset_type, # The data format of the model output\n", + " content_template='{\"instances\":$records}',\n", + " record_template='{\"features\":$features}',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "506b583a-f643-45dc-bdd3-ae29120734fa", + "metadata": {}, + "source": [ + "Currently, the SageMaker Clarify explainer offers a scalable and efficient implementation of SHAP, so the explainability config is `SHAPConfig`, including\n", + "\n", + "* `baseline`: A list of records (at least one) to be used as the baseline dataset in the Kernel SHAP algorithm, each record is JSON object that includes a list of features. It can also be a S3 object URI, the S3 file should be in the same format as dataset.\n", + "* `num_samples`: Number of samples to be used in the Kernel SHAP algorithm. This number determines the size of the generated synthetic dataset to compute the SHAP values.\n", + "* `agg_method`: Aggregation method for global SHAP values. Valid values are\n", + " * \"mean_abs\" (mean of absolute SHAP values for all instances),\n", + " * \"median\" (median of SHAP values for all instances) and\n", + " * \"mean_sq\" (mean of squared SHAP values for all instances).\n", + "* `use_logit`: Indicator of whether the logit function is to be applied to the model predictions. Default is False. If \"use_logit\" is true then the SHAP values will have log-odds units.\n", + "* `save_local_shap_values`: Indicator of whether to save the local SHAP values in the output location. Default is True." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ead08ae-1867-41b9-8c0e-6202760c4175", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Here use the mean value of train dataset as SHAP baseline\n", + "dataset = []\n", + "with open(train_dataset_path) as f:\n", + " instances = json.load(f)[\"instances\"]\n", + " for instance in instances:\n", + " dataset.append(instance[\"features\"])\n", + "mean_values = pd.DataFrame(dataset).mean().round().astype(int).to_list()\n", + "mean_record = {\"features\": mean_values}\n", + "shap_baseline = {\"instances\": [mean_record]}\n", + "print(f\"SHAP baseline: {shap_baseline}\")\n", + "\n", + "shap_config = sagemaker.clarify.SHAPConfig(\n", + " baseline=shap_baseline,\n", + " num_samples=100,\n", + " agg_method=\"mean_abs\",\n", + " save_local_shap_values=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3c9417f1-b2b2-4c23-81ba-256ff4616c5c", + "metadata": {}, + "source": [ + "#### Kick off baselining job\n", + "\n", + "Call the `suggest_baseline()` method to start the baselining job. The model output has a key \"score\" pointing to a confidence score value between `0` and `1`. So, the `model_scores` parameter is set to the `JMESPath` expression \"score\" which can locate the score in the model output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c27e74b-31f6-435a-a0d4-bef52a4cdcdb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "confidence_score_jmespath = \"predictions[*].score\"\n", + "model_explainability_monitor.suggest_baseline(\n", + " explainability_config=shap_config,\n", + " data_config=data_config,\n", + " model_config=model_config,\n", + " model_scores=confidence_score_jmespath, # The JMESPath to locate the confidence score in model output\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9cf396d3-c7ab-4041-8820-64c5ebd15d46", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the baselining job is completed (in about 10 minutes). It then inspects the suggested constraints. This step can be skipped, because the monitor to be scheduled will automatically pick up baselining job name and wait for it before monitoring execution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad0ece68-f130-4b66-b8ab-36d2916502c8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_explainability_monitor.latest_baselining_job.wait(logs=False)\n", + "print()\n", + "model_explainability_constraints = model_explainability_monitor.suggested_constraints()\n", + "print(f\"Suggested constraints: {model_explainability_constraints.file_s3_uri}\")\n", + "print(\n", + " sagemaker.s3.S3Downloader.read_file(\n", + " s3_uri=model_explainability_constraints.file_s3_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5545f7e0-8256-4b33-8385-741c23b9acc6", + "metadata": {}, + "source": [ + "### Monitoring Schedule\n", + "\n", + "With above constraints collected, now call `create_monitoring_schedule()` method to schedule an hourly model explainability monitor." + ] + }, + { + "cell_type": "markdown", + "id": "b99f1d50-d9ce-42c6-84da-a710bfb7b47a", + "metadata": {}, + "source": [ + "If a baselining job has been submitted, then the monitor object will automatically pick up the analysis configuration from the baselining job. But if the baselining step is skipped, or if the capture dataset has different nature than the training dataset, then analysis configuration has to be provided.\n", + "\n", + "`ModelConfig` is required by `ExplainabilityAnalysisConfig` for the same reason as it is required by the baselining job. Note that only features are required for computing feature attribution, so ground truth label should be excluded.\n", + "\n", + "Highlights,\n", + "\n", + "* From `endpoint_name` the monitor can figure out the location of data captured by the endpoint.\n", + "* `features_attribute` is the `JMESPath` expression to locate the features in model input, similar to the `features` parameter of `DataConfig`.\n", + "* `inference_attribute` stores the `JMESPath` expression to locate the confidence score in model output, similar to the `model_scores` parameter of the `suggest_baseline()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d160d3e-0482-4c4b-a171-e62eddb38b87", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "schedule_expression = sagemaker.model_monitor.CronExpressionGenerator.hourly()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c7a1355-2997-46f2-ae02-cb00063e3661", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Remove label because only features are required for the analysis\n", + "headers_without_label_header = copy.deepcopy(all_headers)\n", + "headers_without_label_header.remove(label_header)\n", + "model_explainability_analysis_config = sagemaker.model_monitor.ExplainabilityAnalysisConfig(\n", + " explainability_config=shap_config,\n", + " model_config=model_config,\n", + " headers=headers_without_label_header,\n", + ")\n", + "model_explainability_monitor.create_monitoring_schedule(\n", + " analysis_config=model_explainability_analysis_config,\n", + " endpoint_input=sagemaker.model_monitor.EndpointInput(\n", + " endpoint_name=endpoint_name,\n", + " destination=\"/opt/ml/processing/input/endpoint\",\n", + " features_attribute=features_jmespath,\n", + " inference_attribute=confidence_score_jmespath,\n", + " ),\n", + " output_s3_uri=monitor_output_s3_uri,\n", + " schedule_cron_expression=schedule_expression,\n", + ")\n", + "print(\n", + " f\"Model explainability monitoring schedule: {model_explainability_monitor.monitoring_schedule_name}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bf22401a-4662-4063-b47f-5be6becf3c3b", + "metadata": {}, + "source": [ + "#### Wait for the first execution\n", + "\n", + "The schedule starts jobs at the previously specified intervals. Code below waits until time crosses the hour boundary (in UTC) to see executions kick off.\n", + "\n", + "Note: Even for an hourly schedule, Amazon SageMaker has a buffer period of 20 minutes to schedule executions. The execution might start in anywhere from zero to ~20 minutes from the hour boundary. This is expected and done for load balancing in the backend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae00eb31-bbc7-4cf9-9fae-b323b4d380b2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def wait_for_execution_to_start(model_monitor):\n", + " print(\n", + " \"An hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\"\n", + " )\n", + "\n", + " print(\"Waiting for the first execution to happen\", end=\"\")\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " while \"LastMonitoringExecutionSummary\" not in schedule_desc:\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " print()\n", + " print(\"Done! Execution has been created\")\n", + "\n", + " print(\"Now waiting for execution to start\", end=\"\")\n", + " while schedule_desc[\"LastMonitoringExecutionSummary\"][\"MonitoringExecutionStatus\"] in \"Pending\":\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(10)\n", + "\n", + " print()\n", + " print(\"Done! Execution has started\")" + ] + }, + { + "cell_type": "markdown", + "id": "16fabf1c-8458-4186-9fb2-7bfa2462b705", + "metadata": {}, + "source": [ + "**NOTE**: The following cell waits until the first monitoring execution is started. As explained above, the wait could take more than 60 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b512df1e-57cf-4ba3-9262-0c325c4a600e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "wait_for_execution_to_start(model_explainability_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "210955ae-1709-423f-98c0-ca93476eebde", + "metadata": {}, + "source": [ + "In real world, a monitoring schedule is supposed to be active all the time. But in this example, it can be stopped to avoid incurring extra charges. A stopped schedule will not trigger further executions, but the ongoing execution will continue. And if needed, the schedule can be restarted by `start_monitoring_schedule()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6980d31-c96d-4850-a7fb-c8583eeac54e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_explainability_monitor.stop_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "id": "117a4a1d-4410-4f60-b859-762f18f7370b", + "metadata": {}, + "source": [ + "#### Wait for the execution to finish\n", + "\n", + "In the previous cell, the first execution has started. This section waits for the execution to finish so that its analysis results are available. Here are the possible terminal states and what each of them mean:\n", + "\n", + "* `Completed` - This means the monitoring execution completed, and no issues were found in the violations report.\n", + "* `CompletedWithViolations` - This means the execution completed, but constraint violations were detected.\n", + "* `Failed` - The monitoring execution failed, maybe due to client error (perhaps incorrect role permissions) or infrastructure issues. Further examination of `FailureReason` and `ExitMessage` is necessary to identify what exactly happened.\n", + "* `Stopped` - job exceeded max runtime or was manually stopped." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b07426d-f805-4527-9863-1d3d664734fa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Waits for the schedule to have last execution in a terminal status.\n", + "def wait_for_execution_to_finish(model_monitor):\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + " if execution_summary is not None:\n", + " print(\"Waiting for execution to finish\", end=\"\")\n", + " while execution_summary[\"MonitoringExecutionStatus\"] not in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + " \"Failed\",\n", + " \"Stopped\",\n", + " ]:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc[\"LastMonitoringExecutionSummary\"]\n", + " print()\n", + " print(f\"Done! Execution Status: {execution_summary['MonitoringExecutionStatus']}\")\n", + " else:\n", + " print(\"Last execution not found\")" + ] + }, + { + "cell_type": "markdown", + "id": "01434010-3c04-4ef5-acd2-21a3a0035fc8", + "metadata": {}, + "source": [ + "**NOTE**: The following cell takes about 10 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25e36f00-f488-4a16-867f-92c53d819782", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "wait_for_execution_to_finish(model_explainability_monitor)" + ] + }, + { + "cell_type": "markdown", + "id": "27ecf876-5999-4c2a-adcd-0a8537f082e6", + "metadata": {}, + "source": [ + "#### Inspect execution results\n", + "\n", + "List the generated reports,\n", + "\n", + "* analysis.json includes the global SHAP values.\n", + "* report.* files are static report files to visualize the SHAP values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c767cbd-78c5-433d-a850-e230cb5a55dd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "schedule_desc = model_explainability_monitor.describe_schedule()\n", + "execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + "if execution_summary and execution_summary[\"MonitoringExecutionStatus\"] in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + "]:\n", + " last_model_explainability_monitor_execution = model_explainability_monitor.list_executions()[-1]\n", + " last_model_explainability_monitor_execution_report_uri = (\n", + " last_model_explainability_monitor_execution.output.destination\n", + " )\n", + " print(f\"Report URI: {last_model_explainability_monitor_execution_report_uri}\")\n", + " last_model_explainability_monitor_execution_report_files = sorted(\n", + " sagemaker.s3.S3Downloader.list(\n", + " s3_uri=last_model_explainability_monitor_execution_report_uri,\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " )\n", + " print(\"Found Report Files:\")\n", + " print(\"\\n \".join(last_model_explainability_monitor_execution_report_files))\n", + "else:\n", + " last_model_explainability_monitor_execution = None\n", + " print(\n", + " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", + " )\n", + " print(schedule_desc)" + ] + }, + { + "cell_type": "markdown", + "id": "602a2ef3-4d6c-4d93-974e-77a679fc4757", + "metadata": {}, + "source": [ + "If there are any violations compared to the baseline, they are listed here. See [Feature Attribution Drift Violations](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-model-attribution-drift-violations.html) for the schema of the file, and how violations are detected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7174d2e-9ee4-437f-be9a-c9d984318b76", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "violations = model_explainability_monitor.latest_monitoring_constraint_violations()\n", + "if violations is not None:\n", + " pprint.PrettyPrinter(indent=4).pprint(violations.body_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "1b2e3d97-27cc-4325-814d-04219d25ab76", + "metadata": {}, + "source": [ + "By default, the analysis results are also published to CloudWatch, see [CloudWatch Metrics for Feature Attribution Drift Analysis](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-drift-cw.html)." + ] + }, + { + "cell_type": "markdown", + "id": "f6388287-b810-4522-bcc1-928228982388", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "The endpoint can keep running and capturing data, but if there is no plan to collect more data or use this endpoint further, it should be deleted to avoid incurring additional charges. Note that deleting endpoint does not delete the data that was captured during the model invocations." + ] + }, + { + "cell_type": "markdown", + "id": "554e8db8-4918-420c-9b4d-5c7263a402e7", + "metadata": {}, + "source": [ + "First stop the worker thread," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f813097c-00cc-4ee4-91cc-d03b72915c67", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "invoke_endpoint_thread.terminate()" + ] + }, + { + "cell_type": "markdown", + "id": "80f971c4-c1ae-4766-ab44-a30d361df523", + "metadata": {}, + "source": [ + "Then stop all monitors scheduled for the endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4b99289-3924-4d40-9860-75ccea76646b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_explainability_monitor.stop_monitoring_schedule()\n", + "wait_for_execution_to_finish(model_explainability_monitor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba08b157-b264-450e-8423-81708cc896ee", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_explainability_monitor.delete_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "id": "f2442401-06c9-481a-a04c-e339d618af54", + "metadata": {}, + "source": [ + "Finally, delete the endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6dd0678-66d3-493d-bee4-7e2a9dab901e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sagemaker_session.delete_endpoint(endpoint_name=endpoint_name)\n", + "sagemaker_session.delete_model(model_name=model_name)" + ] + }, + { + "cell_type": "markdown", + "id": "a82317ad-3515-4821-8106-074b2774c1ab", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_monitor|fairness_and_explainability_json|SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint.ipynb)\n" + ] + } + ], + "metadata": { + "availableInstances": [ + { + "_defaultOrder": 0, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.t3.medium", + "vcpuNum": 2 + }, + { + "_defaultOrder": 1, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.t3.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 2, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.t3.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 3, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.t3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 4, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 5, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 6, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 7, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 8, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 9, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 10, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 11, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 12, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5d.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 13, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5d.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 14, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5d.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 15, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5d.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 16, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5d.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 17, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5d.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 18, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + 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\ No newline at end of file diff --git a/sagemaker_model_monitor/index.rst b/sagemaker_model_monitor/index.rst index 019fa0369d..0f29f74666 100644 --- a/sagemaker_model_monitor/index.rst +++ b/sagemaker_model_monitor/index.rst @@ -46,3 +46,7 @@ Model Bias and Model Explainability /sagemaker_model_monitor/fairness_and_explainability_jsonlines/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint /sagemaker_model_monitor/fairness_and_explainability_jsonlines/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform /sagemaker_model_monitor/fairness_and_explainability_jsonlines/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform + /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Endpoint + /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint + /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform + /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform \ No newline at end of file From be54c954d9e6479cf487236a497a12200b31b62d Mon Sep 17 00:00:00 2001 From: ashrawat Date: Sun, 7 Jul 2024 22:32:09 -0400 Subject: [PATCH 05/16] initial commit of Blog content: "using step decorator for bedrock fine tuning" (https://sim.amazon.com/issues/ML-16440) (#4657) * initial commit of using step decorator for bedrock fine tuning * ran black command on the notebook * Added CI badges * Added CI badges * fixed typo in notebook title --------- Co-authored-by: Ashish Rawat Co-authored-by: Zhaoqi --- .../bedrock-examples/config.yaml | 18 + .../fine_tune_bedrock_step_decorator.ipynb | 1892 +++++++++++++++++ .../bedrock-examples/requirements.txt | 10 + 3 files changed, 1920 insertions(+) create mode 100644 sagemaker-pipelines/step-decorator/bedrock-examples/config.yaml create mode 100644 sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb create mode 100644 sagemaker-pipelines/step-decorator/bedrock-examples/requirements.txt diff --git a/sagemaker-pipelines/step-decorator/bedrock-examples/config.yaml b/sagemaker-pipelines/step-decorator/bedrock-examples/config.yaml new file mode 100644 index 0000000000..a13d031716 --- /dev/null +++ b/sagemaker-pipelines/step-decorator/bedrock-examples/config.yaml @@ -0,0 +1,18 @@ +SchemaVersion: '1.0' +SageMaker: + PythonSDK: + Modules: + RemoteFunction: + # role arn is not required if in SageMaker Notebook instance or SageMaker Studio + # Uncomment the following line and replace with the right execution role if in a local IDE + # RoleArn: + InstanceType: ml.c5.2xlarge + Dependencies: ./requirements.txt + IncludeLocalWorkDir: true + CustomFileFilter: + IgnoreNamePatterns: # files or directories to ignore + - "*.ipynb" # all notebook files + + Pipeline: + RoleArn: 'arn:aws:iam::095351214964:role/service-role/AmazonSageMaker-ExecutionRole-20200130T133110' + diff --git a/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb b/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb new file mode 100644 index 0000000000..6e225e9cd5 --- /dev/null +++ b/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb @@ -0,0 +1,1892 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Using SageMaker @step decorator feature convert python functions for creating a custom Bedrock model into a SageMaker pipeline.\n", + "\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-pipelines|step-decorator|bedrock-examples|fine_tune_bedrock_step_decorator.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "> *This notebook has been tested with the **`Python 3`** kernel in SageMaker Studio (JupyterLab version).*\n", + "\n", + "We will fine tune the [Amazon Titan Text Lite](#https://docs.aws.amazon.com/bedrock/latest/userguide/titan-text-models.html) model provided by Amazon Bedrock for a summarization use case. It uses a dataset from CNN that includes news articles and their summaries. The dataset called [cnn_dailymail v3.0](https://huggingface.co/datasets/cnn_dailymail) is available from Hugging Face. \n", + "\n", + "A *config.yaml* file can be found in the same folder as this notebook. This file includes properties that are passed to the @step decorator.\n", + "\n", + "
\n", + "Warning: The last section in this notebook does the clean up by removing the resources created during fine tuning and testing. That includes the Bedrock provisioned throughput which is needed to access the fine tuned custom model. Note that you will continue to incur AWS charges, unless you run the cleanup step.\n", + "
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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m68.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hUsing cached docker-7.0.0-py3-none-any.whl (147 kB)\n", + "Installing collected packages: docker, sagemaker\n", + " Attempting uninstall: sagemaker\n", + " Found existing installation: sagemaker 2.198.1\n", + " Uninstalling sagemaker-2.198.1:\n", + " Successfully uninstalled sagemaker-2.198.1\n", + "Successfully installed docker-7.0.0 sagemaker-2.215.0\n" + ] + } + ], + "source": [ + "!pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# restart kernel for the packages installed above to take effect\n", + "from IPython.core.display import HTML\n", + "\n", + "HTML(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "from itertools import islice\n", + "import pandas as pd\n", + "import sagemaker\n", + "import jsonlines\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "import json\n", + "import os\n", + "import sys\n", + "import boto3\n", + "import time\n", + "import pprint\n", + "import random\n", + "import yaml\n", + "from sagemaker.workflow.function_step import step\n", + "from sagemaker.workflow.parameters import ParameterString\n", + "from sagemaker.workflow.pipeline import Pipeline\n", + "from datetime import datetime\n", + "from botocore.exceptions import ClientError" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set path to config file \"config.yaml\"\n", + "# The config.yaml file contains the arguments that are passed to the step decorator functions.\n", + "os.environ[\"SAGEMAKER_USER_CONFIG_OVERRIDE\"] = os.getcwd()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "1. This notebook uses the default S3 bucket for the user. The default Amazon S3 bucket follows the naming pattern s3://sagemaker-{Region}-{your-account-id}. It is automatically created if it does not exist.\n", + "\n", + "2. This notebook uses the default IAM role for the user. If your studio user role does not have AWS admininstrator access, you will need to add the necessary permissions to the role. These include:\n", + " - [create a training job](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms)\n", + " - [Access to Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples.html)\n", + " - [Customize Amazon Bedrock model](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-iam-role.html)\n", + " - [Access to SageMaker Pipelines](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-access.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Fetched defaults config from location: /home/sagemaker-user/blog\n", + "IAM role: arn:aws:iam::095351214964:role/service-role/AmazonSageMaker-ExecutionRole-20200130T133110\n", + "S3 bucket: sagemaker-us-east-1-095351214964\n" + ] + } + ], + "source": [ + "sagemaker_session = sagemaker.session.Session()\n", + "region = sagemaker_session.boto_region_name\n", + "\n", + "# get the default bucket and IAM role for the user\n", + "bucket_name = sagemaker_session.default_bucket()\n", + "role_arn = sagemaker.get_execution_role()\n", + "\n", + "print(f\"IAM role: {role_arn}\")\n", + "print(f\"S3 bucket: {bucket_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SageMaker:\n", + " PythonSDK:\n", + " Modules:\n", + " RemoteFunction:\n", + " CustomFileFilter:\n", + " IgnoreNamePatterns:\n", + " - '*.ipynb'\n", + " Dependencies: ./requirements.txt\n", + " IncludeLocalWorkDir: true\n", + " InstanceType: ml.c5.2xlarge\n", + "SchemaVersion: '1.0'\n", + "\n" + ] + } + ], + "source": [ + "# let's look at the contemts of config.yaml\n", + "# The properties in congig.ymk are passed into the @step function.\n", + "# print the contents of config.yaml\n", + "# Notice that pipeline step runs on ml.c5.2xlarge as specified in the InstanceType property\n", + "with open(\"./config.yaml\", \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + " print(yaml.dump(config, default_flow_style=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "instruction = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "instruction:\n", + "\n", + "Summarize the news article provided below.\n", + "\n", + "input:\n", + "\n", + "\"\"\"\n", + "\n", + "\n", + "def add_prompt_to_data(dataset):\n", + " # Need to add prompt to the dataset in the format that is\n", + " # required for fine tuning by the Titan test Lite model.\n", + " datapoints = []\n", + "\n", + " for datapoint in dataset:\n", + " # Add insruction prompt to each CNN article\n", + " # and add prefix 'response:' to the article summary.\n", + " temp_dict = {}\n", + " temp_dict[\"prompt\"] = instruction + datapoint[\"article\"]\n", + " temp_dict[\"completion\"] = \"response:\\n\\n\" + datapoint[\"highlights\"]\n", + " datapoints.append(temp_dict)\n", + " return datapoints\n", + "\n", + "\n", + "#### Define step for downloading the dataset\n", + "@step(\n", + " name=\"data-load-step\",\n", + " keep_alive_period_in_seconds=300,\n", + ")\n", + "def data_load(ds_name: str, ds_version: str) -> tuple:\n", + " dataset = load_dataset(ds_name, ds_version)\n", + "\n", + " # the dataset includes data for training, validation, and test.\n", + " # The raw dataset includes the article and its summary.\n", + " # We need to format each row with the LLM prompt.\n", + " datapoints_train = add_prompt_to_data(dataset[\"train\"])\n", + " datapoints_valid = add_prompt_to_data(dataset[\"validation\"])\n", + " datapoints_test = add_prompt_to_data(dataset[\"test\"])\n", + "\n", + " print(f\"Number of training rows: {len(datapoints_train)}\")\n", + " print(f'\\nTraining prompt: {datapoints_train[0][\"prompt\"]}')\n", + " print(f'\\nTraining Completion: {datapoints_train[0][\"completion\"]}')\n", + "\n", + " print(f\"\\nNumber of validation rows: {len(datapoints_valid)}\")\n", + " print(f'\\nValidation prompt: {datapoints_valid[0][\"prompt\"]}')\n", + " print(f'\\nValidation Completion: {datapoints_valid[0][\"completion\"]}')\n", + "\n", + " print(f\"\\nNumber of test rows: {len(datapoints_test)}\")\n", + " print(f'\\nTest prompt: {datapoints_test[0][\"prompt\"]}')\n", + " print(f'\\nTest Completion: {datapoints_test[0][\"completion\"]}')\n", + "\n", + " return datapoints_train, datapoints_valid, datapoints_test" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Restrict the number of rows and row length\n", + "def reduce_dataset_size(data, max_row_length, max_rows):\n", + " datapoints = []\n", + " for datapoint in data:\n", + " if len(datapoint[\"prompt\"] + datapoint[\"completion\"]) <= max_row_length:\n", + " datapoints.append(datapoint)\n", + " random.shuffle(datapoints)\n", + " datapoints = datapoints[:max_rows]\n", + " print(f\"\\nData set size: {len(datapoints)}\")\n", + "\n", + " return datapoints\n", + "\n", + "\n", + "#### Define step for splitting the dataset into training, validation, and testing\n", + "# restrict the size of each row to 3000 words\n", + "# We also select 100 rows for training, 10 for validation, and 5 for testing\n", + "# to keep computation costs low for this example\n", + "@step(\n", + " name=\"data-split-step\",\n", + " keep_alive_period_in_seconds=300,\n", + ")\n", + "def data_split(step_load_result: tuple) -> tuple:\n", + " train_lines = reduce_dataset_size(step_load_result[0], 3000, 100)\n", + " validation_lines = reduce_dataset_size(step_load_result[1], 3000, 10)\n", + " test_lines = reduce_dataset_size(step_load_result[2], 3000, 5)\n", + "\n", + " print(f\"\\nNumber of training rows: {len(train_lines)}\")\n", + " print(f\"\\nNumber of training rows: {len(validation_lines)}\")\n", + " print(f\"\\nNumber of training rows: {len(test_lines)}\")\n", + "\n", + " return train_lines, validation_lines, test_lines" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload the training, validation, and test files to S3\n", + "def upload_file_to_s3(bucket_name: str, file_names: tuple, s3_key_names: tuple):\n", + " import boto3\n", + "\n", + " s3_client = boto3.client(\"s3\")\n", + " for i in range(len(file_names)):\n", + " s3_client.upload_file(file_names[i], bucket_name, s3_key_names[i])\n", + "\n", + "\n", + "# Save the training, validation, and test files in jsonl format\n", + "# to the local file system\n", + "def write_jsonl_file(abs_path: str, file_name: str, data) -> str:\n", + " saved_file_path = f\"{abs_path}/{file_name}\"\n", + "\n", + " with jsonlines.open(saved_file_path, \"w\") as writer:\n", + " for line in data:\n", + " writer.write(line)\n", + "\n", + " return saved_file_path\n", + "\n", + "\n", + "# Save the s3 uri for test data in SSM.\n", + "def save_s3_uri_in_SSM(parameter_name, parameter_value):\n", + " ssm_client = boto3.client(\"ssm\")\n", + " response = ssm_client.put_parameter(\n", + " Name=parameter_name, Value=parameter_value, Type=\"String\", Overwrite=True\n", + " )\n", + "\n", + "\n", + "#### Define step for uploading the training, validation, and test data to S3\n", + "@step(\n", + " name=\"data-upload-to-s3-step\",\n", + " keep_alive_period_in_seconds=300,\n", + ")\n", + "# Convert the data to jsonl format and upload to S3.\n", + "def data_upload_to_s3(data_split_response: tuple, bucket_name: str) -> tuple:\n", + " dataset_folder = \"fine-tuning-datasets\"\n", + "\n", + " if not os.path.exists(dataset_folder):\n", + " # Create the directory\n", + " os.makedirs(dataset_folder)\n", + " print(f\"Directory {dataset_folder} created successfully!\")\n", + " else:\n", + " print(f\"Directory {dataset_folder} already exists!\")\n", + "\n", + " abs_path = os.path.abspath(dataset_folder)\n", + " print(f\"\\nDataset folder path: {abs_path}\")\n", + "\n", + " print(type(data_split_response[0]))\n", + " train_file = write_jsonl_file(abs_path, \"train-cnn.jsonl\", data_split_response[0])\n", + " val_file = write_jsonl_file(abs_path, \"validation-cnn.jsonl\", data_split_response[1])\n", + " test_file = write_jsonl_file(abs_path, \"test-cnn.jsonl\", data_split_response[2])\n", + "\n", + " file_names = train_file, val_file, test_file\n", + "\n", + " s3_keys = (\n", + " f\"{dataset_folder}/train/train-cnn.jsonl\",\n", + " f\"{dataset_folder}/validation/validation-cnn.jsonl\",\n", + " f\"{dataset_folder}/test/test-cnn.jsonl\",\n", + " )\n", + " print(s3_keys)\n", + "\n", + " upload_file_to_s3(bucket_name, file_names, s3_keys)\n", + "\n", + " # save test file S3 uri for use later while testing the model\n", + " save_s3_uri_in_SSM(\"s3_test_uri\", f\"s3://{bucket_name}/{s3_keys[2]}\")\n", + "\n", + " # return the s3 uris for data files\n", + " return (\n", + " f\"s3://{bucket_name}/{s3_keys[0]}\",\n", + " f\"s3://{bucket_name}/{s3_keys[1]}\",\n", + " f\"s3://{bucket_name}/{s3_keys[2]}\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#### Define step for custom training the model\n", + "@step(\n", + " name=\"model-training-step\",\n", + " keep_alive_period_in_seconds=300,\n", + ")\n", + "def train(\n", + " custom_model_name: str, training_job_name: str, step_data_upload_to_s3_result: tuple\n", + ") -> str:\n", + " # Define the hyperparameters for fine-tuning Titan text model\n", + " hyper_parameters = {\n", + " \"epochCount\": \"2\",\n", + " \"batchSize\": \"1\",\n", + " \"learningRate\": \"0.00003\",\n", + " }\n", + "\n", + " # Specify your data path for training, validation(optional) and output\n", + " training_data_config = {\"s3Uri\": step_data_upload_to_s3_result[0]}\n", + " print(f\"Training data config: {training_data_config}\")\n", + "\n", + " validation_data_config = {\n", + " \"validators\": [\n", + " {\n", + " # \"name\": \"validation\",\n", + " \"s3Uri\": step_data_upload_to_s3_result[1]\n", + " }\n", + " ]\n", + " }\n", + " print(f\"Validation data config: {validation_data_config}\")\n", + "\n", + " output_data_config = {\n", + " \"s3Uri\": f\"s3://{bucket_name}/fine-tuning-datasets/outputs/output-{custom_model_name}\"\n", + " }\n", + "\n", + " bedrock = boto3.client(service_name=\"bedrock\")\n", + "\n", + " print(\"Start training....\")\n", + "\n", + " # Create the customization job\n", + " training_job_response = bedrock.create_model_customization_job(\n", + " customizationType=\"FINE_TUNING\",\n", + " jobName=training_job_name,\n", + " customModelName=custom_model_name,\n", + " roleArn=role_arn,\n", + " baseModelIdentifier=\"amazon.titan-text-lite-v1:0:4k\",\n", + " hyperParameters=hyper_parameters,\n", + " trainingDataConfig=training_data_config,\n", + " validationDataConfig=validation_data_config,\n", + " outputDataConfig=output_data_config,\n", + " )\n", + " print(training_job_response)\n", + "\n", + " job_status = bedrock.get_model_customization_job(jobIdentifier=training_job_name)[\"status\"]\n", + " print(job_status)\n", + "\n", + " while job_status == \"InProgress\":\n", + " time.sleep(60)\n", + " job_status = bedrock.get_model_customization_job(jobIdentifier=training_job_name)[\"status\"]\n", + " print(job_status)\n", + "\n", + " fine_tune_job = bedrock.get_model_customization_job(jobIdentifier=training_job_name)\n", + " pprint.pp(fine_tune_job)\n", + " output_job_name = \"model-customization-job-\" + fine_tune_job[\"jobArn\"].split(\"/\")[-1]\n", + " print(f\"output_job_name: {output_job_name}\")\n", + "\n", + " model_id = bedrock.get_custom_model(modelIdentifier=custom_model_name)[\"modelArn\"]\n", + "\n", + " print(f\"Model id: {model_id}\")\n", + " return model_id" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#### Define step for creating Provisioned throughput for the custom model\n", + "@step(\n", + " name=\"create-provisioned-throughput-step\",\n", + " keep_alive_period_in_seconds=300,\n", + ")\n", + "def create_prov_thruput(model_id: str, provisioned_model_name: str) -> str:\n", + " bedrock = boto3.client(service_name=\"bedrock\")\n", + "\n", + " provisioned_model_id = bedrock.create_provisioned_model_throughput(\n", + " modelUnits=1, provisionedModelName=provisioned_model_name, modelId=model_id\n", + " )[\"provisionedModelArn\"]\n", + "\n", + " status = bedrock.get_provisioned_model_throughput(provisionedModelId=provisioned_model_id)[\n", + " \"status\"\n", + " ]\n", + "\n", + " print(status)\n", + "\n", + " while status == \"Creating\":\n", + " time.sleep(60)\n", + " status = bedrock.get_provisioned_model_throughput(provisionedModelId=provisioned_model_id)[\n", + " \"status\"\n", + " ]\n", + " print(status)\n", + " time.sleep(60)\n", + "\n", + " return provisioned_model_id" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Test the custom model\n", + "\n", + "\n", + "def get_ssm_parameter(parameter_name):\n", + " ssm_client = boto3.client(\"ssm\")\n", + " response = ssm_client.get_parameter(Name=parameter_name, WithDecryption=True)\n", + "\n", + " return response[\"Parameter\"][\"Value\"]\n", + "\n", + "\n", + "#### Define step for tesiing the custom model\n", + "@step(\n", + " name=\"model-testing-step\",\n", + " keep_alive_period_in_seconds=300,\n", + ")\n", + "def test_model(provisioned_model_id: str) -> tuple:\n", + " s3_uri = get_ssm_parameter(\"s3_test_uri\")\n", + "\n", + " # Split the s3 uri into bucket name and key\n", + " s3_bucket = s3_uri.split(\"/\")[2]\n", + " s3_key = \"/\".join(s3_uri.split(\"/\")[3:])\n", + " print(f\"s3_bucket : {s3_bucket}, s3_key: {s3_key}\")\n", + "\n", + " # down load the test file\n", + " s3 = boto3.client(\"s3\")\n", + "\n", + " s3.download_file(s3_bucket, s3_key, \"test-cnn.jsonl\")\n", + "\n", + " # Invoke the model\n", + " with open(\"test-cnn.jsonl\") as f:\n", + " lines = f.read().splitlines()\n", + "\n", + " test_prompt = json.loads(lines[0])[\"prompt\"]\n", + " reference_summary = json.loads(lines[0])[\"completion\"]\n", + " pprint.pp(test_prompt)\n", + " print(reference_summary)\n", + "\n", + " prompt = f\"\"\"\n", + " {test_prompt}\n", + " \"\"\"\n", + " body = json.dumps(\n", + " {\n", + " \"inputText\": prompt,\n", + " \"textGenerationConfig\": {\n", + " \"maxTokenCount\": 2048,\n", + " \"stopSequences\": [\"User:\"],\n", + " \"temperature\": 0,\n", + " \"topP\": 0.9,\n", + " },\n", + " }\n", + " )\n", + "\n", + " accept = \"application/json\"\n", + " contentType = \"application/json\"\n", + "\n", + " bedrock_runtime = boto3.client(service_name=\"bedrock-runtime\")\n", + "\n", + " fine_tuned_response = bedrock_runtime.invoke_model(\n", + " body=body, modelId=provisioned_model_id, accept=accept, contentType=contentType\n", + " )\n", + "\n", + " fine_tuned_response_body = json.loads(fine_tuned_response.get(\"body\").read())\n", + " summary = fine_tuned_response_body[\"results\"][0][\"outputText\"]\n", + "\n", + " print(\"Fine tuned model response:\", summary)\n", + " print(\"\\nReference summary from test data: \", reference_summary)\n", + " return prompt, summary" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#### Create the SageMaker pipeline\n", + "# You can see the multi-step directed acyclic graph (DAG) in the Studio UI as a pipeline\n", + "\n", + "pipeline_name = \"bedrock-fine-tune-pipeline\"\n", + "\n", + "ts = datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n", + "custom_model_name = f\"finetuned-model-{ts}\"\n", + "training_job_name = f\"model-finetune-job-{ts}\"\n", + "provisioned_model_name = f\"summarization-model-{ts}\"\n", + "\n", + "param1 = ParameterString(name=\"ds_name\", default_value=\"cnn_dailymail\")\n", + "param2 = ParameterString(name=\"ds_version\", default_value=\"3.0.0\")\n", + "\n", + "data_load_response = data_load(param1, param2)\n", + "\n", + "data_split_response = data_split(data_load_response)\n", + "\n", + "data_upload_to_s3_response = data_upload_to_s3(data_split_response, bucket_name)\n", + "\n", + "train_response = train(custom_model_name, training_job_name, data_upload_to_s3_response)\n", + "\n", + "create_prov_thruput_response = create_prov_thruput(train_response, provisioned_model_name)\n", + "\n", + "test_model_response = test_model(create_prov_thruput_response)\n", + "\n", + "pipeline = Pipeline(name=pipeline_name, steps=[test_model_response], parameters=[param1, param2])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-12 21:36:37,429 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-testing-step/2024-04-12-21-36-35-895/function\n", + "2024-04-12 21:36:37,547 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-testing-step/2024-04-12-21-36-35-895/arguments\n", + "2024-04-12 21:36:37,851 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpv41q6gtg/requirements.txt'\n", + "2024-04-12 21:36:37,912 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-testing-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", + "2024-04-12 21:36:38,000 sagemaker.remote_function INFO Copied user workspace to '/tmp/tmpse97qmlu/temp_workspace/sagemaker_remote_function_workspace'\n", + "2024-04-12 21:36:38,946 sagemaker.remote_function INFO Successfully created workdir archive at '/tmp/tmpse97qmlu/workspace.zip'\n", + "2024-04-12 21:36:39,124 sagemaker.remote_function INFO Successfully uploaded workdir to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/sm_rf_user_ws/2024-04-12-21-36-35-895/workspace.zip'\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-12 21:36:40,298 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/create-provisioned-throughput-step/2024-04-12-21-36-35-895/function\n", + "2024-04-12 21:36:40,411 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/create-provisioned-throughput-step/2024-04-12-21-36-35-895/arguments\n", + "2024-04-12 21:36:40,487 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpnj3veih_/requirements.txt'\n", + "2024-04-12 21:36:40,519 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/create-provisioned-throughput-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-12 21:36:41,695 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-training-step/2024-04-12-21-36-35-895/function\n", + "2024-04-12 21:36:41,792 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-training-step/2024-04-12-21-36-35-895/arguments\n", + "2024-04-12 21:36:41,912 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpzxpgiqlm/requirements.txt'\n", + "2024-04-12 21:36:41,983 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-training-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-12 21:36:43,162 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-upload-to-s3-step/2024-04-12-21-36-35-895/function\n", + "2024-04-12 21:36:43,346 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-upload-to-s3-step/2024-04-12-21-36-35-895/arguments\n", + "2024-04-12 21:36:43,465 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmp7ujlj15s/requirements.txt'\n", + "2024-04-12 21:36:43,528 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-upload-to-s3-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-12 21:36:44,700 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-split-step/2024-04-12-21-36-35-895/function\n", + "2024-04-12 21:36:44,781 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-split-step/2024-04-12-21-36-35-895/arguments\n", + "2024-04-12 21:36:44,891 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmp9p4gw5b6/requirements.txt'\n", + "2024-04-12 21:36:44,919 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-split-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", + "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-12 21:36:46,092 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-load-step/2024-04-12-21-36-35-895/function\n", + "2024-04-12 21:36:46,213 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-load-step/2024-04-12-21-36-35-895/arguments\n", + "2024-04-12 21:36:46,292 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpx3pmqpqv/requirements.txt'\n", + "2024-04-12 21:36:46,319 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-load-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline',\n", + " 'ResponseMetadata': {'RequestId': '8de6e516-fdbf-4d34-bc19-4b61a6cb6474',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'x-amzn-requestid': '8de6e516-fdbf-4d34-bc19-4b61a6cb6474',\n", + " 'content-type': 'application/x-amz-json-1.1',\n", + " 'content-length': '94',\n", + " 'date': 'Fri, 12 Apr 2024 21:36:46 GMT'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.upsert(role_arn)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "execution = pipeline.start()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline',\n", + " 'PipelineExecutionArn': 'arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline/execution/l040kjgtiq4n',\n", + " 'PipelineExecutionDisplayName': 'execution-1712957806959',\n", + " 'PipelineExecutionStatus': 'Executing',\n", + " 'CreationTime': datetime.datetime(2024, 4, 12, 21, 36, 46, 908000, tzinfo=tzlocal()),\n", + " 'LastModifiedTime': datetime.datetime(2024, 4, 12, 21, 36, 46, 908000, tzinfo=tzlocal()),\n", + " 'CreatedBy': {'UserProfileArn': 'arn:aws:sagemaker:us-east-1:095351214964:user-profile/d-ndkfwlyrojeq/blog',\n", + " 'UserProfileName': 'blog',\n", + " 'DomainId': 'd-ndkfwlyrojeq',\n", + " 'IamIdentity': {'Arn': 'arn:aws:sts::095351214964:assumed-role/AmazonSageMaker-ExecutionRole-20200130T133110/SageMaker',\n", + " 'PrincipalId': 'AROARMM3ACN2NE2XC3HPY:SageMaker'}},\n", + " 'LastModifiedBy': {'UserProfileArn': 'arn:aws:sagemaker:us-east-1:095351214964:user-profile/d-ndkfwlyrojeq/blog',\n", + " 'UserProfileName': 'blog',\n", + " 'DomainId': 'd-ndkfwlyrojeq',\n", + " 'IamIdentity': {'Arn': 'arn:aws:sts::095351214964:assumed-role/AmazonSageMaker-ExecutionRole-20200130T133110/SageMaker',\n", + " 'PrincipalId': 'AROARMM3ACN2NE2XC3HPY:SageMaker'}},\n", + " 'ResponseMetadata': {'RequestId': '36b7812f-9de8-4686-9066-107fcda06bee',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'x-amzn-requestid': '36b7812f-9de8-4686-9066-107fcda06bee',\n", + " 'content-type': 'application/x-amz-json-1.1',\n", + " 'content-length': '1041',\n", + " 'date': 'Fri, 12 Apr 2024 21:36:46 GMT'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "execution.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.44 s, sys: 87.4 ms, total: 1.53 s\n", + "Wall time: 1h 31min 17s\n" + ] + } + ], + "source": [ + "%%time\n", + "execution.wait(delay=60, max_attempts=250)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'StepName': 'model-testing-step',\n", + " 'StepDisplayName': '__main__.test_model',\n", + " 'StartTime': datetime.datetime(2024, 4, 12, 23, 4, 43, 688000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 4, 12, 23, 7, 33, 776000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-model-testing-step-pr4gGsj2Rt'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'create-provisioned-throughput-step',\n", + " 'StepDisplayName': '__main__.create_prov_thruput',\n", + " 'StartTime': datetime.datetime(2024, 4, 12, 22, 49, 35, 654000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 4, 12, 23, 4, 42, 774000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-create-provisioned-t-xDN4wVqlsC'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'model-training-step',\n", + " 'StepDisplayName': '__main__.train',\n", + " 'StartTime': datetime.datetime(2024, 4, 12, 21, 46, 28, 754000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 4, 12, 22, 49, 34, 878000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-model-training-step-Kc1rJEbgzv'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'data-upload-to-s3-step',\n", + " 'StepDisplayName': '__main__.data_upload_to_s3',\n", + " 'StartTime': datetime.datetime(2024, 4, 12, 21, 43, 39, 142000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 4, 12, 21, 46, 27, 822000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-data-upload-to-s3-st-eQlNAJKWnc'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'data-split-step',\n", + " 'StepDisplayName': '__main__.data_split',\n", + " 'StartTime': datetime.datetime(2024, 4, 12, 21, 40, 37, 342000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 4, 12, 21, 43, 38, 277000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-data-split-step-68JtnNtXxn'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'data-load-step',\n", + " 'StartTime': datetime.datetime(2024, 4, 12, 21, 36, 48, 342000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 4, 12, 21, 40, 34, 16000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-data-load-step-cYwRdw1Qg1'}},\n", + " 'AttemptCount': 1}]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "execution.list_steps()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('\\n Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\ninstruction:\\n\\nSummarize the news article provided below.\\n\\ninput:\\n\\n(CNN)Remains of up to nearly 400 unaccounted for service members tied to the USS Oklahoma at Pearl Harbor will be exhumed this year, the Defense Department announced Tuesday. The hope is that most of the battleship\\'s sailors and Marines can be identified. \"The secretary of defense and I will work tirelessly to ensure your loved one\\'s remains will be recovered, identified, and returned to you as expeditiously as possible, and we will do so with dignity, respect and care,\" Deputy Secretary of Defense Bob Work said in a statement. \"While not all families will receive an individual identification, we will strive to provide resolution to as many families as possible.\" The USS Oklahoma sank when it was hit by torpedoes on December 7, 1941, during the Japanese attack on Pearl Harbor. A total of 429 sailors and Marines on the ship were killed. Thirty-five crew members were positively identified and buried in the years immediately after the attack, according to the Defense Department. By 1950, all unidentified remains were laid to rest as unknowns at the National Memorial Cemetery of the Pacific. In 2003, five more service members were identified, with the help of historical evidence from Pearl Harbor survivor Ray Emory, 93. Emory, a native of Peoria, Illinois, was serving as a seaman first class on the light cruiser USS Honolulu that fateful day. After the war, Emory worked in Washington state before moving to Hawaii about 30 years ago. The retiree made it his mission to ensure graves are properly identified. \"It\\'s something I looked forward to for a long time,\" he told CNN about Tuesday\\'s announcement. Speaking by phone from Honolulu, Emory said that proper identification means a lot to the families of those who lost loved ones -- and to him. Next of kin were being notified starting Tuesday. Service members who are identified will be returned to their families for burial, with full military honors. WWII pilot, 99, reunited with historic C-47 plane . CNN\\'s Phil Gast contributed to this report.\\n ', '\\nThe USS Oklahoma sank during the Japanese attack on Pearl Harbor on December 7, 1941.\\nThe battleship was hit by torpedoes, killing 429 sailors and Marines.\\nThe Defense Department says it will work to identify as many of the remains as possible.')\n" + ] + } + ], + "source": [ + "print(execution.result(step_name=\"model-testing-step\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup\n", + "Delete the resources that were created to stop incurring charges." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Provisoned throughput deleted for model: arn:aws:bedrock:us-east-1:095351214964:provisioned-model/fj8dou88yq5q\n", + "Custom model arn:aws:bedrock:us-east-1:095351214964:custom-model/amazon.titan-text-lite-v1:0:4k/2zefi5rp4ez1 deleted.\n" + ] + } + ], + "source": [ + "bedrock = boto3.client(service_name=\"bedrock\")\n", + "\n", + "# delete Bedrock provisioned throughput\n", + "provisioned_model_id = execution.result(step_name=\"create-provisioned-throughput-step\")\n", + "try:\n", + " bedrock.delete_provisioned_model_throughput(provisionedModelId=provisioned_model_id)\n", + "except ClientError as e:\n", + " print(e.response[\"Error\"][\"Code\"])\n", + "\n", + "print(f\"Provisoned throughput deleted for model: {provisioned_model_id}\")\n", + "\n", + "# delete the custom model\n", + "custom_model_id = execution.result(step_name=\"model-training-step\")\n", + "try:\n", + " bedrock.delete_custom_model(modelIdentifier=custom_model_id)\n", + "except ClientError as e:\n", + " print(e.response[\"Error\"][\"Code\"])\n", + "\n", + "print(f\"Custom model {custom_model_id} deleted.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ResponseMetadata': {'RequestId': '4a830460-d5d5-48bd-94fa-729f0b5dbfcd',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'server': 'Server',\n", + " 'date': 'Fri, 12 Apr 2024 23:08:07 GMT',\n", + " 'content-type': 'application/x-amz-json-1.1',\n", + " 'content-length': '2',\n", + " 'connection': 'keep-alive',\n", + " 'x-amzn-requestid': '4a830460-d5d5-48bd-94fa-729f0b5dbfcd'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# delete the SSM parameter\n", + "ssm_client = boto3.client(\"ssm\")\n", + "ssm_client.delete_parameter(Name=\"s3_test_uri\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.workflow.pipeline:If triggers have been setup for this target, they will become orphaned.You will need to clean them up manually via the CLI or EventBridge console.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted pipeline arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline\n" + ] + } + ], + "source": [ + "# Delete the SageMaker pipeline\n", + "response = pipeline.delete()\n", + "print(f'Deleted pipeline {response[\"PipelineArn\"]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Objects in Bucket sagemaker-us-east-1-095351214964 have been deleted.\n" + ] + } + ], + "source": [ + "# delete objects in S3\n", + "def delete_objects_with_prefix(bucket_name, prefix):\n", + " s3 = boto3.client(\"s3\")\n", + "\n", + " response = s3.list_objects_v2(Bucket=bucket_name, Delimiter=\"/\", Prefix=prefix)\n", + "\n", + " if \"Contents\" in response:\n", + " contents = response[\"Contents\"]\n", + " for obj in contents:\n", + " s3.delete_object(Bucket=bucket_name, Key=obj[\"Key\"])\n", + "\n", + " while response[\"IsTruncated\"]:\n", + " response = s3.list_objects_v2(\n", + " Bucket=bucket_name,\n", + " Delimiter=\"/\",\n", + " Prefix=prefix,\n", + " ContinuationToken=response[\"NextContinuationToken\"],\n", + " )\n", + " if \"Contents\" in response:\n", + " contents = response[\"Contents\"]\n", + " for obj in contents:\n", + " s3.delete_object(Bucket=bucket_name, Key=obj[\"Key\"])\n", + "\n", + "\n", + "delete_objects_with_prefix(bucket_name, \"fine-tuning-datasets\")\n", + "delete_objects_with_prefix(bucket_name, pipeline_name)\n", + "\n", + "print(f\"Objects in Bucket {bucket_name} have been deleted.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-pipelines|step-decorator|bedrock-examples|fine_tune_bedrock_step_decorator.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-pipelines|step-decorator|bedrock-examples|fine_tune_bedrock_step_decorator.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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example notebooks * Removed geospatial from README.md * Archived remaining workshop notebooks (#4692) * Archived outdated example notebooks between 1-90 views (#4693) --------- Co-authored-by: jsmul --- README.md | 105 +- .../2_object_detection_train_eval.ipynb | 0 ...pStart_Zero_Shot_Text_Classification.ipynb | 0 .../notebooks}/DeployStableCascade.ipynb | 0 ...and Optimization on Amazon SageMaker.ipynb | 0 .../JPMML_Models_SageMaker}/Dockerfile | 0 .../JPMML_Models_SageMaker}/Iris.csv | 0 .../JPMML_Models_SageMaker.ipynb | 0 .../JPMML_Models_SageMaker}/README.md | 0 .../JPMML_Models_SageMaker}/data/iris_rf.pmml | 0 .../data/iris_rf_1.pmml | 0 .../data/iris_rf_2.pmml | 0 .../notebooks/JPMML_Models_SageMaker}/pom.xml | 0 .../JPMML_Models_SageMaker}/server_start.sh | 0 .../pmml/randomforest/app/SGMLauncher.java | 0 .../entrypoint/SGMController.java | 0 ...tomizedResponseEntityExceptionHandler.java | 0 .../randomforest/exception/ErrorDetails.java | 0 .../InsufficientMemoryException.java | 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divided in two repositories: - [Sagemaker Example Community repository](https://github.com/aws/amazon-sagemaker-examples-community) is another SageMaker repository which contains additional examples and reference solutions, beyond the examples showcased in the [official repository](https://github.com/aws/amazon-sagemaker-examples). This repository is maintained by community of engineers and solution architects at AWS. -## Planning to submit a PR to this repository? read this first: +## Planning to submit a PR to this repository? Read this first: + - This repository will only accept notebooks/examples which demonstrate a feature of SageMaker, not yet covered anywhere in this repository. PR submitters are requested to check this before submitting the PR to avoid getting it rejected. - If you still would like to contribute your example, please submit a PR to [Sagemaker Example Community repository](https://github.com/aws/amazon-sagemaker-examples-community) instead. - ## :hammer_and_wrench: Setup The quickest setup to run example notebooks includes: -- An [AWS account](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-account.html) + +- An [AWS Account](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-account.html) - Proper [IAM User and Role](http://docs.aws.amazon.com/sagemaker/latest/dg/authentication-and-access-control.html) setup - An [Amazon SageMaker Notebook Instance](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html) - An [S3 bucket](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-config-permissions.html) @@ -37,22 +38,6 @@ As of February 7, 2022, the default branch is named "main". See our [announcemen ## :notebook: Examples -### Introduction to geospatial capabilities - -These examples introduce SageMaker geospatial capabilities which makes it easy to build, train, and deploy ML models using geospatial data. - -- [How to use SageMaker Processing with geospatial image](sagemaker-geospatial/processing-geospatial-ndvi/geospatial-processing-ndvi-intro.ipynb) shows how to compute the normalized difference vegetation index (NDVI) which indicates health and density of vegetation using SageMaker Processing and satellite imagery -- [Monitoring Lake Drought with SageMaker Geospatial Capabilities](sagemaker-geospatial/lake-mead-drought-monitoring) shows how to monitor Lake Mead drought using SageMaker geospatial capabilities. -- [Digital Farming with Amazon SageMaker Geospatial Capabilities](sagemaker-geospatial/digital-farming-pipelines) shows how geospatial capabilities can help accelerating, optimizing, and easing the processing of the geospatial data for the Digital Farming use cases. -- [Assess wildfire damage with Amazon SageMaker Geospatial Capabilities](sagemaker-geospatial/dixie-wildfire-damage-assessment/dixie-wildfire-damage-assessment.ipynb) demonstrates how Amazon SageMaker geospatial capabilities can be used to identify and assess vegetation loss caused by the Dixie wildfire in Northern California. -- [Monitoring Glacier Melting with SageMaker Geospatial Capabilities](sagemaker-geospatial/mount-shasta-glacier-melting-monitoring) shows how to monitor glacier melting at Mount Shasta using SageMaker geospatial capabilities. -- [Monitoring of methane (CH4) emission point sources using Amazon SageMaker Geospatial Capabilities](sagemaker-geospatial/methane-emission-monitoring/monitor_methane_ch4_emission_point_sources.ipynb) demonstrates how methane emissions can be detected by using open data Satellite imagery (Sentinel-2). -- [Segmenting aerial imagery using geospatial GPU notebook](sagemaker-geospatial/segment-aerial-naip/segment_naip_geospatial_notebook.ipynb) shows how to use the geospatial GPU notebook with open-source libraries to perform segmentation on aerial imagery. -- [Perform Sentinel-1 InSAR using ESA SNAP Toolkit](sagemaker-geospatial/sentinel1-insar-snap/sentinel1_insar_kumamoto.ipynb) shows how the SNAP toolkit can be used within Amazon SageMaker geospatial capabilities to create interferograms on Sentinel-1 SAR data. -- [How to use Vector Enrichment Jobs for Map Matching](sagemaker-geospatial/vector-enrichment-map-matching/vector-enrichment-map-matching.ipynb) shows how to use vector enrichtment operations with Amazon SageMaker Geospatial capabilities to snap GPS coordinates to road segments. -- [How to use Vector Enrichment Jobs for Reverse Geocoding](sagemaker-geospatial/vector-enrichment-reverse-geocoding/vector-enrichment-reverse-geocoding.ipynb) shows how to use Amazon SageMaker Geospatial capabilities for reverse geocoding to obtain human readable addresses from data with latitude/longitude information. -- [Building geospatial pipelines with SageMaker Pipelines](sagemaker-geospatial/geospatial-processing-pipeline/geospatial_pipeline_processing.ipynb) shows how a geospatial data processing workflow can be automated by using Amazon SageMaker Pipelines. - ### Introduction to Ground Truth Labeling Jobs These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth. @@ -69,20 +54,15 @@ These examples provide quick walkthroughs to get you up and running with the lab These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors. - [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives. This uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to create a highly predictive model. -- [Cancer Prediction](introduction_to_applying_machine_learning/breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner. - [Ensembling](introduction_to_applying_machine_learning/ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling. -- [Video Game Sales](introduction_to_applying_machine_learning/video_game_sales) develops a binary prediction model for the success of video games based on review scores. - [MXNet Gluon Recommender System](introduction_to_applying_machine_learning/gluon_recommender_system) uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews. -- [Fair Linear Learner](introduction_to_applying_machine_learning/fair_linear_learner) is an example of an effective way to create fair linear models with respect to sensitive features. - [Population Segmentation of US Census Data using PCA and Kmeans](introduction_to_applying_machine_learning/US-census_population_segmentation_PCA_Kmeans) analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties. -- [Document Embedding using Object2Vec](introduction_to_applying_machine_learning/object2vec_document_embedding) is an example to embed a large collection of documents in a common low-dimensional space, so that the semantic distances between these documents are preserved. - [Traffic violations forecasting using DeepAR](introduction_to_applying_machine_learning/deepar_chicago_traffic_violations) is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm. - [Visual Inspection Automation with Pre-trained Amazon SageMaker Models](introduction_to_applying_machine_learning/visual_object_detection) is an example for fine-tuning pre-trained Amazon Sagemaker models on a target dataset. - [Create SageMaker Models Using the PyTorch Model Zoo](introduction_to_applying_machine_learning/sagemaker_pytorch_model_zoo) contains an example notebook to create a SageMaker model leveraging the PyTorch Model Zoo and visualize the results. - [Deep Demand Forecasting](introduction_to_applying_machine_learning/deep_demand_forecasting) provides an end-to-end solution for Demand Forecasting task using three state-of-the-art time series algorithms LSTNet, Prophet, and SageMaker DeepAR, which are available in GluonTS and Amazon SageMaker. - [Fraud Detection Using Graph Neural Networks](introduction_to_applying_machine_learning/fraud_detection_using_graph_neural_networks) is an example to identify fraudulent transactions from transaction and user identity datasets. - [Identify key insights from textual document](introduction_to_applying_machine_learning/identify_key_insights_from_textual_document) contains comphrensive notebooks for five natural language processing tasks Document Summarization, Text Classification, Question and Answering, Name Entity Recognition, and Semantic Relation Extracion. -- [Synthetic Churn Prediction with Text](introduction_to_applying_machine_learning/synthetic_churn_prediction_with_text) contains an example notebook to train, deploy and use a churn prediction model that processed numerical, categorical and textual features to make its prediction. - [Credit Card Fraud Detector](introduction_to_applying_machine_learning/credit_card_fraud_detector) is an example of the core of a credit card fraud detection system using SageMaker with Random Cut Forest and XGBoost. - [Churn Prediction Multimodality of Text and Tabular](introduction_to_applying_machine_learning/churn_prediction_multimodality_of_text_and_tabular) is an example notebook to train and deploy a churn prediction model that uses state-of-the-art natural language processing model to find useful signals in text. In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. @@ -90,16 +70,11 @@ These examples provide a gentle introduction to machine learning concepts as the These examples introduce SageMaker's hyperparameter tuning functionality which helps deliver the best possible predictions by running a large number of training jobs to determine which hyperparameter values are the most impactful. -- [XGBoost Tuning](hyperparameter_tuning/xgboost_direct_marketing) shows how to use SageMaker hyperparameter tuning to improve your model fit. -- [BlazingText Tuning](hyperparameter_tuning/blazingtext_text_classification_20_newsgroups) shows how to use SageMaker hyperparameter tuning with the BlazingText built-in algorithm and 20_newsgroups dataset.. -- [TensorFlow Tuning](hyperparameter_tuning/tensorflow_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built TensorFlow container and MNIST dataset. -- [MXNet Tuning](hyperparameter_tuning/mxnet_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built MXNet container and MNIST dataset. +- [TensorFlow Tuning](hyperparameter_tuning/tensorflow2_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built TensorFlow container and MNIST dataset. - [HuggingFace Tuning](hyperparameter_tuning/huggingface_multiclass_text_classification_20_newsgroups) shows how to use SageMaker hyperparameter tuning with the pre-built HuggingFace container and 20_newsgroups dataset. - [Keras BYO Tuning](hyperparameter_tuning/keras_bring_your_own) shows how to use SageMaker hyperparameter tuning with a custom container running a Keras convolutional network on CIFAR-10 data. -- [R BYO Tuning](hyperparameter_tuning/r_bring_your_own) shows how to use SageMaker hyperparameter tuning with the custom container from the [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) example. - [Analyzing Results](hyperparameter_tuning/analyze_results) is a shared notebook that can be used after each of the above notebooks to provide analysis on how training jobs with different hyperparameters performed. - [Model tuning for distributed training](hyperparameter_tuning/model_tuning_for_distributed_training) shows how to use SageMaker hyperparameter tuning with Hyperband strategy for optimizing model in distributed training. -- [Neural Architecture Search for Large Language Models](hyperparameter_tuning/neural_architecture_search_llm) shows how to prune fine-tuned large language models via neural architecture search. ### SageMaker Autopilot @@ -107,7 +82,7 @@ These examples introduce SageMaker Autopilot. Autopilot automatically performs f - [Customer Churn AutoML](autopilot/) shows how to use SageMaker Autopilot to automatically train a model for the [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) task. - [Targeted Direct Marketing AutoML](autopilot/) shows how to use SageMaker Autopilot to automatically train a model. -- [Housing Prices AutoML](sagemaker-autopilot/housing_prices) shows how to use SageMaker Autopilot for a linear regression problem (predict housing prices). +- [Housing Prices AutoML](autopilot/autopilot_california_housing.ipynb) shows how to use SageMaker Autopilot for a linear regression problem (predict housing prices). - [Portfolio Churn Prediction with Amazon SageMaker Autopilot and Neo4j](autopilot/sagemaker_autopilot_neo4j_portfolio_churn.ipynb) shows how to use SageMaker Autopilot with graph embeddings to predict investment portfolio churn. - [Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines](autopilot/sagemaker-autopilot-pipelines) shows how to use SageMaker Autopilot in combination with SageMaker Pipelines for end-to-end AutoML training automation. - [Amazon SageMaker Autopilot models to serverless endpoints](autopilot/autopilot-serverless-inference) shows how to deploy Autopilot generated models to serverless endpoints. @@ -123,14 +98,10 @@ These examples provide quick walkthroughs to get you up and running with Amazon - [Neural Topic Model (NTM)](introduction_to_amazon_algorithms/ntm_synthetic) uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known. - [Principal Components Analysis (PCA)](introduction_to_amazon_algorithms/pca_mnist) uses Amazon SageMaker PCA to calculate eigendigits from MNIST. - [Seq2Seq](introduction_to_amazon_algorithms/seq2seq_translation_en-de) uses the Amazon SageMaker Seq2Seq algorithm that's built on top of [Sockeye](https://github.com/awslabs/sockeye), which is a sequence-to-sequence framework for Neural Machine Translation based on MXNet. Seq2Seq implements state-of-the-art encoder-decoder architectures which can also be used for tasks like Abstractive Summarization in addition to Machine Translation. This notebook shows translation from English to German text. -- [Image Classification](introduction_to_amazon_algorithms/imageclassification_caltech) includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm. This uses a ResNet deep convolutional neural network to classify images from the caltech dataset. - [XGBoost for regression](introduction_to_amazon_algorithms/xgboost_abalone) predicts the age of abalone ([Abalone dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html)) using regression from Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost). - [XGBoost for multi-class classification](introduction_to_amazon_algorithms/xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classify handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented. -- [DeepAR for time series forecasting](introduction_to_amazon_algorithms/deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set. - [BlazingText Word2Vec](introduction_to_amazon_algorithms/blazingtext_word2vec_text8) generates Word2Vec embeddings from a cleaned text dump of Wikipedia articles using SageMaker's fast and scalable BlazingText implementation. - [Object detection for bird images](introduction_to_amazon_algorithms/object_detection_birds) demonstrates how to use the Amazon SageMaker Object Detection algorithm with a public dataset of Bird images. -- [Object2Vec for movie recommendation](introduction_to_amazon_algorithms/object2vec_movie_recommendation) demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example. -- [Object2Vec for multi-label classification](introduction_to_amazon_algorithms/object2vec_multilabel_genre_classification) shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions. - [Object2Vec for sentence similarity](introduction_to_amazon_algorithms/object2vec_sentence_similarity) explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application. - [IP Insights for suspicious logins](introduction_to_amazon_algorithms/ipinsights_login) shows how to train IP Insights on a login events for a web server to identify suspicious login attempts. - [Semantic Segmentation](introduction_to_amazon_algorithms/semantic_segmentation_pascalvoc) shows how to train a semantic segmentation algorithm using the Amazon SageMaker Semantic Segmentation algorithm. It also demonstrates how to host the model and produce segmentation masks and probability of segmentation. @@ -147,31 +118,21 @@ These examples provide quick walkthroughs to get you up and running with Amazon - [JumpStart Upscaling](introduction_to_amazon_algorithms/jumpstart_upscaling) demonstrates how to enhance image quality with Stable Diffusion models in JumpStart. - [JumpStart Inpainting](introduction_to_amazon_algorithms/jumpstart_inpainting) demonstrates how to inpaint an image with Stable Diffusion models in JumpStart. - [In-context learning with AlexaTM 20B](introduction_to_amazon_algorithms/jumpstart_alexatm20b) demonstrates how to use AlexaTM 20B for in-context-learning in JumpStart. + ### Amazon SageMaker RL The following provide examples demonstrating different capabilities of Amazon SageMaker RL. -- [Cartpole using Coach](reinforcement_learning/rl_cartpole_coach) demonstrates the simplest usecase of Amazon SageMaker RL using Intel's RL Coach. -- [AWS DeepRacer](reinforcement_learning/rl_deepracer_robomaker_coach_gazebo) demonstrates AWS DeepRacer trainig using RL Coach in the Gazebo environment. - [HVAC using EnergyPlus](reinforcement_learning/rl_hvac_coach_energyplus) demonstrates the training of HVAC systems using the EnergyPlus environment. -- [Knapsack Problem](reinforcement_learning/rl_knapsack_coach_custom) demonstrates how to solve the knapsack problem using a custom environment. - [Mountain Car](reinforcement_learning/rl_mountain_car_coach_gymEnv) Mountain car is a classic RL problem. This notebook explains how to solve this using the OpenAI Gym environment. -- [Distributed Neural Network Compression](reinforcement_learning/rl_network_compression_ray_custom) This notebook explains how to compress ResNets using RL, using a custom environment and the RLLib toolkit. -- [Portfolio Management](reinforcement_learning/rl_portfolio_management_coach_customEnv) This notebook uses a custom Gym environment to manage multiple financial investments. -- [Autoscaling](reinforcement_learning/rl_predictive_autoscaling_coach_customEnv) demonstrates how to adjust load depending on demand. This uses RL Coach and a custom environment. -- [Roboschool](reinforcement_learning/rl_roboschool_ray) is an open source physics simulator that is commonly used to train RL policies for robotic systems. This notebook demonstrates training a few agents using it. -- [Stable Baselines](reinforcement_learning/rl_roboschool_stable_baselines) In this notebook example, we will make the HalfCheetah agent learn to walk using the stable-baselines, which are a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines. - [Travelling Salesman](reinforcement_learning/rl_traveling_salesman_vehicle_routing_coach) is a classic NP hard problem, which this notebook solves with AWS SageMaker RL. -- [Tic-tac-toe](reinforcement_learning/rl_tic_tac_toe_coach_customEnv) is a simple implementation of a custom Gym environment to train and deploy an RL agent in Coach that then plays tic-tac-toe interactively in a Jupyter Notebook. - [Unity Game Agent](reinforcement_learning/rl_unity_ray) shows how to use RL algorithms to train an agent to play Unity3D game. ### Scientific Details of Algorithms These examples provide more thorough mathematical treatment on a select group of algorithms. -- [Streaming Median](scientific_details_of_algorithms/streaming_median) sequentially introduces concepts used in streaming algorithms, which many SageMaker algorithms rely on to deliver speed and scalability. - [Latent Dirichlet Allocation (LDA)](scientific_details_of_algorithms/lda_topic_modeling) dives into Amazon SageMaker's spectral decomposition approach to LDA. -- [Linear Learner features](scientific_details_of_algorithms/linear_learner_class_weights_loss_functions) shows how to use the class weights and loss functions features of the SageMaker Linear Learner algorithm to improve performance on a credit card fraud prediction task ### Amazon SageMaker Debugger @@ -179,24 +140,13 @@ These examples provide and introduction to SageMaker Debugger which allows debug - [Using a built-in rule with TensorFlow](sagemaker-debugger/tensorflow_builtin_rule/) - [Using a custom rule with TensorFlow Keras](sagemaker-debugger/tensorflow_keras_custom_rule/) -- [Interactive tensor analysis in notebook with MXNet](sagemaker-debugger/mnist_tensor_analysis/) -- [Visualizing Debugging Tensors of MXNet training](sagemaker-debugger/mnist_tensor_plot/) -- [Real-time analysis in notebook with MXNet](sagemaker-debugger/mxnet_realtime_analysis/) -- [Using a built in rule with XGBoost](sagemaker-debugger/xgboost_builtin_rules/) -- [Real-time analysis in notebook with XGBoost](sagemaker-debugger/xgboost_realtime_analysis/) -- [Using SageMaker Debugger with Managed Spot Training and MXNet](sagemaker-debugger/mxnet_spot_training/) - [Reacting to CloudWatch Events from Rules to take an action based on status with TensorFlow](sagemaker-debugger/tensorflow_action_on_rule/) -- [Using SageMaker Debugger with a custom PyTorch container](sagemaker-debugger/pytorch_custom_container/) ### Amazon SageMaker Distributed Training These examples provide an introduction to SageMaker Distributed Training Libraries for data parallelism and model parallelism. The libraries are optimized for the SageMaker training environment, help adapt your distributed training jobs to SageMaker, and improve training speed and throughput. More examples for models such as BERT and YOLOv5 can be found in [distributed_training/](https://github.com/aws/amazon-sagemaker-examples/tree/main/training/distributed_training). -- [Train GPT-2 with Sharded Data Parallel](https://github.com/aws/amazon-sagemaker-examples/tree/main/training/distributed_training/pytorch/model_parallel/gpt2/smp-train-gpt-simple-sharded-data-parallel.ipynb) shows how to train GPT-2 with near-linear scaling using Sharded Data Parallelism technique in SageMaker Model Parallelism Library. -- [Train EleutherAI GPT-J with Model Parallel](https://github.com/aws/amazon-sagemaker-examples/blob/main/training/distributed_training/pytorch/model_parallel/gpt-j/11_train_gptj_smp_tensor_parallel_notebook.ipynb) shows how to train EleutherAI GPT-J with PyTorch and Tensor Parallelism technique in the SageMaker Model Parallelism Library. -- [Train MaskRCNN with Data Parallel](https://github.com/aws/amazon-sagemaker-examples/blob/main/training/distributed_training/pytorch/data_parallel/maskrcnn/pytorch_smdataparallel_maskrcnn_demo.ipynb) shows how to train MaskRCNN with PyTorch and SageMaker Data Parallelism Library. - ### Amazon SageMaker Smart Sifting These examples provide an Introduction to Smart Sifting library. Smart Sifting is a framework to speed up training of PyTorch models. The framework implements a set of algorithms that filter out inconsequential training examples during training, reducing the computational cost and accelerating the training process. It is configuration-driven and extensible, allowing users to add custom logic to transform their training examples into a filterable format. Smart sifting provides a generic utility for any DNN model, and can reduce the training cost by up to 35% in infrastructure cost. @@ -208,9 +158,8 @@ These examples provide an Introduction to Smart Sifting library. Smart Sifting i These examples provide an introduction to SageMaker Clarify which provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. -* [Fairness and Explainability with SageMaker Clarify](sagemaker-clarify/fairness_and_explainability) shows how to use SageMaker Clarify Processor API to measure the pre-training bias of a dataset and post-training bias of a model, and explain the importance of the input features on the model's decision. -* [TimeSeries Explainability with SageMaker Clarify](sagemaker-clarify/time_series_deepar) shows how to use SageMaker Clarify Processor API to explain the importance of the input features on the time-series model's decision. -* [Amazon SageMaker Clarify Model Monitors](sagemaker_model_monitor/fairness_and_explainability) shows how to use SageMaker Clarify Model Monitor API to schedule bias monitor to monitor predictions for bias drift on a regular basis, and schedule explainability monitor to monitor predictions for feature attribution drift on a regular basis. +- [Fairness and Explainability with SageMaker Clarify](sagemaker-clarify/fairness_and_explainability) shows how to use SageMaker Clarify Processor API to measure the pre-training bias of a dataset and post-training bias of a model, and explain the importance of the input features on the model's decision. +- [Amazon SageMaker Clarify Model Monitors](sagemaker_model_monitor/fairness_and_explainability) shows how to use SageMaker Clarify Model Monitor API to schedule bias monitor to monitor predictions for bias drift on a regular basis, and schedule explainability monitor to monitor predictions for feature attribution drift on a regular basis. ### Publishing content from RStudio on Amazon SageMaker to RStudio Connect @@ -224,45 +173,32 @@ These examples show you how to run R examples, and publish applications in RStud These examples showcase unique functionality available in Amazon SageMaker. They cover a broad range of topics and utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker. -- [Data Distribution Types](advanced_functionality/data_distribution_types) showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances. This has particular implication for scalability and accuracy of distributed training. - [Distributed Training and Batch Transform with Sentiment Classification](advanced_functionality/sentiment_parallel_batch) shows how to use SageMaker Distributed Data Parallelism, SageMaker Debugger, and distrubted SageMaker Batch Transform on a HuggingFace Estimator, in a sentiment classification use case. -- [Encrypting Your Data](advanced_functionality/handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key. -- [Using Parquet Data](advanced_functionality/parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume. - [Connecting to Redshift](advanced_functionality/working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks. - [Bring Your Own XGBoost Model](advanced_functionality/xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs. - [Bring Your Own k-means Model](advanced_functionality/kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it. -- [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language. -- [Installing the R Kernel](advanced_functionality/install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance. - [Bring Your Own scikit Algorithm](advanced_functionality/scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting. -- [Bring Your Own MXNet Model](advanced_functionality/mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker. - [Bring Your Own TensorFlow Model](advanced_functionality/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker. - [Bring Your Own Model train and deploy BERTopic](advanced_functionality/pytorch_extend_container_train_deploy_bertopic) shows how to bring a model through an external library, how to train it and deploy it into Amazon SageMaker by extending the pytorch base containers. -- [Experiment Management Capabilities with Search](advanced_functionality/search) shows how to organize Training Jobs into projects, and track relationships between Models, Endpoints, and Training Jobs. - [Host Multiple Models with Your Own Algorithm](advanced_functionality/multi_model_bring_your_own) shows how to deploy multiple models to a realtime hosted endpoint with your own custom algorithm. - [Host Multiple Models with XGBoost](advanced_functionality/multi_model_xgboost_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled XGBoost container. - [Host Multiple Models with SKLearn](advanced_functionality/multi_model_sklearn_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled SKLearn container. - [Host Multimodal HuggingFace Model](advanced_functionality/huggingface_deploy_instructpix2pix) shows how to host an instruction based image editing model from HuggingFace as a SageMaker endpoint using single core or multi-core GPU based instances. Inference Recommender is used to run load tests and compare the performance of instances. - [SageMaker Training and Inference with Script Mode](sagemaker-script-mode) shows how to use custom training and inference scripts, similar to those you would use outside of SageMaker, with SageMaker's prebuilt containers for various frameworks like Scikit-learn, PyTorch, and XGBoost. - [Host Models with NVidia Triton Server](sagemaker-triton) shows how to deploy models to a realtime hosted endpoint using [Triton](https://developer.nvidia.com/nvidia-triton-inference-server) as the model inference server. -- [Heterogenous Clusters Training in TensorFlow or PyTorch ](training/heterogeneous-clusters/README.md) shows how to train using TensorFlow tf.data.service (distributed data pipeline) or Pytorch (with gRPC) on top of Amazon SageMaker Heterogenous clusters to overcome CPU bottlenecks by including different instance types (GPU/CPU) in the same training job. +- [Heterogenous Clusters Training in TensorFlow or PyTorch](training/heterogeneous-clusters/README.md) shows how to train using TensorFlow tf.data.service (distributed data pipeline) or Pytorch (with gRPC) on top of Amazon SageMaker Heterogenous clusters to overcome CPU bottlenecks by including different instance types (GPU/CPU) in the same training job. ### Amazon SageMaker Neo Compilation Jobs These examples provide an introduction to how to use Neo to compile and optimize deep learning models. -- [GluonCV SSD Mobilenet](sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet) shows how to train GluonCV SSD MobileNet and use Amazon SageMaker Neo to compile and optimize the trained model. -- [Image Classification](sagemaker_neo_compilation_jobs/imageclassification_caltech) Adapts from [image classification](introduction_to_amazon_algorithms/imageclassification_caltech) including Neo API and comparison against the uncompiled baseline. -- [MNIST with MXNet](sagemaker_neo_compilation_jobs/mxnet_mnist) Adapts from [MXNet MNIST](sagemaker-python-sdk/mxnet_mnist) including Neo API and comparison against the uncompiled baseline. - [Deploying pre-trained PyTorch vision models](sagemaker_neo_compilation_jobs/pytorch_torchvision) shows how to use Amazon SageMaker Neo to compile and optimize pre-trained PyTorch models from TorchVision. -- [Distributed TensorFlow](sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist) includes Neo API and comparison against the uncompiled baseline. -- [Predicting Customer Churn](sagemaker_neo_compilation_jobs/xgboost_customer_churn) Adapts from [XGBoost customer churn](introduction_to_applying_machine_learning/xgboost_customer_churn) including Neo API and comparison against the uncompiled baseline. ### Amazon SageMaker Processing These examples show you how to use SageMaker Processing jobs to run data processing workloads. - [Scikit-Learn Data Processing and Model Evaluation](sagemaker_processing/scikit_learn_data_processing_and_model_evaluation) shows how to use SageMaker Processing and the Scikit-Learn container to run data preprocessing and model evaluation workloads. -- [Feature transformation with Amazon SageMaker Processing and SparkML](sagemaker_processing/feature_transformation_with_sagemaker_processing) shows how to use SageMaker Processing to run data processing workloads using SparkML prior to training. - [Feature transformation with Amazon SageMaker Processing and Dask](sagemaker_processing/feature_transformation_with_sagemaker_processing_dask) shows how to use SageMaker Processing to transform data using Dask distributed clusters - [Distributed Data Processing using Apache Spark and SageMaker Processing](sagemaker_processing/spark_distributed_data_processing) shows how to use the built-in Spark container on SageMaker Processing using the SageMaker Python SDK. @@ -284,17 +220,10 @@ These examples show you how to use [SageMaker Pipelines](https://aws.amazon.com/ These examples show you how to train and host in pre-built deep learning framework containers using the SageMaker Python SDK. -- [Chainer CIFAR-10](sagemaker-python-sdk/chainer_cifar10) trains a VGG image classification network on CIFAR-10 using Chainer (both single machine and multi-machine versions are included) -- [Chainer MNIST](sagemaker-python-sdk/chainer_mnist) trains a basic neural network on MNIST using Chainer (shows how to use local mode) -- [Chainer sentiment analysis](sagemaker-python-sdk/chainer_sentiment_analysis) trains a LSTM network with embeddings to predict text sentiment using Chainer - [IRIS with Scikit-learn](sagemaker-python-sdk/scikit_learn_iris) trains a Scikit-learn classifier on IRIS data - [Model Registry and Batch Transform with Scikit-learn](sagemaker-python-sdk/scikit_learn_model_registry_batch_transform) trains a Scikit-learn Random Forest model, registers it in Model Registry, and runs a Batch Transform Job. -- [MNIST with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_mnist) trains a basic neural network on the MNIST handwritten digit dataset using MXNet Gluon -- [MNIST with MXNet](sagemaker-python-sdk/mxnet_mnist) trains a basic neural network on the MNIST handwritten digit data using MXNet's symbolic syntax -- [Sentiment Analysis with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_sentiment) trains a text classifier using embeddings with MXNet Gluon - [TensorFlow training and serving](sagemaker-python-sdk/tensorflow_script_mode_training_and_serving) trains a basic neural network on MNIST - [TensorFlow with Horovod](sagemaker-python-sdk/tensorflow_script_mode_horovod) trains on MNIST using Horovod for distributed training -- [TensorFlow using shell commands](sagemaker-python-sdk/tensorflow_script_mode_using_shell_commands) shows how to use a shell script for the container's entry point #### Pre-Built Machine Learning Framework Containers @@ -313,8 +242,8 @@ These examples show how to use Amazon SageMaker for model training, hosting, and ### Using Amazon SageMaker with Amazon Keyspaces (for Apache Cassandra) These examples show how to use Amazon SageMaker to read data from [Amazon Keyspaces](https://docs.aws.amazon.com/keyspaces/). -- [Train Machine Learning Models using Amazon Keyspaces as a Data Source](ingest_data/sagemaker-keyspaces) +- [Train Machine Learning Models using Amazon Keyspaces as a Data Source](ingest_data/sagemaker-keyspaces) ### AWS Marketplace @@ -331,7 +260,7 @@ Once you have created an algorithm or a model package to be listed in the AWS Ma - [Curate your AWS Marketplace model package listing and sample notebook](aws_marketplace/curating_aws_marketplace_listing_and_sample_notebook/ModelPackage) provides instructions on how to craft a sample notebook to be associated with your listing and how to curate a good AWS Marketplace listing that makes it easy for AWS customers to consume your model package. - [Curate your AWS Marketplace algorithm listing and sample notebook](aws_marketplace/curating_aws_marketplace_listing_and_sample_notebook/Algorithm) provides instructions on how to craft a sample notebook to be associated with your listing and how to curate a good AWS Marketplace listing that makes it easy for your customers to consume your algorithm. -#### Use algorithms, data, and model packages from AWS Marketplace. +#### Use algorithms, data, and model packages from AWS Marketplace These examples show you how to use model-packages and algorithms from AWS Marketplace and dataset products from AWS Data Exchange, for machine learning. @@ -339,7 +268,6 @@ These examples show you how to use model-packages and algorithms from AWS Market - [Using Algorithm From AWS Marketplace](aws_marketplace/using_algorithms/amazon_demo_product) provides a detailed walkthrough on how to use Algorithm with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs by choosing a canonical product listed on AWS Marketplace. - [Using AutoML algorithm](aws_marketplace/using_algorithms/automl) provides a detailed walkthrough on how to use AutoML algorithm from AWS Marketplace. - [Using Model Packages](aws_marketplace/using_model_packages) - - [Using Model Packages From AWS Marketplace](aws_marketplace/using_model_packages/generic_sample_notebook) is a generic notebook which provides sample code snippets you can modify and use for performing inference on Model Packages from AWS Marketplace, using Amazon SageMaker. - [Using Amazon Demo product From AWS Marketplace](aws_marketplace/using_model_packages/amazon_demo_product) provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace. - [Using models for extracting vehicle metadata](aws_marketplace/using_model_packages/auto_insurance) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of auto-insurance claim processing. - [Using models for identifying non-compliance at a workplace](aws_marketplace/using_model_packages/improving_industrial_workplace_safety) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace. @@ -347,17 +275,21 @@ These examples show you how to use model-packages and algorithms from AWS Market - [Amazon Augmented AI with AWS Marketplace ML models](aws_marketplace/using_model_packages/amazon_augmented_ai_with_aws_marketplace_ml_models) will show you how to use AWS Marketplace pre-trained ML models with Amazon Augmented AI to implement human-in-loop workflow reviews with your ML model predictions. - [Monitoring data quality in third-party models from AWS Marketplace](aws_marketplace/using_model_packages/data_quality_monitoring) will show you how to perform Data Quality monitoring on a pre-trained third-party model from AWS Marketplace. - [Evaluating ML models from AWS Marketplace for person counting use case](aws_marketplace/using_model_packages/evaluating_aws_marketplace_models_for_person_counting_use_case) will show you how to use two AWS Marketplace GluonCV pre-trained ML models for person counting use case and evaluate each model for performance in different types of crowd images. - - [Preprocessing audio data using a pre-trained machine learning model](using_model_packages/preprocessing-audio-data-using-a-machine-learning-model) demonstrates the usage of a pre-trained audio track separation model to create synthetic features and improve an acoustic classification model. + - [Preprocessing audio data using a pre-trained machine learning model](aws_marketplace/using_model_packages/preprocessing-audio-data-using-a-machine-learning-model) demonstrates the usage of a pre-trained audio track separation model to create synthetic features and improve an acoustic classification model. - [Using Dataset Products](aws_marketplace/using_data) - [Using Dataset Product from AWS Data Exchange with ML model from AWS Marketplace](aws_marketplace/using_data/using_data_with_ml_model) is a sample notebook which shows how a dataset from AWS Data Exchange can be used with an ML Model Package from AWS Marketplace. - [Using Shutterstock Image Datasets to train Image Classification Models](aws_marketplace/using_data/image_classification_with_shutterstock_image_datasets) provides a detailed walkthrough on how to use the [Free Sample: Images & Metadata of “Whole Foods” Shoppers](https://aws.amazon.com/marketplace/pp/prodview-y6xuddt42fmbu?qid=1623195111604&sr=0-1&ref_=srh_res_product_title#offers) from Shutterstock's Image Datasets to train a multi-label image classification model using Shutterstock's pre-labeled image assets. You can learn more about this implementation [from this blog post](https://aws.amazon.com/blogs/awsmarketplace/using-shutterstocks-image-datasets-to-train-your-computer-vision-models/). -### Using Amazon SageMaker for Generative AI use cases. +### Using Amazon SageMaker for Generative AI use cases These examples show you how to use AWS services for Generative AI use cases. - Text-to-image - [Fine-tune Stable Diffusion XL model with Kohya](use-cases/text-to-image-fine-tuning) Provides an automated solution to create the necessary components to fine-tune a custom Stable Diffusion XL model. + +### Archived + +This folder houses legacy, low-viewed, and duplicate notebooks, with a 6-month grace period before deletion. If you believe a notebook has been moved into this folder in error, please submit a PR with justification. ## :balance_scale: License @@ -369,3 +301,4 @@ For more details, please take a look at the [LICENSE](https://github.com/aws/ama Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed. Please read our [contributing guidelines](https://github.com/aws/amazon-sagemaker-examples/blob/master/CONTRIBUTING.md) if you'd like to open an issue or submit a pull request. + diff --git a/introduction_to_applying_machine_learning/object_detection_with_tensorflow_and_tfrecords/2_object_detection_train_eval.ipynb b/archived/notebooks/2_object_detection_train_eval.ipynb similarity index 100% rename from introduction_to_applying_machine_learning/object_detection_with_tensorflow_and_tfrecords/2_object_detection_train_eval.ipynb rename to archived/notebooks/2_object_detection_train_eval.ipynb diff --git a/introduction_to_amazon_algorithms/jumpstart_zstc/Amazon_JumpStart_Zero_Shot_Text_Classification.ipynb b/archived/notebooks/Amazon_JumpStart_Zero_Shot_Text_Classification.ipynb similarity index 100% rename from introduction_to_amazon_algorithms/jumpstart_zstc/Amazon_JumpStart_Zero_Shot_Text_Classification.ipynb rename to 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CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "MNIST is a widely used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial shows how to train and test an MNIST model on SageMaker using PyTorch. \n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 5 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [PyTorch Estimator](#PyTorch-Estimator)\n", - "1. [Implement the entry point for training](#Implement-the-entry-point-for-training)\n", - "1. [Set hyperparameters](#Set-hyperparameters)\n", - "1. [Set up channels for the training and testing data](#Set-up-channels-for-the-training-and-testing-data)\n", - "1. [Run the training script on SageMaker](#Run-the-training-script-on-SageMaker)\n", - "1. [Inspect and store model data](#Inspect-and-store-model-data)\n", - "1. [Test and debug the entry point before executing the training container](#Test-and-debug-the-entry-point-before-executing-the-training-container)\n", - "1. [Conclusion](#Conclusion)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "\n", - "import sagemaker\n", - "from sagemaker.pytorch import PyTorch\n", - "from sagemaker import get_execution_role\n", - "\n", - "\n", - "sess = sagemaker.Session()\n", - "region = sess.boto_region_name\n", - "\n", - "role = get_execution_role()\n", - "\n", - "output_path = \"s3://\" + sess.default_bucket() + \"/DEMO-mnist\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PyTorch Estimator\n", - "\n", - "The `PyTorch` class allows you to run your training script on SageMaker\n", - "infrastracture in a containerized environment. In this notebook, we\n", - "refer to this container as *training container*. \n", - "\n", - "You need to configure\n", - "it with the following parameters to set up the environment:\n", - "\n", - "- `entry_point`: A user-defined Python file used by the training container as the \n", - "instructions for training. We further discuss this file in the next subsection.\n", - "\n", - "- `role`: An IAM role to make AWS service requests\n", - "\n", - "- `instance_type`: The type of SageMaker instance to run your training script. \n", - "Set it to `local` if you want to run the training job on \n", - "the SageMaker instance you are using to run this notebook\n", - "\n", - "- `instance_count`: The number of instances to run your training job on. \n", - "Multiple instances are needed for distributed training.\n", - "\n", - "- `output_path`: \n", - "S3 bucket URI to save training output (model artifacts and output files)\n", - "\n", - "- `framework_version`: The version of PyTorch to use\n", - "\n", - "- `py_version`: The Python version to use\n", - "\n", - "For more information, see the [EstimatorBase API reference](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.EstimatorBase)\n", - "\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implement the entry point for training\n", - "\n", - "The entry point for training is a Python script that provides all \n", - "the code for training a PyTorch model. It is used by the SageMaker \n", - "PyTorch Estimator (`PyTorch` class above) as the entry point for running the training job.\n", - "\n", - "Under the hood, SageMaker PyTorch Estimator creates a docker image\n", - "with runtime environemnts \n", - "specified by the parameters you provide to initiate the\n", - "estimator class, and it injects the training script into the \n", - "docker image as the entry point to run the container.\n", - "\n", - "In the rest of the notebook, we use *training image* to refer to the \n", - "docker image specified by the PyTorch Estimator and *training container*\n", - "to refer to the container that runs the training image. \n", - "\n", - "This means your training script is very similar to a training script\n", - "you might run outside Amazon SageMaker, but it can access the useful environment \n", - "variables provided by the training image. See [the complete list of environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a complete \n", - "description of all environment variables your training script\n", - "can access. \n", - "\n", - "In this example, we use the training script `code/train.py`\n", - "as the entry point for our PyTorch Estimator.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pygmentize 'code/train.py'" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set hyperparameters\n", - "\n", - "In addition, the PyTorch estimator allows you to parse command line arguments\n", - "to your training script via `hyperparameters`.\n", - "\n", - "Note: local mode is not supported in SageMaker Studio. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set local_mode to True to run the training script on the machine that runs this notebook\n", - "\n", - "local_mode = False\n", - "\n", - "if local_mode:\n", - " instance_type = \"local\"\n", - "else:\n", - " instance_type = \"ml.c4.xlarge\"\n", - "\n", - "est = PyTorch(\n", - " entry_point=\"train.py\",\n", - " source_dir=\"code\", # directory of your training script\n", - " role=role,\n", - " framework_version=\"1.5.0\",\n", - " py_version=\"py3\",\n", - " instance_type=instance_type,\n", - " instance_count=1,\n", - " volume_size=250,\n", - " output_path=output_path,\n", - " hyperparameters={\"batch-size\": 128, \"epochs\": 1, \"learning-rate\": 1e-3, \"log-interval\": 100},\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training container executes your training script like:\n", - "\n", - "```\n", - "python train.py --batch-size 100 --epochs 1 --learning-rate 1e-3 --log-interval 100\n", - "```" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up channels for the training and testing data\n", - "\n", - "Tell the `PyTorch` estimator where to find the training and \n", - "testing data. It can be a path to an S3 bucket, or a path\n", - "in your local file system if you use local mode. In this example,\n", - "we download the MNIST data from a public S3 bucket and upload it \n", - "to your default bucket. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import boto3\n", - "from botocore.exceptions import ClientError\n", - "\n", - "# Download training and testing data from a public S3 bucket\n", - "\n", - "\n", - "def download_from_s3(data_dir=\"./data\", train=True):\n", - " \"\"\"Download MNIST dataset and convert it to numpy array\n", - "\n", - " Args:\n", - " data_dir (str): directory to save the data\n", - " train (bool): download training set\n", - "\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - "\n", - " if not os.path.exists(data_dir):\n", - " os.makedirs(data_dir)\n", - "\n", - " if train:\n", - " images_file = \"train-images-idx3-ubyte.gz\"\n", - " labels_file = \"train-labels-idx1-ubyte.gz\"\n", - " else:\n", - " images_file = \"t10k-images-idx3-ubyte.gz\"\n", - " labels_file = \"t10k-labels-idx1-ubyte.gz\"\n", - "\n", - " # download objects\n", - " s3 = boto3.client(\"s3\")\n", - " bucket = f\"sagemaker-example-files-prod-{region}\"\n", - " for obj in [images_file, labels_file]:\n", - " key = os.path.join(\"datasets/image/MNIST\", obj)\n", - " dest = os.path.join(data_dir, obj)\n", - " if not os.path.exists(dest):\n", - " s3.download_file(bucket, key, dest)\n", - " return\n", - "\n", - "\n", - "download_from_s3(\"./data\", True)\n", - "download_from_s3(\"./data\", False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload to the default bucket\n", - "\n", - "prefix = \"DEMO-mnist\"\n", - "bucket = sess.default_bucket()\n", - "loc = sess.upload_data(path=\"./data\", bucket=bucket, key_prefix=prefix)\n", - "\n", - "channels = {\"training\": loc, \"testing\": loc}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The keys of the `channels` dictionary are passed to the training image,\n", - "and it creates the environment variable `SM_CHANNEL_`. \n", - "\n", - "In this example, `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` are created in the training image (see \n", - "how `code/train.py` accesses these variables). For more information,\n", - "see: [SM_CHANNEL_{channel_name}](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md#sm_channel_channel_name).\n", - "\n", - "If you want, you can create a channel for validation:\n", - "```\n", - "channels = {\n", - " 'training': train_data_loc,\n", - " 'validation': val_data_loc,\n", - " 'test': test_data_loc\n", - "}\n", - "```\n", - "You can then access this channel within your training script via\n", - "`SM_CHANNEL_VALIDATION`.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run the training script on SageMaker\n", - "Now, the training container has everything to execute your training\n", - "script. Start the container by calling the `fit()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "est.fit(inputs=channels)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inspect and store model data\n", - "\n", - "Now, the training is finished, and the model artifact has been saved in \n", - "the `output_path`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pt_mnist_model_data = est.model_data\n", - "print(\"Model artifact saved at:\\n\", pt_mnist_model_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We store the variable `pt_mnist_model_data` in the current notebook kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%store pt_mnist_model_data" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test and debug the entry point before executing the training container\n", - "\n", - "The entry point `code/train.py` can be executed in the training container. \n", - "When you develop your own training script, it is a good practice to simulate the container environment \n", - "in the local shell and test it before sending it to SageMaker, because debugging in a containerized environment\n", - "is rather cumbersome. The following script shows how you can test your training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pygmentize code/test_train.py" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook, we trained a PyTorch model on the MNIST dataset by fitting a SageMaker estimator. For next steps on how to deploy the trained model and perform inference, see [Deploy a Trained PyTorch Model](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/pytorch/get_started_mnist_deploy.html)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (PyTorch 1.13 Python 3.9 CPU Optimized)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.13-cpu-py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/frameworks/tensorflow/get_started_mnist_train.ipynb b/frameworks/tensorflow/get_started_mnist_train.ipynb deleted file mode 100644 index d5b5233846..0000000000 --- a/frameworks/tensorflow/get_started_mnist_train.ipynb +++ /dev/null @@ -1,460 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train an MNIST model with TensorFlow\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "MNIST is a widely-used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial will show how to train a TensorFlow V2 model on MNIST model on SageMaker.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 5 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [TensorFlow Estimator](#TensorFlow-Estimator)\n", - "1. [Implement the training entry point](#Implement-the-training-entry-point)\n", - "1. [Set hyperparameters](#Set-hyperparameters)\n", - "1. [Set up channels for training and testing data](#Set-up-channels-for-training-and-testing-data)\n", - "1. [Run the training script on SageMaker](#Run-the-training-script-on-SageMaker)\n", - "1. [Inspect and store model data](#Inspect-and-store-model-data)\n", - "1. [Test and debug the entry point before running the training container](#Test-and-debug-the-entry-point-before-running-the-training-container)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "\n", - "import sagemaker\n", - "from sagemaker.tensorflow import TensorFlow\n", - "from sagemaker import get_execution_role\n", - "\n", - "sess = sagemaker.Session()\n", - "\n", - "role = get_execution_role()\n", - "\n", - "output_path = \"s3://\" + sess.default_bucket() + \"/DEMO-tensorflow/mnist\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TensorFlow Estimator\n", - "\n", - "The `TensorFlow` class allows you to run your training script on SageMaker\n", - "infrastracture in a containerized environment. In this notebook, we\n", - "refer to this container as the \"training container.\" \n", - "\n", - "Configure it with the following parameters to set up the environment:\n", - "\n", - "- `entry_point`: A user-defined Python file used by the training container as the instructions for training. We will further discuss this file in the next subsection.\n", - "\n", - "- `role`: An IAM role to make AWS service requests\n", - "\n", - "- `instance_type`: The type of SageMaker instance to run your training script. Set it to `local` if you want to run the training job on the SageMaker instance you are using to run this notebook.\n", - "\n", - "- `model_dir`: S3 bucket URI where the checkpoint data and models can be exported to during training (default: None). \n", - "To disable having model_dir passed to your training script, set `model_dir`=False\n", - "\n", - "- `instance_count`: The number of instances to run your training job on. Multiple instances are needed for distributed training.\n", - "\n", - "- `output_path`: the S3 bucket URI to save training output (model artifacts and output files).\n", - "\n", - "- `framework_version`: The TensorFlow version to use.\n", - "\n", - "- `py_version`: The Python version to use.\n", - "\n", - "For more information, see the [EstimatorBase API reference](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.EstimatorBase).\n", - "\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implement the training entry point\n", - "\n", - "The entry point for training is a Python script that provides all \n", - "the code for training a TensorFlow model. It is used by the SageMaker \n", - "TensorFlow Estimator (`TensorFlow` class above) as the entry point for running the training job.\n", - "\n", - "Under the hood, SageMaker TensorFlow Estimator downloads a docker image\n", - "with runtime environments \n", - "specified by the parameters to initiate the\n", - "estimator class and it injects the training script into the \n", - "docker image as the entry point to run the container.\n", - "\n", - "In the rest of the notebook, we use *training image* to refer to the \n", - "docker image specified by the TensorFlow Estimator and *training container*\n", - "to refer to the container that runs the training image. \n", - "\n", - "This means your training script is very similar to a training script\n", - "you might run outside Amazon SageMaker, but it can access the useful environment \n", - "variables provided by the training image. See [the complete list of environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a complete \n", - "description of all environment variables your training script\n", - "can access. \n", - "\n", - "In this example, we use the training script `code/train.py`\n", - "as the entry point for our TensorFlow Estimator. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pygmentize 'code/train.py'" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set hyperparameters\n", - "\n", - "In addition, the TensorFlow estimator allows you to parse command line arguments\n", - "to your training script via `hyperparameters`.\n", - "\n", - " Note: local mode is not supported in SageMaker Studio. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set local_mode to be True if you want to run the training script on the machine that runs this notebook\n", - "\n", - "local_mode = False\n", - "\n", - "if local_mode:\n", - " instance_type = \"local\"\n", - "else:\n", - " instance_type = \"ml.c4.xlarge\"\n", - "\n", - "est = TensorFlow(\n", - " entry_point=\"train.py\",\n", - " source_dir=\"code\", # directory of your training script\n", - " role=role,\n", - " framework_version=\"2.3.1\",\n", - " model_dir=False, # don't pass --model_dir to your training script\n", - " py_version=\"py37\",\n", - " instance_type=instance_type,\n", - " instance_count=1,\n", - " volume_size=250,\n", - " output_path=output_path,\n", - " hyperparameters={\n", - " \"batch-size\": 512,\n", - " \"epochs\": 1,\n", - " \"learning-rate\": 1e-3,\n", - " \"beta_1\": 0.9,\n", - " \"beta_2\": 0.999,\n", - " },\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training container runs your training script like:\n", - "\n", - "```\n", - "python train.py --batch-size 32 --epochs 1 --learning-rate 0.001 --beta_1 0.9 --beta_2 0.999\n", - "```" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up channels for training and testing data\n", - "\n", - "Tell `TensorFlow` estimator where to find the training and \n", - "testing data. It can be a path to an S3 bucket, or a path\n", - "in your local file system if you use local mode. In this example,\n", - "we download the MNIST data from a public S3 bucket and upload it \n", - "to your default bucket. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import boto3\n", - "from botocore.exceptions import ClientError\n", - "\n", - "# Download training and testing data from a public S3 bucket\n", - "\n", - "\n", - "def download_from_s3(data_dir=\"./data\", train=True):\n", - " \"\"\"Download MNIST dataset and convert it to numpy array\n", - "\n", - " Args:\n", - " data_dir (str): directory to save the data\n", - " train (bool): download training set\n", - "\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - "\n", - " if not os.path.exists(data_dir):\n", - " os.makedirs(data_dir)\n", - "\n", - " if train:\n", - " images_file = \"train-images-idx3-ubyte.gz\"\n", - " labels_file = \"train-labels-idx1-ubyte.gz\"\n", - " else:\n", - " images_file = \"t10k-images-idx3-ubyte.gz\"\n", - " labels_file = \"t10k-labels-idx1-ubyte.gz\"\n", - "\n", - " # download objects\n", - " s3 = boto3.client(\"s3\")\n", - " bucket = f\"sagemaker-example-files-prod-{boto3.session.Session().region_name}\"\n", - " for obj in [images_file, labels_file]:\n", - " key = os.path.join(\"datasets/image/MNIST\", obj)\n", - " dest = os.path.join(data_dir, obj)\n", - " if not os.path.exists(dest):\n", - " s3.download_file(bucket, key, dest)\n", - " return\n", - "\n", - "\n", - "download_from_s3(\"./data\", True)\n", - "download_from_s3(\"./data\", False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload to the default bucket\n", - "\n", - "prefix = \"DEMO-mnist\"\n", - "bucket = sess.default_bucket()\n", - "loc = sess.upload_data(path=\"./data\", bucket=bucket, key_prefix=prefix)\n", - "\n", - "channels = {\"training\": loc, \"testing\": loc}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The keys of the `channels` dictionary are passed to the training image,\n", - "and it creates the environment variable `SM_CHANNEL_`. \n", - "\n", - "In this example, `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` are created in the training image (see \n", - "how `code/train.py` accesses these variables). For more information,\n", - "see: [SM_CHANNEL_{channel_name}](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md#sm_channel_channel_name).\n", - "\n", - "If you want, you can create a channel for validation:\n", - "```\n", - "channels = {\n", - " 'training': train_data_loc,\n", - " 'validation': val_data_loc,\n", - " 'test': test_data_loc\n", - "}\n", - "```\n", - "You can then access this channel within your training script via\n", - "`SM_CHANNEL_VALIDATION`." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run the training script on SageMaker\n", - "Now, the training container has everything to run your training\n", - "script. Start the container by calling the `fit()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "est.fit(inputs=channels)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inspect and store model data\n", - "\n", - "Now, the training is finished, and the model artifact has been saved in \n", - "the `output_path`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tf_mnist_model_data = est.model_data\n", - "print(\"Model artifact saved at:\\n\", tf_mnist_model_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We store the variable `tf_mnist_model_data` in the current notebook kernel. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%store tf_mnist_model_data" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test and debug the entry point before running the training container\n", - "\n", - "The entry point `code/train.py` provided here has been tested and it can be runs in the training container. \n", - "When you develop your own training script, it is a good practice to simulate the container environment \n", - "in the local shell and test it before sending it to SageMaker, because debugging in a containerized environment\n", - "is rather cumbersome. The following script shows how you can test your training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pygmentize code/test_train.py" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook, we trained a TensorFlow model on the MNIST dataset by fitting a SageMaker estimator. For next steps on how to deploy the trained model and perform inference, see [Deploy a Trained TensorFlow V2 Model](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/tensorflow/get_started_mnist_deploy.html)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", - "\n", - "![This ap-northeast-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n" - ] - } - ], - "metadata": { - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (TensorFlow 2.10.0 Python 3.9 CPU Optimized)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/tensorflow-2.10.1-cpu-py39-ubuntu20.04-sagemaker-v1.2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist.ipynb b/hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist.ipynb deleted file mode 100644 index 4a6c6a781c..0000000000 --- a/hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist.ipynb +++ /dev/null @@ -1,454 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hyperparameter Tuning with the SageMaker TensorFlow Container\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This tutorial focuses on how to create a convolutional neural network model to train the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) using the SageMaker TensorFlow container. It leverages hyperparameter tuning to run multiple training jobs with different hyperparameter combinations, to find the one with the best model training result.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 10 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Set Up the Environment](#Set-Up-the-Environment)\n", - "1. [Data](#Data)\n", - "1. [Run a TensorFlow Training Job](#Run-a-TensorFlow-Training-Job)\n", - "1. [Set Up Channels for Training and Testing Data](#Set-Up-Channels-for-Training-and-Testing-Data)\n", - "1. [Run a Hyperparameter Tuning Job](#Run-a-Hyperparameter-Tuning-Job)\n", - "1. [Deploy the Best Model](#Deploy-the-Best-Model)\n", - "1. [Evaluate](#Evaluate)\n", - "1. [Cleanup](#Cleanup)\n", - "\n", - "## Set Up the Environment \n", - "Set up a few things before starting the workflow:\n", - "\n", - "1. A boto3 session object to manage interactions with the Amazon SageMaker APIs. \n", - "2. An execution role which is passed to SageMaker to access your AWS resources." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "\n", - "import sagemaker\n", - "from sagemaker.tensorflow import TensorFlow\n", - "from sagemaker import get_execution_role\n", - "\n", - "sess = sagemaker.Session()\n", - "region = sess.boto_region_name\n", - "role = get_execution_role()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "Download the MNIST data from a public S3 bucket and save it in a temporary directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import boto3\n", - "from botocore.exceptions import ClientError\n", - "\n", - "public_bucket = f\"sagemaker-example-files-prod-{region}\"\n", - "local_data_dir = \"/tmp/data\"\n", - "\n", - "\n", - "# Download training and testing data from a public S3 bucket\n", - "def download_from_s3(data_dir=\"/tmp/data\", train=True):\n", - " \"\"\"Download MNIST dataset and convert it to numpy array\n", - "\n", - " Args:\n", - " data_dir (str): directory to save the data\n", - " train (bool): download training set\n", - "\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - " # project root\n", - " if not os.path.exists(data_dir):\n", - " os.makedirs(data_dir)\n", - "\n", - " if train:\n", - " images_file = \"train-images-idx3-ubyte.gz\"\n", - " labels_file = \"train-labels-idx1-ubyte.gz\"\n", - " else:\n", - " images_file = \"t10k-images-idx3-ubyte.gz\"\n", - " labels_file = \"t10k-labels-idx1-ubyte.gz\"\n", - "\n", - " # download objects\n", - " s3 = boto3.client(\"s3\")\n", - " bucket = public_bucket\n", - " for obj in [images_file, labels_file]:\n", - " key = os.path.join(\"datasets/image/MNIST\", obj)\n", - " dest = os.path.join(data_dir, obj)\n", - " if not os.path.exists(dest):\n", - " s3.download_file(bucket, key, dest)\n", - " return\n", - "\n", - "\n", - "download_from_s3(local_data_dir, True)\n", - "download_from_s3(local_data_dir, False)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run a TensorFlow Training Job\n", - "A TensorFlow training job is defined by using the `TensorFlow` estimator class. It lets you run your training script on SageMaker infrastructure in a containerized environment. For more information on how to instantiate it, see the example [Train an MNIST model with TensorFlow](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/tensorflow/get_started_mnist_train.html#TensorFlow-Estimator)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "est = TensorFlow(\n", - " entry_point=\"train.py\",\n", - " source_dir=\"code\", # directory of your training script\n", - " role=role,\n", - " framework_version=\"2.3.1\",\n", - " model_dir=\"/opt/ml/model\",\n", - " py_version=\"py37\",\n", - " instance_type=\"ml.m5.4xlarge\",\n", - " instance_count=1,\n", - " volume_size=250,\n", - " hyperparameters={\n", - " \"batch-size\": 512,\n", - " \"epochs\": 4,\n", - " },\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Up Channels for Training and Testing Data\n", - "Upload the MNIST data to the default bucket of your AWS account and pass the S3 URI as the channels of training and testing data for the `TensorFlow` estimator class. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prefix = \"mnist\"\n", - "bucket = sess.default_bucket()\n", - "loc = sess.upload_data(path=local_data_dir, bucket=bucket, key_prefix=prefix)\n", - "\n", - "channels = {\"training\": loc, \"testing\": loc}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run a Hyperparameter Tuning Job\n", - "Now that you have set up the training job and the input data channels, you are ready to train the model with hyperparameter search.\n", - "\n", - "Set up the hyperparameter tuning job with the following steps:\n", - "* Define the ranges of hyperparameters we plan to tune. In this example, we tune the learning rate.\n", - "* Define the objective metric for the tuning job to optimize.\n", - "* Create a hyperparameter tuner with the above setting, as well as tuning resource configurations.\n", - "\n", - "\n", - "\n", - "\n", - "For a typical ML model, there are three kinds of hyperparamters:\n", - "\n", - "- Categorical parameters need to take one value from a discrete set. We define this by passing the list of possible values to `CategoricalParameter(list)`\n", - "- Continuous parameters can take any real number value between the minimum and maximum value, defined by `ContinuousParameter(min, max)`\n", - "- Integer parameters can take any integer value between the minimum and maximum value, defined by `IntegerParameter(min, max)`\n", - "\n", - "Learning rate is a continuous variable, so we define its range\n", - "by `ContinuousParameter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.tuner import ContinuousParameter, HyperparameterTuner\n", - "\n", - "hyperparamter_range = {\"learning-rate\": ContinuousParameter(1e-4, 1e-3)}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we specify the objective metric that we'd like to tune and its definition, which includes the regular expression (regex) needed to extract that metric from the CloudWatch logs of the training job. In this particular case, our script emits average loss value and we use it as the objective metric. We set the `objective_type` to `Minimize`, so that hyperparameter tuning seeks to minimize the objective metric when searching for the best hyperparameter value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "objective_metric_name = \"average test loss\"\n", - "objective_type = \"Minimize\"\n", - "metric_definitions = [\n", - " {\n", - " \"Name\": \"average test loss\",\n", - " \"Regex\": \"Test Loss: ([0-9\\\\.]+)\",\n", - " }\n", - "]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, you'll create a `HyperparameterTuner` object. It takes the following parameters:\n", - "- The `TensorFlow` estimator you previously created.\n", - "- Your hyperparameter ranges.\n", - "- Objective metric name and definition.\n", - "- Tuning resource configurations such as the number of training jobs to run in total, and how many training jobs to run in parallel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tuner = HyperparameterTuner(\n", - " est,\n", - " objective_metric_name,\n", - " hyperparamter_range,\n", - " metric_definitions,\n", - " max_jobs=3,\n", - " max_parallel_jobs=3,\n", - " objective_type=objective_type,\n", - ")\n", - "\n", - "tuner.fit(inputs=channels)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the Best Model\n", - "After training with hyperparameter optimization, you can deploy the best-performing model (by the objective metric you defined) to a SageMaker endpoint. For more information about deploying a model to a SageMaker endpoint, see the example [Deploy a Trained TensorFlow V2 Model](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/tensorflow/get_started_mnist_deploy.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictor = tuner.deploy(initial_instance_count=1, instance_type=\"ml.m5.xlarge\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate\n", - "Now, you can evaluate the best-performing model by invoking the endpoint with the MNIST test set. The test data needs to be readily consumable by the model, so we arrange them into the correct shape that is accepted by a TensorFlow model. We also normalize them so that the pixel values have mean 0 and standard deviation 1, since this is the convention used to train the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import gzip\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "\n", - "images_file = \"t10k-images-idx3-ubyte.gz\"\n", - "\n", - "\n", - "def read_mnist(data_dir, images_file):\n", - " \"\"\"Byte string to numpy arrays\"\"\"\n", - " with gzip.open(os.path.join(data_dir, images_file), \"rb\") as f:\n", - " images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n", - " return images\n", - "\n", - "\n", - "X = read_mnist(local_data_dir, images_file)\n", - "\n", - "# randomly sample 16 images to inspect\n", - "mask = random.sample(range(X.shape[0]), 16)\n", - "samples = X[mask]\n", - "\n", - "# plot the images\n", - "fig, axs = plt.subplots(nrows=1, ncols=16, figsize=(16, 1))\n", - "\n", - "for i, splt in enumerate(axs):\n", - " splt.imshow(samples[i])\n", - "\n", - "# preprocess the data to be consumed by the model\n", - "\n", - "\n", - "def normalize(x, axis):\n", - " eps = np.finfo(float).eps\n", - "\n", - " mean = np.mean(x, axis=axis, keepdims=True)\n", - " # avoid division by zero\n", - " std = np.std(x, axis=axis, keepdims=True) + eps\n", - " return (x - mean) / std\n", - "\n", - "\n", - "samples = normalize(samples, axis=(1, 2))\n", - "samples = np.expand_dims(samples, axis=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = predictor.predict(samples)[\"predictions\"]\n", - "\n", - "# softmax to logit\n", - "predictions = np.array(predictions, dtype=np.float32)\n", - "predictions = np.argmax(predictions, axis=1)\n", - "\n", - "print(\"Predictions: \", *predictions)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleanup\n", - "If you do not plan to continue using the endpoint, delete it to free up resources." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictor.delete_endpoint()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Binary Classification with `Trainer` and `sst2` dataset__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Runtime\n", - "\n", - "This notebook takes approximately 45 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Introduction](#Introduction) \n", - "2. [Development environment and permissions](#Development-environment-and-permissions)\n", - " 1. [Installation](#Installation) \n", - " 2. [Development environment](#Development-environment) \n", - " 3. [Permissions](#Permissions)\n", - "3. [Pre-processing](#Pre-processing) \n", - " 1. [Tokenize sentences](#Tokenize-sentences) \n", - " 2. [Upload data to sagemaker_session_bucket](#Upload-data-to-sagemaker_session_bucket) \n", - "4. [Fine-tune the model and start a SageMaker training job](#Fine-tune-the-model-and-start-a-SageMaker-training-job) \n", - " 1. [Create an Estimator and start a training job](#Create-an-Estimator-and-start-a-training-job) \n", - " 2. [Estimator Parameters](#Estimator-Parameters) \n", - " 3. [Attach a previous training job to an estimator](#Attach-a-previous-training-job-to-an-estimator) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Welcome to our end-to-end binary text classification example. This notebook uses Hugging Face's `transformers` library with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer on binary text classification. The pre-trained model is fine-tuned using the `sst2` dataset. To get started, we need to set up the environment with a few prerequisite steps for permissions, configurations, and so on. \n", - "\n", - "This notebook is adapted from Hugging Face's notebook [Huggingface Sagemaker-sdk - Getting Started Demo](https://github.com/huggingface/notebooks/blob/master/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb) and provided here courtesy of Hugging Face.\n", - "\n", - "\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 40 minutes to run.\n", - "\n", - "NOTE: You can run this notebook in SageMaker Studio, a SageMaker notebook instance, or your local machine. This notebook was tested in a notebook instance using the conda\\_pytorch\\_p36 kernel.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Development environment and permissions " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation\n", - "\n", - "_*Note:* We install the required libraries from Hugging Face and AWS. You also need PyTorch, if you haven't installed it already._" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install \"sagemaker\" \"transformers\" \"datasets[s3]\" \"s3fs\" --upgrade" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Development environment " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import sagemaker.huggingface" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Permissions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_If you are going to use SageMaker in a local environment, you need access to an IAM Role with the required permissions for SageMaker. You can read more at [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)._" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import sagemaker\n", - "\n", - "sess = sagemaker.Session()\n", - "# The SageMaker session bucket is used for uploading data, models and logs\n", - "# SageMaker will automatically create this bucket if it doesn't exist\n", - "sagemaker_session_bucket = None\n", - "if sagemaker_session_bucket is None and sess is not None:\n", - " # Set to default bucket if a bucket name is not given\n", - " sagemaker_session_bucket = sess.default_bucket()\n", - "\n", - "role = sagemaker.get_execution_role()\n", - "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n", - "\n", - "print(f\"Role arn: {role}\")\n", - "print(f\"Bucket: {sess.default_bucket()}\")\n", - "print(f\"Region: {sess.boto_region_name}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pre-processing\n", - "\n", - "We use the `datasets` library to pre-process the `sst2` dataset (Stanford Sentiment Treebank). After pre-processing, the dataset is uploaded to the `sagemaker_session_bucket` for use within the training job. The [sst2](https://nlp.stanford.edu/sentiment/index.html) dataset consists of 67349 training samples and _ testing samples of highly polar movie reviews." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from datasets import Dataset\n", - "from transformers import AutoTokenizer\n", - "import pandas as pd\n", - "import boto3\n", - "\n", - "# Tokenizer used in pre-processing\n", - "tokenizer_name = \"distilbert-base-uncased\"\n", - "\n", - "# S3 key prefix for the data\n", - "s3_prefix = \"DEMO-samples/datasets/sst\"\n", - "\n", - "# Download the SST2 data from s3\n", - "s3 = boto3.client(\"s3\")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", - " \"datasets/text/SST2/sst2.test\",\n", - " \"sst2.test\",\n", - ")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", - " \"datasets/text/SST2/sst2.train\",\n", - " \"sst2.train\",\n", - ")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", - " \"datasets/text/SST2/sst2.val\",\n", - " \"sst2.val\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tokenize sentences" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Download tokenizer\n", - "tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n", - "\n", - "\n", - "# Tokenizer helper function\n", - "def tokenize(batch):\n", - " return tokenizer(batch[\"text\"], padding=\"max_length\", truncation=True)\n", - "\n", - "\n", - "# Load dataset\n", - "test_df = pd.read_csv(\"sst2.test\", sep=\"delimiter\", header=None, engine=\"python\", names=[\"line\"])\n", - "train_df = pd.read_csv(\"sst2.train\", sep=\"delimiter\", header=None, engine=\"python\", names=[\"line\"])\n", - "\n", - "test_df[[\"label\", \"text\"]] = test_df[\"line\"].str.split(\" \", 1, expand=True)\n", - "train_df[[\"label\", \"text\"]] = train_df[\"line\"].str.split(\" \", 1, expand=True)\n", - "\n", - "test_df.drop(\"line\", axis=1, inplace=True)\n", - "train_df.drop(\"line\", axis=1, inplace=True)\n", - "\n", - "test_df[\"label\"] = pd.to_numeric(test_df[\"label\"], downcast=\"integer\")\n", - "train_df[\"label\"] = pd.to_numeric(train_df[\"label\"], downcast=\"integer\")\n", - "\n", - "train_dataset = Dataset.from_pandas(train_df)\n", - "test_dataset = Dataset.from_pandas(test_df)\n", - "\n", - "# Tokenize dataset\n", - "train_dataset = train_dataset.map(tokenize, batched=True)\n", - "test_dataset = test_dataset.map(tokenize, batched=True)\n", - "\n", - "# Set format for pytorch\n", - "train_dataset = train_dataset.rename_column(\"label\", \"labels\")\n", - "train_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n", - "\n", - "test_dataset = test_dataset.rename_column(\"label\", \"labels\")\n", - "test_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload data to `sagemaker_session_bucket`\n", - "\n", - "After processing the `datasets`, we use the `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload the dataset to S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import botocore\n", - "from datasets.filesystems import S3FileSystem\n", - "\n", - "s3 = S3FileSystem()\n", - "\n", - "# save train_dataset to s3\n", - "training_input_path = f\"s3://{sess.default_bucket()}/{s3_prefix}/train\"\n", - "train_dataset.save_to_disk(training_input_path, fs=s3)\n", - "\n", - "# save test_dataset to s3\n", - "test_input_path = f\"s3://{sess.default_bucket()}/{s3_prefix}/test\"\n", - "test_dataset.save_to_disk(test_input_path, fs=s3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fine-tune the model and start a SageMaker training job\n", - "\n", - "In order to create a SageMaker training job, we need a `HuggingFace` Estimator. The Estimator handles end-to-end Amazon SageMaker training and deployment tasks. In an Estimator, we define which fine-tuning script should be used as `entry_point`, which `instance_type` should be used, which `hyperparameters` are passed in, etc:\n", - "\n", - "\n", - "\n", - "```python\n", - "hf_estimator = HuggingFace(entry_point=\"train.py\",\n", - " source_dir=\"./scripts\",\n", - " base_job_name=\"huggingface-sdk-extension\",\n", - " instance_type=\"ml.p3.2xlarge\",\n", - " instance_count=1,\n", - " transformers_version=\"4.4\",\n", - " pytorch_version=\"1.6\",\n", - " py_version=\"py36\",\n", - " role=role,\n", - " hyperparameters = {\"epochs\": 1,\n", - " \"train_batch_size\": 32,\n", - " \"model_name\":\"distilbert-base-uncased\"\n", - " })\n", - "```\n", - "\n", - "When we create a SageMaker training job, SageMaker takes care of starting and managing all the required EC2 instances for us with the `huggingface` container, uploads the provided fine-tuning script `train.py`, and downloads the data from the `sagemaker_session_bucket` into the container at `/opt/ml/input/data`. Then, it starts the training job by running:\n", - "\n", - "```python\n", - "/opt/conda/bin/python train.py --epochs 1 --model_name distilbert-base-uncased --train_batch_size 32\n", - "```\n", - "\n", - "The `hyperparameters` defined in the `HuggingFace` estimator are passed in as named arguments. \n", - "\n", - "SageMaker provides useful properties about the training environment through various environment variables, including the following:\n", - "\n", - "* `SM_MODEL_DIR`: A string representing the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting.\n", - "\n", - "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n", - "\n", - "* `SM_CHANNEL_XXXX:` A string representing the path to the directory that contains the input data for the specified channel. For example, if you specify two input channels in the Hugging Face estimator's `fit()` call, named `train` and `test`, the environment variables `SM_CHANNEL_TRAIN` and `SM_CHANNEL_TEST` are set.\n", - "\n", - "\n", - "To run the training job locally, you can define `instance_type=\"local\"` or `instance_type=\"local_gpu\"` for GPU usage.\n", - "\n", - "_Note: local mode is not supported in SageMaker Studio._\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pygmentize ./scripts/train.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an Estimator and start a training job" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from sagemaker.huggingface import HuggingFace\n", - "\n", - "# Hyperparameters which are passed into the training job\n", - "hyperparameters = {\"epochs\": 1, \"train_batch_size\": 32, \"model_name\": \"distilbert-base-uncased\"}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "hf_estimator = HuggingFace(\n", - " entry_point=\"train.py\",\n", - " source_dir=\"./scripts\",\n", - " instance_type=\"ml.p3.2xlarge\",\n", - " instance_count=1,\n", - " role=role,\n", - " transformers_version=\"4.12\",\n", - " pytorch_version=\"1.9\",\n", - " py_version=\"py38\",\n", - " hyperparameters=hyperparameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Start the training job with the uploaded dataset as input\n", - "hf_estimator.fit({\"train\": training_input_path, \"test\": test_input_path})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the endpoint\n", - "\n", - "To deploy the endpoint, call `deploy()` on the HuggingFace estimator object, passing in the desired number of instances and instance type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "predictor = hf_estimator.deploy(1, \"ml.p3.2xlarge\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then use the returned predictor object to perform inference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "sentiment_input = {\"inputs\": \"I love using the new Inference DLC.\"}\n", - "\n", - "predictor.predict(sentiment_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the fine-tuned model classifies the test sentence \"I love using the new Inference DLC.\" as having positive sentiment with 98% probability!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, delete the endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "predictor.delete_endpoint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Estimator Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "print(f\"Container image used for training job: \\n{hf_estimator.image_uri}\\n\")\n", - "print(f\"S3 URI where the trained model is located: \\n{hf_estimator.model_data}\\n\")\n", - "print(f\"Latest training job name for this estimator: \\n{hf_estimator.latest_training_job.name}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "hf_estimator.sagemaker_session.logs_for_job(hf_estimator.latest_training_job.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Attach a previous training job to an estimator\n", - "\n", - "In SageMaker, you can attach a previous training job to an estimator to continue training, get results, etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from sagemaker.estimator import Estimator\n", - "\n", - "# Uncomment the following lines and supply your training job name\n", - "\n", - "# old_training_job_name = \"\"\n", - "# hf_estimator_loaded = Estimator.attach(old_training_job_name)\n", - "# hf_estimator_loaded.model_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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"cells": [ - { - "cell_type": "markdown", - "id": "9b08c378", - "metadata": { - "papermill": { - "duration": 0.018505, - "end_time": "2021-06-07T00:09:44.379517", - "exception": false, - "start_time": "2021-06-07T00:09:44.361012", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Customer Churn Prediction with XGBoost\n" - ] - }, - { - "cell_type": "markdown", - "id": "1b98b6df", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/introduction_to_applying_machine_learning|xgboost_customer_churn|xgboost_customer_churn.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "0bd14a6e", - "metadata": { - "papermill": { - "duration": 0.018505, - "end_time": "2021-06-07T00:09:44.379517", - "exception": false, - "start_time": "2021-06-07T00:09:44.361012", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "_**Using Gradient Boosted Trees to Predict Mobile Customer Departure**_\n", - "\n", - "---\n", - "\n", - "---\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 8 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Background](#Background)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Host](#Host)\n", - " 1. [Evaluate](#Evaluate)\n", - " 1. [Relative cost of errors](#Relative-cost-of-errors)\n", - "1. [Extensions](#Extensions)\n", - "\n", - "---\n", - "\n", - "## Background\n", - "\n", - "_This notebook has been adapted from an [AWS blog post](https://aws.amazon.com/blogs/ai/predicting-customer-churn-with-amazon-machine-learning/)_\n", - "\n", - "Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. This notebook describes using machine learning (ML) for the automated identification of unhappy customers, also known as customer churn prediction. ML models rarely give perfect predictions though, so this notebook is also about how to incorporate the relative costs of prediction mistakes when determining the financial outcome of using ML.\n", - "\n", - "We use a familiar example of churn: leaving a mobile phone operator. Seems like one can always find fault with their provider du jour! And if the provider knows that a customer is thinking of leaving, it can offer timely incentives - such as a phone upgrade or perhaps having a new feature activated – and the customer may stick around. Incentives are often much more cost-effective than losing and reacquiring a customer.\n", - "\n", - "---\n", - "\n", - "## Setup\n", - "\n", - "_This notebook was created and tested on a `ml.m4.xlarge` notebook instance._\n", - "\n", - "Let's start by updating the required packages i.e. SageMaker Python SDK, `pandas` and `numpy`, and specifying:\n", - "\n", - "- The S3 bucket and prefix that you want to use for training and model data. This should be within the same region as the Notebook Instance or Studio, training, and hosting.\n", - "- The IAM role ARN used to give training and hosting access to your data. See the documentation for how to create these. Note: if more than one role is required for notebook instances, training, and/or hosting, please replace the boto regexp with the appropriate full IAM role ARN string(s)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f00baad", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "!{sys.executable} -m pip install sagemaker pandas numpy --upgrade\n", - "!pip3 install -U sagemaker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4c1b3c0", - "metadata": { - "isConfigCell": true, - "papermill": { - "duration": 1.209916, - "end_time": "2021-06-07T00:09:45.607159", - "exception": false, - "start_time": "2021-06-07T00:09:44.397243", - "status": "completed" - }, - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "import sagemaker\n", - "\n", - "sess = sagemaker.Session()\n", - "bucket = sess.default_bucket()\n", - "prefix = \"sagemaker/DEMO-xgboost-churn\"\n", - "\n", - "# Define IAM role\n", - "import boto3\n", - "import re\n", - "from sagemaker import get_execution_role\n", - "\n", - "role = get_execution_role()" - ] - }, - { - "cell_type": "markdown", - "id": "e02e6dbb", - "metadata": { - "papermill": { - "duration": 0.017739, - "end_time": "2021-06-07T00:09:45.683322", - "exception": false, - "start_time": "2021-06-07T00:09:45.665583", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Next, we'll import the Python libraries we'll need for the remainder of the example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08714702", - "metadata": { - "papermill": { - "duration": 0.666347, - "end_time": "2021-06-07T00:09:46.367361", - "exception": false, - "start_time": "2021-06-07T00:09:45.701014", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import io\n", - "import os\n", - "import sys\n", - "import time\n", - "import json\n", - "from IPython.display import display\n", - "from time import strftime, gmtime\n", - "from sagemaker.inputs import TrainingInput\n", - "from sagemaker.serializers import CSVSerializer" - ] - }, - { - "cell_type": "markdown", - "id": "6c810d34", - "metadata": { - "papermill": { - "duration": 0.021555, - "end_time": "2021-06-07T00:09:46.406743", - "exception": false, - "start_time": "2021-06-07T00:09:46.385188", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "---\n", - "## Data\n", - "\n", - "Mobile operators have historical records on which customers ultimately ended up churning and which continued using the service. We can use this historical information to construct an ML model of one mobile operator’s churn using a process called training. After training the model, we can pass the profile information of an arbitrary customer (the same profile information that we used to train the model) to the model, and have the model predict whether this customer is going to churn. Of course, we expect the model to make mistakes. After all, predicting the future is tricky business! But we'll learn how to deal with prediction errors.\n", - "\n", - "The dataset we use is publicly available and was mentioned in the book [Discovering Knowledge in Data](https://www.amazon.com/dp/0470908742/) by Daniel T. Larose. It is attributed by the author to the University of California Irvine Repository of Machine Learning Datasets. Let's download and read that dataset in now:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f01c890", - "metadata": { - "papermill": { - "duration": 1.671215, - "end_time": "2021-06-07T00:09:48.098151", - "exception": false, - "start_time": "2021-06-07T00:09:46.426936", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "s3 = boto3.client(\"s3\")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", - " \"datasets/tabular/synthetic/churn.txt\",\n", - " \"churn.txt\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b89ecb3f", - "metadata": { - "papermill": { - "duration": 0.06925, - "end_time": "2021-06-07T00:09:48.185909", - "exception": false, - "start_time": "2021-06-07T00:09:48.116659", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "churn = pd.read_csv(\"./churn.txt\")\n", - "pd.set_option(\"display.max_columns\", 500)\n", - "churn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d3c3733", - "metadata": {}, - "outputs": [], - "source": [ - "len(churn.columns)" - ] - }, - { - "cell_type": "markdown", - "id": "a1380adb", - "metadata": { - "papermill": { - "duration": 0.019033, - "end_time": "2021-06-07T00:09:48.224277", - "exception": false, - "start_time": "2021-06-07T00:09:48.205244", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "By modern standards, it’s a relatively small dataset, with only 5,000 records, where each record uses 21 attributes to describe the profile of a customer of an unknown US mobile operator. The attributes are:\n", - "\n", - "- `State`: the US state in which the customer resides, indicated by a two-letter abbreviation; for example, OH or NJ\n", - "- `Account Length`: the number of days that this account has been active\n", - "- `Area Code`: the three-digit area code of the corresponding customer’s phone number\n", - "- `Phone`: the remaining seven-digit phone number\n", - "- `Int’l Plan`: whether the customer has an international calling plan: yes/no\n", - "- `VMail Plan`: whether the customer has a voice mail feature: yes/no\n", - "- `VMail Message`: the average number of voice mail messages per month\n", - "- `Day Mins`: the total number of calling minutes used during the day\n", - "- `Day Calls`: the total number of calls placed during the day\n", - "- `Day Charge`: the billed cost of daytime calls\n", - "- `Eve Mins, Eve Calls, Eve Charge`: the billed cost for calls placed during the evening\n", - "- `Night Mins`, `Night Calls`, `Night Charge`: the billed cost for calls placed during nighttime\n", - "- `Intl Mins`, `Intl Calls`, `Intl Charge`: the billed cost for international calls\n", - "- `CustServ Calls`: the number of calls placed to Customer Service\n", - "- `Churn?`: whether the customer left the service: true/false\n", - "\n", - "The last attribute, `Churn?`, is known as the target attribute: the attribute that we want the ML model to predict. Because the target attribute is binary, our model will be performing binary prediction, also known as binary classification.\n", - "\n", - "Let's begin exploring the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a35b9f65", - "metadata": { - "papermill": { - "duration": 2.396119, - "end_time": "2021-06-07T00:09:50.639536", - "exception": false, - "start_time": "2021-06-07T00:09:48.243417", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Frequency tables for each categorical feature\n", - "for column in churn.select_dtypes(include=[\"object\"]).columns:\n", - " display(pd.crosstab(index=churn[column], columns=\"% observations\", normalize=\"columns\"))\n", - "\n", - "# Histograms for each numeric features\n", - "display(churn.describe())\n", - "%matplotlib inline\n", - "hist = churn.hist(bins=30, sharey=True, figsize=(10, 10))" - ] - }, - { - "cell_type": "markdown", - "id": "2046fbb8", - "metadata": { - "papermill": { - "duration": 0.022357, - "end_time": "2021-06-07T00:09:50.685414", - "exception": false, - "start_time": "2021-06-07T00:09:50.663057", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We can see immediately that:\n", - "- `State` appears to be quite evenly distributed.\n", - "- `Phone` takes on too many unique values to be of any practical use. It's possible that parsing out the prefix could have some value, but without more context on how these are allocated, we should avoid using it.\n", - "- Most of the numeric features are surprisingly nicely distributed, with many showing bell-like `gaussianity`. `VMail Message` is a notable exception (and `Area Code` showing up as a feature we should convert to non-numeric)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28552f05", - "metadata": { - "papermill": { - "duration": 0.030406, - "end_time": "2021-06-07T00:09:50.738287", - "exception": false, - "start_time": "2021-06-07T00:09:50.707881", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "churn = churn.drop(\"Phone\", axis=1)\n", - "churn[\"Area Code\"] = churn[\"Area Code\"].astype(object)" - ] - }, - { - "cell_type": "markdown", - "id": "197581c1", - "metadata": { - "papermill": { - "duration": 0.022422, - "end_time": "2021-06-07T00:09:50.783342", - "exception": false, - "start_time": "2021-06-07T00:09:50.760920", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Next let's look at the relationship between each of the features and our target variable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5cee110f", - "metadata": { - "papermill": { - "duration": 4.645229, - "end_time": "2021-06-07T00:09:55.451149", - "exception": false, - "start_time": "2021-06-07T00:09:50.805920", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "for column in churn.select_dtypes(include=[\"object\"]).columns:\n", - " if column != \"Churn?\":\n", - " display(pd.crosstab(index=churn[column], columns=churn[\"Churn?\"], normalize=\"columns\"))\n", - "\n", - "for column in churn.select_dtypes(exclude=[\"object\"]).columns:\n", - " print(column)\n", - " hist = churn[[column, \"Churn?\"]].hist(by=\"Churn?\", bins=30)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1e491a6", - "metadata": { - "papermill": { - "duration": 18.552066, - "end_time": "2021-06-07T00:10:14.041717", - "exception": false, - "start_time": "2021-06-07T00:09:55.489651", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "display(churn.corr(numeric_only=True))\n", - "pd.plotting.scatter_matrix(churn, figsize=(12, 12))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3217f3c5", - "metadata": { - "papermill": { - "duration": 0.050687, - "end_time": "2021-06-07T00:10:14.143830", - "exception": false, - "start_time": "2021-06-07T00:10:14.093143", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We see several features that essentially have 100% correlation with one another. Including these feature pairs in some machine learning algorithms can create catastrophic problems, while in others it will only introduce minor redundancy and bias. Let's remove one feature from each of the highly correlated pairs: `Day Charge` from the pair with `Day Mins`, `Night Charge` from the pair with `Night Mins`, `Intl Charge` from the pair with `Intl Mins`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c904a9d2", - "metadata": { - "papermill": { - "duration": 0.057009, - "end_time": "2021-06-07T00:10:14.251061", - "exception": false, - "start_time": "2021-06-07T00:10:14.194052", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "churn = churn.drop([\"Day Charge\", \"Eve Charge\", \"Night Charge\", \"Intl Charge\"], axis=1)" - ] - }, - { - "cell_type": "markdown", - "id": "a3ce9711", - "metadata": { - "papermill": { - "duration": 0.050512, - "end_time": "2021-06-07T00:10:14.352000", - "exception": false, - "start_time": "2021-06-07T00:10:14.301488", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now that we've cleaned up our dataset, let's determine which algorithm to use. As mentioned above, there appear to be some variables where both high and low (but not intermediate) values are predictive of churn. In order to accommodate this in an algorithm like linear regression, we'd need to generate polynomial (or bucketed) terms. Instead, let's attempt to model this problem using gradient boosted trees. Amazon SageMaker provides an XGBoost container that we can use to train in a managed, distributed setting, and then host as a real-time prediction endpoint. XGBoost uses gradient boosted trees which naturally account for non-linear relationships between features and the target variable, as well as accommodating complex interactions between features.\n", - "\n", - "Amazon SageMaker XGBoost can train on data in either a CSV or LibSVM format. For this example, we'll stick with CSV. It should:\n", - "- Have the predictor variable in the first column\n", - "- Not have a header row\n", - "\n", - "But first, let's convert our categorical features into numeric features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b3ea731", - "metadata": { - "papermill": { - "duration": 0.07096, - "end_time": "2021-06-07T00:10:14.473383", - "exception": false, - "start_time": "2021-06-07T00:10:14.402423", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "model_data = pd.get_dummies(churn)\n", - "model_data = pd.concat(\n", - " [model_data[\"Churn?_True.\"], model_data.drop([\"Churn?_False.\", \"Churn?_True.\"], axis=1)], axis=1\n", - ")\n", - "model_data = model_data.astype(float)" - ] - }, - { - "cell_type": "markdown", - "id": "664ad1dc", - "metadata": { - "papermill": { - "duration": 0.050777, - "end_time": "2021-06-07T00:10:14.574494", - "exception": false, - "start_time": "2021-06-07T00:10:14.523717", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "And now let's split the data into training, validation, and test sets. This will help prevent us from overfitting the model, and allow us to test the model's accuracy on data it hasn't already seen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "298362cf", - "metadata": { - "papermill": { - "duration": 0.246303, - "end_time": "2021-06-07T00:10:14.871668", - "exception": false, - "start_time": "2021-06-07T00:10:14.625365", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "train_data, validation_data, test_data = np.split(\n", - " model_data.sample(frac=1, random_state=1729),\n", - " [int(0.7 * len(model_data)), int(0.9 * len(model_data))],\n", - ")\n", - "train_data.to_csv(\"train.csv\", header=False, index=False)\n", - "validation_data.to_csv(\"validation.csv\", header=False, index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6a5d489", - "metadata": {}, - "outputs": [], - "source": [ - "len(train_data.columns)" - ] - }, - { - "cell_type": "markdown", - "id": "31cd03d7", - "metadata": { - "papermill": { - "duration": 0.050591, - "end_time": "2021-06-07T00:10:14.972677", - "exception": false, - "start_time": "2021-06-07T00:10:14.922086", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now we'll upload these files to S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b8d288f", - "metadata": { - "papermill": { - "duration": 0.79455, - "end_time": "2021-06-07T00:10:15.817950", - "exception": false, - "start_time": "2021-06-07T00:10:15.023400", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "boto3.Session().resource(\"s3\").Bucket(bucket).Object(\n", - " os.path.join(prefix, \"train/train.csv\")\n", - ").upload_file(\"train.csv\")\n", - "boto3.Session().resource(\"s3\").Bucket(bucket).Object(\n", - " os.path.join(prefix, \"validation/validation.csv\")\n", - ").upload_file(\"validation.csv\")" - ] - }, - { - "cell_type": "markdown", - "id": "15beea62", - "metadata": { - "papermill": { - "duration": 0.050157, - "end_time": "2021-06-07T00:10:15.918579", - "exception": false, - "start_time": "2021-06-07T00:10:15.868422", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "---\n", - "## Train\n", - "\n", - "Moving onto training, first we'll need to specify the locations of the XGBoost algorithm containers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79682277", - "metadata": { - "papermill": { - "duration": 0.071985, - "end_time": "2021-06-07T00:10:16.040629", - "exception": false, - "start_time": "2021-06-07T00:10:15.968644", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "container = sagemaker.image_uris.retrieve(\"xgboost\", sess.boto_region_name, \"1.7-1\")\n", - "display(container)" - ] - }, - { - "cell_type": "markdown", - "id": "6be2c94d", - "metadata": { - "papermill": { - "duration": 0.050814, - "end_time": "2021-06-07T00:10:16.142405", - "exception": false, - "start_time": "2021-06-07T00:10:16.091591", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Then, because we're training with the CSV file format, we'll create `TrainingInput`s that our training function can use as a pointer to the files in S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb3b53d1", - "metadata": { - "papermill": { - "duration": 0.05658, - "end_time": "2021-06-07T00:10:16.249848", - "exception": false, - "start_time": "2021-06-07T00:10:16.193268", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "s3_input_train = TrainingInput(\n", - " s3_data=\"s3://{}/{}/train\".format(bucket, prefix), content_type=\"csv\"\n", - ")\n", - "s3_input_validation = TrainingInput(\n", - " s3_data=\"s3://{}/{}/validation/\".format(bucket, prefix), content_type=\"csv\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d0e18e91", - "metadata": { - "papermill": { - "duration": 0.050343, - "end_time": "2021-06-07T00:10:16.350919", - "exception": false, - "start_time": "2021-06-07T00:10:16.300576", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now, we can specify a few parameters like what type of training instances we'd like to use and how many, as well as our XGBoost hyperparameters. A few key hyperparameters are:\n", - "- `max_depth` controls how deep each tree within the algorithm can be built. Deeper trees can lead to better fit, but are more computationally expensive and can lead to overfitting. There is typically some trade-off in model performance that needs to be explored between numerous shallow trees and a smaller number of deeper trees.\n", - "- `subsample` controls sampling of the training data. This technique can help reduce overfitting, but setting it too low can also starve the model of data.\n", - "- `num_round` controls the number of boosting rounds. This is essentially the subsequent models that are trained using the residuals of previous iterations. Again, more rounds should produce a better fit on the training data, but can be computationally expensive or lead to overfitting.\n", - "- `eta` controls how aggressive each round of boosting is. Larger values lead to more conservative boosting.\n", - "- `gamma` controls how aggressively trees are grown. Larger values lead to more conservative models.\n", - "\n", - "More detail on XGBoost's hyper-parameters can be found on their GitHub [page](https://github.com/dmlc/xgboost/blob/master/doc/parameter.md)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3aea5a5c", - "metadata": { - "papermill": { - "duration": 252.035305, - "end_time": "2021-06-07T00:14:28.436818", - "exception": false, - "start_time": "2021-06-07T00:10:16.401513", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "sess = sagemaker.Session()\n", - "\n", - "xgb = sagemaker.estimator.Estimator(\n", - " container,\n", - " role,\n", - " instance_count=1,\n", - " instance_type=\"ml.m4.xlarge\",\n", - " output_path=\"s3://{}/{}/output\".format(bucket, prefix),\n", - " sagemaker_session=sess,\n", - ")\n", - "xgb.set_hyperparameters(\n", - " max_depth=5,\n", - " eta=0.2,\n", - " gamma=4,\n", - " min_child_weight=6,\n", - " subsample=0.8,\n", - " verbosity=0,\n", - " objective=\"binary:logistic\",\n", - " num_round=100,\n", - ")\n", - "\n", - "xgb.fit({\"train\": s3_input_train, \"validation\": s3_input_validation})" - ] - }, - { - "cell_type": "markdown", - "id": "171515b0", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "---\n", - "## Host\n", - "\n", - "Now that we've trained the algorithm, let's create a model and deploy it to a hosted endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f0232f5", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "xgb_predictor = xgb.deploy(\n", - " initial_instance_count=1, instance_type=\"ml.m4.xlarge\", serializer=CSVSerializer()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "29ab4cae", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "### Evaluate\n", - "\n", - "Now that we have a hosted endpoint running, we can make real-time predictions from our model very easily, simply by making a `http` POST request. But first, we'll need to set up serializers and deserializers for passing our `test_data` NumPy arrays to the model behind the endpoint." - ] - }, - { - "cell_type": "markdown", - "id": "6f03c792", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "Now, we'll use a simple function to:\n", - "1. Loop over our test dataset\n", - "1. Split it into mini-batches of rows \n", - "1. Convert those mini-batchs to CSV string payloads\n", - "1. Retrieve mini-batch predictions by invoking the XGBoost endpoint\n", - "1. Collect predictions and convert from the CSV output our model provides into a NumPy array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42d1317f", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def predict(data, rows=500):\n", - " split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n", - " predictions = \"\"\n", - " for array in split_array:\n", - " predictions = \"\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n", - "\n", - " return predictions.split(\"\\n\")[:-1]\n", - "\n", - "\n", - "predictions = predict(test_data.to_numpy()[:, 1:])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "745e08d2", - "metadata": {}, - "outputs": [], - "source": [ - "predictions = np.array([float(num) for num in predictions])\n", - "print(predictions)" - ] - }, - { - "cell_type": "markdown", - "id": "b35e2bf7", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "There are many ways to compare the performance of a machine learning model, but let's start by simply by comparing actual to predicted values. In this case, we're simply predicting whether the customer churned (`1`) or not (`0`), which produces a confusion matrix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d69d58f4", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "pd.crosstab(\n", - " index=test_data.iloc[:, 0],\n", - " columns=np.round(predictions),\n", - " rownames=[\"actual\"],\n", - " colnames=[\"predictions\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "58cc9077", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "_Note, due to randomized elements of the algorithm, your results may differ slightly._\n", - "\n", - "Of the 48 churners, we've correctly predicted 39 of them (true positives). We also incorrectly predicted 4 customers would churn who then ended up not doing so (false positives). There are also 9 customers who ended up churning, that we predicted would not (false negatives).\n", - "\n", - "An important point here is that because of the `np.round()` function above, we are using a simple threshold (or cutoff) of 0.5. Our predictions from `xgboost` yield continuous values between 0 and 1, and we force them into the binary classes that we began with. However, because a customer that churns is expected to cost the company more than proactively trying to retain a customer who we think might churn, we should consider lowering this cutoff. That will almost certainly increase the number of false positives, but it can also be expected to increase the number of true positives and reduce the number of false negatives.\n", - "\n", - "To get a rough intuition here, let's look at the continuous values of our predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2cc8123e", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "plt.hist(predictions)\n", - "plt.xlabel(\"Predicted churn probability\")\n", - "plt.ylabel(\"Number of customers\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "55ce4027", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "The continuous valued predictions coming from our model tend to skew toward 0 or 1, but there is sufficient mass between 0.1 and 0.9 that adjusting the cutoff should indeed shift a number of customers' predictions. For example..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dce5dca1", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "pd.crosstab(index=test_data.iloc[:, 0], columns=np.where(predictions > 0.3, 1, 0))" - ] - }, - { - "cell_type": "markdown", - "id": "18f2c2f1", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "We can see that lowering the cutoff from 0.5 to 0.3 results in 1 more true positive, 3 more false positives, and 1 fewer false negative. The numbers are small overall here, but that's 6-10% of customers overall that are shifting because of a change to the cutoff. Was this the right decision? We may end up retaining 3 extra customers, but we also unnecessarily incentivized 5 more customers who would have stayed anyway. Determining optimal cutoffs is a key step in properly applying machine learning in a real-world setting. Let's discuss this more broadly and then apply a specific, hypothetical solution for our current problem.\n", - "\n", - "### Relative cost of errors\n", - "\n", - "Any practical binary classification problem is likely to produce a similarly sensitive cutoff. That by itself isn’t a problem. After all, if the scores for two classes are really easy to separate, the problem probably isn’t very hard to begin with and might even be solvable with deterministic rules instead of ML.\n", - "\n", - "More important, if we put an ML model into production, there are costs associated with the model erroneously assigning false positives and false negatives. We also need to look at similar costs associated with correct predictions of true positives and true negatives. Because the choice of the cutoff affects all four of these statistics, we need to consider the relative costs to the business for each of these four outcomes for each prediction.\n", - "\n", - "#### Assigning costs\n", - "\n", - "What are the costs for our problem of mobile operator churn? The costs, of course, depend on the specific actions that the business takes. Let's make some assumptions here.\n", - "\n", - "First, assign the true negatives the cost of \\$0. Our model essentially correctly identified a happy customer in this case, and we don’t need to do anything.\n", - "\n", - "False negatives are the most problematic, because they incorrectly predict that a churning customer will stay. We lose the customer and will have to pay all the costs of acquiring a replacement customer, including foregone revenue, advertising costs, administrative costs, point of sale costs, and likely a phone hardware subsidy. A quick search on the Internet reveals that such costs typically run in the hundreds of dollars so, for the purposes of this example, let's assume \\$500. This is the cost of false negatives.\n", - "\n", - "Finally, for customers that our model identifies as churning, let's assume a retention incentive in the amount of \\\\$100. If a provider offered a customer such a concession, they may think twice before leaving. This is the cost of both true positive and false positive outcomes. In the case of false positives (the customer is happy, but the model mistakenly predicted churn), we will “waste” the \\\\$100 concession. We probably could have spent that \\$100 more effectively, but it's possible we increased the loyalty of an already loyal customer, so that’s not so bad." - ] - }, - { - "cell_type": "markdown", - "id": "a51ea034", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "#### Finding the optimal cutoff\n", - "\n", - "It’s clear that false negatives are substantially more costly than false positives. Instead of optimizing for error based on the number of customers, we should be minimizing a cost function that looks like this:\n", - "\n", - "```\n", - "$500 * FN(C) + $0 * TN(C) + $100 * FP(C) + $100 * TP(C)\n", - "```\n", - "\n", - "FN(C) means that the false negative percentage is a function of the cutoff, C, and similar for TN, FP, and TP. We need to find the cutoff, C, where the result of the expression is smallest.\n", - "\n", - "A straightforward way to do this is to simply run a simulation over numerous possible cutoffs. We test 100 possible values in the for-loop below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "324c9f5c", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cutoffs = np.arange(0.01, 1, 0.01)\n", - "costs = []\n", - "for c in cutoffs:\n", - " costs.append(\n", - " np.sum(\n", - " np.sum(\n", - " np.array([[0, 100], [500, 100]])\n", - " * pd.crosstab(index=test_data.iloc[:, 0], columns=np.where(predictions > c, 1, 0))\n", - " )\n", - " )\n", - " )\n", - "\n", - "costs = np.array(costs)\n", - "plt.plot(cutoffs, costs)\n", - "plt.xlabel(\"Cutoff\")\n", - "plt.ylabel(\"Cost\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae213bd8", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "print(\n", - " \"Cost is minimized near a cutoff of:\",\n", - " cutoffs[np.argmin(costs)],\n", - " \"for a cost of:\",\n", - " np.min(costs),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "54e86315", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "The above chart shows how picking a threshold too low results in costs skyrocketing as all customers are given a retention incentive. Meanwhile, setting the threshold too high results in too many lost customers, which ultimately grows to be nearly as costly. The overall cost can be minimized at \\\\$8400 by setting the cutoff to 0.46, which is substantially better than the \\$20k+ we would expect to lose by not taking any action." - ] - }, - { - "cell_type": "markdown", - "id": "ce4a0e5b", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "---\n", - "## Extensions\n", - "\n", - "This notebook showcased how to build a model that predicts whether a customer is likely to churn, and then how to optimally set a threshold that accounts for the cost of true positives, false positives, and false negatives. There are several means of extending it including:\n", - "- Some customers who receive retention incentives will still churn. Including a probability of churning despite receiving an incentive in our cost function would provide a better ROI on our retention programs.\n", - "- Customers who switch to a lower-priced plan or who deactivate a paid feature represent different kinds of churn that could be modeled separately.\n", - "- Modeling the evolution of customer behavior. If usage is dropping and the number of calls placed to Customer Service is increasing, you are more likely to experience churn then if the trend is the opposite. A customer profile should incorporate behavior trends.\n", - "- Actual training data and monetary cost assignments could be more complex.\n", - "- Multiple models for each type of churn could be needed.\n", - "\n", - "Regardless of additional complexity, similar principles described in this notebook are likely applied." - ] - }, - { - "cell_type": "markdown", - "id": "ced6f363", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "### (Optional) Clean-up\n", - "\n", - "If you're ready to be done with this notebook, please run the cell below. This will remove the hosted endpoint you created and avoid any charges from a stray instance being left on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16febdfe", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "xgb_predictor.delete_endpoint()" - ] - }, - { - "cell_type": "markdown", - "id": "f32cb035", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. 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"memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - } - ], - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.", - "papermill": { - "default_parameters": {}, - "duration": 311.728138, - "end_time": "2021-06-07T00:14:55.273560", - "environment_variables": {}, - "exception": true, - "input_path": "xgboost_customer_churn.ipynb", - "output_path": "/opt/ml/processing/output/xgboost_customer_churn-2021-06-07-00-06-03.ipynb", - "parameters": { - "kms_key": "arn:aws:kms:us-west-2:521695447989:key/6e9984db-50cf-4c7e-926c-877ec47a8b25" - }, - "start_time": "2021-06-07T00:09:43.545422", - "version": "2.3.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability.ipynb b/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability.ipynb deleted file mode 100644 index 3b2255959c..0000000000 --- a/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability.ipynb +++ /dev/null @@ -1,1361 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fairness and Explainability with SageMaker Clarify" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Runtime\n", - "\n", - "This notebook takes approximately 30 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Overview](#Overview)\n", - "1. [Prerequisites and Data](#Prerequisites-and-Data)\n", - " 1. [Import Libraries](#Import-Libraries)\n", - " 1. [Set Configurations](#Set-Configurations)\n", - " 1. [Download data](#Download-data)\n", - " 1. [Loading the data: Adult Dataset](#Loading-the-data:-Adult-Dataset) \n", - " 1. [Data inspection](#Data-inspection) \n", - " 1. [Encode and Upload the Dataset](#Encode-and-Upload-the-Dataset) \n", - "1. [Train and Deploy XGBoost Model](#Train-XGBoost-Model)\n", - " 1. [Train Model](#Train-Model)\n", - " 1. [Create Model](#Create-Model)\n", - "1. [Amazon SageMaker Clarify](#Amazon-SageMaker-Clarify)\n", - " 1. [Detecting Bias](#Detecting-Bias)\n", - " 1. [Writing DataConfig](#Writing-DataConfig)\n", - " 1. [Writing ModelConfig](#Writing-ModelConfig)\n", - " 1. [Writing ModelPredictedLabelConfig](#Writing-ModelPredictedLabelConfig)\n", - " 1. [Writing BiasConfig](#Writing-BiasConfig)\n", - " 1. [Pre-training Bias](#Pre-training-Bias)\n", - " 1. [Post-training Bias](#Post-training-Bias)\n", - " 1. [Viewing the Bias Report](#Viewing-the-Bias-Report)\n", - " 1. [Explaining Predictions](#Explaining-Predictions)\n", - " 1. [Viewing the Explainability Report](#Viewing-the-Explainability-Report)\n", - " 1. [Analysis of local explanations](#Analysis-of-local-explanations)\n", - "1. [Clean Up](#Clean-Up)\n", - "\n", - "## Overview\n", - "Amazon SageMaker Clarify helps improve your machine learning models by detecting potential bias and helping explain how these models make predictions. The fairness and explainability functionality provided by SageMaker Clarify takes a step towards enabling AWS customers to build trustworthy and understandable machine learning models. The product comes with the tools to help you with the following tasks.\n", - "\n", - "* Measure biases that can occur during each stage of the ML lifecycle (data collection, model training and tuning, and monitoring of ML models deployed for inference).\n", - "* Generate model governance reports targeting risk and compliance teams and external regulators.\n", - "* Provide explanations of the data, models, and monitoring used to assess predictions.\n", - "\n", - "This sample notebook walks you through: \n", - "1. Key terms and concepts needed to understand SageMaker Clarify\n", - "1. Measuring the pre-training bias of a dataset and post-training bias of a model\n", - "1. Explaining the importance of the various input features on the model's decision\n", - "1. Accessing the reports through SageMaker Studio if you have an instance set up.\n", - "\n", - "In doing so, the notebook first trains a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) to launch SageMaker Clarify jobs to analyze an example dataset in CSV format. \n", - "\n", - "SageMaker Clarify also supports analyzing dataset in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb). Additionally, there is a [peer example available](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_boto3.ipynb) that utilizes the [AWS SDK for Python](https://aws.amazon.com/sdk-for-python/) to launch SageMaker Clarify jobs to analyze data in CSV format. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and Data\n", - "### Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import boto3\n", - "from datetime import datetime\n", - "from sagemaker import get_execution_role, session" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set Configurations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Region: ap-south-1\n", - "Role: arn:aws:iam::000000000000:role/service-role/SMClarifySageMaker-ExecutionRole\n" - ] - } - ], - "source": [ - "# Initialize sagemaker session\n", - "sagemaker_session = session.Session()\n", - "\n", - "region = sagemaker_session.boto_region_name\n", - "print(f\"Region: {region}\")\n", - "\n", - "role = get_execution_role()\n", - "print(f\"Role: {role}\")\n", - "\n", - "bucket = sagemaker_session.default_bucket()\n", - "\n", - "prefix = \"sagemaker/DEMO-sagemaker-clarify\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download data\n", - "Data Source: [https://archive.ics.uci.edu/ml/machine-learning-databases/adult/](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/)\n", - "\n", - "Let's __download__ the data and save it in the local folder with the name adult.data and adult.test from UCI repository$^{[2]}$.\n", - "\n", - "$^{[2]}$Dua Dheeru, and Efi Karra Taniskidou. \"[UCI Machine Learning Repository](http://archive.ics.uci.edu/ml)\". Irvine, CA: University of California, School of Information and Computer Science (2017)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "adult.data already on disk.\n", - "adult.test already on disk.\n" - ] - } - ], - "source": [ - "from sagemaker.s3 import S3Downloader\n", - "\n", - "adult_columns = [\n", - " \"Age\",\n", - " \"Workclass\",\n", - " \"fnlwgt\",\n", - " \"Education\",\n", - " \"Education-Num\",\n", - " \"Marital Status\",\n", - " \"Occupation\",\n", - " \"Relationship\",\n", - " \"Ethnic group\",\n", - " \"Sex\",\n", - " \"Capital Gain\",\n", - " \"Capital Loss\",\n", - " \"Hours per week\",\n", - " \"Country\",\n", - " \"Target\",\n", - "]\n", - "if not os.path.isfile(\"adult.data\"):\n", - " S3Downloader.download(\n", - " s3_uri=\"s3://{}/{}\".format(\n", - " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/uci_adult/adult.data\"\n", - " ),\n", - " local_path=\"./\",\n", - " sagemaker_session=sagemaker_session,\n", - " )\n", - " print(\"adult.data saved!\")\n", - "else:\n", - " print(\"adult.data already on disk.\")\n", - "\n", - "if not os.path.isfile(\"adult.test\"):\n", - " S3Downloader.download(\n", - " s3_uri=\"s3://{}/{}\".format(\n", - " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/uci_adult/adult.test\"\n", - " ),\n", - " local_path=\"./\",\n", - " sagemaker_session=sagemaker_session,\n", - " )\n", - " print(\"adult.test saved!\")\n", - "else:\n", - " print(\"adult.test already on disk.\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the data: Adult Dataset\n", - "From the UCI repository of machine learning datasets, this database contains 14 features concerning demographic characteristics of 45,222 rows (32,561 for training and 12,661 for testing). The task is to predict whether a person has a yearly income that is more or less than $50,000.\n", - "\n", - "Here are the features and their possible values:\n", - "\n", - "1. **Age**: continuous.\n", - "1. **Workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n", - "1. **Fnlwgt**: continuous (the number of people the census takers believe that observation represents).\n", - "1. **Education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n", - "1. **Education-num**: continuous.\n", - "1. **Marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n", - "1. **Occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n", - "1. **Relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n", - "1. **Ethnic group**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n", - "1. **Sex**: Female, Male.\n", - " * **Note**: this data is extracted from the 1994 Census and enforces a binary option on Sex\n", - "1. **Capital-gain**: continuous.\n", - "1. **Capital-loss**: continuous.\n", - "1. **Hours-per-week**: continuous.\n", - "1. **Native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n", - "\n", - "Next, we specify our binary prediction task: \n", - "\n", - "15. **Target**: <=50,000, >$50,000." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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AgeWorkclassfnlwgtEducationEducation-NumMarital StatusOccupationRelationshipEthnic groupSexCapital GainCapital LossHours per weekCountryTarget
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
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" - ], - "text/plain": [ - " Age Workclass fnlwgt Education Education-Num \\\n", - "0 39 State-gov 77516 Bachelors 13 \n", - "1 50 Self-emp-not-inc 83311 Bachelors 13 \n", - "2 38 Private 215646 HS-grad 9 \n", - "3 53 Private 234721 11th 7 \n", - "4 28 Private 338409 Bachelors 13 \n", - "\n", - " Marital Status Occupation Relationship Ethnic group Sex \\\n", - "0 Never-married Adm-clerical Not-in-family White Male \n", - "1 Married-civ-spouse Exec-managerial Husband White Male \n", - "2 Divorced Handlers-cleaners Not-in-family White Male \n", - "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", - "4 Married-civ-spouse Prof-specialty Wife Black Female \n", - "\n", - " Capital Gain Capital Loss Hours per week Country Target \n", - "0 2174 0 40 United-States <=50K \n", - "1 0 0 13 United-States <=50K \n", - "2 0 0 40 United-States <=50K \n", - "3 0 0 40 United-States <=50K \n", - "4 0 0 40 Cuba <=50K " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_data = pd.read_csv(\n", - " \"adult.data\", names=adult_columns, sep=r\"\\s*,\\s*\", engine=\"python\", na_values=\"?\"\n", - ").dropna()\n", - "\n", - "testing_data = pd.read_csv(\n", - " \"adult.test\", names=adult_columns, sep=r\"\\s*,\\s*\", engine=\"python\", na_values=\"?\", skiprows=1\n", - ").dropna()\n", - "\n", - "training_data.head()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data inspection\n", - "Plotting histograms for the distribution of the different features is a good way to visualize the data. Let's plot a few of the features that can be considered _sensitive_. \n", - "Let's take a look specifically at the Sex feature of a census respondent. In the first plot we see that there are fewer Female respondents as a whole but especially in the positive outcomes, where they form ~$\\frac{1}{7}$th of respondents." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "training_data[\"Sex\"].where(training_data[\"Target\"] == \">50K\").value_counts().sort_values().plot(\n", - " kind=\"bar\", title=\"Counts of Sex earning >$50K\", rot=0\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Encode and Upload the Dataset\n", - "Here we encode the training and test data. Encoding input data is not necessary for SageMaker Clarify, but is necessary for the model." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import preprocessing\n", - "\n", - "\n", - "def number_encode_features(df):\n", - " result = df.copy()\n", - " encoders = {}\n", - " for column in result.columns:\n", - " if result.dtypes[column] == np.object:\n", - " encoders[column] = preprocessing.LabelEncoder()\n", - " result[column] = encoders[column].fit_transform(result[column].fillna(\"None\"))\n", - " return result, encoders\n", - "\n", - "\n", - "training_data = pd.concat([training_data[\"Target\"], training_data.drop([\"Target\"], axis=1)], axis=1)\n", - "training_data, _ = number_encode_features(training_data)\n", - "training_data.to_csv(\"train_data.csv\", index=False, header=False)\n", - "\n", - "testing_data, _ = number_encode_features(testing_data)\n", - "test_features = testing_data.drop([\"Target\"], axis=1)\n", - "test_target = testing_data[\"Target\"]\n", - "test_features.to_csv(\"test_features.csv\", index=False, header=False)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A quick note about our encoding: the \"Female\" Sex value has been encoded as 0 and \"Male\" as 1." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " Target Age Workclass fnlwgt Education Education-Num Marital Status \\\n", - "0 0 39 5 77516 9 13 4 \n", - "1 0 50 4 83311 9 13 2 \n", - "2 0 38 2 215646 11 9 0 \n", - "3 0 53 2 234721 1 7 2 \n", - "4 0 28 2 338409 9 13 2 \n", - "\n", - " Occupation Relationship Ethnic group Sex Capital Gain Capital Loss \\\n", - "0 0 1 4 1 2174 0 \n", - "1 3 0 4 1 0 0 \n", - "2 5 1 4 1 0 0 \n", - "3 5 0 2 1 0 0 \n", - "4 9 5 2 0 0 0 \n", - "\n", - " Hours per week Country \n", - "0 40 38 \n", - "1 13 38 \n", - "2 40 38 \n", - "3 40 38 \n", - "4 40 4 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_data.head()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, let's upload the data to S3." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.s3 import S3Uploader\n", - "from sagemaker.inputs import TrainingInput\n", - "\n", - "train_uri = S3Uploader.upload(\"train_data.csv\", \"s3://{}/{}\".format(bucket, prefix))\n", - "train_input = TrainingInput(train_uri, content_type=\"csv\")\n", - "test_uri = S3Uploader.upload(\"test_features.csv\", \"s3://{}/{}\".format(bucket, prefix))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train XGBoost Model\n", - "#### Train Model\n", - "Since our focus is on understanding how to use SageMaker Clarify, we keep it simple by using a standard XGBoost model.\n", - "\n", - "It takes about 5 minutes for the model to be trained." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2023-02-07-05-54-36-442\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "2023-02-07 05:54:36 Starting - Starting the training job..\n", - "2023-02-07 05:54:50 Starting - Preparing the instances for training........\n", - "2023-02-07 05:55:32 Downloading - Downloading input data....\n", - "2023-02-07 05:55:57 Training - Downloading the training image...\n", - "2023-02-07 05:56:18 Training - Training image download completed. Training in progress.......\n", - "2023-02-07 05:56:53 Uploading - Uploading generated training model.\n", - "2023-02-07 05:57:04 Completed - Training job completed\n" - ] - } - ], - "source": [ - "from sagemaker.image_uris import retrieve\n", - "from sagemaker.estimator import Estimator\n", - "\n", - "# This references the AWS managed XGBoost container\n", - "xgboost_image_uri = retrieve(\"xgboost\", region, version=\"1.5-1\")\n", - "\n", - "xgb = Estimator(\n", - " xgboost_image_uri,\n", - " role,\n", - " instance_count=1,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " disable_profiler=True,\n", - " sagemaker_session=sagemaker_session,\n", - ")\n", - "\n", - "xgb.set_hyperparameters(\n", - " max_depth=5,\n", - " eta=0.2,\n", - " gamma=4,\n", - " min_child_weight=6,\n", - " subsample=0.8,\n", - " objective=\"binary:logistic\",\n", - " num_round=800,\n", - ")\n", - "\n", - "xgb.fit({\"train\": train_input}, logs=False)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create Model\n", - "Here we create the SageMaker model." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Creating model with name: DEMO-clarify-model-07-02-2023-05-57-08\n" - ] - }, - { - "data": { - "text/plain": [ - "'DEMO-clarify-model-07-02-2023-05-57-08'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_name = \"DEMO-clarify-model-{}\".format(datetime.now().strftime(\"%d-%m-%Y-%H-%M-%S\"))\n", - "model = xgb.create_model(name=model_name)\n", - "container_def = model.prepare_container_def()\n", - "sagemaker_session.create_model(model_name, role, container_def)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Amazon SageMaker Clarify\n", - "With your model set up, it's time to explore SageMaker Clarify. For a general overview of how SageMaker Clarify processing jobs work, refer [the provided link](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-processing-job-configure-how-it-works.html). " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" - ] - } - ], - "source": [ - "from sagemaker import clarify\n", - "\n", - "# Initialize a SageMakerClarifyProcessor to compute bias metrics and model explanations.\n", - "clarify_processor = clarify.SageMakerClarifyProcessor(\n", - " role=role, instance_count=1, instance_type=\"ml.m5.xlarge\", sagemaker_session=sagemaker_session\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Detecting Bias\n", - "SageMaker Clarify helps you detect possible [pre-training](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-detect-data-bias.html) and [post-training](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-detect-post-training-bias.html) biases using a variety of metrics.\n", - "\n", - "#### Writing DataConfig\n", - "A [DataConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.DataConfig) object communicates some basic information about data I/O to SageMaker Clarify. For our example here we provide the below information:\n", - "\n", - "* `s3_data_input_path`: S3 URI of the train dataset we uploaded above\n", - "* `s3_output_path`: S3 URI at which our output report will be uploaded\n", - "* `label`: Specifies the ground truth label, which is also known as observed label or target attribute. It is used for many bias metrics. In this example, the `Target` column has the ground truth label.\n", - "* `headers`: The list of column names in the dataset\n", - "* `dataset_type`: specifies the format of your dataset, for this example as we are using CSV dataset this will be `text/csv`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "bias_report_output_path = \"s3://{}/{}/clarify-bias\".format(bucket, prefix)\n", - "bias_data_config = clarify.DataConfig(\n", - " s3_data_input_path=train_uri,\n", - " s3_output_path=bias_report_output_path,\n", - " label=\"Target\",\n", - " headers=training_data.columns.to_list(),\n", - " dataset_type=\"text/csv\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Writing ModelConfig\n", - "\n", - "A [ModelConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.ModelConfig) object communicates information about your trained model. To avoid additional traffic to the production models, SageMaker Clarify sets up and tears down a dedicated endpoint when processing. For our example here we provide the below information:\n", - "\n", - "* `model_name`: name of the concerned model, using name of the xgboost model trained earlier\n", - "* `instance_type` and `initial_instance_count` specify your preferred instance type and instance count used to run your model on during SageMaker Clarify's processing. The example dataset is small, so a single standard instance is good enough to run this example.\n", - "* `accept_type` denotes the endpoint response payload format, and `content_type` denotes the payload format of request to the endpoint. As per the example model we created above both of these will be `text/csv`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "model_config = clarify.ModelConfig(\n", - " model_name=model_name,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " instance_count=1,\n", - " accept_type=\"text/csv\",\n", - " content_type=\"text/csv\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Writing ModelPredictedLabelConfig\n", - "\n", - "A [ModelPredictedLabelConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.ModelPredictedLabelConfig) provides information on the format of your predictions. XGBoost model outputs probabilities of samples, so SageMaker Clarify invokes the endpoint then uses `probability_threshold` to convert the probability to binary labels for bias analysis. Prediction above the threshold is interpreted as label value `1` and below or equal as label value `0`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "predictions_config = clarify.ModelPredictedLabelConfig(probability_threshold=0.8)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Writing BiasConfig\n", - "[BiasConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.BiasConfig) contains configuration values for detecting bias using a Clarify container." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "bias_config = clarify.BiasConfig(\n", - " label_values_or_threshold=[1], facet_name=\"Sex\", facet_values_or_threshold=[0], group_name=\"Age\"\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For our demo we provide the following information in BiasConfig API:\n", - "\n", - "* `label_values_or_threshold`: List of label value(s) or threshold to indicate positive outcome used for bias metrics. Here positive outcome is earning >$50,000.\n", - "* `facet_name`: Sensitive columns of the dataset, \"Sex\" is the category\n", - "* `facet_values_or_threshold`: values of the sensitive group, \"Female\" respondents are the sensitive group.\n", - "* `group_name`: This example has selected the \"Age\" column which is used to form subgroups for the measurement of bias metric [Conditional Demographic Disparity (CDD)](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-metric-cddl.html) or [Conditional Demographic Disparity in Predicted Labels (CDDPL)](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cddpl.html).\n", - "\n", - "SageMaker Clarify can handle both categorical and continuous data for `facet: values_or_threshold` and for `label_values_or_threshold`. In this case we are using categorical data. The results will show if the model has a preference for records of one sex over the other." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Pre-training Bias\n", - "Bias can be present in your data before any model training occurs. Inspecting your data for bias before training begins can help detect any data collection gaps, inform your feature engineering, and help you understand what societal biases the data may reflect.\n", - "\n", - "Computing pre-training bias metrics does not require a trained model.\n", - "\n", - "#### Post-training Bias\n", - "Computing post-training bias metrics does require a trained model.\n", - "\n", - "Unbiased training data (as determined by concepts of fairness measured by bias metric) may still result in biased model predictions after training. Whether this occurs depends on several factors including hyperparameter choices.\n", - "\n", - "\n", - "You can run these options separately with [run_pre_training_bias()](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run_pre_training_bias) and [run_post_training_bias()](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run_post_training_bias) or at the same time with `run_bias()` as shown below. We use following additional parameters for the api call:\n", - "\n", - "* `pre_training_methods`: Pre-training bias metrics to be computed. The detailed description of the metrics can be found on [Measure Pre-training Bias](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html). This example sets methods to \"all\" to compute all the pre-training bias metrics.\n", - "* `post_training_methods`: Post-training bias metrics to be computed. The detailed description of the metrics can be found on [Measure Post-training Bias](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-detect-post-training-bias.html). This example sets methods to \"all\" to compute all the post-training bias metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The job takes about 10 minutes to run\n", - "clarify_processor.run_bias(\n", - " data_config=bias_data_config,\n", - " bias_config=bias_config,\n", - " model_config=model_config,\n", - " model_predicted_label_config=predictions_config,\n", - " pre_training_methods=\"all\",\n", - " post_training_methods=\"all\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Viewing the Bias Report\n", - "In Studio, you can view the results under the experiments tab.\n", - "\n", - "\n", - "\n", - "Each bias metric has detailed explanations with examples that you can explore.\n", - "\n", - "\n", - "\n", - "You could also summarize the results in a handy table!\n", - "\n", - "\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you're not a Studio user yet, you can access the bias report in PDF, HTML and ipynb formats in the following S3 bucket:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://sagemaker-ap-south-1-000000000000/sagemaker/DEMO-sagemaker-clarify/clarify-bias'" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bias_report_output_path" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explaining Predictions\n", - "There are expanding business needs and legislative regulations that require explanations of _why_ a model made the decision it did. SageMaker Clarify uses Kernel SHAP to explain the contribution that each input feature makes to the final decision.\n", - "\n", - "For run_explainability API call we need similar `DataConfig` and `ModelConfig` objects we defined above. [SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SHAPConfig) here is the config class for Kernel SHAP algorithm.\n", - "\n", - "For our demo we pass the following information in `SHAPConfig`:\n", - "\n", - "* `baseline`: Kernel SHAP algorithm requires a baseline (also known as background dataset). If not provided, a baseline is calculated automatically by SageMaker Clarify using K-means or K-prototypes in the input dataset. Baseline dataset type shall be the same as dataset_type, and baseline samples shall only include features. By definition, baseline should either be a S3 URI to the baseline dataset file, or an in-place list of samples. In this case we chose the latter, and put the mean of the train dataset to the list. For more details on baseline selection please [refer this documentation](https://docs.aws.amazon.com/en_us/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html).\n", - "* `num_samples`: Number of samples to be used in the Kernel SHAP algorithm. This number determines the size of the generated synthetic dataset to compute the SHAP values. \n", - "* `agg_method`: Aggregation method for global SHAP values. For our example here we are using `mean_abs` i.e. mean of absolute SHAP values for all instances\n", - "* `save_local_shap_values`: Indicates whether to save the local SHAP values in the output location. Default is True." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "explainability_output_path = \"s3://{}/{}/clarify-explainability\".format(bucket, prefix)\n", - "explainability_data_config = clarify.DataConfig(\n", - " s3_data_input_path=train_uri,\n", - " s3_output_path=explainability_output_path,\n", - " label=\"Target\",\n", - " headers=training_data.columns.to_list(),\n", - " dataset_type=\"text/csv\",\n", - ")\n", - "\n", - "baseline = [training_data.mean().iloc[1:].values.tolist()]\n", - "shap_config = clarify.SHAPConfig(\n", - " baseline=baseline,\n", - " num_samples=15,\n", - " agg_method=\"mean_abs\",\n", - " save_local_shap_values=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The job takes about 10 minutes to run\n", - "clarify_processor.run_explainability(\n", - " data_config=explainability_data_config,\n", - " model_config=model_config,\n", - " explainability_config=shap_config,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Viewing the Explainability Report\n", - "As with the bias report, you can view the explainability report in Studio under the experiments tab.\n", - "\n", - "\n", - "\n", - "\n", - "The Model Insights tab contains direct links to the report and model insights.\n", - "\n", - "If you're not a Studio user yet, as with the Bias Report, you can access this report at the following S3 bucket." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://sagemaker-ap-south-1-000000000000/sagemaker/DEMO-sagemaker-clarify/clarify-explainability'" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explainability_output_path" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Analysis of local explanations\n", - "It is possible to visualize the local explanations for single examples in your dataset. You can use the obtained results from running Kernel SHAP algorithm for global explanations.\n", - "\n", - "You can simply load the local explanations stored in your output path, and visualize the explanation (i.e., the impact that the single features have on the prediction of your model) for any single example." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Example number: 111 \n", - "with model prediction: False\n", - "\n", - "Feature values -- Label Target 0\n", - "Age 21\n", - "Workclass 2\n", - "fnlwgt 199915\n", - "Education 15\n", - "Education-Num 10\n", - "Marital Status 4\n", - "Occupation 7\n", - "Relationship 3\n", - "Ethnic group 4\n", - "Sex 0\n", - "Capital Gain 0\n", - "Capital Loss 0\n", - "Hours per week 40\n", - "Country 38\n", - "Name: 120, dtype: int64\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "local_explanations_out = pd.read_csv(explainability_output_path + \"/explanations_shap/out.csv\")\n", - "feature_names = [str.replace(c, \"_label0\", \"\") for c in local_explanations_out.columns.to_series()]\n", - "local_explanations_out.columns = feature_names\n", - "\n", - "selected_example = 111\n", - "print(\n", - " \"Example number:\",\n", - " selected_example,\n", - " \"\\nwith model prediction:\",\n", - " sum(local_explanations_out.iloc[selected_example]) > 0,\n", - ")\n", - "print(\"\\nFeature values -- Label\", training_data.iloc[selected_example])\n", - "local_explanations_out.iloc[selected_example].plot(\n", - " kind=\"bar\", title=\"Local explanation for the example number \" + str(selected_example), rot=90\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** You can run both bias and explainability jobs at the same time with `run_bias_and_explainability()`, refer [API Documentation](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run_bias_and_explainability) for more details. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Clean Up\n", - "Finally, don't forget to clean up the resources we set up and used for this demo!" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Deleting model with name: DEMO-clarify-model-07-02-2023-05-57-08\n" - ] - } - ], - "source": [ - "sagemaker_session.delete_model(model_name)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", - "\n", - "![This ca-central-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n" - ] - } - ], - "metadata": { - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker-geospatial/index.rst b/sagemaker-geospatial/index.rst deleted file mode 100644 index b6a8d67883..0000000000 --- a/sagemaker-geospatial/index.rst +++ /dev/null @@ -1,15 +0,0 @@ -Amazon Sagemaker Geospatial Service -==================================== - -Amazon SageMaker can be utilized to process geosptial data (i.e. satellite imagery). - - -Digital Farming Example ------------------------- - -.. toctree:: - :maxdepth: 1 - - digital-farming-pipelines/digital-farming-sagemaker-geospatial-part-1 - digital-farming-pipelines/digital-farming-sagemaker-geospatial-part-2 - digital-farming-pipelines/pipelines-sagemaker-geospatial diff --git a/sagemaker-lineage/sagemaker-lineage-multihop-queries.ipynb b/sagemaker-lineage/sagemaker-lineage-multihop-queries.ipynb deleted file mode 100644 index 9941703ee2..0000000000 --- a/sagemaker-lineage/sagemaker-lineage-multihop-queries.ipynb +++ /dev/null @@ -1,1094 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "cb187715", - "metadata": {}, - "source": [ - "# Amazon SageMaker Multi-hop Lineage Queries\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "66fa3294", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "367041e5", - "metadata": {}, - "source": [ - "\n", - "Amazon SageMaker Lineage tracks events that happen within SageMaker allowing the relationships between them to be traced via a graph structure. SageMaker Lineage introduces a new API called `LineageQuery` that allows customers to query the lineage graph structure to discover relationship across their Machine Learning entities. \n", - "\n", - "Your machine learning workflows can generate deeply nested relationships, the lineage APIs allow you to answer questions about these relationships. For example find all Data Sets that trained the model deployed to a given Endpoint or find all Models trained by a Data Set.\n", - "\n", - "The lineage graph is created automatically by SageMaker and you can directly create or modify your own lineage.\n", - "\n", - "In addition to the `LineageQuery` API, the SageMaker SDK provides wrapper functions that make it easy to run queries that span across multiple hops of the entity relationship graph. These APIs and helper functions are described in this notebook.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 15 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Key Concepts](#Key-Concepts)\n", - "1. [Prerequisites](#Prerequisites)\n", - "1. [Notebook Overview](#Notebook-Overview)\n", - "1. [Create an Experiment and Trial for a training job](#Create-an-Experiment-and-Trial-for-a-training-job)\n", - "1. [Training Data](#Training-Data)\n", - "1. [Create a training job](#Create-a-training-job)\n", - "1. [Create a Model Package Group for the trained model to be registered](#Create-a-Model-Package-Group-for-the-trained-model-to-be-registered)\n", - "1. [Register the model in the Model Registry](#Register-the-model-in-the-Model-Registry)\n", - "1. [Deploy the model to a SageMaker Endpoint](#Deploy-the-model-to-a-SageMaker-Endpoint)\n", - "1. [SageMaker Lineage Queries](#SageMaker-Lineage-Queries)\n", - " 1. [Using the LineageQuery API to find entity associations](#Using-the-LineageQuery-API-to-find-entity-associations)\n", - " 1. [Find all datasets associated with an Endpoint](#Find-all-datasets-associated-with-an-Endpoint)\n", - " 1. [Find the models associated with an Endpoint](#Find-the-models-associated-with-an-Endpoint)\n", - " 1. [Find the trial components associated with an Endpoint](#Find-the-trial-components-associated-with-an-Endpoint)\n", - " 1. [Change the focal point of lineage](#Change-the-focal-point-of-lineage)\n", - " 1. [Use LineageQueryDirectionEnum.BOTH](#Use-LineageQueryDirectionEnum.BOTH)\n", - " 1. [Directions in LineageQuery: Ascendants vs. Descendants](#Directions-in-LineageQuery:-Ascendants-vs.-Descendants)\n", - " 1. [SDK helper functions](#SDK-helper-functions)\n", - " 1. [Lineage Graph Visualization](#Lineage-Graph-Visualization)\n", - "1. [Conclusion](#Conclusion)\n", - "1. [Cleanup](#Cleanup)\n", - "\n", - "\n", - "## Key Concepts\n", - "\n", - "* **Lineage Graph** - A connected graph tracing your machine learning workflow end to end. \n", - "* **Artifacts** - Represents a URI addressable object or data. Artifacts are typically inputs or outputs to Actions. \n", - "* **Actions** - Represents an action taken such as a computation, transformation, or job. \n", - "* **Contexts** - Provides a method to logically group other entities.\n", - "* **Associations** - A directed edge in the lineage graph that links two entities.\n", - "* **Lineage Traversal** - Starting from an arbitrary point trace the lineage graph to discover and analyze relationships between steps in your workflow.\n", - "* **Experiments** - Experiment entites (Experiments, Trials, and Trial Components) are also part of the lineage graph and can be associated wtih Artifacts, Actions, or Contexts." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25d4a00f", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "[`sagemaker-experiments`](https://github.com/aws/sagemaker-experiments) and [`pyvis`]((https://pyvis.readthedocs.io/en/latest/)) are two Python libraries that need to be installed as part of this notebook execution. `pyvis` is a library designed for interactive network visualization and `sagemaker-experiments` gives users the ability to use SageMaker's Experiment Tracking capabilities. \n", - "\n", - "This notebook should be run with `Python 3.9` using the SageMaker Studio `Python3 (Data Science)` kernel. The `sagemaker` sdk version required for this notebook is `>2.70.0`.\n", - "\n", - "If running in SageMaker Classic Notebooks, use the `conda_python3` kernel. \n", - "\n", - "The AWS account running this notebook should have access to provision two instances of type `ml.m5.xlarge`. These instances are used for training and deploying a model." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0fee7359", - "metadata": {}, - "source": [ - "Let's start by installing the Python SDK, boto and AWS CLI." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93adbfe7", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install sagemaker botocore boto3 awscli --upgrade" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69886125", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install sagemaker-experiments pyvis" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c6cf2db5", - "metadata": {}, - "source": [ - "## Notebook Overview\n", - "\n", - "This notebook demonstrates how to use SageMaker Lineage APIs to query multi-hop relationships across the lineage graph. Multi-hop relationships are those that span beyond single entity relationships, e.g. Model -> Endpoint, Training Job -> Model. Multi-hop queries allow users to search for distant relationships across the Lineage Graph such as Endpoint -> Data Set.\n", - "\n", - "To demonstrate these capabilities, in this notebook we create a training job, register a model to the Model Registry, and deploy the model to an Endpoint. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26efdda2", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import boto3\n", - "import sagemaker\n", - "import pprint\n", - "from botocore.config import Config\n", - "\n", - "config = Config(retries={\"max_attempts\": 50, \"mode\": \"adaptive\"})\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "sm_client = sagemaker_session.sagemaker_client\n", - "\n", - "region = sagemaker_session.boto_region_name\n", - "\n", - "default_bucket = sagemaker_session.default_bucket()\n", - "role = sagemaker.get_execution_role()\n", - "\n", - "# Helper function to print query outputs\n", - "pp = pprint.PrettyPrinter()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9c40701a", - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "training_instance_type = \"ml.m5.xlarge\"\n", - "inference_instance_type = \"ml.m5.xlarge\"\n", - "s3_prefix = \"multihop-example\"\n", - "\n", - "unique_id = str(datetime.now().timestamp()).split(\".\")[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6c51f513", - "metadata": {}, - "source": [ - "## Create an Experiment and Trial for a training job" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8718c000", - "metadata": {}, - "outputs": [], - "source": [ - "from smexperiments.experiment import Experiment\n", - "from smexperiments.trial import Trial\n", - "from smexperiments.trial_component import TrialComponent\n", - "\n", - "experiment_name = f\"MultihopQueryExperiment-{unique_id}\"\n", - "exp = Experiment.create(experiment_name=experiment_name, sagemaker_boto_client=sm_client)\n", - "\n", - "trial = Trial.create(\n", - " experiment_name=exp.experiment_name,\n", - " trial_name=f\"MultihopQueryTrial-{unique_id}\",\n", - " sagemaker_boto_client=sm_client,\n", - ")\n", - "\n", - "print(exp.experiment_name)\n", - "print(trial.trial_name)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f63f088c", - "metadata": {}, - "source": [ - "## Training Data\n", - "\n", - "Creating a `data/` directory to store the preprocessed [UCI Abalone](https://archive.ics.uci.edu/ml/datasets/abalone) dataset. The preprocessing is done using the preprocessing script defined in the notebook [Orchestrating Jobs with Amazon SageMaker Model Building Pipelines](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb) notebook. Then training and validation data is uploaded to S3 so that it can be used in the training and inference job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d020ac3", - "metadata": {}, - "outputs": [], - "source": [ - "default_bucket" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c54bdc99", - "metadata": {}, - "outputs": [], - "source": [ - "if not os.path.exists(\"./data/\"):\n", - " os.makedirs(\"./data/\")\n", - " print(\"Directory Created \")\n", - "else:\n", - " print(\"Directory already exists\")\n", - "\n", - "# Download the processed abalone dataset files\n", - "s3 = boto3.client(\"s3\")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{region}\",\n", - " \"datasets/tabular/uci_abalone/preprocessed/test.csv\",\n", - " \"./data/test.csv\",\n", - ")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{region}\",\n", - " \"datasets/tabular/uci_abalone/preprocessed/train.csv\",\n", - " \"./data/train.csv\",\n", - ")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{region}\",\n", - " \"datasets/tabular/uci_abalone/preprocessed/validation.csv\",\n", - " \"./data/validation.csv\",\n", - ")\n", - "\n", - "# Upload the datasets to the SageMaker session default bucket\n", - "boto3.Session().resource(\"s3\").Bucket(default_bucket).Object(\n", - " \"experiments-demo/train.csv\"\n", - ").upload_file(\"data/train.csv\")\n", - "boto3.Session().resource(\"s3\").Bucket(default_bucket).Object(\n", - " \"experiments-demo/validation.csv\"\n", - ").upload_file(\"data/validation.csv\")\n", - "\n", - "training_data = f\"s3://{default_bucket}/experiments-demo/train.csv\"\n", - "validation_data = f\"s3://{default_bucket}/experiments-demo/validation.csv\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "660c9e25", - "metadata": {}, - "source": [ - "## Create a training job\n", - "\n", - "We train a simple XGBoost model on the Abalone dataset. \n", - "`sagemaker.image_uris.retrieve()` is used to get the sagemaker container for XGBoost so that it can be used in the Estimator. \n", - "\n", - "In the `.fit()` function, we pass in a training and validation dataset along with an `experiment_config`. The `experiment_config` ensures that the metrics, parameters, and artifats associated with this training job are logged to the experiment and trial created above. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fed64de", - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.estimator import Estimator\n", - "\n", - "model_path = f\"s3://{default_bucket}/{s3_prefix}/xgb_model\"\n", - "training_instance_type = \"ml.m5.large\"\n", - "\n", - "image_uri = sagemaker.image_uris.retrieve(\n", - " framework=\"xgboost\",\n", - " region=region,\n", - " version=\"1.5-1\",\n", - " py_version=\"py3\",\n", - " instance_type=training_instance_type,\n", - ")\n", - "\n", - "xgb_train = Estimator(\n", - " image_uri=image_uri,\n", - " instance_type=training_instance_type,\n", - " instance_count=1,\n", - " output_path=model_path,\n", - " sagemaker_session=sagemaker_session,\n", - " role=role,\n", - ")\n", - "\n", - "xgb_train.set_hyperparameters(\n", - " objective=\"reg:squarederror\",\n", - " num_round=50,\n", - " max_depth=5,\n", - " eta=0.2,\n", - " gamma=4,\n", - " min_child_weight=6,\n", - " subsample=0.7,\n", - " verbosity=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5285ba3d", - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.inputs import TrainingInput\n", - "\n", - "xgb_train.fit(\n", - " inputs={\n", - " \"train\": TrainingInput(\n", - " s3_data=training_data,\n", - " content_type=\"text/csv\",\n", - " ),\n", - " \"validation\": TrainingInput(\n", - " s3_data=validation_data,\n", - " content_type=\"text/csv\",\n", - " ),\n", - " },\n", - " experiment_config={\n", - " \"ExperimentName\": experiment_name,\n", - " \"TrialName\": trial.trial_name,\n", - " \"TrialComponentDisplayName\": \"MultiHopQueryTrialComponent\",\n", - " },\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ce43b815", - "metadata": {}, - "source": [ - "## Create a Model Package Group for the trained model to be registered\n", - "\n", - "Create a new Model Package Group or use an existing one to register the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17e9f1e0", - "metadata": {}, - "outputs": [], - "source": [ - "model_package_group_name = \"lineage-test-\" + unique_id\n", - "mpg = sm_client.create_model_package_group(ModelPackageGroupName=model_package_group_name)\n", - "mpg_arn = mpg[\"ModelPackageGroupArn\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d17d04c0", - "metadata": {}, - "source": [ - "## Register the model in the Model Registry\n", - "Once the model is registered, it appears in the Model Registry tab of the SageMaker Studio UI. The model is registered with the `approval_status` set to \"Approved\". By default, the model is registered with the `approval_status` set to \"PendingManualApproval\". Users can then navigate to the Model Registry to manually approve the model based on any criteria set for model evaluation or this can be done via API. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38ab67a1", - "metadata": {}, - "outputs": [], - "source": [ - "inference_instance_type = \"ml.m5.xlarge\"\n", - "model_package = xgb_train.register(\n", - " model_package_group_name=mpg_arn,\n", - " inference_instances=[inference_instance_type],\n", - " transform_instances=[inference_instance_type],\n", - " content_types=[\"text/csv\"],\n", - " response_types=[\"text/csv\"],\n", - " approval_status=\"Approved\",\n", - ")\n", - "\n", - "model_package_arn = model_package.model_package_arn\n", - "print(\"Model Package ARN : \", model_package_arn)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "570f9d6c", - "metadata": {}, - "source": [ - "## Deploy the model to a SageMaker Endpoint\n", - "\n", - "A SageMaker Endpoint is used to host a model that can be used for inference. The type of endpoint deployed in this notebook is a real time inference endpoint. This is ideal for inference workloads where you have real-time, interactive, low latency requirements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8433e1e9", - "metadata": {}, - "outputs": [], - "source": [ - "endpoint_name = \"lineage-test-endpoint-\" + unique_id\n", - "model_package.deploy(\n", - " endpoint_name=endpoint_name,\n", - " initial_instance_count=1,\n", - " instance_type=inference_instance_type,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17178ffe", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the endpoint ARN\n", - "endpoint_arn = sm_client.describe_endpoint(EndpointName=endpoint_name)[\"EndpointArn\"]\n", - "print(endpoint_arn)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1b73bd20", - "metadata": {}, - "source": [ - "## SageMaker Lineage Queries\n", - "\n", - "We explore SageMaker's lineage capabilities to traverse the relationships between the entities created in this notebook - datasets, model, endpoint, and training job. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc2b4ef0", - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.lineage.context import Context, EndpointContext\n", - "from sagemaker.lineage.action import Action\n", - "from sagemaker.lineage.association import Association\n", - "from sagemaker.lineage.artifact import Artifact, ModelArtifact, DatasetArtifact\n", - "\n", - "from sagemaker.lineage.query import (\n", - " LineageQuery,\n", - " LineageFilter,\n", - " LineageSourceEnum,\n", - " LineageEntityEnum,\n", - " LineageQueryDirectionEnum,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "093e985e", - "metadata": {}, - "source": [ - "### Using the LineageQuery API to find entity associations\n", - "\n", - "In this section we use two APIs, `LineageQuery` and `LineageFilter` to construct queries to answer questions about the Lineage Graph and extract entity relationships. \n", - "\n", - "LineageQuery parameters:\n", - "* `start_arns`: A list of ARNs that is used as the starting point for the query.\n", - "* `direction`: The direction of the query.\n", - "* `include_edges`: If true, return edges in addition to vertices.\n", - "* `query_filter`: The query filter.\n", - "\n", - "LineageFilter paramters:\n", - "* `entities`: A list of entity types (Artifact, Association, Action) to filter for when returning the results on LineageQuery\n", - "* `sources`: A list of source types (Endpoint, Model, Dataset) to filter for when returning the results of LineageQuery\n", - "\n", - "A `Context` is automatically created when a SageMaker Endpoint is created, an `Artifact` is automatically created when a Model is created in SageMaker. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a30c455b", - "metadata": {}, - "outputs": [], - "source": [ - "# Find the endpoint context and model artifact that should be used for the lineage queries.\n", - "\n", - "contexts = Context.list(source_uri=endpoint_arn)\n", - "context_name = list(contexts)[0].context_name\n", - "endpoint_context = EndpointContext.load(context_name=context_name)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9963e76e", - "metadata": {}, - "source": [ - "#### Find all datasets associated with an Endpoint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfde258b", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the LineageFilter to look for entities of type `ARTIFACT` and the source of type `DATASET`.\n", - "\n", - "query_filter = LineageFilter(\n", - " entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.DATASET]\n", - ")\n", - "\n", - "# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`\n", - "# and find all datasets.\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[endpoint_context.context_arn],\n", - " query_filter=query_filter,\n", - " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "# Parse through the query results to get the lineage objects corresponding to the datasets\n", - "dataset_artifacts = []\n", - "for vertex in query_result.vertices:\n", - " dataset_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", - "\n", - "pp.pprint(dataset_artifacts)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7dab1c4a", - "metadata": {}, - "source": [ - "#### Find the models associated with an Endpoint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6294fc97", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the LineageFilter to look for entities of type `ARTIFACT` and the source of type `MODEL`.\n", - "\n", - "query_filter = LineageFilter(\n", - " entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.MODEL]\n", - ")\n", - "\n", - "# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`\n", - "# and find all datasets.\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[endpoint_context.context_arn],\n", - " query_filter=query_filter,\n", - " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "# Parse through the query results to get the lineage objects corresponding to the model\n", - "model_artifacts = []\n", - "for vertex in query_result.vertices:\n", - " model_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", - "\n", - "# The results of the `LineageQuery` API call return the ARN of the model deployed to the endpoint along with\n", - "# the S3 URI to the model.tar.gz file associated with the model\n", - "pp.pprint(model_artifacts)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4fa79344", - "metadata": {}, - "source": [ - "#### Find the trial components associated with an Endpoint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d417bf3a", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the LineageFilter to look for entities of type `TRIAL_COMPONENT` and the source of type `TRAINING_JOB`.\n", - "\n", - "query_filter = LineageFilter(\n", - " entities=[LineageEntityEnum.TRIAL_COMPONENT],\n", - " sources=[LineageSourceEnum.TRAINING_JOB],\n", - ")\n", - "\n", - "# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`\n", - "# and find all datasets.\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[endpoint_context.context_arn],\n", - " query_filter=query_filter,\n", - " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "# Parse through the query results to get the ARNs of the training jobs associated with this Endpoint\n", - "trial_components = []\n", - "for vertex in query_result.vertices:\n", - " trial_components.append(vertex.arn)\n", - "\n", - "pp.pprint(trial_components)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9954748f", - "metadata": {}, - "source": [ - "#### Change the focal point of lineage\n", - "\n", - "The `LineageQuery` can be modified to have different `start_arns` which changes the focal point of lineage. In addition, the `LineageFilter` can take multiple sources and entities to expand the scope of the query. \n", - "\n", - "**Here we use the model as the lineage focal point and find the Endpoints and Datasets associated with it.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c28d8ea", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the ModelArtifact\n", - "\n", - "model_artifact_summary = list(Artifact.list(source_uri=model_package_arn))[0]\n", - "model_artifact = ModelArtifact.load(artifact_arn=model_artifact_summary.artifact_arn)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca86919e", - "metadata": {}, - "outputs": [], - "source": [ - "query_filter = LineageFilter(\n", - " entities=[LineageEntityEnum.ARTIFACT],\n", - " sources=[LineageSourceEnum.ENDPOINT, LineageSourceEnum.DATASET],\n", - ")\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[model_artifact.artifact_arn], # Model is the starting artifact\n", - " query_filter=query_filter,\n", - " # Find all the entities that descend from the model, i.e. the endpoint\n", - " direction=LineageQueryDirectionEnum.DESCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "associations = []\n", - "for vertex in query_result.vertices:\n", - " associations.append(vertex.to_lineage_object().source.source_uri)\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[model_artifact.artifact_arn], # Model is the starting artifact\n", - " query_filter=query_filter,\n", - " # Find all the entities that ascend from the model, i.e. the datasets\n", - " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "for vertex in query_result.vertices:\n", - " associations.append(vertex.to_lineage_object().source.source_uri)\n", - "\n", - "pp.pprint(associations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "eaa41ff9", - "metadata": {}, - "source": [ - "#### Use LineageQueryDirectionEnum.BOTH\n", - "\n", - "When the direction is set to `BOTH`, when the query traverses the graph to find ascendant and descendant relationships, the traversal takes place not only from the starting node, but from each node that is visited. \n", - "\n", - "e.g. If the training job is run twice and both models generated by the training job are deployed to endpoints, this result of the query with direction set to `BOTH` shows both endpoints. This is because the same image is used for training and deploying the model. Since the image is common to the model (`start_arn`) and both the endpoints, it appears in the query result. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4bee658", - "metadata": {}, - "outputs": [], - "source": [ - "query_filter = LineageFilter(\n", - " entities=[LineageEntityEnum.ARTIFACT],\n", - " sources=[LineageSourceEnum.ENDPOINT, LineageSourceEnum.DATASET],\n", - ")\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[model_artifact.artifact_arn], # Model is the starting artifact\n", - " query_filter=query_filter,\n", - " # This specifies that the query should look for associations both ascending and descending for the start\n", - " direction=LineageQueryDirectionEnum.BOTH,\n", - " include_edges=False,\n", - ")\n", - "\n", - "associations = []\n", - "for vertex in query_result.vertices:\n", - " associations.append(vertex.to_lineage_object().source.source_uri)\n", - "\n", - "pp.pprint(associations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a69aff24", - "metadata": {}, - "source": [ - "### Directions in LineageQuery: Ascendants vs. Descendants\n", - "\n", - "To understand the direction in the Lineage Graph, take the following entity relationship graph - \n", - "Dataset -> Training Job -> Model -> Endpoint\n", - "\n", - "The endpoint is a **descendant** of the model, and the model is a **descendant** of the dataset. Similarly, the model is an **ascendant** of the endpoint The `direction` parameter can be used to specify whether the query should return entities that are descendants or ascendants of the entity in start_arns. If `start_arns` contains a model and the direction is `DESCENDANTS`, the query returns the endpoint. If the direction is `ASCENDANTS`, the query returns the dataset.\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a273b49f", - "metadata": {}, - "outputs": [], - "source": [ - "# In this example, we'll look at the impact of specifying the direction as ASCENDANT or DESCENDANT in a `LineageQuery`.\n", - "\n", - "query_filter = LineageFilter(\n", - " entities=[LineageEntityEnum.ARTIFACT],\n", - " sources=[\n", - " LineageSourceEnum.ENDPOINT,\n", - " LineageSourceEnum.MODEL,\n", - " LineageSourceEnum.DATASET,\n", - " LineageSourceEnum.TRAINING_JOB,\n", - " ],\n", - ")\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[model_artifact.artifact_arn],\n", - " query_filter=query_filter,\n", - " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "ascendant_artifacts = []\n", - "\n", - "# The lineage entity returned for the Training Job is a TrialComponent which can't be converted to a\n", - "# lineage object using the method `to_lineage_object()` so we extract the TrialComponent ARN.\n", - "for vertex in query_result.vertices:\n", - " try:\n", - " ascendant_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", - " except:\n", - " ascendant_artifacts.append(vertex.arn)\n", - "\n", - "print(\"Ascendant artifacts:\")\n", - "pp.pprint(ascendant_artifacts)\n", - "\n", - "query_result = LineageQuery(sagemaker_session).query(\n", - " start_arns=[model_artifact.artifact_arn],\n", - " query_filter=query_filter,\n", - " direction=LineageQueryDirectionEnum.DESCENDANTS,\n", - " include_edges=False,\n", - ")\n", - "\n", - "descendant_artifacts = []\n", - "for vertex in query_result.vertices:\n", - " try:\n", - " descendant_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", - " except:\n", - " # Handling TrialComponents.\n", - " descendant_artifacts.append(vertex.arn)\n", - "\n", - "print(\"Descendant artifacts:\")\n", - "pp.pprint(descendant_artifacts)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f7ec9d14", - "metadata": {}, - "source": [ - "### SDK helper functions\n", - "\n", - "The classes `EndpointContext`, `ModelArtifact`, and `DatasetArtifact`have helper functions that are wrappers over the `LineageQuery` API to make \n", - "certain lineage queries easier to leverage. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5df166d", - "metadata": {}, - "outputs": [], - "source": [ - "# Find all the datasets associated with the endpoint\n", - "\n", - "datasets = []\n", - "dataset_artifacts = endpoint_context.dataset_artifacts()\n", - "for dataset in dataset_artifacts:\n", - " datasets.append(dataset.source.source_uri)\n", - "print(\"Datasets : \", datasets)\n", - "\n", - "# Find the training jobs associated with the endpoint\n", - "training_job_artifacts = endpoint_context.training_job_arns()\n", - "training_jobs = []\n", - "for training_job in training_job_artifacts:\n", - " training_jobs.append(training_job)\n", - "print(\"Training Jobs : \", training_jobs)\n", - "\n", - "# Get the ARN for the pipeline execution associated with this endpoint (if any)\n", - "pipeline_executions = endpoint_context.pipeline_execution_arn()\n", - "if pipeline_executions:\n", - " for pipeline in pipelines_executions:\n", - " print(pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfc055f5", - "metadata": {}, - "outputs": [], - "source": [ - "# Here we use the `ModelArtifact` class to find all the datasets and endpoints associated with the model\n", - "\n", - "dataset_artifacts = model_artifact.dataset_artifacts()\n", - "endpoint_contexts = model_artifact.endpoint_contexts()\n", - "\n", - "datasets = [dataset.source.source_uri for dataset in dataset_artifacts]\n", - "endpoints = [endpoint.source.source_uri for endpoint in endpoint_contexts]\n", - "\n", - "print(\"Datasets associated with this model : \")\n", - "pp.pprint(datasets)\n", - "\n", - "print(\"Endpoints associated with this model : \")\n", - "pp.pprint(endpoints)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fd69a51", - "metadata": {}, - "outputs": [], - "source": [ - "# Here we use the `DatasetArtifact` class to find all the endpoints hosting models that were trained with a particular dataset\n", - "# Find the artifact associated with the dataset\n", - "\n", - "dataset_artifact_arn = list(Artifact.list(source_uri=training_data))[0].artifact_arn\n", - "dataset_artifact = DatasetArtifact.load(artifact_arn=dataset_artifact_arn)\n", - "\n", - "# Find the endpoints that used this training dataset\n", - "endpoint_contexts = dataset_artifact.endpoint_contexts()\n", - "endpoints = [endpoint.source.source_uri for endpoint in endpoint_contexts]\n", - "\n", - "print(\"Endpoints associated with the training dataset {}\".format(training_data))\n", - "pp.pprint(endpoints)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2f9fdd40", - "metadata": {}, - "source": [ - "### Lineage Graph Visualization\n", - "\n", - "A helper class `Visualizer()` is provided in `visualizer.py` to help plot the lineage graph. When the query response is rendered, a graph with the lineage relationships from the `StartArns` is displayed. From the `StartArns` the visualization shows the relationships with the other lineage entities returned in the `query_lineage` API call. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "106d8d5a", - "metadata": {}, - "outputs": [], - "source": [ - "# Graph APIs\n", - "# Here we use the boto3 `query_lineage` API to generate the query response to plot.\n", - "\n", - "from visualizer import Visualizer\n", - "\n", - "query_response = sm_client.query_lineage(\n", - " StartArns=[endpoint_context.context_arn], Direction=\"Ascendants\", IncludeEdges=True\n", - ")\n", - "\n", - "viz = Visualizer()\n", - "viz.render(query_response, \"Endpoint\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22436292", - "metadata": {}, - "outputs": [], - "source": [ - "query_response = sm_client.query_lineage(\n", - " StartArns=[model_artifact.artifact_arn], Direction=\"Ascendants\", IncludeEdges=True\n", - ")\n", - "viz.render(query_response, \"Model\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b393afa3", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "This notebook demostrated the capabilities of SageMaker Lineage that make it easy for users to keep track of their complex ML workflows. Users can construct their own lineage queries using the `LineageQuery` API and `LineageFilter` or they can use the functions provided on the `EndpointContext`, `ModelArtifact`, and `DatasetArtifact` classes. \n", - "\n", - "In addition, the responses from lineage queries can be plotting using the helper class `Visualizer()` to better understand the relationship between the lineage entities. \n", - "\n", - "When using SageMaker Pipelines as part of their ML workflows, users can find Pipeline execution ARNs using the lineage APIs described in this notebook.\n", - "\n", - "## Cleanup\n", - "In this section we clean up the resources created in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f43ef02", - "metadata": {}, - "outputs": [], - "source": [ - "# Delete endpoint\n", - "\n", - "sm_client.delete_endpoint(EndpointName=endpoint_name)\n", - "\n", - "# # Delete the model package\n", - "sm_client.delete_model_package(ModelPackageName=model_package.model_package_arn)\n", - "\n", - "# Delete the model package group\n", - "sm_client.delete_model_package_group(ModelPackageGroupName=model_package_group_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e19fe85", - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the experiment and trial within it\n", - "\n", - "import time\n", - "\n", - "\n", - "def delete_experiment(experiment):\n", - " for trial_summary in experiment.list_trials():\n", - " trial = Trial.load(trial_name=trial_summary.trial_name)\n", - " for trial_component_summary in trial.list_trial_components():\n", - " tc = TrialComponent.load(\n", - " trial_component_name=trial_component_summary.trial_component_name\n", - " )\n", - " trial.remove_trial_component(tc)\n", - " try:\n", - " # comment out to keep trial components\n", - " tc.delete()\n", - " except:\n", - " # tc is associated with another trial\n", - " continue\n", - " # to prevent throttling\n", - " time.sleep(0.5)\n", - " trial.delete()\n", - " experiment_name = experiment.experiment_name\n", - " experiment.delete()\n", - " print(f\"\\nExperiment {experiment_name} deleted\")\n", - "\n", - "\n", - "# Delete the Experiment and Trials within it\n", - "experiment = Experiment.load(experiment_name=exp.experiment_name)\n", - "delete_experiment(experiment)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7a9fa294", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", - "\n", - "![This eu-west-2 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n" - ] - } - ], - "metadata": { - "instance_type": "ml.t3.medium", - "interpreter": { - "hash": "ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc" - }, - "kernelspec": { - "display_name": "Python 3 (TensorFlow 2.6 Python 3.8 CPU Optimized)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/tensorflow-2.6-cpu-py38-ubuntu20.04-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb deleted file mode 100644 index ba00f7ec9a..0000000000 --- a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb +++ /dev/null @@ -1,1697 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "\n", - "Amazon SageMaker Pipelines offers machine learning (ML) application developers and operations engineers the ability to orchestrate SageMaker jobs and author reproducible ML pipelines. It also enables them to deploy custom-built models for inference in real-time with low latency, run offline inferences with Batch Transform, and track lineage of artifacts. They can institute sound operational practices in deploying and monitoring production workflows, deploying model artifacts, and tracking artifact lineage through a simple interface, adhering to safety and best practice paradigms for ML application development.\n", - "\n", - "The SageMaker Pipelines service supports a SageMaker Pipeline domain specific language (DSL), which is a declarative JSON specification. This DSL defines a directed acyclic graph (DAG) of pipeline parameters and SageMaker job steps. The SageMaker Python Software Developer Kit (SDK) streamlines the generation of the pipeline DSL using constructs that engineers and scientists are already familiar with.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately an hour to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [SageMaker Pipelines](#SageMaker-Pipelines)\n", - "1. [Notebook Overview](#Notebook-Overview)\n", - "1. [A SageMaker Pipeline](#A-SageMaker-Pipeline)\n", - "1. [Dataset](#Dataset)\n", - "1. [Define Parameters to Parametrize Pipeline Execution](#Define-Parameters-to-Parametrize-Pipeline-Execution)\n", - "1. [Define a Processing Step for Feature Engineering](#Define-a-Processing-Step-for-Feature-Engineering)\n", - "1. [Define a Training Step to Train a Model](#Define-a-Training-Step-to-Train-a-Model)\n", - "1. [Define a Model Evaluation Step to Evaluate the Trained Model](#Define-a-Model-Evaluation-Step-to-Evaluate-the-Trained-Model)\n", - "1. [Define a Create Model Step to Create a Model](#Define-a-Create-Model-Step-to-Create-a-Model)\n", - "1. [Define a Transform Step to Perform Batch Transformation](#Define-a-Transform-Step-to-Perform-Batch-Transformation)\n", - "1. [Define a Register Model Step to Create a Model Package](#Define-a-Register-Model-Step-to-Create-a-Model-Package)\n", - "1. [Define a Fail Step to Terminate the Pipeline Execution and Mark it as Failed](#Define-a-Fail-Step-to-Terminate-the-Pipeline-Execution-and-Mark-it-as-Failed)\n", - "1. [Define a Condition Step to Check Accuracy and Conditionally Create a Model and Run a Batch Transformation and Register a Model in the Model Registry, Or Terminate the Execution in Failed State](#Define-a-Condition-Step-to-Check-Accuracy-and-Conditionally-Create-a-Model-and-Run-a-Batch-Transformation-and-Register-a-Model-in-the-Model-Registry,-Or-Terminate-the-Execution-in-Failed-State)\n", - "1. [Define a Pipeline of Parameters, Steps, and Conditions](#Define-a-Pipeline-of-Parameters,-Steps,-and-Conditions)\n", - "1. [Submit the pipeline to SageMaker and start execution](#Submit-the-pipeline-to-SageMaker-and-start-execution)\n", - "1. [Pipeline Operations: Examining and Waiting for Pipeline Execution](#Pipeline-Operations:-Examining-and-Waiting-for-Pipeline-Execution)\n", - " 1. [Examining the Evaluation](#Examining-the-Evaluation)\n", - " 1. [Lineage](#Lineage)\n", - " 1. [Parametrized Executions](#Parametrized-Executions)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## SageMaker Pipelines\n", - "\n", - "SageMaker Pipelines supports the following activities, which are demonstrated in this notebook:\n", - "\n", - "* Pipelines - A DAG of steps and conditions to orchestrate SageMaker jobs and resource creation.\n", - "* Processing job steps - A simplified, managed experience on SageMaker to run data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation.\n", - "* Training job steps - An iterative process that teaches a model to make predictions by presenting examples from a training dataset.\n", - "* Conditional execution steps - A step that provides conditional execution of branches in a pipeline.\n", - "* Register model steps - A step that creates a model package resource in the Model Registry that can be used to create deployable models in Amazon SageMaker.\n", - "* Create model steps - A step that creates a model for use in transform steps or later publication as an endpoint.\n", - "* Transform job steps - A batch transform to preprocess datasets to remove noise or bias that interferes with training or inference from a dataset, get inferences from large datasets, and run inference when a persistent endpoint is not needed.\n", - "* Fail steps - A step that stops a pipeline execution and marks the pipeline execution as failed.\n", - "* Parametrized Pipeline executions - Enables variation in pipeline executions according to specified parameters." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Notebook Overview\n", - "\n", - "This notebook shows how to:\n", - "\n", - "* Define a set of Pipeline parameters that can be used to parametrize a SageMaker Pipeline.\n", - "* Define a Processing step that performs cleaning, feature engineering, and splitting the input data into train and test data sets.\n", - "* Define a Training step that trains a model on the preprocessed train data set.\n", - "* Define a Processing step that evaluates the trained model's performance on the test dataset.\n", - "* Define a Create Model step that creates a model from the model artifacts used in training.\n", - "* Define a Transform step that performs batch transformation based on the model that was created.\n", - "* Define a Register Model step that creates a model package from the estimator and model artifacts used to train the model.\n", - "* Define a Conditional step that measures a condition based on output from prior steps and conditionally executes other steps.\n", - "* Define a Fail step with a customized error message indicating the cause of the execution failure.\n", - "* Define and create a Pipeline definition in a DAG, with the defined parameters and steps.\n", - "* Start a Pipeline execution and wait for execution to complete.\n", - "* Download the model evaluation report from the S3 bucket for examination.\n", - "* Start a second Pipeline execution." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## A SageMaker Pipeline\n", - "\n", - "The pipeline that you create follows a typical machine learning (ML) application pattern of preprocessing, training, evaluation, model creation, batch transformation, and model registration:\n", - "\n", - "![A typical ML Application pipeline](img/pipeline-full.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Dataset\n", - "\n", - "The dataset you use is the [UCI Machine Learning Abalone Dataset](https://archive.ics.uci.edu/ml/datasets/abalone) [1]. The aim for this task is to determine the age of an abalone snail from its physical measurements. At the core, this is a regression problem.\n", - "\n", - "The dataset contains several features: length (the longest shell measurement), diameter (the diameter perpendicular to length), height (the height with meat in the shell), whole_weight (the weight of whole abalone), shucked_weight (the weight of meat), viscera_weight (the gut weight after bleeding), shell_weight (the weight after being dried), sex ('M', 'F', 'I' where 'I' is Infant), and rings (integer).\n", - "\n", - "The number of rings turns out to be a good approximation for age (age is rings + 1.5). However, to obtain this number requires cutting the shell through the cone, staining the section, and counting the number of rings through a microscope, which is a time-consuming task. However, the other physical measurements are easier to determine. You use the dataset to build a predictive model of the variable rings through these other physical measurements.\n", - "\n", - "Before you upload the data to an S3 bucket, install the SageMaker Python SDK and gather some constants you can use later in this notebook.\n", - "\n", - "> [1] Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "!pip install -U sagemaker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "import boto3\n", - "import sagemaker\n", - "from sagemaker.workflow.pipeline_context import PipelineSession\n", - "\n", - "sagemaker_session = sagemaker.session.Session()\n", - "region = sagemaker_session.boto_region_name\n", - "role = sagemaker.get_execution_role()\n", - "pipeline_session = PipelineSession()\n", - "default_bucket = sagemaker_session.default_bucket()\n", - "model_package_group_name = f\"AbaloneModelPackageGroupName\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Now, upload the data into the default bucket. You can select our own data set for the `input_data_uri` as is appropriate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "!mkdir -p data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "local_path = \"data/abalone-dataset.csv\"\n", - "\n", - "s3 = boto3.resource(\"s3\")\n", - "s3.Bucket(f\"sagemaker-example-files-prod-{region}\").download_file(\n", - " \"datasets/tabular/uci_abalone/abalone.csv\", local_path\n", - ")\n", - "\n", - "base_uri = f\"s3://{default_bucket}/abalone\"\n", - "input_data_uri = sagemaker.s3.S3Uploader.upload(\n", - " local_path=local_path,\n", - " desired_s3_uri=base_uri,\n", - ")\n", - "print(input_data_uri)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Download a second dataset for batch transformation after model creation. You can select our own dataset for the `batch_data_uri` as is appropriate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "local_path = \"data/abalone-dataset-batch\"\n", - "\n", - "s3 = boto3.resource(\"s3\")\n", - "s3.Bucket(f\"sagemaker-servicecatalog-seedcode-{region}\").download_file(\n", - " \"dataset/abalone-dataset-batch\", local_path\n", - ")\n", - "\n", - "base_uri = f\"s3://{default_bucket}/abalone\"\n", - "batch_data_uri = sagemaker.s3.S3Uploader.upload(\n", - " local_path=local_path,\n", - " desired_s3_uri=base_uri,\n", - ")\n", - "print(batch_data_uri)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define Parameters to Parametrize Pipeline Execution\n", - "\n", - "Define Pipeline parameters that you can use to parametrize the pipeline. Parameters enable custom pipeline executions and schedules without having to modify the Pipeline definition.\n", - "\n", - "The supported parameter types include:\n", - "\n", - "* `ParameterString` - represents a `str` Python type\n", - "* `ParameterInteger` - represents an `int` Python type\n", - "* `ParameterFloat` - represents a `float` Python type\n", - "\n", - "These parameters support providing a default value, which can be overridden on pipeline execution. The default value specified should be an instance of the type of the parameter.\n", - "\n", - "The parameters defined in this workflow include:\n", - "\n", - "* `processing_instance_count` - The instance count of the processing job.\n", - "* `instance_type` - The `ml.*` instance type of the training job.\n", - "* `model_approval_status` - The approval status to register with the trained model for CI/CD purposes (\"PendingManualApproval\" is the default).\n", - "* `input_data` - The S3 bucket URI location of the input data.\n", - "* `batch_data` - The S3 bucket URI location of the batch data.\n", - "* `mse_threshold` - The Mean Squared Error (MSE) threshold used to verify the accuracy of a model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.workflow.parameters import (\n", - " ParameterInteger,\n", - " ParameterString,\n", - " ParameterFloat,\n", - ")\n", - "\n", - "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n", - "instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n", - "model_approval_status = ParameterString(\n", - " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", - ")\n", - "input_data = ParameterString(\n", - " name=\"InputData\",\n", - " default_value=input_data_uri,\n", - ")\n", - "batch_data = ParameterString(\n", - " name=\"BatchData\",\n", - " default_value=batch_data_uri,\n", - ")\n", - "mse_threshold = ParameterFloat(name=\"MseThreshold\", default_value=6.0)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define Parameters](img/pipeline-1.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Processing Step for Feature Engineering\n", - "\n", - "First, develop a preprocessing script that is specified in the Processing step.\n", - "\n", - "This notebook cell writes a file `preprocessing_abalone.py`, which contains the preprocessing script. You can update the script, and rerun this cell to overwrite. The preprocessing script uses `scikit-learn` to do the following:\n", - "\n", - "* Fill in missing sex category data and encode it so that it is suitable for training.\n", - "* Scale and normalize all numerical fields, aside from sex and rings numerical data.\n", - "* Split the data into training, validation, and test datasets.\n", - "\n", - "The Processing step executes the script on the input data. The Training step uses the preprocessed training features and labels to train a model. The Evaluation step uses the trained model and preprocessed test features and labels to evaluate the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "!mkdir -p code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%%writefile code/preprocessing.py\n", - "import argparse\n", - "import os\n", - "import requests\n", - "import tempfile\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "\n", - "\n", - "# Since we get a headerless CSV file, we specify the column names here.\n", - "feature_columns_names = [\n", - " \"sex\",\n", - " \"length\",\n", - " \"diameter\",\n", - " \"height\",\n", - " \"whole_weight\",\n", - " \"shucked_weight\",\n", - " \"viscera_weight\",\n", - " \"shell_weight\",\n", - "]\n", - "label_column = \"rings\"\n", - "\n", - "feature_columns_dtype = {\n", - " \"sex\": str,\n", - " \"length\": np.float64,\n", - " \"diameter\": np.float64,\n", - " \"height\": np.float64,\n", - " \"whole_weight\": np.float64,\n", - " \"shucked_weight\": np.float64,\n", - " \"viscera_weight\": np.float64,\n", - " \"shell_weight\": np.float64,\n", - "}\n", - "label_column_dtype = {\"rings\": np.float64}\n", - "\n", - "\n", - "def merge_two_dicts(x, y):\n", - " z = x.copy()\n", - " z.update(y)\n", - " return z\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " base_dir = \"/opt/ml/processing\"\n", - "\n", - " df = pd.read_csv(\n", - " f\"{base_dir}/input/abalone-dataset.csv\",\n", - " header=None,\n", - " names=feature_columns_names + [label_column],\n", - " dtype=merge_two_dicts(feature_columns_dtype, label_column_dtype),\n", - " )\n", - " numeric_features = list(feature_columns_names)\n", - " numeric_features.remove(\"sex\")\n", - " numeric_transformer = Pipeline(\n", - " steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n", - " )\n", - "\n", - " categorical_features = [\"sex\"]\n", - " categorical_transformer = Pipeline(\n", - " steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n", - " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n", - " ]\n", - " )\n", - "\n", - " preprocess = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features),\n", - " ]\n", - " )\n", - "\n", - " y = df.pop(\"rings\")\n", - " X_pre = preprocess.fit_transform(df)\n", - " y_pre = y.to_numpy().reshape(len(y), 1)\n", - "\n", - " X = np.concatenate((y_pre, X_pre), axis=1)\n", - "\n", - " np.random.shuffle(X)\n", - " train, validation, test = np.split(X, [int(0.7 * len(X)), int(0.85 * len(X))])\n", - "\n", - " pd.DataFrame(train).to_csv(f\"{base_dir}/train/train.csv\", header=False, index=False)\n", - " pd.DataFrame(validation).to_csv(\n", - " f\"{base_dir}/validation/validation.csv\", header=False, index=False\n", - " )\n", - " pd.DataFrame(test).to_csv(f\"{base_dir}/test/test.csv\", header=False, index=False)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Next, create an instance of a `SKLearnProcessor` processor and use that in our `ProcessingStep`.\n", - "\n", - "You also specify the `framework_version` to use throughout this notebook.\n", - "\n", - "Note the `processing_instance_count` parameter used by the processor instance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.sklearn.processing import SKLearnProcessor\n", - "\n", - "\n", - "framework_version = \"1.2-1\"\n", - "\n", - "sklearn_processor = SKLearnProcessor(\n", - " framework_version=framework_version,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " instance_count=processing_instance_count,\n", - " base_job_name=\"sklearn-abalone-process\",\n", - " role=role,\n", - " sagemaker_session=pipeline_session,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Finally, we take the output of the processor's `run` method and pass that as arguments to the `ProcessingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.run()` does not launch the processing job, it returns the arguments needed to run the job as a step in the pipeline.\n", - "\n", - "Note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", - "from sagemaker.workflow.steps import ProcessingStep\n", - "\n", - "processor_args = sklearn_processor.run(\n", - " inputs=[\n", - " ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"),\n", - " ],\n", - " outputs=[\n", - " ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n", - " ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n", - " ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n", - " ],\n", - " code=\"code/preprocessing.py\",\n", - ")\n", - "\n", - "step_process = ProcessingStep(name=\"AbaloneProcess\", step_args=processor_args)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Processing Step for Feature Engineering](img/pipeline-2.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Training Step to Train a Model\n", - "\n", - "In this section, use Amazon SageMaker's [XGBoost Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) to train on this dataset. Configure an Estimator for the XGBoost algorithm and the input dataset. A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to `model_dir` so that it can be hosted later.\n", - "\n", - "The model path where the models from training are saved is also specified.\n", - "\n", - "Note the `instance_type` parameter may be used in multiple places in the pipeline. In this case, the `instance_type` is passed into the estimator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.estimator import Estimator\n", - "from sagemaker.inputs import TrainingInput\n", - "\n", - "model_path = f\"s3://{default_bucket}/AbaloneTrain\"\n", - "image_uri = sagemaker.image_uris.retrieve(\n", - " framework=\"xgboost\",\n", - " region=region,\n", - " version=\"1.0-1\",\n", - " py_version=\"py3\",\n", - " instance_type=\"ml.m5.xlarge\",\n", - ")\n", - "xgb_train = Estimator(\n", - " image_uri=image_uri,\n", - " instance_type=instance_type,\n", - " instance_count=1,\n", - " output_path=model_path,\n", - " role=role,\n", - " sagemaker_session=pipeline_session,\n", - ")\n", - "xgb_train.set_hyperparameters(\n", - " objective=\"reg:linear\",\n", - " num_round=50,\n", - " max_depth=5,\n", - " eta=0.2,\n", - " gamma=4,\n", - " min_child_weight=6,\n", - " subsample=0.7,\n", - ")\n", - "\n", - "train_args = xgb_train.fit(\n", - " inputs={\n", - " \"train\": TrainingInput(\n", - " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n", - " content_type=\"text/csv\",\n", - " ),\n", - " \"validation\": TrainingInput(\n", - " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", - " \"validation\"\n", - " ].S3Output.S3Uri,\n", - " content_type=\"text/csv\",\n", - " ),\n", - " }\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Finally, we use the output of the estimator's `.fit()` method as arguments to the `TrainingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.fit()` does not launch the training job, it returns the arguments needed to run the job as a step in the pipeline.\n", - "\n", - "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `.fit()` method. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.inputs import TrainingInput\n", - "from sagemaker.workflow.steps import TrainingStep\n", - "\n", - "\n", - "step_train = TrainingStep(\n", - " name=\"AbaloneTrain\",\n", - " step_args=train_args,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Training Step to Train a Model](img/pipeline-3.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Model Evaluation Step to Evaluate the Trained Model\n", - "\n", - "First, develop an evaluation script that is specified in a Processing step that performs the model evaluation.\n", - "\n", - "After pipeline execution, you can examine the resulting `evaluation.json` for analysis.\n", - "\n", - "The evaluation script uses `xgboost` to do the following:\n", - "\n", - "* Load the model.\n", - "* Read the test data.\n", - "* Issue predictions against the test data.\n", - "* Build a classification report, including accuracy and ROC curve.\n", - "* Save the evaluation report to the evaluation directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%%writefile code/evaluation.py\n", - "import json\n", - "import pathlib\n", - "import pickle\n", - "import tarfile\n", - "\n", - "import joblib\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xgboost\n", - "\n", - "from sklearn.metrics import mean_squared_error\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " model_path = f\"/opt/ml/processing/model/model.tar.gz\"\n", - " with tarfile.open(model_path) as tar:\n", - " tar.extractall(path=\".\")\n", - "\n", - " model = pickle.load(open(\"xgboost-model\", \"rb\"))\n", - "\n", - " test_path = \"/opt/ml/processing/test/test.csv\"\n", - " df = pd.read_csv(test_path, header=None)\n", - "\n", - " y_test = df.iloc[:, 0].to_numpy()\n", - " df.drop(df.columns[0], axis=1, inplace=True)\n", - "\n", - " X_test = xgboost.DMatrix(df.values)\n", - "\n", - " predictions = model.predict(X_test)\n", - "\n", - " mse = mean_squared_error(y_test, predictions)\n", - " std = np.std(y_test - predictions)\n", - " report_dict = {\n", - " \"regression_metrics\": {\n", - " \"mse\": {\"value\": mse, \"standard_deviation\": std},\n", - " },\n", - " }\n", - "\n", - " output_dir = \"/opt/ml/processing/evaluation\"\n", - " pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n", - "\n", - " evaluation_path = f\"{output_dir}/evaluation.json\"\n", - " with open(evaluation_path, \"w\") as f:\n", - " f.write(json.dumps(report_dict))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.processing import ScriptProcessor\n", - "\n", - "\n", - "script_eval = ScriptProcessor(\n", - " image_uri=image_uri,\n", - " command=[\"python3\"],\n", - " instance_type=\"ml.m5.xlarge\",\n", - " instance_count=1,\n", - " base_job_name=\"script-abalone-eval\",\n", - " role=role,\n", - " sagemaker_session=pipeline_session,\n", - ")\n", - "\n", - "eval_args = script_eval.run(\n", - " inputs=[\n", - " ProcessingInput(\n", - " source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", - " destination=\"/opt/ml/processing/model\",\n", - " ),\n", - " ProcessingInput(\n", - " source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n", - " destination=\"/opt/ml/processing/test\",\n", - " ),\n", - " ],\n", - " outputs=[\n", - " ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n", - " ],\n", - " code=\"code/evaluation.py\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Use the processor's arguments returned by `.run()` to construct a `ProcessingStep`, along with the input and output channels and the code that will be executed when the pipeline invokes pipeline execution.\n", - "\n", - "Specifically, the `S3ModelArtifacts` from the `step_train` `properties` and the `S3Uri` of the `\"test_data\"` output channel of the `step_process` `properties` are passed as inputs. The `TrainingStep` and `ProcessingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) and [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response objects, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.workflow.properties import PropertyFile\n", - "\n", - "\n", - "evaluation_report = PropertyFile(\n", - " name=\"EvaluationReport\", output_name=\"evaluation\", path=\"evaluation.json\"\n", - ")\n", - "step_eval = ProcessingStep(\n", - " name=\"AbaloneEval\",\n", - " step_args=eval_args,\n", - " property_files=[evaluation_report],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Model Evaluation Step to Evaluate the Trained Model](img/pipeline-4.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Create Model Step to Create a Model\n", - "\n", - "In order to perform batch transformation using the example model, create a SageMaker model.\n", - "\n", - "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.model import Model\n", - "\n", - "model = Model(\n", - " image_uri=image_uri,\n", - " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", - " sagemaker_session=pipeline_session,\n", - " role=role,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Define the `ModelStep` by providing the return values from `model.create()` as the step arguments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.inputs import CreateModelInput\n", - "from sagemaker.workflow.model_step import ModelStep\n", - "\n", - "step_create_model = ModelStep(\n", - " name=\"AbaloneCreateModel\",\n", - " step_args=model.create(instance_type=\"ml.m5.large\", accelerator_type=\"ml.eia1.medium\"),\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Transform Step to Perform Batch Transformation\n", - "\n", - "Now that a model instance is defined, create a `Transformer` instance with the appropriate model type, compute instance type, and desired output S3 URI.\n", - "\n", - "Specifically, pass in the `ModelName` from the `CreateModelStep`, `step_create_model` properties. The `CreateModelStep` `properties` attribute matches the object model of the [DescribeModel](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeModel.html) response object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.transformer import Transformer\n", - "\n", - "\n", - "transformer = Transformer(\n", - " model_name=step_create_model.properties.ModelName,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " instance_count=1,\n", - " output_path=f\"s3://{default_bucket}/AbaloneTransform\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Pass in the transformer instance and the `TransformInput` with the `batch_data` pipeline parameter defined earlier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.inputs import TransformInput\n", - "from sagemaker.workflow.steps import TransformStep\n", - "\n", - "\n", - "step_transform = TransformStep(\n", - " name=\"AbaloneTransform\", transformer=transformer, inputs=TransformInput(data=batch_data)\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Register Model Step to Create a Model Package\n", - "\n", - "A model package is an abstraction of reusable model artifacts that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n", - "\n", - "A model package group is a collection of model packages. A model package group can be created for a specific ML business problem, and new versions of the model packages can be added to it. Typically, customers are expected to create a ModelPackageGroup for a SageMaker pipeline so that model package versions can be added to the group for every SageMaker Pipeline run.\n", - "\n", - "To register a model in the Model Registry, we take the model created in the previous steps\n", - "```\n", - "model = Model(\n", - " image_uri=image_uri,\n", - " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", - " sagemaker_session=pipeline_session,\n", - " role=role,\n", - ")\n", - "```\n", - "and call the `.register()` function on it while passing all the parameters needed for registering the model.\n", - "\n", - "We take the outputs of the `.register()` call and pass that to the `ModelStep` as step arguments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.model_metrics import MetricsSource, ModelMetrics\n", - "\n", - "model_metrics = ModelMetrics(\n", - " model_statistics=MetricsSource(\n", - " s3_uri=\"{}/evaluation.json\".format(\n", - " step_eval.arguments[\"ProcessingOutputConfig\"][\"Outputs\"][0][\"S3Output\"][\"S3Uri\"]\n", - " ),\n", - " content_type=\"application/json\",\n", - " )\n", - ")\n", - "\n", - "register_args = model.register(\n", - " content_types=[\"text/csv\"],\n", - " response_types=[\"text/csv\"],\n", - " inference_instances=[\"ml.t2.medium\", \"ml.m5.xlarge\"],\n", - " transform_instances=[\"ml.m5.xlarge\"],\n", - " model_package_group_name=model_package_group_name,\n", - " approval_status=model_approval_status,\n", - " model_metrics=model_metrics,\n", - ")\n", - "step_register = ModelStep(name=\"AbaloneRegisterModel\", step_args=register_args)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Create Model Step and Batch Transform to Process Data in Batch at Scale](img/pipeline-5.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Fail Step to Terminate the Pipeline Execution and Mark it as Failed\n", - "\n", - "This section walks you through the following steps:\n", - "\n", - "* Define a `FailStep` with customized error message, which indicates the cause of the execution failure.\n", - "* Enter the `FailStep` error message with a `Join` function, which appends a static text string with the dynamic `mse_threshold` parameter to build a more informative error message." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.workflow.fail_step import FailStep\n", - "from sagemaker.workflow.functions import Join\n", - "\n", - "step_fail = FailStep(\n", - " name=\"AbaloneMSEFail\",\n", - " error_message=Join(on=\" \", values=[\"Execution failed due to MSE >\", mse_threshold]),\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Fail Step to Terminate the Execution in Failed State](img/pipeline-8.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Condition Step to Check Accuracy and Conditionally Create a Model and Run a Batch Transformation and Register a Model in the Model Registry, Or Terminate the Execution in Failed State\n", - "\n", - "In this step, the model is registered only if the accuracy of the model, as determined by the evaluation step `step_eval`, exceeded a specified value. Otherwise, the pipeline execution fails and terminates. A `ConditionStep` enables pipelines to support conditional execution in the pipeline DAG based on the conditions of the step properties.\n", - "\n", - "In the following section, you:\n", - "\n", - "* Define a `ConditionLessThanOrEqualTo` on the accuracy value found in the output of the evaluation step, `step_eval`.\n", - "* Use the condition in the list of conditions in a `ConditionStep`.\n", - "* Pass the `CreateModelStep` and `TransformStep` steps, and the `RegisterModel` step collection into the `if_steps` of the `ConditionStep`, which are only executed if the condition evaluates to `True`.\n", - "* Pass the `FailStep` step into the `else_steps`of the `ConditionStep`, which is only executed if the condition evaluates to `False`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n", - "from sagemaker.workflow.condition_step import ConditionStep\n", - "from sagemaker.workflow.functions import JsonGet\n", - "\n", - "\n", - "cond_lte = ConditionLessThanOrEqualTo(\n", - " left=JsonGet(\n", - " step_name=step_eval.name,\n", - " property_file=evaluation_report,\n", - " json_path=\"regression_metrics.mse.value\",\n", - " ),\n", - " right=mse_threshold,\n", - ")\n", - "\n", - "step_cond = ConditionStep(\n", - " name=\"AbaloneMSECond\",\n", - " conditions=[cond_lte],\n", - " if_steps=[step_register, step_create_model, step_transform],\n", - " else_steps=[step_fail],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Condition Step to Check Accuracy and Conditionally Execute Steps](img/pipeline-6.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Define a Pipeline of Parameters, Steps, and Conditions\n", - "\n", - "In this section, combine the steps into a Pipeline so it can be executed.\n", - "\n", - "A pipeline requires a `name`, `parameters`, and `steps`. Names must be unique within an `(account, region)` pair.\n", - "\n", - "Note:\n", - "\n", - "* All the parameters used in the definitions must be present.\n", - "* Steps passed into the pipeline do not have to be listed in the order of execution. The SageMaker Pipeline service resolves the data dependency DAG as steps for the execution to complete.\n", - "* Steps must be unique to across the pipeline step list and all condition step if/else lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.workflow.pipeline import Pipeline\n", - "\n", - "\n", - "pipeline_name = f\"AbalonePipeline\"\n", - "pipeline = Pipeline(\n", - " name=pipeline_name,\n", - " parameters=[\n", - " processing_instance_count,\n", - " instance_type,\n", - " model_approval_status,\n", - " input_data,\n", - " batch_data,\n", - " mse_threshold,\n", - " ],\n", - " steps=[step_process, step_train, step_eval, step_cond],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "![Define a Pipeline of Parameters, Steps, and Conditions](img/pipeline-7.png)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### (Optional) Examining the pipeline definition\n", - "\n", - "The JSON of the pipeline definition can be examined to confirm the pipeline is well-defined and the parameters and step properties resolve correctly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "\n", - "definition = json.loads(pipeline.definition())\n", - "definition" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Submit the pipeline to SageMaker and start execution\n", - "\n", - "Submit the pipeline definition to the Pipeline service. The Pipeline service uses the role that is passed in to create all the jobs defined in the steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "pipeline.upsert(role_arn=role)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Start the pipeline and accept all the default parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution = pipeline.start()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Pipeline Operations: Examining and Waiting for Pipeline Execution\n", - "\n", - "Describe the pipeline execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution.describe()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Wait for the execution to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution.wait()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "List the steps in the execution. These are the steps in the pipeline that have been resolved by the step executor service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution.list_steps()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Examining the Evaluation\n", - "\n", - "Examine the resulting model evaluation after the pipeline completes. Download the resulting `evaluation.json` file from S3 and print the report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from pprint import pprint\n", - "\n", - "\n", - "evaluation_json = sagemaker.s3.S3Downloader.read_file(\n", - " \"{}/evaluation.json\".format(\n", - " step_eval.arguments[\"ProcessingOutputConfig\"][\"Outputs\"][0][\"S3Output\"][\"S3Uri\"]\n", - " )\n", - ")\n", - "pprint(json.loads(evaluation_json))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Lineage\n", - "\n", - "Review the lineage of the artifacts generated by the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import time\n", - "from sagemaker.lineage.visualizer import LineageTableVisualizer\n", - "\n", - "\n", - "viz = LineageTableVisualizer(sagemaker.session.Session())\n", - "for execution_step in reversed(execution.list_steps()):\n", - " print(execution_step)\n", - " display(viz.show(pipeline_execution_step=execution_step))\n", - " time.sleep(5)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Parametrized Executions\n", - "\n", - "You can run additional executions of the pipeline and specify different pipeline parameters. The `parameters` argument is a dictionary containing parameter names, and where the values are used to override the defaults values.\n", - "\n", - "Based on the performance of the model, you might want to kick off another pipeline execution on a compute-optimized instance type and set the model approval status to \"Approved\" automatically. This means that the model package version generated by the `RegisterModel` step is automatically ready for deployment through CI/CD pipelines, such as with SageMaker Projects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution = pipeline.start(\n", - " parameters=dict(\n", - " ModelApprovalStatus=\"Approved\",\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution.wait()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution.list_steps()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Apart from that, you might also want to adjust the MSE threshold to a smaller value and raise the bar for the accuracy of the registered model. In this case you can override the MSE threshold like the following:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution = pipeline.start(parameters=dict(MseThreshold=3.0))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "If the MSE threshold is not satisfied, the pipeline execution enters the `FailStep` and is marked as failed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "try:\n", - " execution.wait()\n", - "except Exception as error:\n", - " print(error)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "execution.list_steps()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", - "\n", - "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", - "\n", - "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n" - ] - } - ], - "metadata": { - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb b/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb deleted file mode 100644 index e1861407da..0000000000 --- a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb +++ /dev/null @@ -1,1709 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SageMaker Pipelines Lambda Step\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-pipelines|tabular|lambda-step|sagemaker-pipelines-lambda-step.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This notebook illustrates how a Lambda function can be run as a step in a SageMaker Pipeline.\n", - "\n", - "The steps in this pipeline include:\n", - "* Preprocess the Abalone dataset\n", - "* Train an XGBoost Model\n", - "* Evaluate the model performance\n", - "* Create a model\n", - "* Deploy the model to a SageMaker Hosted Endpoint using a Lambda Function, through SageMaker Pipelines\n", - "\n", - "A step to register the model into a Model Registry can be added to the pipeline using the `RegisterModel` step." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Runtime\n", - "\n", - "This notebook takes approximately 15 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Prerequisites](#Prerequisites)\n", - "1. [Configuration Setup](#Configuration-Setup)\n", - "1. [Data Preparation](#Data-Preparation)\n", - "1. [Model Training and Evaluation](#Model-Training-and-Evaluation)\n", - "1. [Setting up Lambda](#Setting-up-Lambda)\n", - "1. [Execute the Pipeline](#Execute-the-Pipeline)\n", - "1. [Clean up resources](#Clean-up-resources)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "The notebook execution role should have policies which enable the notebook to create a Lambda function. The Amazon managed policy `AmazonSageMakerPipelinesIntegrations` can be added to the notebook execution role to achieve the same effect.\n", - "\n", - "The policy description is as follows:\n", - "\n", - "```\n", - "\n", - "{\n", - " \"Version\": \"2012-10-17\",\n", - " \"Statement\": [\n", - " {\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": [\n", - " \"lambda:CreateFunction\",\n", - " \"lambda:DeleteFunction\",\n", - " \"lambda:InvokeFunction\",\n", - " \"lambda:UpdateFunctionCode\"\n", - " ],\n", - " \"Resource\": [\n", - " \"arn:aws:lambda:*:*:function:*sagemaker*\",\n", - " \"arn:aws:lambda:*:*:function:*sageMaker*\",\n", - " \"arn:aws:lambda:*:*:function:*SageMaker*\"\n", - " ]\n", - " },\n", - " {\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": [\n", - " \"sqs:CreateQueue\",\n", - " \"sqs:SendMessage\"\n", - " ],\n", - " \"Resource\": [\n", - " \"arn:aws:sqs:*:*:*sagemaker*\",\n", - " \"arn:aws:sqs:*:*:*sageMaker*\",\n", - " \"arn:aws:sqs:*:*:*SageMaker*\"\n", - " ]\n", - " },\n", - " {\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": [\n", - " \"iam:PassRole\"\n", - " ],\n", - " \"Resource\": \"arn:aws:iam::*:role/*\",\n", - " \"Condition\": {\n", - " \"StringEquals\": {\n", - " \"iam:PassedToService\": [\n", - " \"lambda.amazonaws.com\"\n", - " ]\n", - " }\n", - " }\n", - " }\n", - " ]\n", - "}\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Let's start by importing necessary packages and installing the SageMaker Python SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "import time\n", - "import boto3\n", - "import sagemaker\n", - "\n", - "from sagemaker.estimator import Estimator\n", - "from sagemaker.inputs import TrainingInput\n", - "\n", - "from sagemaker.processing import (\n", - " ProcessingInput,\n", - " ProcessingOutput,\n", - " Processor,\n", - " ScriptProcessor,\n", - ")\n", - "\n", - "from sagemaker import Model\n", - "from sagemaker.xgboost import XGBoostPredictor\n", - "from sagemaker.sklearn.processing import SKLearnProcessor\n", - "\n", - "from sagemaker.workflow.parameters import (\n", - " ParameterInteger,\n", - " ParameterString,\n", - ")\n", - "from sagemaker.workflow.pipeline import Pipeline\n", - "from sagemaker.workflow.properties import PropertyFile\n", - "from sagemaker.workflow.steps import ProcessingStep, TrainingStep, CacheConfig\n", - "from sagemaker.workflow.lambda_step import (\n", - " LambdaStep,\n", - " LambdaOutput,\n", - " LambdaOutputTypeEnum,\n", - ")\n", - "from sagemaker.workflow.model_step import ModelStep\n", - "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n", - "from sagemaker.workflow.condition_step import ConditionStep\n", - "from sagemaker.workflow.functions import JsonGet\n", - "from sagemaker.workflow.pipeline_context import PipelineSession\n", - "\n", - "from sagemaker.lambda_helper import Lambda\n", - "import sys" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!{sys.executable} -m pip install \"sagemaker>=2.99.0\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now configure the setup we need, which includes the session object from the SageMaker Python SDK, and neccessary configurations for the pipelines, such as object types, input and output buckets and so on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Create the SageMaker Session\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "pipeline_session = PipelineSession()\n", - "sm_client = sagemaker_session.sagemaker_client\n", - "region = sagemaker_session.boto_region_name\n", - "prefix = \"lambda-step-pipeline\"\n", - "\n", - "account_id = sagemaker_session.account_id()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Define variables and parameters needed for the Pipeline steps\n", - "\n", - "role = sagemaker.get_execution_role()\n", - "default_bucket = sagemaker_session.default_bucket()\n", - "base_job_prefix = \"lambda-step-example\"\n", - "s3_prefix = \"lambda-step-pipeline\"\n", - "\n", - "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n", - "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n", - "model_approval_status = ParameterString(\n", - " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", - ")\n", - "input_data = ParameterString(\n", - " name=\"InputDataUrl\",\n", - " default_value=f\"s3://sagemaker-example-files-prod-{boto3.Session().region_name}/datasets/tabular/uci_abalone/abalone.csv\",\n", - ")\n", - "model_approval_status = ParameterString(\n", - " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", - ")\n", - "\n", - "# Cache Pipeline steps to reduce execution time on subsequent executions\n", - "cache_config = CacheConfig(enable_caching=True, expire_after=\"30d\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preparation\n", - "\n", - "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets.\n", - "\n", - "The output of this step is used as the input to the TrainingStep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!mkdir -p code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%%writefile code/preprocess.py\n", - "\n", - "\"\"\"Feature engineers the abalone dataset.\"\"\"\n", - "import argparse\n", - "import logging\n", - "import os\n", - "import pathlib\n", - "import requests\n", - "import tempfile\n", - "\n", - "import boto3\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.INFO)\n", - "logger.addHandler(logging.StreamHandler())\n", - "\n", - "\n", - "# Since we get a headerless CSV file we specify the column names here.\n", - "feature_columns_names = [\n", - " \"sex\",\n", - " \"length\",\n", - " \"diameter\",\n", - " \"height\",\n", - " \"whole_weight\",\n", - " \"shucked_weight\",\n", - " \"viscera_weight\",\n", - " \"shell_weight\",\n", - "]\n", - "label_column = \"rings\"\n", - "\n", - "feature_columns_dtype = {\n", - " \"sex\": str,\n", - " \"length\": np.float64,\n", - " \"diameter\": np.float64,\n", - " \"height\": np.float64,\n", - " \"whole_weight\": np.float64,\n", - " \"shucked_weight\": np.float64,\n", - " \"viscera_weight\": np.float64,\n", - " \"shell_weight\": np.float64,\n", - "}\n", - "label_column_dtype = {\"rings\": np.float64}\n", - "\n", - "\n", - "def merge_two_dicts(x, y):\n", - " \"\"\"Merges two dicts, returning a new copy.\"\"\"\n", - " z = x.copy()\n", - " z.update(y)\n", - " return z\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " logger.debug(\"Starting preprocessing.\")\n", - " parser = argparse.ArgumentParser()\n", - " parser.add_argument(\"--input-data\", type=str, required=True)\n", - " args = parser.parse_args()\n", - "\n", - " base_dir = \"/opt/ml/processing\"\n", - " pathlib.Path(f\"{base_dir}/data\").mkdir(parents=True, exist_ok=True)\n", - " input_data = args.input_data\n", - " bucket = input_data.split(\"/\")[2]\n", - " key = \"/\".join(input_data.split(\"/\")[3:])\n", - "\n", - " logger.info(\"Downloading data from bucket: %s, key: %s\", bucket, key)\n", - " fn = f\"{base_dir}/data/abalone-dataset.csv\"\n", - " s3 = boto3.resource(\"s3\")\n", - " s3.Bucket(bucket).download_file(key, fn)\n", - "\n", - " logger.debug(\"Reading downloaded data.\")\n", - " df = pd.read_csv(\n", - " fn,\n", - " header=None,\n", - " names=feature_columns_names + [label_column],\n", - " dtype=merge_two_dicts(feature_columns_dtype, label_column_dtype),\n", - " )\n", - " os.unlink(fn)\n", - "\n", - " logger.debug(\"Defining transformers.\")\n", - " numeric_features = list(feature_columns_names)\n", - " numeric_features.remove(\"sex\")\n", - " numeric_transformer = Pipeline(\n", - " steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", - " (\"scaler\", StandardScaler()),\n", - " ]\n", - " )\n", - "\n", - " categorical_features = [\"sex\"]\n", - " categorical_transformer = Pipeline(\n", - " steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n", - " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n", - " ]\n", - " )\n", - "\n", - " preprocess = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features),\n", - " ]\n", - " )\n", - "\n", - " logger.info(\"Applying transforms.\")\n", - " y = df.pop(\"rings\")\n", - " X_pre = preprocess.fit_transform(df)\n", - " y_pre = y.to_numpy().reshape(len(y), 1)\n", - "\n", - " X = np.concatenate((y_pre, X_pre), axis=1)\n", - "\n", - " logger.info(\"Splitting %d rows of data into train, validation, test datasets.\", len(X))\n", - " np.random.shuffle(X)\n", - " train, validation, test = np.split(X, [int(0.7 * len(X)), int(0.85 * len(X))])\n", - "\n", - " logger.info(\"Writing out datasets to %s.\", base_dir)\n", - " pd.DataFrame(train).to_csv(f\"{base_dir}/train/train.csv\", header=False, index=False)\n", - " pd.DataFrame(validation).to_csv(\n", - " f\"{base_dir}/validation/validation.csv\", header=False, index=False\n", - " )\n", - " pd.DataFrame(test).to_csv(f\"{base_dir}/test/test.csv\", header=False, index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Process the training data step using a python script.\n", - "# Split the training data set into train, test, and validation datasets\n", - "\n", - "sklearn_processor = SKLearnProcessor(\n", - " framework_version=\"0.23-1\",\n", - " instance_type=\"ml.m5.xlarge\",\n", - " instance_count=processing_instance_count,\n", - " base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n", - " sagemaker_session=pipeline_session,\n", - " role=role,\n", - ")\n", - "\n", - "processor_args = sklearn_processor.run(\n", - " outputs=[\n", - " ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n", - " ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n", - " ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n", - " ],\n", - " code=\"code/preprocess.py\",\n", - " arguments=[\"--input-data\", input_data],\n", - ")\n", - "\n", - "step_process = ProcessingStep(\n", - " name=\"PreprocessAbaloneData\",\n", - " step_args=processor_args,\n", - " cache_config=cache_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Training and Evaluation\n", - "\n", - "We will now train an XGBoost model using the SageMaker Python SDK and the output of the ProcessingStep." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training the Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Define the output path for the model artifacts from the Hyperparameter Tuning Job\n", - "model_path = f\"s3://{default_bucket}/{base_job_prefix}/AbaloneTrain\"\n", - "\n", - "image_uri = sagemaker.image_uris.retrieve(\n", - " framework=\"xgboost\",\n", - " region=region,\n", - " version=\"1.0-1\",\n", - " py_version=\"py3\",\n", - " instance_type=\"ml.m5.xlarge\",\n", - ")\n", - "\n", - "xgb_train = Estimator(\n", - " image_uri=image_uri,\n", - " instance_type=training_instance_type,\n", - " instance_count=1,\n", - " output_path=model_path,\n", - " base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n", - " sagemaker_session=pipeline_session,\n", - " role=role,\n", - ")\n", - "\n", - "xgb_train.set_hyperparameters(\n", - " objective=\"reg:linear\",\n", - " num_round=50,\n", - " max_depth=5,\n", - " eta=0.2,\n", - " gamma=4,\n", - " min_child_weight=6,\n", - " subsample=0.7,\n", - " silent=0,\n", - ")\n", - "\n", - "train_args = xgb_train.fit(\n", - " inputs={\n", - " \"train\": TrainingInput(\n", - " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n", - " content_type=\"text/csv\",\n", - " ),\n", - " \"validation\": TrainingInput(\n", - " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", - " \"validation\"\n", - " ].S3Output.S3Uri,\n", - " content_type=\"text/csv\",\n", - " ),\n", - " },\n", - ")\n", - "\n", - "step_train = TrainingStep(\n", - " name=\"TrainAbaloneModel\",\n", - " step_args=train_args,\n", - " cache_config=cache_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluating the model\n", - "\n", - "Use a processing job to evaluate the model from the TrainingStep. If the output of the evaluation is True, a model is created and a Lambda function is invoked to deploy the model to a SageMaker Endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%%writefile code/evaluate.py\n", - "\n", - "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n", - "import json\n", - "import logging\n", - "import pathlib\n", - "import pickle\n", - "import tarfile\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xgboost\n", - "\n", - "from sklearn.metrics import mean_squared_error\n", - "\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.INFO)\n", - "logger.addHandler(logging.StreamHandler())\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " logger.debug(\"Starting evaluation.\")\n", - " model_path = \"/opt/ml/processing/model/model.tar.gz\"\n", - " with tarfile.open(model_path) as tar:\n", - " tar.extractall(path=\".\")\n", - "\n", - " logger.debug(\"Loading xgboost model.\")\n", - " model = pickle.load(open(\"xgboost-model\", \"rb\"))\n", - "\n", - " logger.debug(\"Reading test data.\")\n", - " test_path = \"/opt/ml/processing/test/test.csv\"\n", - " df = pd.read_csv(test_path, header=None)\n", - "\n", - " logger.debug(\"Reading test data.\")\n", - " y_test = df.iloc[:, 0].to_numpy()\n", - " df.drop(df.columns[0], axis=1, inplace=True)\n", - " X_test = xgboost.DMatrix(df.values)\n", - "\n", - " logger.info(\"Performing predictions against test data.\")\n", - " predictions = model.predict(X_test)\n", - "\n", - " logger.debug(\"Calculating mean squared error.\")\n", - " mse = mean_squared_error(y_test, predictions)\n", - " std = np.std(y_test - predictions)\n", - " report_dict = {\n", - " \"regression_metrics\": {\n", - " \"mse\": {\"value\": mse, \"standard_deviation\": std},\n", - " },\n", - " }\n", - "\n", - " output_dir = \"/opt/ml/processing/evaluation\"\n", - " pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n", - "\n", - " logger.info(\"Writing out evaluation report with mse: %f\", mse)\n", - " evaluation_path = f\"{output_dir}/evaluation.json\"\n", - " with open(evaluation_path, \"w\") as f:\n", - " f.write(json.dumps(report_dict))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# A ProcessingStep is used to evaluate the performance of the trained model.\n", - "# Based on the results of the evaluation, the model is created and deployed.\n", - "\n", - "script_eval = ScriptProcessor(\n", - " image_uri=image_uri,\n", - " command=[\"python3\"],\n", - " instance_type=\"ml.m5.xlarge\",\n", - " instance_count=1,\n", - " base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n", - " sagemaker_session=pipeline_session,\n", - " role=role,\n", - ")\n", - "\n", - "evaluation_report = PropertyFile(\n", - " name=\"AbaloneEvaluationReport\",\n", - " output_name=\"evaluation\",\n", - " path=\"evaluation.json\",\n", - ")\n", - "\n", - "eval_args = script_eval.run(\n", - " inputs=[\n", - " ProcessingInput(\n", - " source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", - " destination=\"/opt/ml/processing/model\",\n", - " ),\n", - " ProcessingInput(\n", - " source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n", - " destination=\"/opt/ml/processing/test\",\n", - " ),\n", - " ],\n", - " outputs=[\n", - " ProcessingOutput(\n", - " output_name=\"evaluation\",\n", - " source=\"/opt/ml/processing/evaluation\",\n", - " destination=f\"s3://{default_bucket}/{s3_prefix}/evaluation_report\",\n", - " ),\n", - " ],\n", - " code=\"code/evaluate.py\",\n", - ")\n", - "step_eval = ProcessingStep(\n", - " name=\"EvaluateAbaloneModel\",\n", - " step_args=eval_args,\n", - " property_files=[evaluation_report],\n", - " cache_config=cache_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating the final model object\n", - "\n", - "The model is created and the name of the model is provided to the Lambda function for deployment. The `CreateModelStep` dynamically assigns a name to the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Create Model\n", - "model = Model(\n", - " image_uri=image_uri,\n", - " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", - " sagemaker_session=pipeline_session,\n", - " role=role,\n", - " predictor_cls=XGBoostPredictor,\n", - ")\n", - "\n", - "step_create_model = ModelStep(\n", - " name=\"CreateModel\",\n", - " step_args=model.create(\"ml.m4.large\"),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up Lambda\n", - "\n", - "When defining the LambdaStep, the SageMaker Lambda helper class provides helper functions for creating the Lambda function. Users can either use the `lambda_func` argument to provide the function ARN to an already deployed Lambda function OR use the `Lambda` class to create a Lambda function by providing a script, function name and role for the Lambda function.\n", - "\n", - "When passing inputs to the Lambda, the `inputs` argument can be used and within the Lambda function's handler, the `event` argument can be used to retrieve the inputs.\n", - "\n", - "The dictionary response from the Lambda function is parsed through the `LambdaOutput` objects provided to the `outputs` argument. The `output_name` in `LambdaOutput` corresponds to the dictionary key in the Lambda's return dictionary." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define the Lambda function\n", - "\n", - "Users can choose the leverage the Lambda helper class to create a Lambda function and provide that function object to the LambdaStep. Alternatively, users can use a pre-deployed Lambda function and provide the function ARN to the `Lambda` helper class in the Lambda step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%%writefile code/lambda_helper.py\n", - "\n", - "\"\"\"\n", - "This Lambda function creates an Endpoint Configuration and deploys a model to an Endpoint.\n", - "The name of the model to deploy is provided via the `event` argument\n", - "\"\"\"\n", - "\n", - "import json\n", - "import boto3\n", - "\n", - "\n", - "def lambda_handler(event, context):\n", - " \"\"\" \"\"\"\n", - " sm_client = boto3.client(\"sagemaker\")\n", - "\n", - " # The name of the model created in the Pipeline CreateModelStep\n", - " model_name = event[\"model_name\"]\n", - "\n", - " endpoint_config_name = event[\"endpoint_config_name\"]\n", - " endpoint_name = event[\"endpoint_name\"]\n", - "\n", - " create_endpoint_config_response = sm_client.create_endpoint_config(\n", - " EndpointConfigName=endpoint_config_name,\n", - " ProductionVariants=[\n", - " {\n", - " \"InstanceType\": \"ml.m4.xlarge\",\n", - " \"InitialVariantWeight\": 1,\n", - " \"InitialInstanceCount\": 1,\n", - " \"ModelName\": model_name,\n", - " \"VariantName\": \"AllTraffic\",\n", - " }\n", - " ],\n", - " )\n", - "\n", - " create_endpoint_response = sm_client.create_endpoint(\n", - " EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name\n", - " )\n", - "\n", - " return {\n", - " \"statusCode\": 200,\n", - " \"body\": json.dumps(\"Created Endpoint!\"),\n", - " \"other_key\": \"example_value\",\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setting up the custom IAM Role\n", - "\n", - "The Lambda function needs an IAM role that allows it to deploy a SageMaker Endpoint. The role ARN must be provided in the LambdaStep.\n", - "\n", - "The Lambda role should at minimum have policies to allow `sagemaker:CreateModel`, `sagemaker:CreateEndpointConfig`, `sagemaker:CreateEndpoint` in addition to the based Lambda execution policies.\n", - "\n", - "A helper function in `iam_helper.py` is available to create the Lambda function role. Please note that the role uses the Amazon managed policy - `SageMakerFullAccess`. This should be replaced with an IAM policy with least privileges as per AWS IAM best practices." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from iam_helper import create_lambda_role\n", - "\n", - "lambda_role = create_lambda_role(\"lambda-deployment-role\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Custom Lambda Step\n", - "\n", - "current_time = time.strftime(\"%m-%d-%H-%M-%S\", time.localtime())\n", - "model_name = \"demo-lambda-model\" + current_time\n", - "endpoint_config_name = \"demo-lambda-deploy-endpoint-config-\" + current_time\n", - "endpoint_name = \"demo-lambda-deploy-endpoint-\" + current_time\n", - "\n", - "function_name = \"sagemaker-lambda-step-endpoint-deploy-\" + current_time\n", - "\n", - "# Lambda helper class can be used to create the Lambda function\n", - "func = Lambda(\n", - " function_name=function_name,\n", - " execution_role_arn=lambda_role,\n", - " script=\"code/lambda_helper.py\",\n", - " handler=\"lambda_helper.lambda_handler\",\n", - ")\n", - "\n", - "output_param_1 = LambdaOutput(output_name=\"statusCode\", output_type=LambdaOutputTypeEnum.String)\n", - "output_param_2 = LambdaOutput(output_name=\"body\", output_type=LambdaOutputTypeEnum.String)\n", - "output_param_3 = LambdaOutput(output_name=\"other_key\", output_type=LambdaOutputTypeEnum.String)\n", - "\n", - "step_deploy_lambda = LambdaStep(\n", - " name=\"LambdaStep\",\n", - " lambda_func=func,\n", - " inputs={\n", - " \"model_name\": step_create_model.properties.ModelName,\n", - " \"endpoint_config_name\": endpoint_config_name,\n", - " \"endpoint_name\": endpoint_name,\n", - " },\n", - " outputs=[output_param_1, output_param_2, output_param_3],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# ConditionStep for evaluating model quality and branching execution.\n", - "# The `json_path` value is based on the `report_dict` variable in `evaluate.py`\n", - "\n", - "cond_lte = ConditionLessThanOrEqualTo(\n", - " left=JsonGet(\n", - " step_name=step_eval.name,\n", - " property_file=evaluation_report,\n", - " json_path=\"regression_metrics.mse.value\",\n", - " ),\n", - " right=6.0,\n", - ")\n", - "\n", - "step_cond = ConditionStep(\n", - " name=\"CheckMSEAbaloneEvaluation\",\n", - " conditions=[cond_lte],\n", - " if_steps=[step_create_model, step_deploy_lambda],\n", - " else_steps=[],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Use the same pipeline name across executions for cache usage.\n", - "\n", - "pipeline_name = \"lambda-step-pipeline\" + current_time\n", - "\n", - "pipeline = Pipeline(\n", - " name=pipeline_name,\n", - " parameters=[\n", - " processing_instance_count,\n", - " training_instance_type,\n", - " input_data,\n", - " model_approval_status,\n", - " ],\n", - " steps=[step_process, step_train, step_eval, step_cond],\n", - " sagemaker_session=pipeline_session,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Execute the Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "definition = json.loads(pipeline.definition())\n", - "definition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "pipeline.upsert(role_arn=role)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "execution = pipeline.start()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "execution.wait()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Create a SageMaker client\n", - "sm_client = sagemaker.Session().sagemaker_client\n", - "\n", - "# Wait for the endpoint to be in service\n", - "waiter = sm_client.get_waiter(\"endpoint_in_service\")\n", - "waiter.wait(EndpointName=endpoint_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources\n", - "\n", - "Running the following cell will delete the following resources created in this notebook -\n", - "* SageMaker Model\n", - "* SageMaker Endpoint Configuration\n", - "* SageMaker Endpoint\n", - "* SageMaker Pipeline\n", - "* Lambda Function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Get the model name from the EndpointCofig. The CreateModelStep properties are not available\n", - "# outside the Pipeline execution context so `step_create_model.properties.ModelName`\n", - "# cannot be used while deleting the model.\n", - "\n", - "model_name = sm_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)[\n", - " \"ProductionVariants\"\n", - "][0][\"ModelName\"]\n", - "\n", - "# Delete the Model\n", - "sm_client.delete_model(ModelName=model_name)\n", - "\n", - "# Delete the EndpointConfig\n", - "sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n", - "\n", - "# Delete the Endpoint\n", - "sm_client.delete_endpoint(EndpointName=endpoint_name)\n", - "\n", - "# Delete the Lambda function\n", - "func.delete()\n", - "\n", - "# Delete the Pipeline\n", - "sm_client.delete_pipeline(PipelineName=pipeline_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-pipelines|tabular|lambda-step|sagemaker-pipelines-lambda-step.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-pipelines|tabular|lambda-step|sagemaker-pipelines-lambda-step.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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"mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "metadata": { - "interpreter": { - "hash": "ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb b/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb deleted file mode 100644 index 4523a10420..0000000000 --- a/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb +++ /dev/null @@ -1,684 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Iris Training and Prediction with Sagemaker Scikit-learn\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "This tutorial shows you how to use [Scikit-learn](https://scikit-learn.org/stable/) with SageMaker by utilizing the pre-built container. Scikit-learn is a popular Python machine learning framework. It includes a number of different algorithms for classification, regression, clustering, dimensionality reduction, and data/feature pre-processing. \n", - "\n", - "The [sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk) module makes it easy to take existing scikit-learn code, which we show by training a model on the Iris dataset and generating a set of predictions. For more information about the Scikit-learn container, see the [sagemaker-scikit-learn-containers](https://github.com/aws/sagemaker-scikit-learn-container) repository and the [sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk) repository.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 15 minutes to run.\n", - "\n", - "## Contents\n", - "* [Upload the data for training](#upload_data)\n", - "* [Create a Scikit-learn script to train with](#create_sklearn_script)\n", - "* [Create the SageMaker Scikit Estimator](#create_sklearn_estimator)\n", - "* [Train the SKLearn Estimator on the Iris data](#train_sklearn)\n", - "* [Use the trained model to make inference requests](#inference)\n", - " * [Deploy the model](#deploy)\n", - " * [Choose some data and use it for a prediction](#prediction_request)\n", - " * [Endpoint cleanup](#endpoint_cleanup)\n", - "* [Batch Transform](#batch_transform)\n", - " * [Prepare Input Data](#prepare_input_data)\n", - " * [Run Transform Job](#run_transform_job)\n", - " * [Check Output Data](#check_output_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%pip install -U sagemaker>=2.15" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "First, let's create our Sagemaker session and role, and create a S3 prefix to use for the notebook example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# S3 prefix\n", - "prefix = \"DEMO-scikit-iris\"\n", - "\n", - "import sagemaker\n", - "from sagemaker import get_execution_role\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "region = sagemaker_session.boto_region_name\n", - "role = get_execution_role()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Upload the data for training \n", - "\n", - "When training large models with huge amounts of data, you may use big data tools like Amazon Athena, AWS Glue, or Amazon EMR to process your data backed by S3. For the purposes of this example, we're using a sample of the classic [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris). We load the dataset, write it locally, then upload it to S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import boto3\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "\n", - "os.makedirs(\"./data\", exist_ok=True)\n", - "\n", - "s3_client = boto3.client(\"s3\")\n", - "s3_client.download_file(\n", - " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/iris/iris.data\", \"./data/iris.csv\"\n", - ")\n", - "\n", - "df_iris = pd.read_csv(\"./data/iris.csv\", header=None)\n", - "df_iris[4] = df_iris[4].map({\"Iris-setosa\": 0, \"Iris-versicolor\": 1, \"Iris-virginica\": 2})\n", - "iris = df_iris[[4, 0, 1, 2, 3]].to_numpy()\n", - "np.savetxt(\"./data/iris.csv\", iris, delimiter=\",\", fmt=\"%1.1f, %1.3f, %1.3f, %1.3f, %1.3f\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Once we have the data locally, we can use use the tools provided by the SageMaker Python SDK to upload the data to a default bucket. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "WORK_DIRECTORY = \"data\"\n", - "\n", - "train_input = sagemaker_session.upload_data(\n", - " WORK_DIRECTORY, key_prefix=\"{}/{}\".format(prefix, WORK_DIRECTORY)\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Create a Scikit-learn script for training \n", - "SageMaker can run a scikit-learn script using the `SKLearn` estimator. When run on SageMaker, a number of helpful environment variables are available to access properties of the training environment, such as:\n", - "\n", - "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to. Any artifacts saved in this folder are uploaded to S3 for model hosting after the training job completes.\n", - "* `SM_OUTPUT_DIR`: A string representing the file system path to write output artifacts to. Output artifacts may include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed and uploaded to S3 to the same S3 prefix as the model artifacts.\n", - "\n", - "Supposing two input channels, 'train' and 'test', were used in the call to the `SKLearn` estimator's `fit()` method, the following environment variables are set, following the format `SM_CHANNEL_[channel_name]`:\n", - "\n", - "* `SM_CHANNEL_TRAIN`: A string representing the path to the directory containing data in the 'train' channel.\n", - "* `SM_CHANNEL_TEST`: Same as above, but for the 'test' channel.\n", - "\n", - "A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to the `model_dir` so that it can be hosted later. Hyperparameters are passed to your script as arguments and can be retrieved with an `argparse.ArgumentParser` instance. For example, the script that we run in this notebook is below:\n", - "\n", - "```python\n", - "from __future__ import print_function\n", - "\n", - "import argparse\n", - "import joblib\n", - "import os\n", - "import pandas as pd\n", - "\n", - "from sklearn import tree\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # Hyperparameters are described here. In this simple example we are just including one hyperparameter.\n", - " parser.add_argument('--max_leaf_nodes', type=int, default=-1)\n", - "\n", - " # Sagemaker specific arguments. Defaults are set in the environment variables.\n", - " parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])\n", - " parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n", - " parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n", - "\n", - " args = parser.parse_args()\n", - "\n", - " # Take the set of files and read them all into a single pandas dataframe\n", - " input_files = [ os.path.join(args.train, file) for file in os.listdir(args.train) ]\n", - " if len(input_files) == 0:\n", - " raise ValueError(('There are no files in {}.\\n' +\n", - " 'This usually indicates that the channel ({}) was incorrectly specified,\\n' +\n", - " 'the data specification in S3 was incorrectly specified or the role specified\\n' +\n", - " 'does not have permission to access the data.').format(args.train, \"train\"))\n", - " raw_data = [ pd.read_csv(file, header=None, engine=\"python\") for file in input_files ]\n", - " train_data = pd.concat(raw_data)\n", - "\n", - " # labels are in the first column\n", - " train_y = train_data.iloc[:, 0]\n", - " train_X = train_data.iloc[:, 1:]\n", - "\n", - " # Here we support a single hyperparameter, 'max_leaf_nodes'. Note that you can add as many\n", - " # as your training my require in the ArgumentParser above.\n", - " max_leaf_nodes = args.max_leaf_nodes\n", - "\n", - " # Now use scikit-learn's decision tree classifier to train the model.\n", - " clf = tree.DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)\n", - " clf = clf.fit(train_X, train_y)\n", - "\n", - " # Print the coefficients of the trained classifier, and save the coefficients\n", - " joblib.dump(clf, os.path.join(args.model_dir, \"model.joblib\"))\n", - "\n", - "\n", - "def model_fn(model_dir):\n", - " \"\"\"Deserialized and return fitted model\n", - " \n", - " Note that this should have the same name as the serialized model in the main method\n", - " \"\"\"\n", - " clf = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n", - " return clf\n", - "```" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Because the Scikit-learn container imports your training script, you should always put your training code in a main guard `(if __name__=='__main__':)` so that the container does not inadvertently run your training code at the wrong point in execution.\n", - "\n", - "For more information about training environment variables, please visit https://github.com/aws/sagemaker-containers." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Create a SageMaker SKLearn Estimator \n", - "\n", - "To run our Scikit-learn training script on SageMaker, we construct a `sagemaker.sklearn.estimator.sklearn` estimator, which accepts several constructor arguments:\n", - "\n", - "* __entry_point__: The path to the Python script SageMaker runs for training and prediction.\n", - "* __role__: The IAM role ARN.\n", - "* __instance_type__ *(optional)*: The type of SageMaker instances for training. __Note__: Because Scikit-learn does not natively support GPU training, SageMaker Scikit-learn does not currently support training on GPU instance types.\n", - "* __sagemaker_session__ *(optional)*: The session used to train on SageMaker.\n", - "* __hyperparameters__ *(optional)*: A dictionary passed to the train function as hyperparameters.\n", - "\n", - "To see the code for the SKLearn Estimator, see: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/sklearn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from sagemaker.sklearn.estimator import SKLearn\n", - "\n", - "FRAMEWORK_VERSION = \"1.2-1\"\n", - "script_path = \"scikit_learn_iris.py\"\n", - "\n", - "sklearn = SKLearn(\n", - " entry_point=script_path,\n", - " framework_version=FRAMEWORK_VERSION,\n", - " instance_type=\"ml.c4.xlarge\",\n", - " role=role,\n", - " sagemaker_session=sagemaker_session,\n", - " hyperparameters={\"max_leaf_nodes\": 30},\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Train SKLearn Estimator on Iris data \n", - "Training is straightforward, just call `fit()` on the Estimator! This starts a SageMaker training job that downloads the data, invokes our scikit-learn code (in the provided script file), and saves any model artifacts that the script creates." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "sklearn.fit({\"train\": train_input})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Use the trained model to make inference requests \n", - "\n", - "### Deploy the model \n", - "\n", - "Deploying the model to SageMaker hosting just requires a `deploy()` call on the fitted model. This call takes an instance count and instance type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "predictor = sklearn.deploy(initial_instance_count=1, instance_type=\"ml.m5.xlarge\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Choose some data and use it for a prediction \n", - "\n", - "We extract some data we used for training and make predictions on it. This is not a recommended statistical practice, but it demonstrates how to run inference using the deployed endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import itertools\n", - "import pandas as pd\n", - "\n", - "shape = pd.read_csv(\"data/iris.csv\", header=None)\n", - "\n", - "a = [50 * i for i in range(3)]\n", - "b = [40 + i for i in range(10)]\n", - "indices = [i + j for i, j in itertools.product(a, b)]\n", - "\n", - "test_data = shape.iloc[indices[:-1]]\n", - "test_X = test_data.iloc[:, 1:]\n", - "test_y = test_data.iloc[:, 0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "To make a prediction, call `predict()` on the predictor returned from `deploy()`, passing the data to do predictions on. The output from the endpoint returns a numerical representation of the classification prediction; in the original dataset, these are three flower category names, but in this example the labels are numerical. We can compare against the original label that we parsed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "print(predictor.predict(test_X.values))\n", - "print(test_y.values)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Endpoint cleanup \n", - "\n", - "When you're done with the endpoint, delete it to release the resources and avoid incurring additional cost." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "predictor.delete_endpoint()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Batch Transform \n", - "We can also use the trained model for asynchronous batch inference on S3 data using SageMaker Batch Transform." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Define an SKLearn Transformer from the trained SKLearn Estimator\n", - "transformer = sklearn.transformer(instance_count=1, instance_type=\"ml.m5.xlarge\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Prepare Input Data \n", - "We extract 10 random samples of 100 rows from the training data, split the features (X) from the labels (Y), and upload the input data to a given location in S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%%bash\n", - "# Randomly sample the iris dataset 10 times, then split X and Y\n", - "mkdir -p batch_data/XY batch_data/X batch_data/Y\n", - "for i in {0..9}; do\n", - " cat data/iris.csv | shuf -n 100 > batch_data/XY/iris_sample_${i}.csv\n", - " cat batch_data/XY/iris_sample_${i}.csv | cut -d',' -f2- > batch_data/X/iris_sample_X_${i}.csv\n", - " cat batch_data/XY/iris_sample_${i}.csv | cut -d',' -f1 > batch_data/Y/iris_sample_Y_${i}.csv\n", - "done" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Upload input data from local file system to S3\n", - "batch_input_s3 = sagemaker_session.upload_data(\"batch_data/X\", key_prefix=prefix + \"/batch_input\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Run Transform Job \n", - "Using the Transformer, run a transform job on the S3 input data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Start a transform job and wait for it to finish\n", - "transformer.transform(batch_input_s3, content_type=\"text/csv\")\n", - "print(\"Waiting for transform job: \" + transformer.latest_transform_job.job_name)\n", - "transformer.wait()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Check Output Data \n", - "After the transform job has completed, download the output data from S3. For each file \"f\" in the input data, we have a corresponding file \"f.out\" containing the predicted labels from each input row. We can compare the predicted labels to the true labels saved earlier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Download the output data from S3 to local file system\n", - "batch_output = transformer.output_path\n", - "!mkdir -p batch_data/output\n", - "!aws s3 cp --recursive $batch_output/ batch_data/output/\n", - "# Head to see what the batch output looks like\n", - "!head batch_data/output/*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%%bash\n", - "# For each sample file, compare the predicted labels from batch output to the true labels\n", - "for i in {1..9}; do\n", - " diff -s batch_data/Y/iris_sample_Y_${i}.csv \\\n", - " <(cat batch_data/output/iris_sample_X_${i}.csv.out | sed 's/[[\"]//g' | sed 's/, \\|]/\\n/g') \\\n", - " | sed \"s/\\/dev\\/fd\\/63/batch_data\\/output\\/iris_sample_X_${i}.csv.out/\"\n", - "done" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n" - ] - } - ], - "metadata": { - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker-script-mode/pytorch_bert/deploy_bert.ipynb b/sagemaker-script-mode/pytorch_bert/deploy_bert.ipynb deleted file mode 100644 index 3672db392b..0000000000 --- a/sagemaker-script-mode/pytorch_bert/deploy_bert.ipynb +++ /dev/null @@ -1,295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Host a Pretrained Model on SageMaker\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "Amazon SageMaker is a service to accelerate the entire machine learning lifecycle. It includes components for building, training and deploying machine learning models. Each SageMaker component is modular, so you're welcome to only use the features needed for your use case. One of the most popular features of SageMaker is model hosting. Using SageMaker hosting, you can deploy your model as a scalable, highly available, multi-process API endpoint with a few lines of code. Read more at [Deploy a Model in Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html). In this notebook, we demonstrate how to host a pretrained BERT model in Amazon SageMaker to extract embeddings from text.\n", - "\n", - "SageMaker provides prebuilt containers that can be used for training, hosting, or data processing. The inference containers include a web serving stack, so you don't need to install and configure one. We use the SageMaker PyTorch container, but you may use the TensorFlow container, or bring your own container if needed. See all containers at [AWS Deep Learning Containers](https://github.com/aws/deep-learning-containers).\n", - "\n", - "This notebook walks you through how to deploy a pretrained Hugging Face model as a scalable, highly available, production-ready API.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 5 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Retrieve Model Artifacts](#Retrieve-Model-Artifacts)\n", - "1. [Write the Inference Script](#Write-the-Inference-Script)\n", - "1. [Package Model](#Package-Model)\n", - "1. [Deploy Model](#Deploy-Model)\n", - "1. [Get Predictions](#Get-Predictions)\n", - "1. [Conclusion](#Conclusion)\n", - "1. [Cleanup](#Cleanup)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve Model Artifacts\n", - "\n", - "First we download the model artifacts for the pretrained BERT model. BERT is a popular natural language processing (NLP) model that extracts meaning and context from text. You can read the original paper, [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "!pip install transformers==3.3.1 sagemaker==2.15.0 --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from transformers import BertTokenizer, BertModel\n", - "\n", - "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n", - "model = BertModel.from_pretrained(\"bert-base-uncased\")\n", - "\n", - "model_path = \"model/\"\n", - "code_path = \"code/\"\n", - "\n", - "if not os.path.exists(model_path):\n", - " os.mkdir(model_path)\n", - "\n", - "model.save_pretrained(save_directory=model_path)\n", - "tokenizer.save_pretrained(save_directory=model_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Write the Inference Script\n", - "\n", - "Since we are bringing a model to SageMaker, we must create an inference script. The script runs inside our PyTorch container. Our script should include a function for model loading, and optionally functions generating predictions, and input/output processing. The PyTorch container provides default implementations for generating a prediction and input/output processing. By including these functions in your script you are overriding the default functions. You can find additional details at [Serve a PyTorch Model](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#serve-a-pytorch-model).\n", - "\n", - "The next cell shows our inference script, whcich uses the [Transformers library from HuggingFace](https://huggingface.co/transformers/). This library is not installed in the container by default, so we add it in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pygmentize code/inference_code.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Package Model\n", - "\n", - "For hosting, SageMaker requires that the deployment package be structured in a compatible format. It expects all files to be packaged in a tar archive named \"model.tar.gz\" with gzip compression. To install additional libraries at container startup, we can add a requirements.txt file that specifies the libraries to be installed using [pip](https://pypi.org/project/pip/). Read more at [Using Third-Party Libraries](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#using-third-party-libraries). Within the archive, the PyTorch container expects all inference code and requirements.txt file to be inside the code/ directory. See the [Model Directory Structure](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#model-directory-structure) guide for a thorough explanation of the required directory structure. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tarfile\n", - "\n", - "zipped_model_path = os.path.join(model_path, \"model.tar.gz\")\n", - "\n", - "with tarfile.open(zipped_model_path, \"w:gz\") as tar:\n", - " tar.add(model_path)\n", - " tar.add(code_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy Model\n", - "\n", - "Now that we have our deployment package, we can use the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/index.html) to deploy our API endpoint with two lines of code. We need to specify an IAM role for the SageMaker endpoint to use. Minimally, it needs read access to the default SageMaker bucket (usually named `s3://sagemaker-{region}-{your account ID}`) so it can read the deployment package. When we call `deploy()`, the SDK saves our deployment archive to S3 for the SageMaker endpoint to use. We use the helper function [get_execution_role()](https://sagemaker.readthedocs.io/en/stable/api/utility/session.html?highlight=get_execution_role#sagemaker.session.get_execution_role) to retrieve our current IAM role so we can pass it to the SageMaker endpoint. Minimally it requires read access to the model artifacts in S3 and the [ECR repository](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) where the container image is stored by AWS.\n", - "\n", - "\n", - "You may notice that we specify our PyTorch version and Python version when creating the PyTorchModel object. The SageMaker SDK uses these parameters to determine which PyTorch container to use. \n", - "\n", - "We use an m5.xlarge instance for our endpoint to ensure we have sufficient memory to serve our model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.pytorch import PyTorchModel\n", - "from sagemaker import get_execution_role\n", - "import time\n", - "\n", - "endpoint_name = \"bert-base-\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n", - "\n", - "model = PyTorchModel(\n", - " entry_point=\"inference_code.py\",\n", - " model_data=zipped_model_path,\n", - " role=get_execution_role(),\n", - " framework_version=\"1.5\",\n", - " py_version=\"py3\",\n", - ")\n", - "\n", - "predictor = model.deploy(\n", - " initial_instance_count=1, instance_type=\"ml.m5.xlarge\", endpoint_name=endpoint_name\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get Predictions\n", - "\n", - "Now that our API endpoint is deployed, we send it text to get predictions from our BERT model. You can use the SageMaker SDK or the [InvokeEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html) method of the SageMaker Runtime API to invoke the endpoint. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sagemaker\n", - "\n", - "sm = sagemaker.Session().sagemaker_runtime_client\n", - "\n", - "prompt = \"The best part of Amazon SageMaker is that it makes machine learning easy.\"\n", - "\n", - "response = sm.invoke_endpoint(\n", - " EndpointName=endpoint_name, Body=prompt.encode(encoding=\"UTF-8\"), ContentType=\"text/csv\"\n", - ")\n", - "\n", - "response[\"Body\"].read()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleanup\n", - "\n", - "Delete the model and endpoint to release resources and stop incurring costs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictor.delete_model()\n", - "predictor.delete_endpoint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "We have successfully created a scalable, highly available, RESTful API that is backed by a BERT model! It can be used for downstream NLP tasks like text classification. If you are still interested in learning more, check out some of the more advanced features of SageMaker hosting, like [Monitor models for data and model quality, bias, and explainability](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) to detect concept drift, [Automatically Scale Amazon SageMaker Models](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html) to dynamically adjust the number of instances, or [Give SageMaker Hosted Endpoints Access to Resources in Your Amazon VPC](https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) to control network access to/from your endpoint.\n", - "\n", - "You can also read the blog [Deploy machine learning models to Amazon SageMaker using the ezsmdeploy Python package and a few lines of code](https://aws.amazon.com/blogs/opensource/deploy-machine-learning-models-to-amazon-sagemaker-using-the-ezsmdeploy-python-package-and-a-few-lines-of-code/). The ezsmdeploy package automates most of this process." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", - "\n", - "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (PyTorch 1.10 Python 3.8 CPU Optimized)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.10-cpu-py38" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker-script-mode/sklearn/sklearn_byom.ipynb b/sagemaker-script-mode/sklearn/sklearn_byom.ipynb deleted file mode 100644 index 7d63f2d915..0000000000 --- a/sagemaker-script-mode/sklearn/sklearn_byom.ipynb +++ /dev/null @@ -1,445 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "e950fa8e", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Train a SKLearn Model using Script Mode\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0abdc17b", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "90e7cac6", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "\n", - "The aim of this notebook is to demonstrate how to train and deploy a scikit-learn model in Amazon SageMaker. The method used is called Script Mode, in which we write a script to train our model and submit it to the SageMaker Python SDK. For more information, feel free to read [Using Scikit-learn with the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/using_sklearn.html).\n", - "\n", - "## Runtime\n", - "This notebook takes approximately 15 minutes to run.\n", - "\n", - "## Contents\n", - "1. [Download data](#Download-data)\n", - "1. [Prepare data](#Prepare-data)\n", - "1. [Train model](#Train-model)\n", - "1. [Deploy and test endpoint](#Deploy-and-test-endpoint)\n", - "1. [Cleanup](#Cleanup)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a16db1a6", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Download data \n", - "Download the [Iris Data Set](https://archive.ics.uci.edu/ml/datasets/iris), which is the data used to trained the model in this demo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2d5c27c", - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "!pip install -U sagemaker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a670c242", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import boto3\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "s3 = boto3.client(\"s3\")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{boto3.session.Session().region_name}\",\n", - " \"datasets/tabular/iris/iris.data\",\n", - " \"iris.data\",\n", - ")\n", - "\n", - "df = pd.read_csv(\n", - " \"iris.data\", header=None, names=[\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\", \"class\"]\n", - ")\n", - "df.head()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7c03b3d2", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Prepare data\n", - "Next, we prepare the data for training by first converting the labels from string to integers. Then we split the data into a train dataset (80% of the data) and test dataset (the remaining 20% of the data) before saving them into CSV files. Then, these files are uploaded to S3 where the SageMaker SDK can access and use them to train the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72748b04", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Convert the three classes from strings to integers in {0,1,2}\n", - "df[\"class_cat\"] = df[\"class\"].astype(\"category\").cat.codes\n", - "categories_map = dict(enumerate(df[\"class\"].astype(\"category\").cat.categories))\n", - "print(categories_map)\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb5ea6cf", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Split the data into 80-20 train-test split\n", - "num_samples = df.shape[0]\n", - "split = round(num_samples * 0.8)\n", - "train = df.iloc[:split, :]\n", - "test = df.iloc[split:, :]\n", - "print(\"{} train, {} test\".format(split, num_samples - split))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48770a6b", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Write train and test CSV files\n", - "train.to_csv(\"train.csv\", index=False)\n", - "test.to_csv(\"test.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba40dab3", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Create a sagemaker session to upload data to S3\n", - "import sagemaker\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "\n", - "# Upload data to default S3 bucket\n", - "prefix = \"DEMO-sklearn-iris\"\n", - "training_input_path = sagemaker_session.upload_data(\"train.csv\", key_prefix=prefix + \"/training\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d52c534", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Train model\n", - "The model is trained using the SageMaker SDK's Estimator class. Firstly, get the execution role for training. This role allows us to access the S3 bucket in the last step, where the train and test data set is located." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7cbdad2", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Use the current execution role for training. It needs access to S3\n", - "role = sagemaker.get_execution_role()\n", - "print(role)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "10cdcfb6", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Then, it is time to define the SageMaker SDK Estimator class. We use an Estimator class specifically desgined to train scikit-learn models called `SKLearn`. In this estimator, we define the following parameters:\n", - "1. The script that we want to use to train the model (i.e. `entry_point`). This is the heart of the Script Mode method. Additionally, set the `script_mode` parameter to `True`.\n", - "1. The role which allows us access to the S3 bucket containing the train and test data set (i.e. `role`)\n", - "1. How many instances we want to use in training (i.e. `instance_count`) and what type of instance we want to use in training (i.e. `instance_type`)\n", - "1. Which version of scikit-learn to use (i.e. `framework_version`)\n", - "1. Training hyperparameters (i.e. `hyperparameters`)\n", - "\n", - "After setting these parameters, the `fit` function is invoked to train the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac14dcb7", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Docs: https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html\n", - "\n", - "from sagemaker.sklearn import SKLearn\n", - "\n", - "sk_estimator = SKLearn(\n", - " entry_point=\"train.py\",\n", - " role=role,\n", - " instance_count=1,\n", - " instance_type=\"ml.c5.xlarge\",\n", - " py_version=\"py3\",\n", - " framework_version=\"1.2-1\",\n", - " script_mode=True,\n", - " hyperparameters={\"estimators\": 20},\n", - ")\n", - "\n", - "# Train the estimator\n", - "sk_estimator.fit({\"train\": training_input_path})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "3813b62c", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Deploy and test endpoint\n", - "After training the model, it is time to deploy it as an endpoint. To do so, we invoke the `deploy` function within the scikit-learn estimator. As shown in the code below, one can define the number of instances (i.e. `initial_instance_count`) and instance type (i.e. `instance_type`) used to deploy the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06aace5c", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import time\n", - "\n", - "sk_endpoint_name = \"sklearn-rf-model\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n", - "sk_predictor = sk_estimator.deploy(\n", - " initial_instance_count=1, instance_type=\"ml.m5.large\", endpoint_name=sk_endpoint_name\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bbc747e1", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "After the endpoint has been completely deployed, it can be invoked using the [SageMaker Runtime Client](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime.html) (which is the method used in the code cell below) or [Scikit Learn Predictor](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html#scikit-learn-predictor). If you plan to use the latter method, make sure to use a [Serializer](https://sagemaker.readthedocs.io/en/stable/api/inference/serializers.html) to serialize your data properly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85491166", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "client = sagemaker_session.sagemaker_runtime_client\n", - "\n", - "request_body = {\"Input\": [[9.0, 3571, 1976, 0.525]]}\n", - "data = json.loads(json.dumps(request_body))\n", - "payload = json.dumps(data)\n", - "\n", - "response = client.invoke_endpoint(\n", - " EndpointName=sk_endpoint_name, ContentType=\"application/json\", Body=payload\n", - ")\n", - "\n", - "result = json.loads(response[\"Body\"].read().decode())[\"Output\"]\n", - "print(\"Predicted class category {} ({})\".format(result, categories_map[result]))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "90f26921", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Cleanup\n", - "If the model and endpoint are no longer in use, they should be deleted to save costs and free up resources." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec5a3a83", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "sk_predictor.delete_model()\n", - "sk_predictor.delete_endpoint()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "454a7ca7", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", - "\n", - "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb b/sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb deleted file mode 100644 index 606743e2a9..0000000000 --- a/sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb +++ /dev/null @@ -1,2290 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "8c8a3cea", - "metadata": { - "papermill": { - "duration": 0.009489, - "end_time": "2021-06-03T00:10:10.266437", - "exception": false, - "start_time": "2021-06-03T00:10:10.256948", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Use SageMaker Batch Transform for PyTorch Batch Inference\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ac52b806", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_batch_transform|pytorch_mnist_batch_transform|pytorch-mnist-batch-transform.ipynb)\n", - "\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea2e8bde", - "metadata": { - "papermill": { - "duration": 0.009489, - "end_time": "2021-06-03T00:10:10.266437", - "exception": false, - "start_time": "2021-06-03T00:10:10.256948", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "In this notebook, we examine how to do a Batch Transform task with PyTorch in Amazon SageMaker. \n", - "\n", - "First, an image classification model is built on the MNIST dataset. Then, we demonstrate batch transform by using the SageMaker Python SDK PyTorch framework with different configurations:\n", - "- `data_type=S3Prefix`: uses all objects that match the specified S3 prefix for batch inference.\n", - "- `data_type=ManifestFile`: a manifest file contains a list of object keys to use in batch inference.\n", - "- `instance_count>1`: distributes the batch inference dataset to multiple inference instances.\n", - "\n", - "For batch transform in TensorFlow in Amazon SageMaker, you can follow other Jupyter notebooks in the [sagemaker_batch_transform](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_batch_transform) directory.\n", - "\n", - "### Runtime\n", - "\n", - "This notebook takes approximately 15 minutes to run.\n", - "\n", - "### Contents\n", - "\n", - "1. [Setup](#Setup)\n", - "1. [Model training](#Model-training)\n", - "1. [Prepare batch inference data](#Prepare-batch-inference-data)\n", - "1. [Create model transformer](#Create-model-transformer)\n", - "1. [Batch inference](#Batch-inference)\n", - "1. [Look at all transform jobs](#Look-at-all-transform-jobs)\n", - "1. [Conclusion](#Conclusion)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "cb8aa488", - "metadata": { - "papermill": { - "duration": 0.009319, - "end_time": "2021-06-03T00:10:10.285106", - "exception": false, - "start_time": "2021-06-03T00:10:10.275787", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Setup\n", - "We'll begin with some necessary installs and imports, and get an Amazon SageMaker session to help perform certain tasks, as well as an IAM role with the necessary permissions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "347fb3de", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install nvidia-ml-py3\n", - "!yes | pip uninstall torchvision\n", - "!pip install torchvision" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53e1a695", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-03T00:10:10.310480Z", - "iopub.status.busy": "2021-06-03T00:10:10.309977Z", - "iopub.status.idle": "2021-06-03T00:10:11.972019Z", - "shell.execute_reply": "2021-06-03T00:10:11.971547Z" - }, - "papermill": { - "duration": 1.677667, - "end_time": "2021-06-03T00:10:11.972131", - "exception": false, - "start_time": "2021-06-03T00:10:10.294464", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import os\n", - "from os import listdir\n", - "from os.path import isfile, join\n", - "from shutil import copyfile\n", - "import sagemaker\n", - "from sagemaker.pytorch import PyTorchModel\n", - "from sagemaker import get_execution_role\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "region = sagemaker_session.boto_region_name\n", - "role = get_execution_role()\n", - "\n", - "bucket = sagemaker_session.default_bucket()\n", - "prefix = \"sagemaker/DEMO-pytorch-batch-inference-script\"\n", - "print(\"Bucket: {}\".format(bucket))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1df34f4f", - "metadata": { - "papermill": { - "duration": 0.009748, - "end_time": "2021-06-03T00:10:11.992188", - "exception": false, - "start_time": "2021-06-03T00:10:11.982440", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Model training" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2e50d7ed", - "metadata": { - "papermill": { - "duration": 0.009924, - "end_time": "2021-06-03T00:10:12.012090", - "exception": false, - "start_time": "2021-06-03T00:10:12.002166", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Since the main purpose of this notebook is to demonstrate SageMaker PyTorch batch transform, we reuse a SageMaker Python SDK [PyTorch MNIST example](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/pytorch_mnist) to train a PyTorch model. It takes around 7 minutes to finish the training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfa3102c", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-03T00:10:12.038135Z", - "iopub.status.busy": "2021-06-03T00:10:12.037362Z", - "iopub.status.idle": "2021-06-03T00:15:42.451109Z", - "shell.execute_reply": "2021-06-03T00:15:42.449969Z" - }, - "papermill": { - "duration": 330.429296, - "end_time": "2021-06-03T00:15:42.451328", - "exception": true, - "start_time": "2021-06-03T00:10:12.022032", - "status": "failed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from torchvision.datasets import MNIST\n", - "from torchvision import transforms\n", - "\n", - "local_dir = \"data\"\n", - "MNIST.mirrors = [\n", - " f\"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/\"\n", - "]\n", - "MNIST(\n", - " local_dir,\n", - " download=True,\n", - " transform=transforms.Compose(\n", - " [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]\n", - " ),\n", - ")\n", - "\n", - "\n", - "inputs = sagemaker_session.upload_data(path=local_dir, bucket=bucket, key_prefix=prefix)\n", - "print(\"input spec (in this case, just an S3 path): {}\".format(inputs))\n", - "\n", - "from sagemaker.pytorch import PyTorch\n", - "\n", - "estimator = PyTorch(\n", - " entry_point=\"model-script/mnist.py\",\n", - " role=role,\n", - " framework_version=\"1.8.0\",\n", - " py_version=\"py3\",\n", - " instance_count=3,\n", - " instance_type=\"ml.c5.2xlarge\",\n", - " hyperparameters={\n", - " \"epochs\": 1,\n", - " \"backend\": \"gloo\",\n", - " }, # set epochs to a more realistic number for real training\n", - ")\n", - "\n", - "estimator.fit({\"training\": inputs})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a0f0249f", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "## Prepare batch inference data\n", - "\n", - "Convert the test data into PNG image format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "343a2a68", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!ls data/MNIST/raw" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a29e9c07", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# untar gz => png\n", - "\n", - "import gzip\n", - "import numpy as np\n", - "import os\n", - "\n", - "with gzip.open(os.path.join(local_dir, \"MNIST/raw\", \"t10k-images-idx3-ubyte.gz\"), \"rb\") as f:\n", - " images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91f0f659", - "metadata": {}, - "outputs": [], - "source": [ - "print(len(images), \"test images\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b617160c", - "metadata": {}, - "source": [ - "Randomly sample 100 test images and upload them to S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62f06915", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "from PIL import Image as im\n", - "\n", - "ids = random.sample(range(len(images)), 100)\n", - "ids = np.array(ids, dtype=np.int)\n", - "selected_images = images[ids]\n", - "\n", - "image_dir = \"data/images\"\n", - "\n", - "if not os.path.exists(image_dir):\n", - " os.makedirs(image_dir)\n", - "\n", - "for i, img in enumerate(selected_images):\n", - " pngimg = im.fromarray(img)\n", - " pngimg.save(os.path.join(image_dir, f\"{i}.png\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf93b71e", - "metadata": {}, - "outputs": [], - "source": [ - "inference_prefix = \"batch_transform\"\n", - "inference_inputs = sagemaker_session.upload_data(\n", - " path=image_dir, bucket=bucket, key_prefix=inference_prefix\n", - ")\n", - "print(\"Input S3 path: {}\".format(inference_inputs))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ff8b9b66", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "## Create model transformer\n", - "Now, we create a transformer object for creating and interacting with Amazon SageMaker transform jobs. We can create the transformer in two ways:\n", - "1. Use a fitted estimator directly.\n", - "1. First create a PyTorchModel from a saved model artifact, and then create a transformer from the PyTorchModel object.\n", - "\n", - "\n", - "Here, we implement the `model_fn`, `input_fn`, `predict_fn` and `output_fn` function to override the default [PyTorch inference handler](https://github.com/aws/sagemaker-pytorch-inference-toolkit/blob/master/src/sagemaker_pytorch_serving_container/default_inference_handler.py). \n", - "\n", - "In the `input_fn()` function, the inferenced images are encoded as a Python ByteArray. That's why we use the `load_from_bytearray()` function to load images from `io.BytesIO` and then use `PIL.image` to read the images.\n", - "\n", - "```python\n", - "def model_fn(model_dir):\n", - " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - " model = torch.nn.DataParallel(Net())\n", - " with open(os.path.join(model_dir, \"model.pth\"), \"rb\") as f:\n", - " model.load_state_dict(torch.load(f))\n", - " return model.to(device)\n", - "\n", - " \n", - "def load_from_bytearray(request_body):\n", - " image_as_bytes = io.BytesIO(request_body)\n", - " image = Image.open(image_as_bytes)\n", - " image_tensor = ToTensor()(image).unsqueeze(0) \n", - " return image_tensor\n", - "\n", - "\n", - "def input_fn(request_body, request_content_type):\n", - " # if set content_type as \"image/jpg\" or \"application/x-npy\", \n", - " # the input is also a python bytearray\n", - " if request_content_type == \"application/x-image\": \n", - " image_tensor = load_from_bytearray(request_body)\n", - " else:\n", - " print(\"not support this type yet\")\n", - " raise ValueError(\"not support this type yet\")\n", - " return image_tensor\n", - "\n", - "\n", - "# Perform prediction on the deserialized object, with the loaded model\n", - "def predict_fn(input_object, model):\n", - " output = model.forward(input_object)\n", - " pred = output.max(1, keepdim=True)[1]\n", - "\n", - " return {\"predictions\": pred.item()}\n", - "\n", - "\n", - "# Serialize the prediction result into the desired response content type\n", - "def output_fn(predictions, response_content_type):\n", - " return json.dumps(predictions)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86782070", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Use fitted estimator directly\n", - "transformer = estimator.transformer(instance_count=1, instance_type=\"ml.c5.xlarge\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09735ff2", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# You can also create a Transformer object from saved model artifact\n", - "\n", - "# Get model artifact location by estimator.model_data, or give an S3 key directly\n", - "model_artifact_s3_location = estimator.model_data # \"s3:////model.tar.gz\"\n", - "\n", - "# Create PyTorchModel from saved model artifact\n", - "pytorch_model = PyTorchModel(\n", - " model_data=model_artifact_s3_location,\n", - " role=role,\n", - " framework_version=\"1.8.0\",\n", - " py_version=\"py3\",\n", - " source_dir=\"model-script/\",\n", - " entry_point=\"mnist.py\",\n", - ")\n", - "\n", - "# Create transformer from PyTorchModel object\n", - "transformer = pytorch_model.transformer(instance_count=1, instance_type=\"ml.c5.xlarge\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f024f81c", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "## Batch inference\n", - "Next, we perform inference on the sampled 100 MNIST images in a batch manner. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e3aafd66", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "### Input images directly from S3 location\n", - "We set `S3DataType=S3Prefix` to use all objects that match the specified S3 prefix for batch inference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f666cde", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "transformer.transform(\n", - " data=inference_inputs,\n", - " data_type=\"S3Prefix\",\n", - " content_type=\"application/x-image\",\n", - " wait=True,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d42055d", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "### Input images by manifest file\n", - "First, we generate a manifest file. Then we use the manifest file containing a list of object keys as inputs to batch inference. Some key points:\n", - "- `content_type = \"application/x-image\"` (here the `content_type` is for the actual object for inference, not for the manifest file)\n", - "- `data_type = \"ManifestFile\"`\n", - "- Manifest file format must follow the format as [S3DataSource](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_S3DataSource.html#SageMaker-Type-S3DataSource-S3DataType) points out. We create the manifest file by using the jsonlines package.\n", - "``` json\n", - "[\n", - " {\"prefix\": \"s3://customer_bucket/some/prefix/\"},\n", - " \"relative/path/to/custdata-1\",\n", - " \"relative/path/custdata-2\",\n", - " ...\n", - " \"relative/path/custdata-N\"\n", - "]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "295c39fc", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install -q jsonlines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b279b271", - "metadata": {}, - "outputs": [], - "source": [ - "import jsonlines\n", - "\n", - "# Build image list\n", - "manifest_prefix = f\"s3://{bucket}/{prefix}/images/\"\n", - "\n", - "path = image_dir\n", - "img_files = [f for f in listdir(path) if isfile(join(path, f))]\n", - "\n", - "print(\"img_files\\n\", img_files)\n", - "\n", - "manifest_content = [{\"prefix\": manifest_prefix}]\n", - "manifest_content.extend(img_files)\n", - "\n", - "print(\"manifest_content\\n\", manifest_content)\n", - "\n", - "# Write jsonl file\n", - "manifest_file = \"manifest.json\"\n", - "with jsonlines.open(manifest_file, mode=\"w\") as writer:\n", - " writer.write(manifest_content)\n", - "\n", - "# Upload to S3\n", - "manifest_obj = sagemaker_session.upload_data(path=manifest_file, key_prefix=prefix)\n", - "\n", - "print(\"manifest_obj\\n\", manifest_obj)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b58e5fe6", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Batch transform with manifest file\n", - "transform_job = transformer.transform(\n", - " data=manifest_obj,\n", - " data_type=\"ManifestFile\",\n", - " content_type=\"application/x-image\",\n", - " wait=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aaa60562", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Latest transform job:\", transformer.latest_transform_job.name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56dde353", - "metadata": {}, - "outputs": [], - "source": [ - "# look at the status of the transform job\n", - "import pprint as pp\n", - "\n", - "sm_cli = sagemaker_session.sagemaker_client\n", - "\n", - "job_info = sm_cli.describe_transform_job(TransformJobName=transformer.latest_transform_job.name)\n", - "\n", - "pp.pprint(job_info)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f4a43f63", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "source": [ - "### Multiple instance\n", - "We use `instance_count > 1` to create multiple inference instances. When a batch transform job starts, Amazon SageMaker initializes compute instances and distributes the inference or preprocessing workload between them. Batch Transform partitions the Amazon S3 objects in the input by key and maps Amazon S3 objects to instances. Given multiple files, one instance might process input1.csv, and another instance might process input2.csv. Read more at [Use Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9661fe0e", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "dist_transformer = estimator.transformer(instance_count=2, instance_type=\"ml.c4.xlarge\")\n", - "\n", - "dist_transformer.transform(\n", - " data=inference_inputs,\n", - " data_type=\"S3Prefix\",\n", - " content_type=\"application/x-image\",\n", - " wait=True,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "57d2f7f8", - "metadata": {}, - "source": [ - "## Look at all transform jobs" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "942c6f2e", - "metadata": {}, - "source": [ - "We list and describe the transform jobs to retrieve information about them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7725d230", - "metadata": {}, - "outputs": [], - "source": [ - "transform_jobs = sm_cli.list_transform_jobs()[\"TransformJobSummaries\"]\n", - "for job in transform_jobs:\n", - " pp.pprint(job)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b694abf", - "metadata": {}, - "outputs": [], - "source": [ - "job_info = sm_cli.describe_transform_job(\n", - " TransformJobName=dist_transformer.latest_transform_job.name\n", - ")\n", - "\n", - "pp.pprint(job_info)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e682401", - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "\n", - "\n", - "def get_bucket_and_prefix(s3_output_path):\n", - " trim = re.sub(\"s3://\", \"\", s3_output_path)\n", - " bucket, prefix = trim.split(\"/\")\n", - " return bucket, prefix\n", - "\n", - "\n", - "local_path = \"output\" # Where to save the output locally\n", - "\n", - "bucket, output_prefix = get_bucket_and_prefix(job_info[\"TransformOutput\"][\"S3OutputPath\"])\n", - "print(bucket, output_prefix)\n", - "\n", - "sagemaker_session.download_data(path=local_path, bucket=bucket, key_prefix=output_prefix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ae24be8", - "metadata": {}, - "outputs": [], - "source": [ - "!ls {local_path}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c336288", - "metadata": {}, - "outputs": [], - "source": [ - "# Inspect the output\n", - "\n", - "import json\n", - "\n", - "for f in os.listdir(local_path):\n", - " path = os.path.join(local_path, f)\n", - " with open(path, \"r\") as f:\n", - " pred = json.load(f)\n", - " print(pred)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e3cbd160", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook, we trained a PyTorch model, created a transformer from it, and then performed batch inference using S3 inputs, manifest files, and on multiple instances. This shows a variety of options that are available when running SageMaker Batch Transform jobs for batch inference." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "cdb3abb1", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_batch_transform|pytorch_mnist_batch_transform|pytorch-mnist-batch-transform.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Amazon SageMaker Model Monitor\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook shows how to:\n", - "* Host a machine learning model in Amazon SageMaker and capture inference requests, results, and metadata \n", - "* Analyze a training dataset to generate baseline constraints\n", - "* Monitor a live endpoint for violations against constraints\n", - "\n", - "---\n", - "## Background\n", - "\n", - "Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. Amazon SageMaker is a fully-managed service that encompasses the entire machine learning workflow. You can label and prepare your data, choose an algorithm, train a model, and then tune and optimize it for deployment. You can deploy your models to production with Amazon SageMaker to make predictions and lower costs than was previously possible.\n", - "\n", - "In addition, Amazon SageMaker enables you to capture the input, output and metadata for invocations of the models that you deploy. It also enables you to analyze the data and monitor its quality. In this notebook, you learn how Amazon SageMaker enables these capabilities.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook uses an hourly monitor, so it takes between 30-90 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [PART A: Capturing real-time inference data from Amazon SageMaker endpoints](#PART-A:-Capturing-real-time-inference-data-from-Amazon-SageMaker-endpoints)\n", - "1. [PART B: Model Monitor - Baselining and continuous monitoring](#PART-B:-Model-Monitor---Baselining-and-continuous-monitoring)\n", - " 1. [Constraint suggestion with baseline/training dataset](#1.-Constraint-suggestion-with-baseline/training-dataset)\n", - " 1. [Analyze collected data for data quality issues](#2.-Analyze-collected-data-for-data-quality-issues)\n", - "---\n", - "## Setup\n", - "\n", - "To get started, make sure you have these prerequisites completed:\n", - "\n", - "* Specify an AWS Region to host your model.\n", - "* An IAM role ARN exists that is used to give Amazon SageMaker access to your data in Amazon Simple Storage Service (Amazon S3).\n", - "* Use the default S3 bucket to store the data used to train your model, any additional model data, and the data captured from model invocations. For demonstration purposes, you are using the same bucket for these. In reality, you might want to separate them with different security policies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "isConfigCell": true - }, - "outputs": [], - "source": [ - "import os\n", - "import boto3\n", - "import re\n", - "import json\n", - "import sagemaker\n", - "from sagemaker import get_execution_role, session\n", - "\n", - "sm_session = sagemaker.Session()\n", - "region = sm_session.boto_region_name\n", - "\n", - "role = get_execution_role()\n", - "print(\"Role ARN: {}\".format(role))\n", - "\n", - "bucket = sm_session.default_bucket()\n", - "print(\"Demo Bucket: {}\".format(bucket))\n", - "prefix = \"sagemaker/DEMO-ModelMonitor\"\n", - "\n", - "data_capture_prefix = \"{}/datacapture\".format(prefix)\n", - "s3_capture_upload_path = \"s3://{}/{}\".format(bucket, data_capture_prefix)\n", - "reports_prefix = \"{}/reports\".format(prefix)\n", - "s3_report_path = \"s3://{}/{}\".format(bucket, reports_prefix)\n", - "code_prefix = \"{}/code\".format(prefix)\n", - "s3_code_preprocessor_uri = \"s3://{}/{}/{}\".format(bucket, code_prefix, \"preprocessor.py\")\n", - "s3_code_postprocessor_uri = \"s3://{}/{}/{}\".format(bucket, code_prefix, \"postprocessor.py\")\n", - "\n", - "print(\"Capture path: {}\".format(s3_capture_upload_path))\n", - "print(\"Report path: {}\".format(s3_report_path))\n", - "print(\"Preproc Code path: {}\".format(s3_code_preprocessor_uri))\n", - "print(\"Postproc Code path: {}\".format(s3_code_postprocessor_uri))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PART A: Capturing real-time inference data from Amazon SageMaker endpoints\n", - "Create an endpoint to showcase the data capture capability in action.\n", - "\n", - "### Upload the pre-trained model to Amazon S3\n", - "This code uploads a pre-trained XGBoost model that is ready for you to deploy. This model was trained using the XGB Churn Prediction Notebook in SageMaker. You can also use your own pre-trained model in this step. If you already have a pretrained model in Amazon S3, you can add it instead by specifying the s3_key." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_file = open(\"model/xgb-churn-prediction-model.tar.gz\", \"rb\")\n", - "s3_key = os.path.join(prefix, \"xgb-churn-prediction-model.tar.gz\")\n", - "boto3.Session().resource(\"s3\").Bucket(bucket).Object(s3_key).upload_fileobj(model_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model to Amazon SageMaker\n", - "Start with deploying a pre-trained churn prediction model. Here, you create the model object with the image and model data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from time import gmtime, strftime\n", - "from sagemaker.model import Model\n", - "from sagemaker.image_uris import retrieve\n", - "\n", - "model_name = \"DEMO-xgb-churn-pred-model-monitor-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", - "model_url = \"https://{}.s3-{}.amazonaws.com/{}/xgb-churn-prediction-model.tar.gz\".format(\n", - " bucket, region, prefix\n", - ")\n", - "\n", - "image_uri = retrieve(\"xgboost\", region, \"0.90-1\")\n", - "\n", - "model = Model(image_uri=image_uri, model_data=model_url, role=role)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To enable data capture for monitoring the model data quality, you specify the new capture option called `DataCaptureConfig`. You can capture the request payload, the response payload or both with this configuration. The capture config applies to all variants. Go ahead with the deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.model_monitor import DataCaptureConfig\n", - "\n", - "endpoint_name = \"DEMO-xgb-churn-pred-model-monitor-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", - "print(\"EndpointName={}\".format(endpoint_name))\n", - "\n", - "data_capture_config = DataCaptureConfig(\n", - " enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_upload_path\n", - ")\n", - "\n", - "predictor = model.deploy(\n", - " initial_instance_count=1,\n", - " instance_type=\"ml.m4.xlarge\",\n", - " endpoint_name=endpoint_name,\n", - " data_capture_config=data_capture_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Invoke the deployed model\n", - "\n", - "You can now send data to this endpoint to get inferences in real time. Because you enabled the data capture in the previous steps, the request and response payload, along with some additional metadata, is saved in the Amazon Simple Storage Service (Amazon S3) location you have specified in the DataCaptureConfig." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This step invokes the endpoint with included sample data for about 3 minutes. Data is captured based on the sampling percentage specified and the capture continues until the data capture option is turned off." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.predictor import Predictor\n", - "from sagemaker.serializers import CSVSerializer\n", - "import time\n", - "\n", - "predictor = Predictor(endpoint_name=endpoint_name, serializer=CSVSerializer())\n", - "\n", - "# Get a subset of test data for a quick test\n", - "!head -180 test_data/test-dataset-input-cols.csv > test_data/test_sample.csv\n", - "print(\"Sending test traffic to the endpoint {}. \\nPlease wait...\".format(endpoint_name))\n", - "\n", - "with open(\"test_data/test_sample.csv\", \"r\") as f:\n", - " for row in f:\n", - " payload = row.rstrip(\"\\n\")\n", - " response = predictor.predict(data=payload)\n", - " time.sleep(1)\n", - "\n", - "print(\"Done!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View captured data\n", - "\n", - "Now list the data capture files stored in Amazon S3. You should expect to see different files from different time periods organized based on the hour in which the invocation occurred. The format of the Amazon S3 path is:\n", - "\n", - "`s3://{destination-bucket-prefix}/{endpoint-name}/{variant-name}/yyyy/mm/dd/hh/filename.jsonl`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "s3_client = boto3.Session().client(\"s3\")\n", - "current_endpoint_capture_prefix = \"{}/{}\".format(data_capture_prefix, endpoint_name)\n", - "result = s3_client.list_objects(Bucket=bucket, Prefix=current_endpoint_capture_prefix)\n", - "capture_files = [capture_file.get(\"Key\") for capture_file in result.get(\"Contents\")]\n", - "print(\"Found Capture Files:\")\n", - "print(\"\\n \".join(capture_files))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, view the contents of a single capture file. Here you should see all the data captured in an Amazon SageMaker specific JSON-line formatted file. Take a quick peek at the first few lines in the captured file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_obj_body(obj_key):\n", - " return s3_client.get_object(Bucket=bucket, Key=obj_key).get(\"Body\").read().decode(\"utf-8\")\n", - "\n", - "\n", - "capture_file = get_obj_body(capture_files[-1])\n", - "print(capture_file[:2000])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the contents of a single line is present below in a formatted JSON file so that you can observe a little better." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "print(json.dumps(json.loads(capture_file.split(\"\\n\")[0]), indent=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, each inference request is captured in one line in the jsonl file. The line contains both the input and output merged together. In the example, you provided the ContentType as `text/csv` which is reflected in the `observedContentType` value. Also, you expose the encoding that you used to encode the input and output payloads in the capture format with the `encoding` value.\n", - "\n", - "To recap, you observed how you can enable capturing the input or output payloads to an endpoint with a new parameter. You have also observed what the captured format looks like in Amazon S3. Next, continue to explore how Amazon SageMaker helps with monitoring the data collected in Amazon S3." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PART B: Model Monitor - Baselining and continuous monitoring" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to collecting the data, Amazon SageMaker provides the capability for you to monitor and evaluate the data observed by the endpoints. For this:\n", - "1. Create a baseline with which you compare the realtime traffic. \n", - "1. Once a baseline is ready, setup a schedule to continously evaluate and compare against the baseline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Constraint suggestion with baseline/training dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training dataset with which you trained the model is usually a good baseline dataset. Note that the training dataset data schema and the inference dataset schema should exactly match (i.e. the number and order of the features).\n", - "\n", - "From the training dataset you can ask Amazon SageMaker to suggest a set of baseline `constraints` and generate descriptive `statistics` to explore the data. For this example, upload the training dataset that was used to train the pre-trained model included in this example. If you already have it in Amazon S3, you can directly point to it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# copy over the training dataset to Amazon S3 (if you already have it in Amazon S3, you could reuse it)\n", - "baseline_prefix = prefix + \"/baselining\"\n", - "baseline_data_prefix = baseline_prefix + \"/data\"\n", - "baseline_results_prefix = baseline_prefix + \"/results\"\n", - "\n", - "baseline_data_uri = \"s3://{}/{}\".format(bucket, baseline_data_prefix)\n", - "baseline_results_uri = \"s3://{}/{}\".format(bucket, baseline_results_prefix)\n", - "print(\"Baseline data uri: {}\".format(baseline_data_uri))\n", - "print(\"Baseline results uri: {}\".format(baseline_results_uri))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_data_file = open(\"test_data/training-dataset-with-header.csv\", \"rb\")\n", - "s3_key = os.path.join(baseline_prefix, \"data\", \"training-dataset-with-header.csv\")\n", - "boto3.Session().resource(\"s3\").Bucket(bucket).Object(s3_key).upload_fileobj(training_data_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a baselining job with training dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have the training data ready in Amazon S3, start a job to `suggest` constraints. `DefaultModelMonitor.suggest_baseline(..)` starts a `ProcessingJob` using an Amazon SageMaker provided Model Monitor container to generate the constraints." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.model_monitor import DefaultModelMonitor\n", - "from sagemaker.model_monitor.dataset_format import DatasetFormat\n", - "\n", - "my_default_monitor = DefaultModelMonitor(\n", - " role=role,\n", - " instance_count=1,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " volume_size_in_gb=20,\n", - " max_runtime_in_seconds=3600,\n", - ")\n", - "\n", - "my_default_monitor.suggest_baseline(\n", - " baseline_dataset=baseline_data_uri + \"/training-dataset-with-header.csv\",\n", - " dataset_format=DatasetFormat.csv(header=True),\n", - " output_s3_uri=baseline_results_uri,\n", - " wait=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Explore the generated constraints and statistics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s3_client = boto3.Session().client(\"s3\")\n", - "result = s3_client.list_objects(Bucket=bucket, Prefix=baseline_results_prefix)\n", - "report_files = [report_file.get(\"Key\") for report_file in result.get(\"Contents\")]\n", - "print(\"Found Files:\")\n", - "print(\"\\n \".join(report_files))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "baseline_job = my_default_monitor.latest_baselining_job\n", - "schema_df = pd.io.json.json_normalize(baseline_job.baseline_statistics().body_dict[\"features\"])\n", - "schema_df.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "constraints_df = pd.io.json.json_normalize(\n", - " baseline_job.suggested_constraints().body_dict[\"features\"]\n", - ")\n", - "constraints_df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Analyze collected data for data quality issues\n", - "\n", - "When you have collected the data above, analyze and monitor the data with Monitoring Schedules." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a schedule" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload some test scripts to the S3 bucket for pre- and post-processing\n", - "bucket = boto3.Session().resource(\"s3\").Bucket(bucket)\n", - "bucket.Object(code_prefix + \"/preprocessor.py\").upload_file(\"preprocessor.py\")\n", - "bucket.Object(code_prefix + \"/postprocessor.py\").upload_file(\"postprocessor.py\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can create a model monitoring schedule for the endpoint created earlier. Use the baseline resources (constraints and statistics) to compare against the realtime traffic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.model_monitor import CronExpressionGenerator\n", - "\n", - "mon_schedule_name = \"DEMO-xgb-churn-pred-model-monitor-schedule-\" + strftime(\n", - " \"%Y-%m-%d-%H-%M-%S\", gmtime()\n", - ")\n", - "my_default_monitor.create_monitoring_schedule(\n", - " monitor_schedule_name=mon_schedule_name,\n", - " endpoint_input=predictor.endpoint,\n", - " # record_preprocessor_script=pre_processor_script,\n", - " post_analytics_processor_script=s3_code_postprocessor_uri,\n", - " output_s3_uri=s3_report_path,\n", - " statistics=my_default_monitor.baseline_statistics(),\n", - " constraints=my_default_monitor.suggested_constraints(),\n", - " schedule_cron_expression=CronExpressionGenerator.hourly(),\n", - " enable_cloudwatch_metrics=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Start generating some artificial traffic\n", - "The cell below starts a thread to send some traffic to the endpoint. Note that you need to stop the kernel to terminate this thread. If there is no traffic, the monitoring jobs are marked as `Failed` since there is no data to process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from threading import Thread\n", - "from time import sleep\n", - "\n", - "endpoint_name = predictor.endpoint\n", - "runtime_client = sm_session.sagemaker_runtime_client\n", - "\n", - "\n", - "# (just repeating code from above for convenience/ able to run this section independently)\n", - "def invoke_endpoint(ep_name, file_name, runtime_client):\n", - " with open(file_name, \"r\") as f:\n", - " for row in f:\n", - " payload = row.rstrip(\"\\n\")\n", - " response = runtime_client.invoke_endpoint(\n", - " EndpointName=ep_name, ContentType=\"text/csv\", Body=payload\n", - " )\n", - " response[\"Body\"].read()\n", - " time.sleep(1)\n", - "\n", - "\n", - "def invoke_endpoint_forever():\n", - " while True:\n", - " try:\n", - " invoke_endpoint(endpoint_name, \"test_data/test-dataset-input-cols.csv\", runtime_client)\n", - " except runtime_client.exceptions.ValidationError:\n", - " pass\n", - "\n", - "\n", - "thread = Thread(target=invoke_endpoint_forever)\n", - "thread.start()\n", - "\n", - "# Note that you need to stop the kernel to stop the invocations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Describe and inspect the schedule\n", - "Once you describe, observe that the MonitoringScheduleStatus changes to Scheduled." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "desc_schedule_result = my_default_monitor.describe_schedule()\n", - "print(\"Schedule status: {}\".format(desc_schedule_result[\"MonitoringScheduleStatus\"]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### List executions\n", - "The schedule starts jobs at the previously specified intervals. Here, you list the latest five executions. Note that if you are kicking this off after creating the hourly schedule, you might find the executions empty. You might have to wait until you cross the hour boundary (in UTC) to see executions kick off. The code below has the logic for waiting.\n", - "\n", - "Note: Even for an hourly schedule, Amazon SageMaker has a buffer period of 20 minutes to schedule your execution. You might see your execution start in anywhere from zero to ~20 minutes from the hour boundary. This is expected and done for load balancing in the backend." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mon_executions = my_default_monitor.list_executions()\n", - "print(\n", - " \"We created a hourly schedule above that begins executions ON the hour (plus 0-20 min buffer.\\nWe will have to wait till we hit the hour...\"\n", - ")\n", - "\n", - "while len(mon_executions) == 0:\n", - " print(\"Waiting for the first execution to happen...\")\n", - " time.sleep(60)\n", - " mon_executions = my_default_monitor.list_executions()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Inspect a specific execution (latest execution)\n", - "In the previous cell, you picked up the latest completed or failed scheduled execution. Here are the possible terminal states and what each of them mean: \n", - "* `Completed` - The monitoring execution completed and no issues were found in the violations report.\n", - "* `CompletedWithViolations` - The execution completed, but constraint violations were detected.\n", - "* `Failed` - The monitoring execution failed, maybe due to client error (perhaps incorrect role premissions) or infrastructure issues. Further examination of `FailureReason` and `ExitMessage` is necessary to identify what exactly happened.\n", - "* `Stopped` - The job exceeded max runtime or was manually stopped." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "latest_execution = mon_executions[-1] # Latest execution's index is -1, second to last is -2, etc\n", - "time.sleep(60)\n", - "latest_execution.wait(logs=False)\n", - "\n", - "print(\"Latest execution status: {}\".format(latest_execution.describe()[\"ProcessingJobStatus\"]))\n", - "print(\"Latest execution result: {}\".format(latest_execution.describe()[\"ExitMessage\"]))\n", - "\n", - "latest_job = latest_execution.describe()\n", - "if latest_job[\"ProcessingJobStatus\"] != \"Completed\":\n", - " print(\n", - " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "report_uri = latest_execution.output.destination\n", - "print(\"Report Uri: {}\".format(report_uri))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### List the generated reports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from urllib.parse import urlparse\n", - "\n", - "s3uri = urlparse(report_uri)\n", - "report_bucket = s3uri.netloc\n", - "report_key = s3uri.path.lstrip(\"/\")\n", - "print(\"Report bucket: {}\".format(report_bucket))\n", - "print(\"Report key: {}\".format(report_key))\n", - "\n", - "s3_client = boto3.Session().client(\"s3\")\n", - "result = s3_client.list_objects(Bucket=report_bucket, Prefix=report_key)\n", - "report_files = [report_file.get(\"Key\") for report_file in result.get(\"Contents\")]\n", - "print(\"Found Report Files:\")\n", - "print(\"\\n \".join(report_files))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Violations report" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Any violations compared to the baseline are listed below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "violations = my_default_monitor.latest_monitoring_constraint_violations()\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "constraints_df = pd.io.json.json_normalize(violations.body_dict[\"violations\"])\n", - "constraints_df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Other commands\n", - "We can also start and stop the monitoring schedules." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# my_default_monitor.stop_monitoring_schedule()\n", - "# my_default_monitor.start_monitoring_schedule()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete resources\n", - "\n", - "You can keep your endpoint running to continue capturing data. If you do not plan to collect more data or use this endpoint further, delete the endpoint to avoid incurring additional charges. Note that deleting your endpoint does not delete the data that was captured during the model invocations. That data persists in Amazon S3 until you delete it yourself.\n", - "\n", - "You need to delete the schedule before deleting the model and endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "my_default_monitor.stop_monitoring_schedule()\n", - "my_default_monitor.delete_monitoring_schedule()\n", - "time.sleep(60) # Wait for the deletion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "predictor.delete_model()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictor.delete_endpoint()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", - "\n", - "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", - "\n", - "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", - "\n", - "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb b/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb deleted file mode 100644 index 215a037530..0000000000 --- a/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb +++ /dev/null @@ -1,975 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Amazon SageMaker Neo is an API to compile machine learning models to optimize them for our choice of hardware targets. Currently, Neo supports pre-trained PyTorch models from [TorchVision](https://pytorch.org/docs/stable/torchvision/models.html). General support for other PyTorch models is forthcoming.\n", - "\n", - "### Runtime\n", - "\n", - "This notebook takes approximately 8 minutes to run.\n", - "\n", - "### Contents\n", - "\n", - "1. [Import ResNet18 from TorchVision](#Import-ResNet18-from-TorchVision)\n", - "1. [Invoke Neo Compilation API](#Invoke-Neo-Compilation-API)\n", - "1. [Deploy the model](#Deploy-the-model)\n", - "1. [Send requests](#Send-requests)\n", - "1. [Delete the Endpoint](#Delete-the-Endpoint)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import ResNet18 from TorchVision" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We import the [ResNet18](https://arxiv.org/abs/1512.03385) model from TorchVision and create a model artifact `model.tar.gz`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "!{sys.executable} -m pip install torch==1.13.0 torchvision==0.14.0\n", - "!{sys.executable} -m pip install s3transfer==0.5.0\n", - "!{sys.executable} -m pip install --upgrade sagemaker" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify the input data shape. For more information, see [Prepare Model for Compilation](https://docs.aws.amazon.com/sagemaker/latest/dg/neo-compilation-preparing-model.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import sagemaker\n", - "import torch\n", - "import torchvision.models as models\n", - "import tarfile\n", - "\n", - "resnet18 = models.resnet18(pretrained=True)\n", - "input_shape = [1, 3, 224, 224]\n", - "trace = torch.jit.trace(resnet18.float().eval(), torch.zeros(input_shape).float())\n", - "trace.save(\"model.pth\")\n", - "\n", - "with tarfile.open(\"model.tar.gz\", \"w:gz\") as f:\n", - " f.add(\"model.pth\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload the model archive to S3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify parameters for the compilation job and upload the `model.tar.gz` archive file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import boto3\n", - "import sagemaker\n", - "import time\n", - "from sagemaker.utils import name_from_base\n", - "\n", - "role = sagemaker.get_execution_role()\n", - "sess = sagemaker.Session()\n", - "region = sess.boto_region_name\n", - "bucket = sess.default_bucket()\n", - "\n", - "compilation_job_name = name_from_base(\"TorchVision-ResNet18-Neo\")\n", - "prefix = compilation_job_name + \"/model\"\n", - "\n", - "model_path = sess.upload_data(path=\"model.tar.gz\", key_prefix=prefix)\n", - "\n", - "data_shape = '{\"input0\":[1,3,224,224]}'\n", - "target_device = \"ml_c5\"\n", - "framework = \"PYTORCH\"\n", - "framework_version = \"1.13\"\n", - "compiled_model_path = \"s3://{}/{}/output\".format(bucket, compilation_job_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Invoke Neo Compilation API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a PyTorch SageMaker model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `PyTorchModel` and define parameters including the path to the model, the `entry_point` script that is used to perform inference, and other version and environment variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from sagemaker.pytorch.model import PyTorchModel\n", - "from sagemaker.predictor import Predictor\n", - "\n", - "sagemaker_model = PyTorchModel(\n", - " model_data=model_path,\n", - " predictor_cls=Predictor,\n", - " framework_version=framework_version,\n", - " role=role,\n", - " sagemaker_session=sess,\n", - " entry_point=\"resnet18.py\",\n", - " source_dir=\"code\",\n", - " py_version=\"py3\",\n", - " env={\"MMS_DEFAULT_RESPONSE_TIMEOUT\": \"500\"},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use Neo compiler to compile the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the compilation job, which is saved in S3 at the specified `compiled_model_path` location." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "compiled_model = sagemaker_model.compile(\n", - " target_instance_family=target_device,\n", - " input_shape=data_shape,\n", - " job_name=compilation_job_name,\n", - " role=role,\n", - " framework=framework.lower(),\n", - " framework_version=framework_version,\n", - " output_path=compiled_model_path,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Deploy the compiled model to an endpoint so it can be used for inference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "predictor = compiled_model.deploy(initial_instance_count=1, instance_type=\"ml.c5.9xlarge\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Send requests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's send a picture to the endpoint to predict the image subject.\n", - "\n", - "![title](cat.jpg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Open the image and pass the payload as a bytearray to the predictor, receiving a response." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import json\n", - "\n", - "with open(\"cat.jpg\", \"rb\") as f:\n", - " payload = f.read()\n", - " payload = bytearray(payload)\n", - "\n", - "response = predictor.predict(payload)\n", - "result = json.loads(response.decode())\n", - "print(\"Most likely class: {}\".format(np.argmax(result)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the ImageNet class ID response to look up which subject the image contains, and with what probability." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Load names for ImageNet classes\n", - "object_categories = {}\n", - "with open(\"imagenet1000_clsidx_to_labels.txt\", \"r\") as f:\n", - " for line in f:\n", - " key, val = line.strip().split(\":\")\n", - " object_categories[key] = val.strip(\" \").strip(\",\")\n", - "print(\n", - " \"The label is\",\n", - " object_categories[str(np.argmax(result))],\n", - " \"with probability\",\n", - " str(np.amax(result))[:5],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete the Endpoint\n", - "Delete the endpoint to avoid incurring costs now that it is no longer needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "predictor.delete_model()\n", - "sess.delete_endpoint(predictor.endpoint_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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"memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 7, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 8, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 9, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 10, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 11, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 12, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5d.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 13, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5d.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 14, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5d.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 15, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5d.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 16, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5d.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 17, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5d.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 18, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5d.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 19, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 20, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": true, - "memoryGiB": 0, - "name": "ml.geospatial.interactive", - "supportedImageNames": [ - "sagemaker-geospatial-v1-0" - ], - "vcpuNum": 0 - }, - { - "_defaultOrder": 21, - "_isFastLaunch": true, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.c5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 22, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.c5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 23, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.c5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 24, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.c5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 25, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 72, - "name": "ml.c5.9xlarge", - "vcpuNum": 36 - }, - { - "_defaultOrder": 26, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 96, - "name": "ml.c5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 27, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 144, - "name": "ml.c5.18xlarge", - "vcpuNum": 72 - }, - { - "_defaultOrder": 28, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.c5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 29, - "_isFastLaunch": true, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g4dn.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 30, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g4dn.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 31, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g4dn.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 32, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g4dn.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 33, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g4dn.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 34, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g4dn.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 35, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 61, - "name": "ml.p3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 36, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 244, - "name": "ml.p3.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 37, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 488, - "name": "ml.p3.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 38, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.p3dn.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 39, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.r5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 40, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.r5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 41, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.r5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 42, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.r5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 43, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.r5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 44, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.r5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 45, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.r5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 46, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.r5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 47, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 48, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 49, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 50, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 51, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 52, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 53, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - } - ], - "kernelspec": { - "display_name": "Python 3 (PyTorch 1.12 Python 3.8 CPU Optimized)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.12-cpu-py38" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb b/sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb deleted file mode 100644 index 86324ba686..0000000000 --- a/sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb +++ /dev/null @@ -1,378 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Get started with SageMaker Processing\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "\n", - "This notebook corresponds to the section \"Preprocessing Data With The Built-In Scikit-Learn Container\" in the blog post [Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/). \n", - "It shows a lightweight example of using SageMaker Processing to create train, test, and validation datasets. SageMaker Processing is used to create these datasets, which then are written back to S3.\n", - "\n", - "## Runtime\n", - "\n", - "This notebook takes approximately 5 minutes to run.\n", - "\n", - "## Contents\n", - "\n", - "1. [Prepare resources](#Prepare-resources)\n", - "1. [Download data](#Download-data)\n", - "1. [Prepare Processing script](#Prepare-Processing-script)\n", - "1. [Run Processing job](#Run-Processing-job)\n", - "1. [Conclusion](#Conclusion)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Prepare resources\n", - "\n", - "First, let’s create an SKLearnProcessor object, passing the scikit-learn version we want to use, as well as our managed infrastructure requirements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "!pip install -U sagemaker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import boto3\n", - "import sagemaker\n", - "from sagemaker import get_execution_role\n", - "from sagemaker.sklearn.processing import SKLearnProcessor\n", - "\n", - "region = sagemaker.Session().boto_region_name\n", - "role = get_execution_role()\n", - "sklearn_processor = SKLearnProcessor(\n", - " framework_version=\"1.2-1\", role=role, instance_type=\"ml.m5.xlarge\", instance_count=1\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Download data\n", - "\n", - "Read in the raw data from a public S3 bucket. This example uses the [Census-Income (KDD) Dataset](https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29) from the UCI Machine Learning Repository.\n", - "\n", - "> Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "s3 = boto3.client(\"s3\")\n", - "s3.download_file(\n", - " \"sagemaker-sample-data-{}\".format(region),\n", - " \"processing/census/census-income.csv\",\n", - " \"census-income.csv\",\n", - ")\n", - "df = pd.read_csv(\"census-income.csv\")\n", - "df.to_csv(\"dataset.csv\")\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Prepare Processing script\n", - "\n", - "Write the Python script that will be run by SageMaker Processing. This script reads the single data file from S3; splits the rows into train, test, and validation sets; and then writes the three output files to S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%%writefile preprocessing.py\n", - "import pandas as pd\n", - "import os\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "input_data_path = os.path.join(\"/opt/ml/processing/input\", \"dataset.csv\")\n", - "df = pd.read_csv(input_data_path)\n", - "print(\"Shape of data is:\", df.shape)\n", - "train, test = train_test_split(df, test_size=0.2)\n", - "train, validation = train_test_split(train, test_size=0.2)\n", - "\n", - "try:\n", - " os.makedirs(\"/opt/ml/processing/output/train\")\n", - " os.makedirs(\"/opt/ml/processing/output/validation\")\n", - " os.makedirs(\"/opt/ml/processing/output/test\")\n", - " print(\"Successfully created directories\")\n", - "except Exception as e:\n", - " # if the Processing call already creates these directories (or directory otherwise cannot be created)\n", - " print(e)\n", - " print(\"Could not make directories\")\n", - " pass\n", - "\n", - "try:\n", - " train.to_csv(\"/opt/ml/processing/output/train/train.csv\")\n", - " validation.to_csv(\"/opt/ml/processing/output/validation/validation.csv\")\n", - " test.to_csv(\"/opt/ml/processing/output/test/test.csv\")\n", - " print(\"Wrote files successfully\")\n", - "except Exception as e:\n", - " print(\"Failed to write the files\")\n", - " print(e)\n", - " pass\n", - "\n", - "print(\"Completed running the processing job\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Run Processing job" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Run the Processing job, specifying the script name, input file, and output files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "%%capture output\n", - "\n", - "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", - "\n", - "sklearn_processor.run(\n", - " code=\"preprocessing.py\",\n", - " # arguments = [\"arg1\", \"arg2\"], # Arguments can optionally be specified here\n", - " inputs=[ProcessingInput(source=\"dataset.csv\", destination=\"/opt/ml/processing/input\")],\n", - " outputs=[\n", - " ProcessingOutput(source=\"/opt/ml/processing/output/train\"),\n", - " ProcessingOutput(source=\"/opt/ml/processing/output/validation\"),\n", - " ProcessingOutput(source=\"/opt/ml/processing/output/test\"),\n", - " ],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Get the Processing job logs and retrieve the job name." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "print(output)\n", - "job_name = str(output).split(\"\\n\")[1].split(\" \")[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Confirm that the output dataset files were written to S3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import boto3\n", - "\n", - "s3_client = boto3.client(\"s3\")\n", - "default_bucket = sagemaker.Session().default_bucket()\n", - "for i in range(1, 4):\n", - " prefix = s3_client.list_objects(Bucket=default_bucket, Prefix=\"sagemaker-scikit-learn\")[\n", - " \"Contents\"\n", - " ][-i][\"Key\"]\n", - " print(\"s3://\" + default_bucket + \"/\" + prefix)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook, we read a dataset from S3 and processed it into train, test, and validation sets using a SageMaker Processing job. You can extend this example for preprocessing your own datasets in preparation for machine learning or other applications." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n", - "\n", - "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb b/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb deleted file mode 100644 index b48847305e..0000000000 --- a/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb +++ /dev/null @@ -1,705 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distributed Data Processing using Apache Spark and SageMaker Processing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", - "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Apache Spark is a unified analytics engine for large-scale data processing. The Spark framework is often used within the context of machine learning workflows to run data transformation or feature engineering workloads at scale. Amazon SageMaker provides a set of prebuilt Docker images that include Apache Spark and other dependencies needed to run distributed data processing jobs on Amazon SageMaker. This example notebook demonstrates how to use the prebuilt Spark images on SageMaker Processing using the SageMaker Python SDK.\n", - "\n", - "This notebook walks through the following scenarios to illustrate the functionality of the SageMaker Spark Container:\n", - "\n", - "* Running a basic PySpark application using the SageMaker Python SDK's `PySparkProcessor` class\n", - "* Viewing the Spark UI via the `start_history_server()` function of a `PySparkProcessor` object\n", - "* Adding additional Python and jar file dependencies to jobs\n", - "* Running a basic Java/Scala-based Spark job using the SageMaker Python SDK's `SparkJarProcessor` class\n", - "* Specifying additional Spark configuration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Runtime\n", - "\n", - "This notebook takes approximately 22 minutes to run." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contents\n", - "\n", - "1. [Setup](#Setup)\n", - "1. [Example 1: Running a basic PySpark application](#Example-1:-Running-a-basic-PySpark-application)\n", - "1. [Example 2: Specify additional Python and jar file dependencies](#Example-2:-Specify-additional-Python-and-jar-file-dependencies)\n", - "1. [Example 3: Run a Java/Scala Spark application](#Example-3:-Run-a-Java/Scala-Spark-application)\n", - "1. [Example 4: Specifying additional Spark configuration](#Example-4:-Specifying-additional-Spark-configuration)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install the latest SageMaker Python SDK\n", - "\n", - "This notebook requires the latest v2.x version of the SageMaker Python SDK. First, ensure that the latest version is installed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -U \"sagemaker>2.0\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Restart your notebook kernel after upgrading the SDK*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example 1: Running a basic PySpark application\n", - "\n", - "The first example is a basic Spark MLlib data processing script. This script will take a raw data set and do some transformations on it such as string indexing and one hot encoding.\n", - "\n", - "### Setup S3 bucket locations and roles\n", - "\n", - "First, setup some locations in the default SageMaker bucket to store the raw input datasets and the Spark job output. Here, you'll also define the role that will be used to run all SageMaker Processing jobs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import sagemaker\n", - "from time import gmtime, strftime\n", - "\n", - "sagemaker_logger = logging.getLogger(\"sagemaker\")\n", - "sagemaker_logger.setLevel(logging.INFO)\n", - "sagemaker_logger.addHandler(logging.StreamHandler())\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "bucket = sagemaker_session.default_bucket()\n", - "role = sagemaker.get_execution_role()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, you'll download the example dataset from a SageMaker staging bucket." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fetch the dataset from the SageMaker bucket\n", - "import boto3\n", - "\n", - "s3 = boto3.client(\"s3\")\n", - "s3.download_file(\n", - " f\"sagemaker-example-files-prod-{sagemaker_session.boto_region_name}\",\n", - " \"datasets/tabular/uci_abalone/abalone.csv\",\n", - " \"./data/abalone.csv\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write the PySpark script\n", - "\n", - "The source for a preprocessing script is in the cell below. The cell uses the `%%writefile` directive to save this file locally. This script does some basic feature engineering on a raw input dataset. In this example, the dataset is the [Abalone Data Set](https://archive.ics.uci.edu/ml/datasets/abalone) and the code below performs string indexing, one hot encoding, vector assembly, and combines them into a pipeline to perform these transformations in order. The script then does an 80-20 split to produce training and validation datasets as output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile ./code/preprocess.py\n", - "from __future__ import print_function\n", - "from __future__ import unicode_literals\n", - "\n", - "import argparse\n", - "import csv\n", - "import os\n", - "import shutil\n", - "import sys\n", - "import time\n", - "\n", - "import pyspark\n", - "from pyspark.sql import SparkSession\n", - "from pyspark.ml import Pipeline\n", - "from pyspark.ml.feature import (\n", - " OneHotEncoder,\n", - " StringIndexer,\n", - " VectorAssembler,\n", - " VectorIndexer,\n", - ")\n", - "from pyspark.sql.functions import *\n", - "from pyspark.sql.types import (\n", - " DoubleType,\n", - " StringType,\n", - " StructField,\n", - " StructType,\n", - ")\n", - "\n", - "\n", - "def csv_line(data):\n", - " r = \",\".join(str(d) for d in data[1])\n", - " return str(data[0]) + \",\" + r\n", - "\n", - "\n", - "def main():\n", - " parser = argparse.ArgumentParser(description=\"app inputs and outputs\")\n", - " parser.add_argument(\"--s3_input_bucket\", type=str, help=\"s3 input bucket\")\n", - " parser.add_argument(\"--s3_input_key_prefix\", type=str, help=\"s3 input key prefix\")\n", - " parser.add_argument(\"--s3_output_bucket\", type=str, help=\"s3 output bucket\")\n", - " parser.add_argument(\"--s3_output_key_prefix\", type=str, help=\"s3 output key prefix\")\n", - " args = parser.parse_args()\n", - "\n", - " spark = SparkSession.builder.appName(\"PySparkApp\").getOrCreate()\n", - "\n", - " # This is needed to save RDDs which is the only way to write nested Dataframes into CSV format\n", - " spark.sparkContext._jsc.hadoopConfiguration().set(\n", - " \"mapred.output.committer.class\", \"org.apache.hadoop.mapred.FileOutputCommitter\"\n", - " )\n", - "\n", - " # Defining the schema corresponding to the input data. The input data does not contain the headers\n", - " schema = StructType(\n", - " [\n", - " StructField(\"sex\", StringType(), True),\n", - " StructField(\"length\", DoubleType(), True),\n", - " StructField(\"diameter\", DoubleType(), True),\n", - " StructField(\"height\", DoubleType(), True),\n", - " StructField(\"whole_weight\", DoubleType(), True),\n", - " StructField(\"shucked_weight\", DoubleType(), True),\n", - " StructField(\"viscera_weight\", DoubleType(), True),\n", - " StructField(\"shell_weight\", DoubleType(), True),\n", - " StructField(\"rings\", DoubleType(), True),\n", - " ]\n", - " )\n", - "\n", - " # Downloading the data from S3 into a Dataframe\n", - " total_df = spark.read.csv(\n", - " (\"s3://\" + os.path.join(args.s3_input_bucket, args.s3_input_key_prefix, \"abalone.csv\")),\n", - " header=False,\n", - " schema=schema,\n", - " )\n", - "\n", - " # StringIndexer on the sex column which has categorical value\n", - " sex_indexer = StringIndexer(inputCol=\"sex\", outputCol=\"indexed_sex\")\n", - "\n", - " # one-hot-encoding is being performed on the string-indexed sex column (indexed_sex)\n", - " sex_encoder = OneHotEncoder(inputCol=\"indexed_sex\", outputCol=\"sex_vec\")\n", - "\n", - " # vector-assembler will bring all the features to a 1D vector for us to save easily into CSV format\n", - " assembler = VectorAssembler(\n", - " inputCols=[\n", - " \"sex_vec\",\n", - " \"length\",\n", - " \"diameter\",\n", - " \"height\",\n", - " \"whole_weight\",\n", - " \"shucked_weight\",\n", - " \"viscera_weight\",\n", - " \"shell_weight\",\n", - " ],\n", - " outputCol=\"features\",\n", - " )\n", - "\n", - " # The pipeline is comprised of the steps added above\n", - " pipeline = Pipeline(stages=[sex_indexer, sex_encoder, assembler])\n", - "\n", - " # This step trains the feature transformers\n", - " model = pipeline.fit(total_df)\n", - "\n", - " # This step transforms the dataset with information obtained from the previous fit\n", - " transformed_total_df = model.transform(total_df)\n", - "\n", - " # Split the overall dataset into 80-20 training and validation\n", - " (train_df, validation_df) = transformed_total_df.randomSplit([0.8, 0.2])\n", - "\n", - " # Convert the train dataframe to RDD to save in CSV format and upload to S3\n", - " train_rdd = train_df.rdd.map(lambda x: (x.rings, x.features))\n", - " train_lines = train_rdd.map(csv_line)\n", - " train_lines.saveAsTextFile(\n", - " \"s3://\" + os.path.join(args.s3_output_bucket, args.s3_output_key_prefix, \"train\")\n", - " )\n", - "\n", - " # Convert the validation dataframe to RDD to save in CSV format and upload to S3\n", - " validation_rdd = validation_df.rdd.map(lambda x: (x.rings, x.features))\n", - " validation_lines = validation_rdd.map(csv_line)\n", - " validation_lines.saveAsTextFile(\n", - " \"s3://\" + os.path.join(args.s3_output_bucket, args.s3_output_key_prefix, \"validation\")\n", - " )\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the SageMaker Processing Job\n", - "\n", - "Next, you'll use the `PySparkProcessor` class to define a Spark job and run it using SageMaker Processing. A few things to note in the definition of the `PySparkProcessor`:\n", - "\n", - "* This is a multi-node job with two m5.xlarge instances (which is specified via the `instance_count` and `instance_type` parameters)\n", - "* Spark framework version 3.1 is specified via the `framework_version` parameter\n", - "* The PySpark script defined above is passed via via the `submit_app` parameter\n", - "* Command-line arguments to the PySpark script (such as the S3 input and output locations) are passed via the `arguments` parameter\n", - "* Spark event logs will be offloaded to the S3 location specified in `spark_event_logs_s3_uri` and can be used to view the Spark UI while the job is in progress or after it completes\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.spark.processing import PySparkProcessor\n", - "\n", - "# Upload the raw input dataset to a unique S3 location\n", - "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", - "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", - "input_prefix_abalone = \"{}/input/raw/abalone\".format(prefix)\n", - "input_preprocessed_prefix_abalone = \"{}/input/preprocessed/abalone\".format(prefix)\n", - "\n", - "sagemaker_session.upload_data(\n", - " path=\"./data/abalone.csv\", bucket=bucket, key_prefix=input_prefix_abalone\n", - ")\n", - "\n", - "# Run the processing job\n", - "spark_processor = PySparkProcessor(\n", - " base_job_name=\"sm-spark\",\n", - " framework_version=\"3.1\",\n", - " role=role,\n", - " instance_count=2,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " max_runtime_in_seconds=1200,\n", - ")\n", - "\n", - "spark_processor.run(\n", - " submit_app=\"./code/preprocess.py\",\n", - " arguments=[\n", - " \"--s3_input_bucket\",\n", - " bucket,\n", - " \"--s3_input_key_prefix\",\n", - " input_prefix_abalone,\n", - " \"--s3_output_bucket\",\n", - " bucket,\n", - " \"--s3_output_key_prefix\",\n", - " input_preprocessed_prefix_abalone,\n", - " ],\n", - " spark_event_logs_s3_uri=\"s3://{}/{}/spark_event_logs\".format(bucket, prefix),\n", - " logs=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validate Data Processing Results\n", - "\n", - "Next, validate the output of our data preprocessing job by looking at the first 5 rows of the output dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Top 5 rows from s3://{}/{}/train/\".format(bucket, input_preprocessed_prefix_abalone))\n", - "!aws s3 cp --quiet s3://$bucket/$input_preprocessed_prefix_abalone/train/part-00000 - | head -n5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View the Spark UI\n", - "\n", - "Next, you can view the Spark UI by running the history server locally in this notebook. (**Note:** this feature will only work in a local development environment with docker installed or on a Sagemaker Notebook Instance. This feature does not currently work in SageMaker Studio.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uses docker\n", - "spark_processor.start_history_server()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After viewing the Spark UI, you can terminate the history server before proceeding." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "spark_processor.terminate_history_server()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example 2: Specify additional Python and jar file dependencies\n", - "\n", - "The next example demonstrates a scenario where additional Python file dependencies are required by the PySpark script. You'll use a sample PySpark script that requires additional user-defined functions (UDFs) defined in a local module." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile ./code/hello_py_spark_app.py\n", - "import argparse\n", - "import time\n", - "\n", - "# Import local module to test spark-submit--py-files dependencies\n", - "import hello_py_spark_udfs as udfs\n", - "from pyspark.sql import SparkSession, SQLContext\n", - "from pyspark.sql.functions import udf\n", - "from pyspark.sql.types import IntegerType\n", - "import time\n", - "\n", - "if __name__ == \"__main__\":\n", - " print(\"Hello World, this is PySpark!\")\n", - "\n", - " parser = argparse.ArgumentParser(description=\"inputs and outputs\")\n", - " parser.add_argument(\"--input\", type=str, help=\"path to input data\")\n", - " parser.add_argument(\"--output\", required=False, type=str, help=\"path to output data\")\n", - " args = parser.parse_args()\n", - " spark = SparkSession.builder.appName(\"SparkTestApp\").getOrCreate()\n", - " sqlContext = SQLContext(spark.sparkContext)\n", - "\n", - " # Load test data set\n", - " inputPath = args.input\n", - " outputPath = args.output\n", - " salesDF = spark.read.json(inputPath)\n", - " salesDF.printSchema()\n", - "\n", - " salesDF.createOrReplaceTempView(\"sales\")\n", - "\n", - " # Define a UDF that doubles an integer column\n", - " # The UDF function is imported from local module to test spark-submit--py-files dependencies\n", - " double_udf_int = udf(udfs.double_x, IntegerType())\n", - "\n", - " # Save transformed data set to disk\n", - " salesDF.select(\"date\", \"sale\", double_udf_int(\"sale\").alias(\"sale_double\")).write.json(\n", - " outputPath\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile ./code/hello_py_spark_udfs.py\n", - "def double_x(x):\n", - " return x + x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a processing job with Python file dependencies\n", - "\n", - "Then, you'll create a processing job where the additional Python file dependencies are specified via the `submit_py_files` argument in the `run()` function. If your Spark application requires additional jar file dependencies, these can be specified via the `submit_jars` argument of the `run()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define job input/output URIs\n", - "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", - "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", - "input_prefix_sales = \"{}/input/sales\".format(prefix)\n", - "output_prefix_sales = \"{}/output/sales\".format(prefix)\n", - "input_s3_uri = \"s3://{}/{}\".format(bucket, input_prefix_sales)\n", - "output_s3_uri = \"s3://{}/{}\".format(bucket, output_prefix_sales)\n", - "\n", - "sagemaker_session.upload_data(\n", - " path=\"./data/data.jsonl\", bucket=bucket, key_prefix=input_prefix_sales\n", - ")\n", - "\n", - "spark_processor = PySparkProcessor(\n", - " base_job_name=\"sm-spark-udfs\",\n", - " framework_version=\"3.1\",\n", - " role=role,\n", - " instance_count=2,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " max_runtime_in_seconds=1200,\n", - ")\n", - "\n", - "spark_processor.run(\n", - " submit_app=\"./code/hello_py_spark_app.py\",\n", - " submit_py_files=[\"./code/hello_py_spark_udfs.py\"],\n", - " arguments=[\"--input\", input_s3_uri, \"--output\", output_s3_uri],\n", - " logs=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validate Data Processing Results\n", - "\n", - "Next, validate the output of the Spark job by ensuring that the output URI contains the Spark `_SUCCESS` file along with the output json lines file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Output files in {}\".format(output_s3_uri))\n", - "!aws s3 ls $output_s3_uri/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example 3: Run a Java/Scala Spark application\n", - "\n", - "In the next example, you'll take a Spark application jar (located in `./code/spark-test-app.jar`) that is already built and run it using SageMaker Processing. Here, you'll use the `SparkJarProcessor` class to define the job parameters. \n", - "\n", - "In the `run()` function you'll specify: \n", - "\n", - "* The location of the Spark application jar file in the `submit_app` argument\n", - "* The main class for the Spark application in the `submit_class` argument\n", - "* Input/output arguments for the Spark application" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.spark.processing import SparkJarProcessor\n", - "\n", - "# Upload the raw input dataset to S3\n", - "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", - "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", - "input_prefix_sales = \"{}/input/sales\".format(prefix)\n", - "output_prefix_sales = \"{}/output/sales\".format(prefix)\n", - "input_s3_uri = \"s3://{}/{}\".format(bucket, input_prefix_sales)\n", - "output_s3_uri = \"s3://{}/{}\".format(bucket, output_prefix_sales)\n", - "\n", - "sagemaker_session.upload_data(\n", - " path=\"./data/data.jsonl\", bucket=bucket, key_prefix=input_prefix_sales\n", - ")\n", - "\n", - "spark_processor = SparkJarProcessor(\n", - " base_job_name=\"sm-spark-java\",\n", - " framework_version=\"3.1\",\n", - " role=role,\n", - " instance_count=2,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " max_runtime_in_seconds=1200,\n", - ")\n", - "\n", - "spark_processor.run(\n", - " submit_app=\"./code/spark-test-app.jar\",\n", - " submit_class=\"com.amazonaws.sagemaker.spark.test.HelloJavaSparkApp\",\n", - " arguments=[\"--input\", input_s3_uri, \"--output\", output_s3_uri],\n", - " logs=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example 4: Specifying additional Spark configuration\n", - "\n", - "Overriding Spark configuration is crucial for a number of tasks such as tuning your Spark application or configuring the Hive metastore. Using the SageMaker Python SDK, you can easily override Spark/Hive/Hadoop configuration.\n", - "\n", - "The next example demonstrates this by overriding Spark executor memory/cores.\n", - "\n", - "For more information on configuring your Spark application, see the EMR documentation on [Configuring Applications](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-configure-apps.html)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload the raw input dataset to a unique S3 location\n", - "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", - "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", - "input_prefix_abalone = \"{}/input/raw/abalone\".format(prefix)\n", - "input_preprocessed_prefix_abalone = \"{}/input/preprocessed/abalone\".format(prefix)\n", - "\n", - "sagemaker_session.upload_data(\n", - " path=\"./data/abalone.csv\", bucket=bucket, key_prefix=input_prefix_abalone\n", - ")\n", - "\n", - "spark_processor = PySparkProcessor(\n", - " base_job_name=\"sm-spark\",\n", - " framework_version=\"3.1\",\n", - " role=role,\n", - " instance_count=2,\n", - " instance_type=\"ml.m5.xlarge\",\n", - " max_runtime_in_seconds=1200,\n", - ")\n", - "\n", - "configuration = [\n", - " {\n", - " \"Classification\": \"spark-defaults\",\n", - " \"Properties\": {\"spark.executor.memory\": \"2g\", \"spark.executor.cores\": \"1\"},\n", - " }\n", - "]\n", - "\n", - "spark_processor.run(\n", - " submit_app=\"./code/preprocess.py\",\n", - " arguments=[\n", - " \"--s3_input_bucket\",\n", - " bucket,\n", - " \"--s3_input_key_prefix\",\n", - " input_prefix_abalone,\n", - " \"--s3_output_bucket\",\n", - " bucket,\n", - " \"--s3_output_key_prefix\",\n", - " input_preprocessed_prefix_abalone,\n", - " ],\n", - " configuration=configuration,\n", - " logs=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook CI Test Results\n", - "\n", - "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", - "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n", - "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n", - "\n", - "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n" - ] - } - ], - "metadata": { - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "conda_python3", - "language": "python", - "name": "conda_python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 4f6b1f6b5276b40b0dabad55ac8a4cdcd040dda3 Mon Sep 17 00:00:00 2001 From: Zhaoqi Date: Tue, 9 Jul 2024 12:54:45 -0400 Subject: [PATCH 07/16] Revert "New folder structure (#4694)" (#4701) This reverts commit 970d88ee18a217610c5c7005bcedb8330c41b774 due to broken blog 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(100%) create mode 100644 sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb rename {archived/notebooks => sagemaker_neo_compilation_jobs/tensorflow_unet}/sagemaker-neo-tf-unet.ipynb (100%) create mode 100644 sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb create mode 100644 sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb rename {archived/notebooks => step-functions-data-science-sdk}/training_pipeline_pytorch_mnist/code/mnist.py (100%) rename {archived/notebooks => step-functions-data-science-sdk}/training_pipeline_pytorch_mnist/code/requirements.txt (100%) rename {archived/notebooks => step-functions-data-science-sdk}/training_pipeline_pytorch_mnist/training_pipeline_pytorch_mnist.ipynb (100%) diff --git a/README.md b/README.md index 4402f8c150..cc8b819911 100644 --- a/README.md +++ b/README.md @@ -12,17 +12,16 @@ Amazon SageMaker examples are divided in two repositories: - [Sagemaker Example Community repository](https://github.com/aws/amazon-sagemaker-examples-community) is another SageMaker repository which contains additional examples and reference solutions, beyond the examples showcased in the [official repository](https://github.com/aws/amazon-sagemaker-examples). This repository is maintained by community of engineers and solution architects at AWS. -## Planning to submit a PR to this repository? Read this first: - +## Planning to submit a PR to this repository? read this first: - This repository will only accept notebooks/examples which demonstrate a feature of SageMaker, not yet covered anywhere in this repository. PR submitters are requested to check this before submitting the PR to avoid getting it rejected. - If you still would like to contribute your example, please submit a PR to [Sagemaker Example Community repository](https://github.com/aws/amazon-sagemaker-examples-community) instead. + ## :hammer_and_wrench: Setup The quickest setup to run example notebooks includes: - -- An [AWS Account](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-account.html) +- An [AWS account](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-account.html) - Proper [IAM User and Role](http://docs.aws.amazon.com/sagemaker/latest/dg/authentication-and-access-control.html) setup - An [Amazon SageMaker Notebook Instance](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html) - An [S3 bucket](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-config-permissions.html) @@ -38,6 +37,22 @@ As of February 7, 2022, the default branch is named "main". See our [announcemen ## :notebook: Examples +### Introduction to geospatial capabilities + +These examples introduce SageMaker geospatial capabilities which makes it easy to build, train, and deploy ML models using geospatial data. + +- [How to use SageMaker Processing with geospatial image](sagemaker-geospatial/processing-geospatial-ndvi/geospatial-processing-ndvi-intro.ipynb) shows how to compute the normalized difference vegetation index (NDVI) which indicates health and density of vegetation using SageMaker Processing and satellite imagery +- [Monitoring Lake Drought with SageMaker Geospatial Capabilities](sagemaker-geospatial/lake-mead-drought-monitoring) shows how to monitor Lake Mead drought using SageMaker geospatial capabilities. +- [Digital Farming with Amazon SageMaker Geospatial Capabilities](sagemaker-geospatial/digital-farming-pipelines) shows how geospatial capabilities can help accelerating, optimizing, and easing the processing of the geospatial data for the Digital Farming use cases. +- [Assess wildfire damage with Amazon SageMaker Geospatial Capabilities](sagemaker-geospatial/dixie-wildfire-damage-assessment/dixie-wildfire-damage-assessment.ipynb) demonstrates how Amazon SageMaker geospatial capabilities can be used to identify and assess vegetation loss caused by the Dixie wildfire in Northern California. +- [Monitoring Glacier Melting with SageMaker Geospatial Capabilities](sagemaker-geospatial/mount-shasta-glacier-melting-monitoring) shows how to monitor glacier melting at Mount Shasta using SageMaker geospatial capabilities. +- [Monitoring of methane (CH4) emission point sources using Amazon SageMaker Geospatial Capabilities](sagemaker-geospatial/methane-emission-monitoring/monitor_methane_ch4_emission_point_sources.ipynb) demonstrates how methane emissions can be detected by using open data Satellite imagery (Sentinel-2). +- [Segmenting aerial imagery using geospatial GPU notebook](sagemaker-geospatial/segment-aerial-naip/segment_naip_geospatial_notebook.ipynb) shows how to use the geospatial GPU notebook with open-source libraries to perform segmentation on aerial imagery. +- [Perform Sentinel-1 InSAR using ESA SNAP Toolkit](sagemaker-geospatial/sentinel1-insar-snap/sentinel1_insar_kumamoto.ipynb) shows how the SNAP toolkit can be used within Amazon SageMaker geospatial capabilities to create interferograms on Sentinel-1 SAR data. +- [How to use Vector Enrichment Jobs for Map Matching](sagemaker-geospatial/vector-enrichment-map-matching/vector-enrichment-map-matching.ipynb) shows how to use vector enrichtment operations with Amazon SageMaker Geospatial capabilities to snap GPS coordinates to road segments. +- [How to use Vector Enrichment Jobs for Reverse Geocoding](sagemaker-geospatial/vector-enrichment-reverse-geocoding/vector-enrichment-reverse-geocoding.ipynb) shows how to use Amazon SageMaker Geospatial capabilities for reverse geocoding to obtain human readable addresses from data with latitude/longitude information. +- [Building geospatial pipelines with SageMaker Pipelines](sagemaker-geospatial/geospatial-processing-pipeline/geospatial_pipeline_processing.ipynb) shows how a geospatial data processing workflow can be automated by using Amazon SageMaker Pipelines. + ### Introduction to Ground Truth Labeling Jobs These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth. @@ -54,15 +69,20 @@ These examples provide quick walkthroughs to get you up and running with the lab These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors. - [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives. This uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to create a highly predictive model. +- [Cancer Prediction](introduction_to_applying_machine_learning/breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner. - [Ensembling](introduction_to_applying_machine_learning/ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling. +- [Video Game Sales](introduction_to_applying_machine_learning/video_game_sales) develops a binary prediction model for the success of video games based on review scores. - [MXNet Gluon Recommender System](introduction_to_applying_machine_learning/gluon_recommender_system) uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews. +- [Fair Linear Learner](introduction_to_applying_machine_learning/fair_linear_learner) is an example of an effective way to create fair linear models with respect to sensitive features. - [Population Segmentation of US Census Data using PCA and Kmeans](introduction_to_applying_machine_learning/US-census_population_segmentation_PCA_Kmeans) analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties. +- [Document Embedding using Object2Vec](introduction_to_applying_machine_learning/object2vec_document_embedding) is an example to embed a large collection of documents in a common low-dimensional space, so that the semantic distances between these documents are preserved. - [Traffic violations forecasting using DeepAR](introduction_to_applying_machine_learning/deepar_chicago_traffic_violations) is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm. - [Visual Inspection Automation with Pre-trained Amazon SageMaker Models](introduction_to_applying_machine_learning/visual_object_detection) is an example for fine-tuning pre-trained Amazon Sagemaker models on a target dataset. - [Create SageMaker Models Using the PyTorch Model Zoo](introduction_to_applying_machine_learning/sagemaker_pytorch_model_zoo) contains an example notebook to create a SageMaker model leveraging the PyTorch Model Zoo and visualize the results. - [Deep Demand Forecasting](introduction_to_applying_machine_learning/deep_demand_forecasting) provides an end-to-end solution for Demand Forecasting task using three state-of-the-art time series algorithms LSTNet, Prophet, and SageMaker DeepAR, which are available in GluonTS and Amazon SageMaker. - [Fraud Detection Using Graph Neural Networks](introduction_to_applying_machine_learning/fraud_detection_using_graph_neural_networks) is an example to identify fraudulent transactions from transaction and user identity datasets. - [Identify key insights from textual document](introduction_to_applying_machine_learning/identify_key_insights_from_textual_document) contains comphrensive notebooks for five natural language processing tasks Document Summarization, Text Classification, Question and Answering, Name Entity Recognition, and Semantic Relation Extracion. +- [Synthetic Churn Prediction with Text](introduction_to_applying_machine_learning/synthetic_churn_prediction_with_text) contains an example notebook to train, deploy and use a churn prediction model that processed numerical, categorical and textual features to make its prediction. - [Credit Card Fraud Detector](introduction_to_applying_machine_learning/credit_card_fraud_detector) is an example of the core of a credit card fraud detection system using SageMaker with Random Cut Forest and XGBoost. - [Churn Prediction Multimodality of Text and Tabular](introduction_to_applying_machine_learning/churn_prediction_multimodality_of_text_and_tabular) is an example notebook to train and deploy a churn prediction model that uses state-of-the-art natural language processing model to find useful signals in text. In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. @@ -70,11 +90,16 @@ These examples provide a gentle introduction to machine learning concepts as the These examples introduce SageMaker's hyperparameter tuning functionality which helps deliver the best possible predictions by running a large number of training jobs to determine which hyperparameter values are the most impactful. -- [TensorFlow Tuning](hyperparameter_tuning/tensorflow2_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built TensorFlow container and MNIST dataset. +- [XGBoost Tuning](hyperparameter_tuning/xgboost_direct_marketing) shows how to use SageMaker hyperparameter tuning to improve your model fit. +- [BlazingText Tuning](hyperparameter_tuning/blazingtext_text_classification_20_newsgroups) shows how to use SageMaker hyperparameter tuning with the BlazingText built-in algorithm and 20_newsgroups dataset.. +- [TensorFlow Tuning](hyperparameter_tuning/tensorflow_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built TensorFlow container and MNIST dataset. +- [MXNet Tuning](hyperparameter_tuning/mxnet_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built MXNet container and MNIST dataset. - [HuggingFace Tuning](hyperparameter_tuning/huggingface_multiclass_text_classification_20_newsgroups) shows how to use SageMaker hyperparameter tuning with the pre-built HuggingFace container and 20_newsgroups dataset. - [Keras BYO Tuning](hyperparameter_tuning/keras_bring_your_own) shows how to use SageMaker hyperparameter tuning with a custom container running a Keras convolutional network on CIFAR-10 data. +- [R BYO Tuning](hyperparameter_tuning/r_bring_your_own) shows how to use SageMaker hyperparameter tuning with the custom container from the [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) example. - [Analyzing Results](hyperparameter_tuning/analyze_results) is a shared notebook that can be used after each of the above notebooks to provide analysis on how training jobs with different hyperparameters performed. - [Model tuning for distributed training](hyperparameter_tuning/model_tuning_for_distributed_training) shows how to use SageMaker hyperparameter tuning with Hyperband strategy for optimizing model in distributed training. +- [Neural Architecture Search for Large Language Models](hyperparameter_tuning/neural_architecture_search_llm) shows how to prune fine-tuned large language models via neural architecture search. ### SageMaker Autopilot @@ -82,7 +107,7 @@ These examples introduce SageMaker Autopilot. Autopilot automatically performs f - [Customer Churn AutoML](autopilot/) shows how to use SageMaker Autopilot to automatically train a model for the [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) task. - [Targeted Direct Marketing AutoML](autopilot/) shows how to use SageMaker Autopilot to automatically train a model. -- [Housing Prices AutoML](autopilot/autopilot_california_housing.ipynb) shows how to use SageMaker Autopilot for a linear regression problem (predict housing prices). +- [Housing Prices AutoML](sagemaker-autopilot/housing_prices) shows how to use SageMaker Autopilot for a linear regression problem (predict housing prices). - [Portfolio Churn Prediction with Amazon SageMaker Autopilot and Neo4j](autopilot/sagemaker_autopilot_neo4j_portfolio_churn.ipynb) shows how to use SageMaker Autopilot with graph embeddings to predict investment portfolio churn. - [Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines](autopilot/sagemaker-autopilot-pipelines) shows how to use SageMaker Autopilot in combination with SageMaker Pipelines for end-to-end AutoML training automation. - [Amazon SageMaker Autopilot models to serverless endpoints](autopilot/autopilot-serverless-inference) shows how to deploy Autopilot generated models to serverless endpoints. @@ -98,10 +123,14 @@ These examples provide quick walkthroughs to get you up and running with Amazon - [Neural Topic Model (NTM)](introduction_to_amazon_algorithms/ntm_synthetic) uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known. - [Principal Components Analysis (PCA)](introduction_to_amazon_algorithms/pca_mnist) uses Amazon SageMaker PCA to calculate eigendigits from MNIST. - [Seq2Seq](introduction_to_amazon_algorithms/seq2seq_translation_en-de) uses the Amazon SageMaker Seq2Seq algorithm that's built on top of [Sockeye](https://github.com/awslabs/sockeye), which is a sequence-to-sequence framework for Neural Machine Translation based on MXNet. Seq2Seq implements state-of-the-art encoder-decoder architectures which can also be used for tasks like Abstractive Summarization in addition to Machine Translation. This notebook shows translation from English to German text. +- [Image Classification](introduction_to_amazon_algorithms/imageclassification_caltech) includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm. This uses a ResNet deep convolutional neural network to classify images from the caltech dataset. - [XGBoost for regression](introduction_to_amazon_algorithms/xgboost_abalone) predicts the age of abalone ([Abalone dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html)) using regression from Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost). - [XGBoost for multi-class classification](introduction_to_amazon_algorithms/xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classify handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented. +- [DeepAR for time series forecasting](introduction_to_amazon_algorithms/deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set. - [BlazingText Word2Vec](introduction_to_amazon_algorithms/blazingtext_word2vec_text8) generates Word2Vec embeddings from a cleaned text dump of Wikipedia articles using SageMaker's fast and scalable BlazingText implementation. - [Object detection for bird images](introduction_to_amazon_algorithms/object_detection_birds) demonstrates how to use the Amazon SageMaker Object Detection algorithm with a public dataset of Bird images. +- [Object2Vec for movie recommendation](introduction_to_amazon_algorithms/object2vec_movie_recommendation) demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example. +- [Object2Vec for multi-label classification](introduction_to_amazon_algorithms/object2vec_multilabel_genre_classification) shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions. - [Object2Vec for sentence similarity](introduction_to_amazon_algorithms/object2vec_sentence_similarity) explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application. - [IP Insights for suspicious logins](introduction_to_amazon_algorithms/ipinsights_login) shows how to train IP Insights on a login events for a web server to identify suspicious login attempts. - [Semantic Segmentation](introduction_to_amazon_algorithms/semantic_segmentation_pascalvoc) shows how to train a semantic segmentation algorithm using the Amazon SageMaker Semantic Segmentation algorithm. It also demonstrates how to host the model and produce segmentation masks and probability of segmentation. @@ -118,21 +147,31 @@ These examples provide quick walkthroughs to get you up and running with Amazon - [JumpStart Upscaling](introduction_to_amazon_algorithms/jumpstart_upscaling) demonstrates how to enhance image quality with Stable Diffusion models in JumpStart. - [JumpStart Inpainting](introduction_to_amazon_algorithms/jumpstart_inpainting) demonstrates how to inpaint an image with Stable Diffusion models in JumpStart. - [In-context learning with AlexaTM 20B](introduction_to_amazon_algorithms/jumpstart_alexatm20b) demonstrates how to use AlexaTM 20B for in-context-learning in JumpStart. - ### Amazon SageMaker RL The following provide examples demonstrating different capabilities of Amazon SageMaker RL. +- [Cartpole using Coach](reinforcement_learning/rl_cartpole_coach) demonstrates the simplest usecase of Amazon SageMaker RL using Intel's RL Coach. +- [AWS DeepRacer](reinforcement_learning/rl_deepracer_robomaker_coach_gazebo) demonstrates AWS DeepRacer trainig using RL Coach in the Gazebo environment. - [HVAC using EnergyPlus](reinforcement_learning/rl_hvac_coach_energyplus) demonstrates the training of HVAC systems using the EnergyPlus environment. +- [Knapsack Problem](reinforcement_learning/rl_knapsack_coach_custom) demonstrates how to solve the knapsack problem using a custom environment. - [Mountain Car](reinforcement_learning/rl_mountain_car_coach_gymEnv) Mountain car is a classic RL problem. This notebook explains how to solve this using the OpenAI Gym environment. +- [Distributed Neural Network Compression](reinforcement_learning/rl_network_compression_ray_custom) This notebook explains how to compress ResNets using RL, using a custom environment and the RLLib toolkit. +- [Portfolio Management](reinforcement_learning/rl_portfolio_management_coach_customEnv) This notebook uses a custom Gym environment to manage multiple financial investments. +- [Autoscaling](reinforcement_learning/rl_predictive_autoscaling_coach_customEnv) demonstrates how to adjust load depending on demand. This uses RL Coach and a custom environment. +- [Roboschool](reinforcement_learning/rl_roboschool_ray) is an open source physics simulator that is commonly used to train RL policies for robotic systems. This notebook demonstrates training a few agents using it. +- [Stable Baselines](reinforcement_learning/rl_roboschool_stable_baselines) In this notebook example, we will make the HalfCheetah agent learn to walk using the stable-baselines, which are a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines. - [Travelling Salesman](reinforcement_learning/rl_traveling_salesman_vehicle_routing_coach) is a classic NP hard problem, which this notebook solves with AWS SageMaker RL. +- [Tic-tac-toe](reinforcement_learning/rl_tic_tac_toe_coach_customEnv) is a simple implementation of a custom Gym environment to train and deploy an RL agent in Coach that then plays tic-tac-toe interactively in a Jupyter Notebook. - [Unity Game Agent](reinforcement_learning/rl_unity_ray) shows how to use RL algorithms to train an agent to play Unity3D game. ### Scientific Details of Algorithms These examples provide more thorough mathematical treatment on a select group of algorithms. +- [Streaming Median](scientific_details_of_algorithms/streaming_median) sequentially introduces concepts used in streaming algorithms, which many SageMaker algorithms rely on to deliver speed and scalability. - [Latent Dirichlet Allocation (LDA)](scientific_details_of_algorithms/lda_topic_modeling) dives into Amazon SageMaker's spectral decomposition approach to LDA. +- [Linear Learner features](scientific_details_of_algorithms/linear_learner_class_weights_loss_functions) shows how to use the class weights and loss functions features of the SageMaker Linear Learner algorithm to improve performance on a credit card fraud prediction task ### Amazon SageMaker Debugger @@ -140,13 +179,24 @@ These examples provide and introduction to SageMaker Debugger which allows debug - [Using a built-in rule with TensorFlow](sagemaker-debugger/tensorflow_builtin_rule/) - [Using a custom rule with TensorFlow Keras](sagemaker-debugger/tensorflow_keras_custom_rule/) +- [Interactive tensor analysis in notebook with MXNet](sagemaker-debugger/mnist_tensor_analysis/) +- [Visualizing Debugging Tensors of MXNet training](sagemaker-debugger/mnist_tensor_plot/) +- [Real-time analysis in notebook with MXNet](sagemaker-debugger/mxnet_realtime_analysis/) +- [Using a built in rule with XGBoost](sagemaker-debugger/xgboost_builtin_rules/) +- [Real-time analysis in notebook with XGBoost](sagemaker-debugger/xgboost_realtime_analysis/) +- [Using SageMaker Debugger with Managed Spot Training and MXNet](sagemaker-debugger/mxnet_spot_training/) - [Reacting to CloudWatch Events from Rules to take an action based on status with TensorFlow](sagemaker-debugger/tensorflow_action_on_rule/) +- [Using SageMaker Debugger with a custom PyTorch container](sagemaker-debugger/pytorch_custom_container/) ### Amazon SageMaker Distributed Training These examples provide an introduction to SageMaker Distributed Training Libraries for data parallelism and model parallelism. The libraries are optimized for the SageMaker training environment, help adapt your distributed training jobs to SageMaker, and improve training speed and throughput. More examples for models such as BERT and YOLOv5 can be found in [distributed_training/](https://github.com/aws/amazon-sagemaker-examples/tree/main/training/distributed_training). +- [Train GPT-2 with Sharded Data Parallel](https://github.com/aws/amazon-sagemaker-examples/tree/main/training/distributed_training/pytorch/model_parallel/gpt2/smp-train-gpt-simple-sharded-data-parallel.ipynb) shows how to train GPT-2 with near-linear scaling using Sharded Data Parallelism technique in SageMaker Model Parallelism Library. +- [Train EleutherAI GPT-J with Model Parallel](https://github.com/aws/amazon-sagemaker-examples/blob/main/training/distributed_training/pytorch/model_parallel/gpt-j/11_train_gptj_smp_tensor_parallel_notebook.ipynb) shows how to train EleutherAI GPT-J with PyTorch and Tensor Parallelism technique in the SageMaker Model Parallelism Library. +- [Train MaskRCNN with Data Parallel](https://github.com/aws/amazon-sagemaker-examples/blob/main/training/distributed_training/pytorch/data_parallel/maskrcnn/pytorch_smdataparallel_maskrcnn_demo.ipynb) shows how to train MaskRCNN with PyTorch and SageMaker Data Parallelism Library. + ### Amazon SageMaker Smart Sifting These examples provide an Introduction to Smart Sifting library. Smart Sifting is a framework to speed up training of PyTorch models. The framework implements a set of algorithms that filter out inconsequential training examples during training, reducing the computational cost and accelerating the training process. It is configuration-driven and extensible, allowing users to add custom logic to transform their training examples into a filterable format. Smart sifting provides a generic utility for any DNN model, and can reduce the training cost by up to 35% in infrastructure cost. @@ -158,8 +208,9 @@ These examples provide an Introduction to Smart Sifting library. Smart Sifting i These examples provide an introduction to SageMaker Clarify which provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. -- [Fairness and Explainability with SageMaker Clarify](sagemaker-clarify/fairness_and_explainability) shows how to use SageMaker Clarify Processor API to measure the pre-training bias of a dataset and post-training bias of a model, and explain the importance of the input features on the model's decision. -- [Amazon SageMaker Clarify Model Monitors](sagemaker_model_monitor/fairness_and_explainability) shows how to use SageMaker Clarify Model Monitor API to schedule bias monitor to monitor predictions for bias drift on a regular basis, and schedule explainability monitor to monitor predictions for feature attribution drift on a regular basis. +* [Fairness and Explainability with SageMaker Clarify](sagemaker-clarify/fairness_and_explainability) shows how to use SageMaker Clarify Processor API to measure the pre-training bias of a dataset and post-training bias of a model, and explain the importance of the input features on the model's decision. +* [TimeSeries Explainability with SageMaker Clarify](sagemaker-clarify/time_series_deepar) shows how to use SageMaker Clarify Processor API to explain the importance of the input features on the time-series model's decision. +* [Amazon SageMaker Clarify Model Monitors](sagemaker_model_monitor/fairness_and_explainability) shows how to use SageMaker Clarify Model Monitor API to schedule bias monitor to monitor predictions for bias drift on a regular basis, and schedule explainability monitor to monitor predictions for feature attribution drift on a regular basis. ### Publishing content from RStudio on Amazon SageMaker to RStudio Connect @@ -173,32 +224,45 @@ These examples show you how to run R examples, and publish applications in RStud These examples showcase unique functionality available in Amazon SageMaker. They cover a broad range of topics and utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker. +- [Data Distribution Types](advanced_functionality/data_distribution_types) showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances. This has particular implication for scalability and accuracy of distributed training. - [Distributed Training and Batch Transform with Sentiment Classification](advanced_functionality/sentiment_parallel_batch) shows how to use SageMaker Distributed Data Parallelism, SageMaker Debugger, and distrubted SageMaker Batch Transform on a HuggingFace Estimator, in a sentiment classification use case. +- [Encrypting Your Data](advanced_functionality/handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key. +- [Using Parquet Data](advanced_functionality/parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume. - [Connecting to Redshift](advanced_functionality/working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks. - [Bring Your Own XGBoost Model](advanced_functionality/xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs. - [Bring Your Own k-means Model](advanced_functionality/kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it. +- [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language. +- [Installing the R Kernel](advanced_functionality/install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance. - [Bring Your Own scikit Algorithm](advanced_functionality/scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting. +- [Bring Your Own MXNet Model](advanced_functionality/mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker. - [Bring Your Own TensorFlow Model](advanced_functionality/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker. - [Bring Your Own Model train and deploy BERTopic](advanced_functionality/pytorch_extend_container_train_deploy_bertopic) shows how to bring a model through an external library, how to train it and deploy it into Amazon SageMaker by extending the pytorch base containers. +- [Experiment Management Capabilities with Search](advanced_functionality/search) shows how to organize Training Jobs into projects, and track relationships between Models, Endpoints, and Training Jobs. - [Host Multiple Models with Your Own Algorithm](advanced_functionality/multi_model_bring_your_own) shows how to deploy multiple models to a realtime hosted endpoint with your own custom algorithm. - [Host Multiple Models with XGBoost](advanced_functionality/multi_model_xgboost_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled XGBoost container. - [Host Multiple Models with SKLearn](advanced_functionality/multi_model_sklearn_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled SKLearn container. - [Host Multimodal HuggingFace Model](advanced_functionality/huggingface_deploy_instructpix2pix) shows how to host an instruction based image editing model from HuggingFace as a SageMaker endpoint using single core or multi-core GPU based instances. Inference Recommender is used to run load tests and compare the performance of instances. - [SageMaker Training and Inference with Script Mode](sagemaker-script-mode) shows how to use custom training and inference scripts, similar to those you would use outside of SageMaker, with SageMaker's prebuilt containers for various frameworks like Scikit-learn, PyTorch, and XGBoost. - [Host Models with NVidia Triton Server](sagemaker-triton) shows how to deploy models to a realtime hosted endpoint using [Triton](https://developer.nvidia.com/nvidia-triton-inference-server) as the model inference server. -- [Heterogenous Clusters Training in TensorFlow or PyTorch](training/heterogeneous-clusters/README.md) shows how to train using TensorFlow tf.data.service (distributed data pipeline) or Pytorch (with gRPC) on top of Amazon SageMaker Heterogenous clusters to overcome CPU bottlenecks by including different instance types (GPU/CPU) in the same training job. +- [Heterogenous Clusters Training in TensorFlow or PyTorch ](training/heterogeneous-clusters/README.md) shows how to train using TensorFlow tf.data.service (distributed data pipeline) or Pytorch (with gRPC) on top of Amazon SageMaker Heterogenous clusters to overcome CPU bottlenecks by including different instance types (GPU/CPU) in the same training job. ### Amazon SageMaker Neo Compilation Jobs These examples provide an introduction to how to use Neo to compile and optimize deep learning models. +- [GluonCV SSD Mobilenet](sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet) shows how to train GluonCV SSD MobileNet and use Amazon SageMaker Neo to compile and optimize the trained model. +- [Image Classification](sagemaker_neo_compilation_jobs/imageclassification_caltech) Adapts from [image classification](introduction_to_amazon_algorithms/imageclassification_caltech) including Neo API and comparison against the uncompiled baseline. +- [MNIST with MXNet](sagemaker_neo_compilation_jobs/mxnet_mnist) Adapts from [MXNet MNIST](sagemaker-python-sdk/mxnet_mnist) including Neo API and comparison against the uncompiled baseline. - [Deploying pre-trained PyTorch vision models](sagemaker_neo_compilation_jobs/pytorch_torchvision) shows how to use Amazon SageMaker Neo to compile and optimize pre-trained PyTorch models from TorchVision. +- [Distributed TensorFlow](sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist) includes Neo API and comparison against the uncompiled baseline. +- [Predicting Customer Churn](sagemaker_neo_compilation_jobs/xgboost_customer_churn) Adapts from [XGBoost customer churn](introduction_to_applying_machine_learning/xgboost_customer_churn) including Neo API and comparison against the uncompiled baseline. ### Amazon SageMaker Processing These examples show you how to use SageMaker Processing jobs to run data processing workloads. - [Scikit-Learn Data Processing and Model Evaluation](sagemaker_processing/scikit_learn_data_processing_and_model_evaluation) shows how to use SageMaker Processing and the Scikit-Learn container to run data preprocessing and model evaluation workloads. +- [Feature transformation with Amazon SageMaker Processing and SparkML](sagemaker_processing/feature_transformation_with_sagemaker_processing) shows how to use SageMaker Processing to run data processing workloads using SparkML prior to training. - [Feature transformation with Amazon SageMaker Processing and Dask](sagemaker_processing/feature_transformation_with_sagemaker_processing_dask) shows how to use SageMaker Processing to transform data using Dask distributed clusters - [Distributed Data Processing using Apache Spark and SageMaker Processing](sagemaker_processing/spark_distributed_data_processing) shows how to use the built-in Spark container on SageMaker Processing using the SageMaker Python SDK. @@ -220,10 +284,17 @@ These examples show you how to use [SageMaker Pipelines](https://aws.amazon.com/ These examples show you how to train and host in pre-built deep learning framework containers using the SageMaker Python SDK. +- [Chainer CIFAR-10](sagemaker-python-sdk/chainer_cifar10) trains a VGG image classification network on CIFAR-10 using Chainer (both single machine and multi-machine versions are included) +- [Chainer MNIST](sagemaker-python-sdk/chainer_mnist) trains a basic neural network on MNIST using Chainer (shows how to use local mode) +- [Chainer sentiment analysis](sagemaker-python-sdk/chainer_sentiment_analysis) trains a LSTM network with embeddings to predict text sentiment using Chainer - [IRIS with Scikit-learn](sagemaker-python-sdk/scikit_learn_iris) trains a Scikit-learn classifier on IRIS data - [Model Registry and Batch Transform with Scikit-learn](sagemaker-python-sdk/scikit_learn_model_registry_batch_transform) trains a Scikit-learn Random Forest model, registers it in Model Registry, and runs a Batch Transform Job. +- [MNIST with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_mnist) trains a basic neural network on the MNIST handwritten digit dataset using MXNet Gluon +- [MNIST with MXNet](sagemaker-python-sdk/mxnet_mnist) trains a basic neural network on the MNIST handwritten digit data using MXNet's symbolic syntax +- [Sentiment Analysis with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_sentiment) trains a text classifier using embeddings with MXNet Gluon - [TensorFlow training and serving](sagemaker-python-sdk/tensorflow_script_mode_training_and_serving) trains a basic neural network on MNIST - [TensorFlow with Horovod](sagemaker-python-sdk/tensorflow_script_mode_horovod) trains on MNIST using Horovod for distributed training +- [TensorFlow using shell commands](sagemaker-python-sdk/tensorflow_script_mode_using_shell_commands) shows how to use a shell script for the container's entry point #### Pre-Built Machine Learning Framework Containers @@ -242,9 +313,9 @@ These examples show how to use Amazon SageMaker for model training, hosting, and ### Using Amazon SageMaker with Amazon Keyspaces (for Apache Cassandra) These examples show how to use Amazon SageMaker to read data from [Amazon Keyspaces](https://docs.aws.amazon.com/keyspaces/). - - [Train Machine Learning Models using Amazon Keyspaces as a Data Source](ingest_data/sagemaker-keyspaces) + ### AWS Marketplace #### Create algorithms/model packages for listing in AWS Marketplace for machine learning. @@ -260,7 +331,7 @@ Once you have created an algorithm or a model package to be listed in the AWS Ma - [Curate your AWS Marketplace model package listing and sample notebook](aws_marketplace/curating_aws_marketplace_listing_and_sample_notebook/ModelPackage) provides instructions on how to craft a sample notebook to be associated with your listing and how to curate a good AWS Marketplace listing that makes it easy for AWS customers to consume your model package. - [Curate your AWS Marketplace algorithm listing and sample notebook](aws_marketplace/curating_aws_marketplace_listing_and_sample_notebook/Algorithm) provides instructions on how to craft a sample notebook to be associated with your listing and how to curate a good AWS Marketplace listing that makes it easy for your customers to consume your algorithm. -#### Use algorithms, data, and model packages from AWS Marketplace +#### Use algorithms, data, and model packages from AWS Marketplace. These examples show you how to use model-packages and algorithms from AWS Marketplace and dataset products from AWS Data Exchange, for machine learning. @@ -268,6 +339,7 @@ These examples show you how to use model-packages and algorithms from AWS Market - [Using Algorithm From AWS Marketplace](aws_marketplace/using_algorithms/amazon_demo_product) provides a detailed walkthrough on how to use Algorithm with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs by choosing a canonical product listed on AWS Marketplace. - [Using AutoML algorithm](aws_marketplace/using_algorithms/automl) provides a detailed walkthrough on how to use AutoML algorithm from AWS Marketplace. - [Using Model Packages](aws_marketplace/using_model_packages) + - [Using Model Packages From AWS Marketplace](aws_marketplace/using_model_packages/generic_sample_notebook) is a generic notebook which provides sample code snippets you can modify and use for performing inference on Model Packages from AWS Marketplace, using Amazon SageMaker. - [Using Amazon Demo product From AWS Marketplace](aws_marketplace/using_model_packages/amazon_demo_product) provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace. - [Using models for extracting vehicle metadata](aws_marketplace/using_model_packages/auto_insurance) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of auto-insurance claim processing. - [Using models for identifying non-compliance at a workplace](aws_marketplace/using_model_packages/improving_industrial_workplace_safety) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace. @@ -275,21 +347,17 @@ These examples show you how to use model-packages and algorithms from AWS Market - [Amazon Augmented AI with AWS Marketplace ML models](aws_marketplace/using_model_packages/amazon_augmented_ai_with_aws_marketplace_ml_models) will show you how to use AWS Marketplace pre-trained ML models with Amazon Augmented AI to implement human-in-loop workflow reviews with your ML model predictions. - [Monitoring data quality in third-party models from AWS Marketplace](aws_marketplace/using_model_packages/data_quality_monitoring) will show you how to perform Data Quality monitoring on a pre-trained third-party model from AWS Marketplace. - [Evaluating ML models from AWS Marketplace for person counting use case](aws_marketplace/using_model_packages/evaluating_aws_marketplace_models_for_person_counting_use_case) will show you how to use two AWS Marketplace GluonCV pre-trained ML models for person counting use case and evaluate each model for performance in different types of crowd images. - - [Preprocessing audio data using a pre-trained machine learning model](aws_marketplace/using_model_packages/preprocessing-audio-data-using-a-machine-learning-model) demonstrates the usage of a pre-trained audio track separation model to create synthetic features and improve an acoustic classification model. + - [Preprocessing audio data using a pre-trained machine learning model](using_model_packages/preprocessing-audio-data-using-a-machine-learning-model) demonstrates the usage of a pre-trained audio track separation model to create synthetic features and improve an acoustic classification model. - [Using Dataset Products](aws_marketplace/using_data) - [Using Dataset Product from AWS Data Exchange with ML model from AWS Marketplace](aws_marketplace/using_data/using_data_with_ml_model) is a sample notebook which shows how a dataset from AWS Data Exchange can be used with an ML Model Package from AWS Marketplace. - [Using Shutterstock Image Datasets to train Image Classification Models](aws_marketplace/using_data/image_classification_with_shutterstock_image_datasets) provides a detailed walkthrough on how to use the [Free Sample: Images & Metadata of “Whole Foods” Shoppers](https://aws.amazon.com/marketplace/pp/prodview-y6xuddt42fmbu?qid=1623195111604&sr=0-1&ref_=srh_res_product_title#offers) from Shutterstock's Image Datasets to train a multi-label image classification model using Shutterstock's pre-labeled image assets. You can learn more about this implementation [from this blog post](https://aws.amazon.com/blogs/awsmarketplace/using-shutterstocks-image-datasets-to-train-your-computer-vision-models/). -### Using Amazon SageMaker for Generative AI use cases +### Using Amazon SageMaker for Generative AI use cases. These examples show you how to use AWS services for Generative AI use cases. - Text-to-image - [Fine-tune Stable Diffusion XL model with Kohya](use-cases/text-to-image-fine-tuning) Provides an automated solution to create the necessary components to fine-tune a custom Stable Diffusion XL model. - -### Archived - -This folder houses legacy, low-viewed, and duplicate notebooks, with a 6-month grace period before deletion. If you believe a notebook has been moved into this folder in error, please submit a PR with justification. ## :balance_scale: License @@ -301,4 +369,3 @@ For more details, please take a look at the [LICENSE](https://github.com/aws/ama Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed. Please read our [contributing guidelines](https://github.com/aws/amazon-sagemaker-examples/blob/master/CONTRIBUTING.md) if you'd like to open an issue or submit a pull request. - diff --git a/archived/notebooks/distributed_tensorflow_mask_rcnn/README.md b/advanced_functionality/distributed_tensorflow_mask_rcnn/README.md similarity index 100% rename from archived/notebooks/distributed_tensorflow_mask_rcnn/README.md rename to advanced_functionality/distributed_tensorflow_mask_rcnn/README.md diff --git a/archived/notebooks/distributed_tensorflow_mask_rcnn/aws-mask-rcnn.py b/advanced_functionality/distributed_tensorflow_mask_rcnn/aws-mask-rcnn.py similarity index 100% rename from archived/notebooks/distributed_tensorflow_mask_rcnn/aws-mask-rcnn.py rename to advanced_functionality/distributed_tensorflow_mask_rcnn/aws-mask-rcnn.py diff --git a/archived/notebooks/distributed_tensorflow_mask_rcnn/cfn-fsx.yaml b/advanced_functionality/distributed_tensorflow_mask_rcnn/cfn-fsx.yaml similarity 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{}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "MNIST is a widely used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial shows how to train and test an MNIST model on SageMaker using PyTorch. \n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 5 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [PyTorch Estimator](#PyTorch-Estimator)\n", + "1. [Implement the entry point for training](#Implement-the-entry-point-for-training)\n", + "1. [Set hyperparameters](#Set-hyperparameters)\n", + "1. [Set up channels for the training and testing data](#Set-up-channels-for-the-training-and-testing-data)\n", + "1. [Run the training script on SageMaker](#Run-the-training-script-on-SageMaker)\n", + "1. [Inspect and store model data](#Inspect-and-store-model-data)\n", + "1. [Test and debug the entry point before executing the training container](#Test-and-debug-the-entry-point-before-executing-the-training-container)\n", + "1. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "import sagemaker\n", + "from sagemaker.pytorch import PyTorch\n", + "from sagemaker import get_execution_role\n", + "\n", + "\n", + "sess = sagemaker.Session()\n", + "region = sess.boto_region_name\n", + "\n", + "role = get_execution_role()\n", + "\n", + "output_path = \"s3://\" + sess.default_bucket() + \"/DEMO-mnist\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PyTorch Estimator\n", + "\n", + "The `PyTorch` class allows you to run your training script on SageMaker\n", + "infrastracture in a containerized environment. In this notebook, we\n", + "refer to this container as *training container*. \n", + "\n", + "You need to configure\n", + "it with the following parameters to set up the environment:\n", + "\n", + "- `entry_point`: A user-defined Python file used by the training container as the \n", + "instructions for training. We further discuss this file in the next subsection.\n", + "\n", + "- `role`: An IAM role to make AWS service requests\n", + "\n", + "- `instance_type`: The type of SageMaker instance to run your training script. \n", + "Set it to `local` if you want to run the training job on \n", + "the SageMaker instance you are using to run this notebook\n", + "\n", + "- `instance_count`: The number of instances to run your training job on. \n", + "Multiple instances are needed for distributed training.\n", + "\n", + "- `output_path`: \n", + "S3 bucket URI to save training output (model artifacts and output files)\n", + "\n", + "- `framework_version`: The version of PyTorch to use\n", + "\n", + "- `py_version`: The Python version to use\n", + "\n", + "For more information, see the [EstimatorBase API reference](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.EstimatorBase)\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implement the entry point for training\n", + "\n", + "The entry point for training is a Python script that provides all \n", + "the code for training a PyTorch model. It is used by the SageMaker \n", + "PyTorch Estimator (`PyTorch` class above) as the entry point for running the training job.\n", + "\n", + "Under the hood, SageMaker PyTorch Estimator creates a docker image\n", + "with runtime environemnts \n", + "specified by the parameters you provide to initiate the\n", + "estimator class, and it injects the training script into the \n", + "docker image as the entry point to run the container.\n", + "\n", + "In the rest of the notebook, we use *training image* to refer to the \n", + "docker image specified by the PyTorch Estimator and *training container*\n", + "to refer to the container that runs the training image. \n", + "\n", + "This means your training script is very similar to a training script\n", + "you might run outside Amazon SageMaker, but it can access the useful environment \n", + "variables provided by the training image. See [the complete list of environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a complete \n", + "description of all environment variables your training script\n", + "can access. \n", + "\n", + "In this example, we use the training script `code/train.py`\n", + "as the entry point for our PyTorch Estimator.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pygmentize 'code/train.py'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set hyperparameters\n", + "\n", + "In addition, the PyTorch estimator allows you to parse command line arguments\n", + "to your training script via `hyperparameters`.\n", + "\n", + "Note: local mode is not supported in SageMaker Studio. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set local_mode to True to run the training script on the machine that runs this notebook\n", + "\n", + "local_mode = False\n", + "\n", + "if local_mode:\n", + " instance_type = \"local\"\n", + "else:\n", + " instance_type = \"ml.c4.xlarge\"\n", + "\n", + "est = PyTorch(\n", + " entry_point=\"train.py\",\n", + " source_dir=\"code\", # directory of your training script\n", + " role=role,\n", + " framework_version=\"1.5.0\",\n", + " py_version=\"py3\",\n", + " instance_type=instance_type,\n", + " instance_count=1,\n", + " volume_size=250,\n", + " output_path=output_path,\n", + " hyperparameters={\"batch-size\": 128, \"epochs\": 1, \"learning-rate\": 1e-3, \"log-interval\": 100},\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training container executes your training script like:\n", + "\n", + "```\n", + "python train.py --batch-size 100 --epochs 1 --learning-rate 1e-3 --log-interval 100\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up channels for the training and testing data\n", + "\n", + "Tell the `PyTorch` estimator where to find the training and \n", + "testing data. It can be a path to an S3 bucket, or a path\n", + "in your local file system if you use local mode. In this example,\n", + "we download the MNIST data from a public S3 bucket and upload it \n", + "to your default bucket. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import boto3\n", + "from botocore.exceptions import ClientError\n", + "\n", + "# Download training and testing data from a public S3 bucket\n", + "\n", + "\n", + "def download_from_s3(data_dir=\"./data\", train=True):\n", + " \"\"\"Download MNIST dataset and convert it to numpy array\n", + "\n", + " Args:\n", + " data_dir (str): directory to save the data\n", + " train (bool): download training set\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " if not os.path.exists(data_dir):\n", + " os.makedirs(data_dir)\n", + "\n", + " if train:\n", + " images_file = \"train-images-idx3-ubyte.gz\"\n", + " labels_file = \"train-labels-idx1-ubyte.gz\"\n", + " else:\n", + " images_file = \"t10k-images-idx3-ubyte.gz\"\n", + " labels_file = \"t10k-labels-idx1-ubyte.gz\"\n", + "\n", + " # download objects\n", + " s3 = boto3.client(\"s3\")\n", + " bucket = f\"sagemaker-example-files-prod-{region}\"\n", + " for obj in [images_file, labels_file]:\n", + " key = os.path.join(\"datasets/image/MNIST\", obj)\n", + " dest = os.path.join(data_dir, obj)\n", + " if not os.path.exists(dest):\n", + " s3.download_file(bucket, key, dest)\n", + " return\n", + "\n", + "\n", + "download_from_s3(\"./data\", True)\n", + "download_from_s3(\"./data\", False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload to the default bucket\n", + "\n", + "prefix = \"DEMO-mnist\"\n", + "bucket = sess.default_bucket()\n", + "loc = sess.upload_data(path=\"./data\", bucket=bucket, key_prefix=prefix)\n", + "\n", + "channels = {\"training\": loc, \"testing\": loc}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The keys of the `channels` dictionary are passed to the training image,\n", + "and it creates the environment variable `SM_CHANNEL_`. \n", + "\n", + "In this example, `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` are created in the training image (see \n", + "how `code/train.py` accesses these variables). For more information,\n", + "see: [SM_CHANNEL_{channel_name}](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md#sm_channel_channel_name).\n", + "\n", + "If you want, you can create a channel for validation:\n", + "```\n", + "channels = {\n", + " 'training': train_data_loc,\n", + " 'validation': val_data_loc,\n", + " 'test': test_data_loc\n", + "}\n", + "```\n", + "You can then access this channel within your training script via\n", + "`SM_CHANNEL_VALIDATION`.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the training script on SageMaker\n", + "Now, the training container has everything to execute your training\n", + "script. Start the container by calling the `fit()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "est.fit(inputs=channels)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect and store model data\n", + "\n", + "Now, the training is finished, and the model artifact has been saved in \n", + "the `output_path`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pt_mnist_model_data = est.model_data\n", + "print(\"Model artifact saved at:\\n\", pt_mnist_model_data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We store the variable `pt_mnist_model_data` in the current notebook kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%store pt_mnist_model_data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test and debug the entry point before executing the training container\n", + "\n", + "The entry point `code/train.py` can be executed in the training container. \n", + "When you develop your own training script, it is a good practice to simulate the container environment \n", + "in the local shell and test it before sending it to SageMaker, because debugging in a containerized environment\n", + "is rather cumbersome. The following script shows how you can test your training script:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pygmentize code/test_train.py" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we trained a PyTorch model on the MNIST dataset by fitting a SageMaker estimator. For next steps on how to deploy the trained model and perform inference, see [Deploy a Trained PyTorch Model](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/pytorch/get_started_mnist_deploy.html)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/frameworks|pytorch|get_started_mnist_train.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/frameworks|pytorch|get_started_mnist_train.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (PyTorch 1.13 Python 3.9 CPU Optimized)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.13-cpu-py39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/frameworks/tensorflow/get_started_mnist_train.ipynb b/frameworks/tensorflow/get_started_mnist_train.ipynb new file mode 100644 index 0000000000..d5b5233846 --- /dev/null +++ b/frameworks/tensorflow/get_started_mnist_train.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train an MNIST model with TensorFlow\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "MNIST is a widely-used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial will show how to train a TensorFlow V2 model on MNIST model on SageMaker.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 5 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [TensorFlow Estimator](#TensorFlow-Estimator)\n", + "1. [Implement the training entry point](#Implement-the-training-entry-point)\n", + "1. [Set hyperparameters](#Set-hyperparameters)\n", + "1. [Set up channels for training and testing data](#Set-up-channels-for-training-and-testing-data)\n", + "1. [Run the training script on SageMaker](#Run-the-training-script-on-SageMaker)\n", + "1. [Inspect and store model data](#Inspect-and-store-model-data)\n", + "1. [Test and debug the entry point before running the training container](#Test-and-debug-the-entry-point-before-running-the-training-container)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "import sagemaker\n", + "from sagemaker.tensorflow import TensorFlow\n", + "from sagemaker import get_execution_role\n", + "\n", + "sess = sagemaker.Session()\n", + "\n", + "role = get_execution_role()\n", + "\n", + "output_path = \"s3://\" + sess.default_bucket() + \"/DEMO-tensorflow/mnist\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow Estimator\n", + "\n", + "The `TensorFlow` class allows you to run your training script on SageMaker\n", + "infrastracture in a containerized environment. In this notebook, we\n", + "refer to this container as the \"training container.\" \n", + "\n", + "Configure it with the following parameters to set up the environment:\n", + "\n", + "- `entry_point`: A user-defined Python file used by the training container as the instructions for training. We will further discuss this file in the next subsection.\n", + "\n", + "- `role`: An IAM role to make AWS service requests\n", + "\n", + "- `instance_type`: The type of SageMaker instance to run your training script. Set it to `local` if you want to run the training job on the SageMaker instance you are using to run this notebook.\n", + "\n", + "- `model_dir`: S3 bucket URI where the checkpoint data and models can be exported to during training (default: None). \n", + "To disable having model_dir passed to your training script, set `model_dir`=False\n", + "\n", + "- `instance_count`: The number of instances to run your training job on. Multiple instances are needed for distributed training.\n", + "\n", + "- `output_path`: the S3 bucket URI to save training output (model artifacts and output files).\n", + "\n", + "- `framework_version`: The TensorFlow version to use.\n", + "\n", + "- `py_version`: The Python version to use.\n", + "\n", + "For more information, see the [EstimatorBase API reference](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.EstimatorBase).\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implement the training entry point\n", + "\n", + "The entry point for training is a Python script that provides all \n", + "the code for training a TensorFlow model. It is used by the SageMaker \n", + "TensorFlow Estimator (`TensorFlow` class above) as the entry point for running the training job.\n", + "\n", + "Under the hood, SageMaker TensorFlow Estimator downloads a docker image\n", + "with runtime environments \n", + "specified by the parameters to initiate the\n", + "estimator class and it injects the training script into the \n", + "docker image as the entry point to run the container.\n", + "\n", + "In the rest of the notebook, we use *training image* to refer to the \n", + "docker image specified by the TensorFlow Estimator and *training container*\n", + "to refer to the container that runs the training image. \n", + "\n", + "This means your training script is very similar to a training script\n", + "you might run outside Amazon SageMaker, but it can access the useful environment \n", + "variables provided by the training image. See [the complete list of environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a complete \n", + "description of all environment variables your training script\n", + "can access. \n", + "\n", + "In this example, we use the training script `code/train.py`\n", + "as the entry point for our TensorFlow Estimator. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pygmentize 'code/train.py'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set hyperparameters\n", + "\n", + "In addition, the TensorFlow estimator allows you to parse command line arguments\n", + "to your training script via `hyperparameters`.\n", + "\n", + " Note: local mode is not supported in SageMaker Studio. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set local_mode to be True if you want to run the training script on the machine that runs this notebook\n", + "\n", + "local_mode = False\n", + "\n", + "if local_mode:\n", + " instance_type = \"local\"\n", + "else:\n", + " instance_type = \"ml.c4.xlarge\"\n", + "\n", + "est = TensorFlow(\n", + " entry_point=\"train.py\",\n", + " source_dir=\"code\", # directory of your training script\n", + " role=role,\n", + " framework_version=\"2.3.1\",\n", + " model_dir=False, # don't pass --model_dir to your training script\n", + " py_version=\"py37\",\n", + " instance_type=instance_type,\n", + " instance_count=1,\n", + " volume_size=250,\n", + " output_path=output_path,\n", + " hyperparameters={\n", + " \"batch-size\": 512,\n", + " \"epochs\": 1,\n", + " \"learning-rate\": 1e-3,\n", + " \"beta_1\": 0.9,\n", + " \"beta_2\": 0.999,\n", + " },\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training container runs your training script like:\n", + "\n", + "```\n", + "python train.py --batch-size 32 --epochs 1 --learning-rate 0.001 --beta_1 0.9 --beta_2 0.999\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up channels for training and testing data\n", + "\n", + "Tell `TensorFlow` estimator where to find the training and \n", + "testing data. It can be a path to an S3 bucket, or a path\n", + "in your local file system if you use local mode. In this example,\n", + "we download the MNIST data from a public S3 bucket and upload it \n", + "to your default bucket. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import boto3\n", + "from botocore.exceptions import ClientError\n", + "\n", + "# Download training and testing data from a public S3 bucket\n", + "\n", + "\n", + "def download_from_s3(data_dir=\"./data\", train=True):\n", + " \"\"\"Download MNIST dataset and convert it to numpy array\n", + "\n", + " Args:\n", + " data_dir (str): directory to save the data\n", + " train (bool): download training set\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " if not os.path.exists(data_dir):\n", + " os.makedirs(data_dir)\n", + "\n", + " if train:\n", + " images_file = \"train-images-idx3-ubyte.gz\"\n", + " labels_file = \"train-labels-idx1-ubyte.gz\"\n", + " else:\n", + " images_file = \"t10k-images-idx3-ubyte.gz\"\n", + " labels_file = \"t10k-labels-idx1-ubyte.gz\"\n", + "\n", + " # download objects\n", + " s3 = boto3.client(\"s3\")\n", + " bucket = f\"sagemaker-example-files-prod-{boto3.session.Session().region_name}\"\n", + " for obj in [images_file, labels_file]:\n", + " key = os.path.join(\"datasets/image/MNIST\", obj)\n", + " dest = os.path.join(data_dir, obj)\n", + " if not os.path.exists(dest):\n", + " s3.download_file(bucket, key, dest)\n", + " return\n", + "\n", + "\n", + "download_from_s3(\"./data\", True)\n", + "download_from_s3(\"./data\", False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload to the default bucket\n", + "\n", + "prefix = \"DEMO-mnist\"\n", + "bucket = sess.default_bucket()\n", + "loc = sess.upload_data(path=\"./data\", bucket=bucket, key_prefix=prefix)\n", + "\n", + "channels = {\"training\": loc, \"testing\": loc}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The keys of the `channels` dictionary are passed to the training image,\n", + "and it creates the environment variable `SM_CHANNEL_`. \n", + "\n", + "In this example, `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` are created in the training image (see \n", + "how `code/train.py` accesses these variables). For more information,\n", + "see: [SM_CHANNEL_{channel_name}](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md#sm_channel_channel_name).\n", + "\n", + "If you want, you can create a channel for validation:\n", + "```\n", + "channels = {\n", + " 'training': train_data_loc,\n", + " 'validation': val_data_loc,\n", + " 'test': test_data_loc\n", + "}\n", + "```\n", + "You can then access this channel within your training script via\n", + "`SM_CHANNEL_VALIDATION`." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the training script on SageMaker\n", + "Now, the training container has everything to run your training\n", + "script. Start the container by calling the `fit()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "est.fit(inputs=channels)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect and store model data\n", + "\n", + "Now, the training is finished, and the model artifact has been saved in \n", + "the `output_path`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tf_mnist_model_data = est.model_data\n", + "print(\"Model artifact saved at:\\n\", tf_mnist_model_data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We store the variable `tf_mnist_model_data` in the current notebook kernel. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%store tf_mnist_model_data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test and debug the entry point before running the training container\n", + "\n", + "The entry point `code/train.py` provided here has been tested and it can be runs in the training container. \n", + "When you develop your own training script, it is a good practice to simulate the container environment \n", + "in the local shell and test it before sending it to SageMaker, because debugging in a containerized environment\n", + "is rather cumbersome. The following script shows how you can test your training script:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pygmentize code/test_train.py" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we trained a TensorFlow model on the MNIST dataset by fitting a SageMaker estimator. For next steps on how to deploy the trained model and perform inference, see [Deploy a Trained TensorFlow V2 Model](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/tensorflow/get_started_mnist_deploy.html)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/frameworks|tensorflow|get_started_mnist_train.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. 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You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archived/notebooks/nas_for_llm_with_amt/estimate_efficency.py b/hyperparameter_tuning/neural_architecture_search_llm/estimate_efficency.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/estimate_efficency.py rename to hyperparameter_tuning/neural_architecture_search_llm/estimate_efficency.py diff --git a/archived/notebooks/nas_for_llm_with_amt/evaluate_subnetwork.py b/hyperparameter_tuning/neural_architecture_search_llm/evaluate_subnetwork.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/evaluate_subnetwork.py rename to hyperparameter_tuning/neural_architecture_search_llm/evaluate_subnetwork.py diff --git a/archived/notebooks/nas_for_llm_with_amt/extract_subnetworks.py b/hyperparameter_tuning/neural_architecture_search_llm/extract_subnetworks.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/extract_subnetworks.py rename to hyperparameter_tuning/neural_architecture_search_llm/extract_subnetworks.py diff --git a/archived/notebooks/nas_for_llm_with_amt/hf_args.py b/hyperparameter_tuning/neural_architecture_search_llm/hf_args.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/hf_args.py rename to hyperparameter_tuning/neural_architecture_search_llm/hf_args.py diff --git a/archived/notebooks/nas_for_llm_with_amt/inference.py b/hyperparameter_tuning/neural_architecture_search_llm/inference.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/inference.py rename to hyperparameter_tuning/neural_architecture_search_llm/inference.py diff --git a/archived/notebooks/nas_for_llm_with_amt/load_glue_datasets.py b/hyperparameter_tuning/neural_architecture_search_llm/load_glue_datasets.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/load_glue_datasets.py rename to hyperparameter_tuning/neural_architecture_search_llm/load_glue_datasets.py diff --git a/archived/notebooks/nas_for_llm_with_amt/mask/__init__.py b/hyperparameter_tuning/neural_architecture_search_llm/mask/__init__.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/mask/__init__.py rename to hyperparameter_tuning/neural_architecture_search_llm/mask/__init__.py diff --git a/archived/notebooks/nas_for_llm_with_amt/mask/mask_bert.py b/hyperparameter_tuning/neural_architecture_search_llm/mask/mask_bert.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/mask/mask_bert.py rename to hyperparameter_tuning/neural_architecture_search_llm/mask/mask_bert.py diff --git a/archived/notebooks/nas_for_llm_with_amt/mask/utils.py b/hyperparameter_tuning/neural_architecture_search_llm/mask/utils.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/mask/utils.py rename to hyperparameter_tuning/neural_architecture_search_llm/mask/utils.py diff --git a/archived/notebooks/nas_for_llm_with_amt/multi_objective.py b/hyperparameter_tuning/neural_architecture_search_llm/multi_objective.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/multi_objective.py rename to hyperparameter_tuning/neural_architecture_search_llm/multi_objective.py diff --git a/archived/notebooks/nas_for_llm_with_amt/nas_for_llm_with_amt.ipynb b/hyperparameter_tuning/neural_architecture_search_llm/nas_for_llm_with_amt.ipynb similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/nas_for_llm_with_amt.ipynb rename to hyperparameter_tuning/neural_architecture_search_llm/nas_for_llm_with_amt.ipynb diff --git a/archived/notebooks/nas_for_llm_with_amt/requirements.txt b/hyperparameter_tuning/neural_architecture_search_llm/requirements.txt similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/requirements.txt rename to hyperparameter_tuning/neural_architecture_search_llm/requirements.txt diff --git a/archived/notebooks/nas_for_llm_with_amt/sampling.py b/hyperparameter_tuning/neural_architecture_search_llm/sampling.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/sampling.py rename to hyperparameter_tuning/neural_architecture_search_llm/sampling.py diff --git a/archived/notebooks/nas_for_llm_with_amt/task_data.py b/hyperparameter_tuning/neural_architecture_search_llm/task_data.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/task_data.py rename to hyperparameter_tuning/neural_architecture_search_llm/task_data.py diff --git a/archived/notebooks/nas_for_llm_with_amt/training.py b/hyperparameter_tuning/neural_architecture_search_llm/training.py similarity index 100% rename from archived/notebooks/nas_for_llm_with_amt/training.py rename to hyperparameter_tuning/neural_architecture_search_llm/training.py diff --git a/hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist.ipynb b/hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist.ipynb new file mode 100644 index 0000000000..4a6c6a781c --- /dev/null +++ b/hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameter Tuning with the SageMaker TensorFlow Container\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This tutorial focuses on how to create a convolutional neural network model to train the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) using the SageMaker TensorFlow container. It leverages hyperparameter tuning to run multiple training jobs with different hyperparameter combinations, to find the one with the best model training result.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 10 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Set Up the Environment](#Set-Up-the-Environment)\n", + "1. [Data](#Data)\n", + "1. [Run a TensorFlow Training Job](#Run-a-TensorFlow-Training-Job)\n", + "1. [Set Up Channels for Training and Testing Data](#Set-Up-Channels-for-Training-and-Testing-Data)\n", + "1. [Run a Hyperparameter Tuning Job](#Run-a-Hyperparameter-Tuning-Job)\n", + "1. [Deploy the Best Model](#Deploy-the-Best-Model)\n", + "1. [Evaluate](#Evaluate)\n", + "1. [Cleanup](#Cleanup)\n", + "\n", + "## Set Up the Environment \n", + "Set up a few things before starting the workflow:\n", + "\n", + "1. A boto3 session object to manage interactions with the Amazon SageMaker APIs. \n", + "2. An execution role which is passed to SageMaker to access your AWS resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "import sagemaker\n", + "from sagemaker.tensorflow import TensorFlow\n", + "from sagemaker import get_execution_role\n", + "\n", + "sess = sagemaker.Session()\n", + "region = sess.boto_region_name\n", + "role = get_execution_role()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "Download the MNIST data from a public S3 bucket and save it in a temporary directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import boto3\n", + "from botocore.exceptions import ClientError\n", + "\n", + "public_bucket = f\"sagemaker-example-files-prod-{region}\"\n", + "local_data_dir = \"/tmp/data\"\n", + "\n", + "\n", + "# Download training and testing data from a public S3 bucket\n", + "def download_from_s3(data_dir=\"/tmp/data\", train=True):\n", + " \"\"\"Download MNIST dataset and convert it to numpy array\n", + "\n", + " Args:\n", + " data_dir (str): directory to save the data\n", + " train (bool): download training set\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " # project root\n", + " if not os.path.exists(data_dir):\n", + " os.makedirs(data_dir)\n", + "\n", + " if train:\n", + " images_file = \"train-images-idx3-ubyte.gz\"\n", + " labels_file = \"train-labels-idx1-ubyte.gz\"\n", + " else:\n", + " images_file = \"t10k-images-idx3-ubyte.gz\"\n", + " labels_file = \"t10k-labels-idx1-ubyte.gz\"\n", + "\n", + " # download objects\n", + " s3 = boto3.client(\"s3\")\n", + " bucket = public_bucket\n", + " for obj in [images_file, labels_file]:\n", + " key = os.path.join(\"datasets/image/MNIST\", obj)\n", + " dest = os.path.join(data_dir, obj)\n", + " if not os.path.exists(dest):\n", + " s3.download_file(bucket, key, dest)\n", + " return\n", + "\n", + "\n", + "download_from_s3(local_data_dir, True)\n", + "download_from_s3(local_data_dir, False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run a TensorFlow Training Job\n", + "A TensorFlow training job is defined by using the `TensorFlow` estimator class. It lets you run your training script on SageMaker infrastructure in a containerized environment. For more information on how to instantiate it, see the example [Train an MNIST model with TensorFlow](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/tensorflow/get_started_mnist_train.html#TensorFlow-Estimator)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "est = TensorFlow(\n", + " entry_point=\"train.py\",\n", + " source_dir=\"code\", # directory of your training script\n", + " role=role,\n", + " framework_version=\"2.3.1\",\n", + " model_dir=\"/opt/ml/model\",\n", + " py_version=\"py37\",\n", + " instance_type=\"ml.m5.4xlarge\",\n", + " instance_count=1,\n", + " volume_size=250,\n", + " hyperparameters={\n", + " \"batch-size\": 512,\n", + " \"epochs\": 4,\n", + " },\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Up Channels for Training and Testing Data\n", + "Upload the MNIST data to the default bucket of your AWS account and pass the S3 URI as the channels of training and testing data for the `TensorFlow` estimator class. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prefix = \"mnist\"\n", + "bucket = sess.default_bucket()\n", + "loc = sess.upload_data(path=local_data_dir, bucket=bucket, key_prefix=prefix)\n", + "\n", + "channels = {\"training\": loc, \"testing\": loc}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run a Hyperparameter Tuning Job\n", + "Now that you have set up the training job and the input data channels, you are ready to train the model with hyperparameter search.\n", + "\n", + "Set up the hyperparameter tuning job with the following steps:\n", + "* Define the ranges of hyperparameters we plan to tune. In this example, we tune the learning rate.\n", + "* Define the objective metric for the tuning job to optimize.\n", + "* Create a hyperparameter tuner with the above setting, as well as tuning resource configurations.\n", + "\n", + "\n", + "\n", + "\n", + "For a typical ML model, there are three kinds of hyperparamters:\n", + "\n", + "- Categorical parameters need to take one value from a discrete set. We define this by passing the list of possible values to `CategoricalParameter(list)`\n", + "- Continuous parameters can take any real number value between the minimum and maximum value, defined by `ContinuousParameter(min, max)`\n", + "- Integer parameters can take any integer value between the minimum and maximum value, defined by `IntegerParameter(min, max)`\n", + "\n", + "Learning rate is a continuous variable, so we define its range\n", + "by `ContinuousParameter`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.tuner import ContinuousParameter, HyperparameterTuner\n", + "\n", + "hyperparamter_range = {\"learning-rate\": ContinuousParameter(1e-4, 1e-3)}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we specify the objective metric that we'd like to tune and its definition, which includes the regular expression (regex) needed to extract that metric from the CloudWatch logs of the training job. In this particular case, our script emits average loss value and we use it as the objective metric. We set the `objective_type` to `Minimize`, so that hyperparameter tuning seeks to minimize the objective metric when searching for the best hyperparameter value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "objective_metric_name = \"average test loss\"\n", + "objective_type = \"Minimize\"\n", + "metric_definitions = [\n", + " {\n", + " \"Name\": \"average test loss\",\n", + " \"Regex\": \"Test Loss: ([0-9\\\\.]+)\",\n", + " }\n", + "]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you'll create a `HyperparameterTuner` object. It takes the following parameters:\n", + "- The `TensorFlow` estimator you previously created.\n", + "- Your hyperparameter ranges.\n", + "- Objective metric name and definition.\n", + "- Tuning resource configurations such as the number of training jobs to run in total, and how many training jobs to run in parallel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tuner = HyperparameterTuner(\n", + " est,\n", + " objective_metric_name,\n", + " hyperparamter_range,\n", + " metric_definitions,\n", + " max_jobs=3,\n", + " max_parallel_jobs=3,\n", + " objective_type=objective_type,\n", + ")\n", + "\n", + "tuner.fit(inputs=channels)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy the Best Model\n", + "After training with hyperparameter optimization, you can deploy the best-performing model (by the objective metric you defined) to a SageMaker endpoint. For more information about deploying a model to a SageMaker endpoint, see the example [Deploy a Trained TensorFlow V2 Model](https://sagemaker-examples.readthedocs.io/en/latest/frameworks/tensorflow/get_started_mnist_deploy.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictor = tuner.deploy(initial_instance_count=1, instance_type=\"ml.m5.xlarge\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate\n", + "Now, you can evaluate the best-performing model by invoking the endpoint with the MNIST test set. The test data needs to be readily consumable by the model, so we arrange them into the correct shape that is accepted by a TensorFlow model. We also normalize them so that the pixel values have mean 0 and standard deviation 1, since this is the convention used to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import gzip\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "\n", + "images_file = \"t10k-images-idx3-ubyte.gz\"\n", + "\n", + "\n", + "def read_mnist(data_dir, images_file):\n", + " \"\"\"Byte string to numpy arrays\"\"\"\n", + " with gzip.open(os.path.join(data_dir, images_file), \"rb\") as f:\n", + " images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n", + " return images\n", + "\n", + "\n", + "X = read_mnist(local_data_dir, images_file)\n", + "\n", + "# randomly sample 16 images to inspect\n", + "mask = random.sample(range(X.shape[0]), 16)\n", + "samples = X[mask]\n", + "\n", + "# plot the images\n", + "fig, axs = plt.subplots(nrows=1, ncols=16, figsize=(16, 1))\n", + "\n", + "for i, splt in enumerate(axs):\n", + " splt.imshow(samples[i])\n", + "\n", + "# preprocess the data to be consumed by the model\n", + "\n", + "\n", + "def normalize(x, axis):\n", + " eps = np.finfo(float).eps\n", + "\n", + " mean = np.mean(x, axis=axis, keepdims=True)\n", + " # avoid division by zero\n", + " std = np.std(x, axis=axis, keepdims=True) + eps\n", + " return (x - mean) / std\n", + "\n", + "\n", + "samples = normalize(samples, axis=(1, 2))\n", + "samples = np.expand_dims(samples, axis=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = predictor.predict(samples)[\"predictions\"]\n", + "\n", + "# softmax to logit\n", + "predictions = np.array(predictions, dtype=np.float32)\n", + "predictions = np.argmax(predictions, axis=1)\n", + "\n", + "print(\"Predictions: \", *predictions)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup\n", + "If you do not plan to continue using the endpoint, delete it to free up resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictor.delete_endpoint()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/hyperparameter_tuning|tensorflow2_mnist|hpo_tensorflow2_mnist.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. 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similarity index 100% rename from archived/notebooks/Dynamic Pricing with Causal Machine Learning and Optimization on Amazon SageMaker.ipynb rename to introduction_to_applying_machine_learning/dynamic_pricing_with_causal_ml_and_optimization/Dynamic Pricing with Causal Machine Learning and Optimization on Amazon SageMaker.ipynb diff --git a/introduction_to_applying_machine_learning/huggingface_sentiment_classification/huggingface_sentiment.ipynb b/introduction_to_applying_machine_learning/huggingface_sentiment_classification/huggingface_sentiment.ipynb new file mode 100644 index 0000000000..c3e0729705 --- /dev/null +++ b/introduction_to_applying_machine_learning/huggingface_sentiment_classification/huggingface_sentiment.ipynb @@ -0,0 +1,1158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hugging Face Sentiment Classification\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Binary Classification with `Trainer` and `sst2` dataset__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 45 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Introduction](#Introduction) \n", + "2. [Development environment and permissions](#Development-environment-and-permissions)\n", + " 1. [Installation](#Installation) \n", + " 2. [Development environment](#Development-environment) \n", + " 3. [Permissions](#Permissions)\n", + "3. [Pre-processing](#Pre-processing) \n", + " 1. [Tokenize sentences](#Tokenize-sentences) \n", + " 2. [Upload data to sagemaker_session_bucket](#Upload-data-to-sagemaker_session_bucket) \n", + "4. [Fine-tune the model and start a SageMaker training job](#Fine-tune-the-model-and-start-a-SageMaker-training-job) \n", + " 1. [Create an Estimator and start a training job](#Create-an-Estimator-and-start-a-training-job) \n", + " 2. [Estimator Parameters](#Estimator-Parameters) \n", + " 3. [Attach a previous training job to an estimator](#Attach-a-previous-training-job-to-an-estimator) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Welcome to our end-to-end binary text classification example. This notebook uses Hugging Face's `transformers` library with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer on binary text classification. The pre-trained model is fine-tuned using the `sst2` dataset. To get started, we need to set up the environment with a few prerequisite steps for permissions, configurations, and so on. \n", + "\n", + "This notebook is adapted from Hugging Face's notebook [Huggingface Sagemaker-sdk - Getting Started Demo](https://github.com/huggingface/notebooks/blob/master/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb) and provided here courtesy of Hugging Face.\n", + "\n", + "\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 40 minutes to run.\n", + "\n", + "NOTE: You can run this notebook in SageMaker Studio, a SageMaker notebook instance, or your local machine. This notebook was tested in a notebook instance using the conda\\_pytorch\\_p36 kernel.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Development environment and permissions " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation\n", + "\n", + "_*Note:* We install the required libraries from Hugging Face and AWS. You also need PyTorch, if you haven't installed it already._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install \"sagemaker\" \"transformers\" \"datasets[s3]\" \"s3fs\" --upgrade" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Development environment " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sagemaker.huggingface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Permissions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_If you are going to use SageMaker in a local environment, you need access to an IAM Role with the required permissions for SageMaker. You can read more at [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sagemaker\n", + "\n", + "sess = sagemaker.Session()\n", + "# The SageMaker session bucket is used for uploading data, models and logs\n", + "# SageMaker will automatically create this bucket if it doesn't exist\n", + "sagemaker_session_bucket = None\n", + "if sagemaker_session_bucket is None and sess is not None:\n", + " # Set to default bucket if a bucket name is not given\n", + " sagemaker_session_bucket = sess.default_bucket()\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n", + "\n", + "print(f\"Role arn: {role}\")\n", + "print(f\"Bucket: {sess.default_bucket()}\")\n", + "print(f\"Region: {sess.boto_region_name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-processing\n", + "\n", + "We use the `datasets` library to pre-process the `sst2` dataset (Stanford Sentiment Treebank). After pre-processing, the dataset is uploaded to the `sagemaker_session_bucket` for use within the training job. The [sst2](https://nlp.stanford.edu/sentiment/index.html) dataset consists of 67349 training samples and _ testing samples of highly polar movie reviews." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from datasets import Dataset\n", + "from transformers import AutoTokenizer\n", + "import pandas as pd\n", + "import boto3\n", + "\n", + "# Tokenizer used in pre-processing\n", + "tokenizer_name = \"distilbert-base-uncased\"\n", + "\n", + "# S3 key prefix for the data\n", + "s3_prefix = \"DEMO-samples/datasets/sst\"\n", + "\n", + "# Download the SST2 data from s3\n", + "s3 = boto3.client(\"s3\")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", + " \"datasets/text/SST2/sst2.test\",\n", + " \"sst2.test\",\n", + ")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", + " \"datasets/text/SST2/sst2.train\",\n", + " \"sst2.train\",\n", + ")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", + " \"datasets/text/SST2/sst2.val\",\n", + " \"sst2.val\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tokenize sentences" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Download tokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n", + "\n", + "\n", + "# Tokenizer helper function\n", + "def tokenize(batch):\n", + " return tokenizer(batch[\"text\"], padding=\"max_length\", truncation=True)\n", + "\n", + "\n", + "# Load dataset\n", + "test_df = pd.read_csv(\"sst2.test\", sep=\"delimiter\", header=None, engine=\"python\", names=[\"line\"])\n", + "train_df = pd.read_csv(\"sst2.train\", sep=\"delimiter\", header=None, engine=\"python\", names=[\"line\"])\n", + "\n", + "test_df[[\"label\", \"text\"]] = test_df[\"line\"].str.split(\" \", 1, expand=True)\n", + "train_df[[\"label\", \"text\"]] = train_df[\"line\"].str.split(\" \", 1, expand=True)\n", + "\n", + "test_df.drop(\"line\", axis=1, inplace=True)\n", + "train_df.drop(\"line\", axis=1, inplace=True)\n", + "\n", + "test_df[\"label\"] = pd.to_numeric(test_df[\"label\"], downcast=\"integer\")\n", + "train_df[\"label\"] = pd.to_numeric(train_df[\"label\"], downcast=\"integer\")\n", + "\n", + "train_dataset = Dataset.from_pandas(train_df)\n", + "test_dataset = Dataset.from_pandas(test_df)\n", + "\n", + "# Tokenize dataset\n", + "train_dataset = train_dataset.map(tokenize, batched=True)\n", + "test_dataset = test_dataset.map(tokenize, batched=True)\n", + "\n", + "# Set format for pytorch\n", + "train_dataset = train_dataset.rename_column(\"label\", \"labels\")\n", + "train_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n", + "\n", + "test_dataset = test_dataset.rename_column(\"label\", \"labels\")\n", + "test_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload data to `sagemaker_session_bucket`\n", + "\n", + "After processing the `datasets`, we use the `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload the dataset to S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import botocore\n", + "from datasets.filesystems import S3FileSystem\n", + "\n", + "s3 = S3FileSystem()\n", + "\n", + "# save train_dataset to s3\n", + "training_input_path = f\"s3://{sess.default_bucket()}/{s3_prefix}/train\"\n", + "train_dataset.save_to_disk(training_input_path, fs=s3)\n", + "\n", + "# save test_dataset to s3\n", + "test_input_path = f\"s3://{sess.default_bucket()}/{s3_prefix}/test\"\n", + "test_dataset.save_to_disk(test_input_path, fs=s3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fine-tune the model and start a SageMaker training job\n", + "\n", + "In order to create a SageMaker training job, we need a `HuggingFace` Estimator. The Estimator handles end-to-end Amazon SageMaker training and deployment tasks. In an Estimator, we define which fine-tuning script should be used as `entry_point`, which `instance_type` should be used, which `hyperparameters` are passed in, etc:\n", + "\n", + "\n", + "\n", + "```python\n", + "hf_estimator = HuggingFace(entry_point=\"train.py\",\n", + " source_dir=\"./scripts\",\n", + " base_job_name=\"huggingface-sdk-extension\",\n", + " instance_type=\"ml.p3.2xlarge\",\n", + " instance_count=1,\n", + " transformers_version=\"4.4\",\n", + " pytorch_version=\"1.6\",\n", + " py_version=\"py36\",\n", + " role=role,\n", + " hyperparameters = {\"epochs\": 1,\n", + " \"train_batch_size\": 32,\n", + " \"model_name\":\"distilbert-base-uncased\"\n", + " })\n", + "```\n", + "\n", + "When we create a SageMaker training job, SageMaker takes care of starting and managing all the required EC2 instances for us with the `huggingface` container, uploads the provided fine-tuning script `train.py`, and downloads the data from the `sagemaker_session_bucket` into the container at `/opt/ml/input/data`. Then, it starts the training job by running:\n", + "\n", + "```python\n", + "/opt/conda/bin/python train.py --epochs 1 --model_name distilbert-base-uncased --train_batch_size 32\n", + "```\n", + "\n", + "The `hyperparameters` defined in the `HuggingFace` estimator are passed in as named arguments. \n", + "\n", + "SageMaker provides useful properties about the training environment through various environment variables, including the following:\n", + "\n", + "* `SM_MODEL_DIR`: A string representing the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting.\n", + "\n", + "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n", + "\n", + "* `SM_CHANNEL_XXXX:` A string representing the path to the directory that contains the input data for the specified channel. For example, if you specify two input channels in the Hugging Face estimator's `fit()` call, named `train` and `test`, the environment variables `SM_CHANNEL_TRAIN` and `SM_CHANNEL_TEST` are set.\n", + "\n", + "\n", + "To run the training job locally, you can define `instance_type=\"local\"` or `instance_type=\"local_gpu\"` for GPU usage.\n", + "\n", + "_Note: local mode is not supported in SageMaker Studio._\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pygmentize ./scripts/train.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Estimator and start a training job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.huggingface import HuggingFace\n", + "\n", + "# Hyperparameters which are passed into the training job\n", + "hyperparameters = {\"epochs\": 1, \"train_batch_size\": 32, \"model_name\": \"distilbert-base-uncased\"}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "hf_estimator = HuggingFace(\n", + " entry_point=\"train.py\",\n", + " source_dir=\"./scripts\",\n", + " instance_type=\"ml.p3.2xlarge\",\n", + " instance_count=1,\n", + " role=role,\n", + " transformers_version=\"4.12\",\n", + " pytorch_version=\"1.9\",\n", + " py_version=\"py38\",\n", + " hyperparameters=hyperparameters,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Start the training job with the uploaded dataset as input\n", + "hf_estimator.fit({\"train\": training_input_path, \"test\": test_input_path})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the endpoint\n", + "\n", + "To deploy the endpoint, call `deploy()` on the HuggingFace estimator object, passing in the desired number of instances and instance type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "predictor = hf_estimator.deploy(1, \"ml.p3.2xlarge\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then use the returned predictor object to perform inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sentiment_input = {\"inputs\": \"I love using the new Inference DLC.\"}\n", + "\n", + "predictor.predict(sentiment_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the fine-tuned model classifies the test sentence \"I love using the new Inference DLC.\" as having positive sentiment with 98% probability!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, delete the endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "predictor.delete_endpoint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimator Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "print(f\"Container image used for training job: \\n{hf_estimator.image_uri}\\n\")\n", + "print(f\"S3 URI where the trained model is located: \\n{hf_estimator.model_data}\\n\")\n", + "print(f\"Latest training job name for this estimator: \\n{hf_estimator.latest_training_job.name}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "hf_estimator.sagemaker_session.logs_for_job(hf_estimator.latest_training_job.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Attach a previous training job to an estimator\n", + "\n", + "In SageMaker, you can attach a previous training job to an estimator to continue training, get results, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.estimator import Estimator\n", + "\n", + "# Uncomment the following lines and supply your training job name\n", + "\n", + "# old_training_job_name = \"\"\n", + "# hf_estimator_loaded = Estimator.attach(old_training_job_name)\n", + "# hf_estimator_loaded.model_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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"memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + } + ], + "instance_type": "ml.t3.medium", + "interpreter": { + "hash": "c281c456f1b8161c8906f4af2c08ed2c40c50136979eaae69688b01f70e9f4a9" + }, + "kernelspec": { + "display_name": "Python 3 (PyTorch 1.13 Python 3.9 CPU Optimized)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.13-cpu-py39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archived/notebooks/2_object_detection_train_eval.ipynb b/introduction_to_applying_machine_learning/object_detection_with_tensorflow_and_tfrecords/2_object_detection_train_eval.ipynb similarity index 100% rename from archived/notebooks/2_object_detection_train_eval.ipynb rename to introduction_to_applying_machine_learning/object_detection_with_tensorflow_and_tfrecords/2_object_detection_train_eval.ipynb diff --git a/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb b/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb new file mode 100644 index 0000000000..841e87e101 --- /dev/null +++ b/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb @@ -0,0 +1,1844 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9b08c378", + "metadata": { + "papermill": { + "duration": 0.018505, + "end_time": "2021-06-07T00:09:44.379517", + "exception": false, + "start_time": "2021-06-07T00:09:44.361012", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Customer Churn Prediction with XGBoost\n" + ] + }, + { + "cell_type": "markdown", + "id": "1b98b6df", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/introduction_to_applying_machine_learning|xgboost_customer_churn|xgboost_customer_churn.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0bd14a6e", + "metadata": { + "papermill": { + "duration": 0.018505, + "end_time": "2021-06-07T00:09:44.379517", + "exception": false, + "start_time": "2021-06-07T00:09:44.361012", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "_**Using Gradient Boosted Trees to Predict Mobile Customer Departure**_\n", + "\n", + "---\n", + "\n", + "---\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 8 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Background](#Background)\n", + "1. [Setup](#Setup)\n", + "1. [Data](#Data)\n", + "1. [Train](#Train)\n", + "1. [Host](#Host)\n", + " 1. [Evaluate](#Evaluate)\n", + " 1. [Relative cost of errors](#Relative-cost-of-errors)\n", + "1. [Extensions](#Extensions)\n", + "\n", + "---\n", + "\n", + "## Background\n", + "\n", + "_This notebook has been adapted from an [AWS blog post](https://aws.amazon.com/blogs/ai/predicting-customer-churn-with-amazon-machine-learning/)_\n", + "\n", + "Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. This notebook describes using machine learning (ML) for the automated identification of unhappy customers, also known as customer churn prediction. ML models rarely give perfect predictions though, so this notebook is also about how to incorporate the relative costs of prediction mistakes when determining the financial outcome of using ML.\n", + "\n", + "We use a familiar example of churn: leaving a mobile phone operator. Seems like one can always find fault with their provider du jour! And if the provider knows that a customer is thinking of leaving, it can offer timely incentives - such as a phone upgrade or perhaps having a new feature activated – and the customer may stick around. Incentives are often much more cost-effective than losing and reacquiring a customer.\n", + "\n", + "---\n", + "\n", + "## Setup\n", + "\n", + "_This notebook was created and tested on a `ml.m4.xlarge` notebook instance._\n", + "\n", + "Let's start by updating the required packages i.e. SageMaker Python SDK, `pandas` and `numpy`, and specifying:\n", + "\n", + "- The S3 bucket and prefix that you want to use for training and model data. This should be within the same region as the Notebook Instance or Studio, training, and hosting.\n", + "- The IAM role ARN used to give training and hosting access to your data. See the documentation for how to create these. Note: if more than one role is required for notebook instances, training, and/or hosting, please replace the boto regexp with the appropriate full IAM role ARN string(s)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f00baad", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "!{sys.executable} -m pip install sagemaker pandas numpy --upgrade\n", + "!pip3 install -U sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4c1b3c0", + "metadata": { + "isConfigCell": true, + "papermill": { + "duration": 1.209916, + "end_time": "2021-06-07T00:09:45.607159", + "exception": false, + "start_time": "2021-06-07T00:09:44.397243", + "status": "completed" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "import sagemaker\n", + "\n", + "sess = sagemaker.Session()\n", + "bucket = sess.default_bucket()\n", + "prefix = \"sagemaker/DEMO-xgboost-churn\"\n", + "\n", + "# Define IAM role\n", + "import boto3\n", + "import re\n", + "from sagemaker import get_execution_role\n", + "\n", + "role = get_execution_role()" + ] + }, + { + "cell_type": "markdown", + "id": "e02e6dbb", + "metadata": { + "papermill": { + "duration": 0.017739, + "end_time": "2021-06-07T00:09:45.683322", + "exception": false, + "start_time": "2021-06-07T00:09:45.665583", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we'll import the Python libraries we'll need for the remainder of the example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08714702", + "metadata": { + "papermill": { + "duration": 0.666347, + "end_time": "2021-06-07T00:09:46.367361", + "exception": false, + "start_time": "2021-06-07T00:09:45.701014", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import io\n", + "import os\n", + "import sys\n", + "import time\n", + "import json\n", + "from IPython.display import display\n", + "from time import strftime, gmtime\n", + "from sagemaker.inputs import TrainingInput\n", + "from sagemaker.serializers import CSVSerializer" + ] + }, + { + "cell_type": "markdown", + "id": "6c810d34", + "metadata": { + "papermill": { + "duration": 0.021555, + "end_time": "2021-06-07T00:09:46.406743", + "exception": false, + "start_time": "2021-06-07T00:09:46.385188", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "---\n", + "## Data\n", + "\n", + "Mobile operators have historical records on which customers ultimately ended up churning and which continued using the service. We can use this historical information to construct an ML model of one mobile operator’s churn using a process called training. After training the model, we can pass the profile information of an arbitrary customer (the same profile information that we used to train the model) to the model, and have the model predict whether this customer is going to churn. Of course, we expect the model to make mistakes. After all, predicting the future is tricky business! But we'll learn how to deal with prediction errors.\n", + "\n", + "The dataset we use is publicly available and was mentioned in the book [Discovering Knowledge in Data](https://www.amazon.com/dp/0470908742/) by Daniel T. Larose. It is attributed by the author to the University of California Irvine Repository of Machine Learning Datasets. Let's download and read that dataset in now:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f01c890", + "metadata": { + "papermill": { + "duration": 1.671215, + "end_time": "2021-06-07T00:09:48.098151", + "exception": false, + "start_time": "2021-06-07T00:09:46.426936", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "s3 = boto3.client(\"s3\")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{sess.boto_region_name}\",\n", + " \"datasets/tabular/synthetic/churn.txt\",\n", + " \"churn.txt\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b89ecb3f", + "metadata": { + "papermill": { + "duration": 0.06925, + "end_time": "2021-06-07T00:09:48.185909", + "exception": false, + "start_time": "2021-06-07T00:09:48.116659", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "churn = pd.read_csv(\"./churn.txt\")\n", + "pd.set_option(\"display.max_columns\", 500)\n", + "churn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d3c3733", + "metadata": {}, + "outputs": [], + "source": [ + "len(churn.columns)" + ] + }, + { + "cell_type": "markdown", + "id": "a1380adb", + "metadata": { + "papermill": { + "duration": 0.019033, + "end_time": "2021-06-07T00:09:48.224277", + "exception": false, + "start_time": "2021-06-07T00:09:48.205244", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "By modern standards, it’s a relatively small dataset, with only 5,000 records, where each record uses 21 attributes to describe the profile of a customer of an unknown US mobile operator. The attributes are:\n", + "\n", + "- `State`: the US state in which the customer resides, indicated by a two-letter abbreviation; for example, OH or NJ\n", + "- `Account Length`: the number of days that this account has been active\n", + "- `Area Code`: the three-digit area code of the corresponding customer’s phone number\n", + "- `Phone`: the remaining seven-digit phone number\n", + "- `Int’l Plan`: whether the customer has an international calling plan: yes/no\n", + "- `VMail Plan`: whether the customer has a voice mail feature: yes/no\n", + "- `VMail Message`: the average number of voice mail messages per month\n", + "- `Day Mins`: the total number of calling minutes used during the day\n", + "- `Day Calls`: the total number of calls placed during the day\n", + "- `Day Charge`: the billed cost of daytime calls\n", + "- `Eve Mins, Eve Calls, Eve Charge`: the billed cost for calls placed during the evening\n", + "- `Night Mins`, `Night Calls`, `Night Charge`: the billed cost for calls placed during nighttime\n", + "- `Intl Mins`, `Intl Calls`, `Intl Charge`: the billed cost for international calls\n", + "- `CustServ Calls`: the number of calls placed to Customer Service\n", + "- `Churn?`: whether the customer left the service: true/false\n", + "\n", + "The last attribute, `Churn?`, is known as the target attribute: the attribute that we want the ML model to predict. Because the target attribute is binary, our model will be performing binary prediction, also known as binary classification.\n", + "\n", + "Let's begin exploring the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a35b9f65", + "metadata": { + "papermill": { + "duration": 2.396119, + "end_time": "2021-06-07T00:09:50.639536", + "exception": false, + "start_time": "2021-06-07T00:09:48.243417", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Frequency tables for each categorical feature\n", + "for column in churn.select_dtypes(include=[\"object\"]).columns:\n", + " display(pd.crosstab(index=churn[column], columns=\"% observations\", normalize=\"columns\"))\n", + "\n", + "# Histograms for each numeric features\n", + "display(churn.describe())\n", + "%matplotlib inline\n", + "hist = churn.hist(bins=30, sharey=True, figsize=(10, 10))" + ] + }, + { + "cell_type": "markdown", + "id": "2046fbb8", + "metadata": { + "papermill": { + "duration": 0.022357, + "end_time": "2021-06-07T00:09:50.685414", + "exception": false, + "start_time": "2021-06-07T00:09:50.663057", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can see immediately that:\n", + "- `State` appears to be quite evenly distributed.\n", + "- `Phone` takes on too many unique values to be of any practical use. It's possible that parsing out the prefix could have some value, but without more context on how these are allocated, we should avoid using it.\n", + "- Most of the numeric features are surprisingly nicely distributed, with many showing bell-like `gaussianity`. `VMail Message` is a notable exception (and `Area Code` showing up as a feature we should convert to non-numeric)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28552f05", + "metadata": { + "papermill": { + "duration": 0.030406, + "end_time": "2021-06-07T00:09:50.738287", + "exception": false, + "start_time": "2021-06-07T00:09:50.707881", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "churn = churn.drop(\"Phone\", axis=1)\n", + "churn[\"Area Code\"] = churn[\"Area Code\"].astype(object)" + ] + }, + { + "cell_type": "markdown", + "id": "197581c1", + "metadata": { + "papermill": { + "duration": 0.022422, + "end_time": "2021-06-07T00:09:50.783342", + "exception": false, + "start_time": "2021-06-07T00:09:50.760920", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next let's look at the relationship between each of the features and our target variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cee110f", + "metadata": { + "papermill": { + "duration": 4.645229, + "end_time": "2021-06-07T00:09:55.451149", + "exception": false, + "start_time": "2021-06-07T00:09:50.805920", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "for column in churn.select_dtypes(include=[\"object\"]).columns:\n", + " if column != \"Churn?\":\n", + " display(pd.crosstab(index=churn[column], columns=churn[\"Churn?\"], normalize=\"columns\"))\n", + "\n", + "for column in churn.select_dtypes(exclude=[\"object\"]).columns:\n", + " print(column)\n", + " hist = churn[[column, \"Churn?\"]].hist(by=\"Churn?\", bins=30)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1e491a6", + "metadata": { + "papermill": { + "duration": 18.552066, + "end_time": "2021-06-07T00:10:14.041717", + "exception": false, + "start_time": "2021-06-07T00:09:55.489651", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "display(churn.corr(numeric_only=True))\n", + "pd.plotting.scatter_matrix(churn, figsize=(12, 12))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3217f3c5", + "metadata": { + "papermill": { + "duration": 0.050687, + "end_time": "2021-06-07T00:10:14.143830", + "exception": false, + "start_time": "2021-06-07T00:10:14.093143", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We see several features that essentially have 100% correlation with one another. Including these feature pairs in some machine learning algorithms can create catastrophic problems, while in others it will only introduce minor redundancy and bias. Let's remove one feature from each of the highly correlated pairs: `Day Charge` from the pair with `Day Mins`, `Night Charge` from the pair with `Night Mins`, `Intl Charge` from the pair with `Intl Mins`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c904a9d2", + "metadata": { + "papermill": { + "duration": 0.057009, + "end_time": "2021-06-07T00:10:14.251061", + "exception": false, + "start_time": "2021-06-07T00:10:14.194052", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "churn = churn.drop([\"Day Charge\", \"Eve Charge\", \"Night Charge\", \"Intl Charge\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "a3ce9711", + "metadata": { + "papermill": { + "duration": 0.050512, + "end_time": "2021-06-07T00:10:14.352000", + "exception": false, + "start_time": "2021-06-07T00:10:14.301488", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now that we've cleaned up our dataset, let's determine which algorithm to use. As mentioned above, there appear to be some variables where both high and low (but not intermediate) values are predictive of churn. In order to accommodate this in an algorithm like linear regression, we'd need to generate polynomial (or bucketed) terms. Instead, let's attempt to model this problem using gradient boosted trees. Amazon SageMaker provides an XGBoost container that we can use to train in a managed, distributed setting, and then host as a real-time prediction endpoint. XGBoost uses gradient boosted trees which naturally account for non-linear relationships between features and the target variable, as well as accommodating complex interactions between features.\n", + "\n", + "Amazon SageMaker XGBoost can train on data in either a CSV or LibSVM format. For this example, we'll stick with CSV. It should:\n", + "- Have the predictor variable in the first column\n", + "- Not have a header row\n", + "\n", + "But first, let's convert our categorical features into numeric features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b3ea731", + "metadata": { + "papermill": { + "duration": 0.07096, + "end_time": "2021-06-07T00:10:14.473383", + "exception": false, + "start_time": "2021-06-07T00:10:14.402423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "model_data = pd.get_dummies(churn)\n", + "model_data = pd.concat(\n", + " [model_data[\"Churn?_True.\"], model_data.drop([\"Churn?_False.\", \"Churn?_True.\"], axis=1)], axis=1\n", + ")\n", + "model_data = model_data.astype(float)" + ] + }, + { + "cell_type": "markdown", + "id": "664ad1dc", + "metadata": { + "papermill": { + "duration": 0.050777, + "end_time": "2021-06-07T00:10:14.574494", + "exception": false, + "start_time": "2021-06-07T00:10:14.523717", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "And now let's split the data into training, validation, and test sets. This will help prevent us from overfitting the model, and allow us to test the model's accuracy on data it hasn't already seen." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "298362cf", + "metadata": { + "papermill": { + "duration": 0.246303, + "end_time": "2021-06-07T00:10:14.871668", + "exception": false, + "start_time": "2021-06-07T00:10:14.625365", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "train_data, validation_data, test_data = np.split(\n", + " model_data.sample(frac=1, random_state=1729),\n", + " [int(0.7 * len(model_data)), int(0.9 * len(model_data))],\n", + ")\n", + "train_data.to_csv(\"train.csv\", header=False, index=False)\n", + "validation_data.to_csv(\"validation.csv\", header=False, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6a5d489", + "metadata": {}, + "outputs": [], + "source": [ + "len(train_data.columns)" + ] + }, + { + "cell_type": "markdown", + "id": "31cd03d7", + "metadata": { + "papermill": { + "duration": 0.050591, + "end_time": "2021-06-07T00:10:14.972677", + "exception": false, + "start_time": "2021-06-07T00:10:14.922086", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now we'll upload these files to S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b8d288f", + "metadata": { + "papermill": { + "duration": 0.79455, + "end_time": "2021-06-07T00:10:15.817950", + "exception": false, + "start_time": "2021-06-07T00:10:15.023400", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "boto3.Session().resource(\"s3\").Bucket(bucket).Object(\n", + " os.path.join(prefix, \"train/train.csv\")\n", + ").upload_file(\"train.csv\")\n", + "boto3.Session().resource(\"s3\").Bucket(bucket).Object(\n", + " os.path.join(prefix, \"validation/validation.csv\")\n", + ").upload_file(\"validation.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "15beea62", + "metadata": { + "papermill": { + "duration": 0.050157, + "end_time": "2021-06-07T00:10:15.918579", + "exception": false, + "start_time": "2021-06-07T00:10:15.868422", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "---\n", + "## Train\n", + "\n", + "Moving onto training, first we'll need to specify the locations of the XGBoost algorithm containers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79682277", + "metadata": { + "papermill": { + "duration": 0.071985, + "end_time": "2021-06-07T00:10:16.040629", + "exception": false, + "start_time": "2021-06-07T00:10:15.968644", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "container = sagemaker.image_uris.retrieve(\"xgboost\", sess.boto_region_name, \"1.7-1\")\n", + "display(container)" + ] + }, + { + "cell_type": "markdown", + "id": "6be2c94d", + "metadata": { + "papermill": { + "duration": 0.050814, + "end_time": "2021-06-07T00:10:16.142405", + "exception": false, + "start_time": "2021-06-07T00:10:16.091591", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Then, because we're training with the CSV file format, we'll create `TrainingInput`s that our training function can use as a pointer to the files in S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb3b53d1", + "metadata": { + "papermill": { + "duration": 0.05658, + "end_time": "2021-06-07T00:10:16.249848", + "exception": false, + "start_time": "2021-06-07T00:10:16.193268", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "s3_input_train = TrainingInput(\n", + " s3_data=\"s3://{}/{}/train\".format(bucket, prefix), content_type=\"csv\"\n", + ")\n", + "s3_input_validation = TrainingInput(\n", + " s3_data=\"s3://{}/{}/validation/\".format(bucket, prefix), content_type=\"csv\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d0e18e91", + "metadata": { + "papermill": { + "duration": 0.050343, + "end_time": "2021-06-07T00:10:16.350919", + "exception": false, + "start_time": "2021-06-07T00:10:16.300576", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now, we can specify a few parameters like what type of training instances we'd like to use and how many, as well as our XGBoost hyperparameters. A few key hyperparameters are:\n", + "- `max_depth` controls how deep each tree within the algorithm can be built. Deeper trees can lead to better fit, but are more computationally expensive and can lead to overfitting. There is typically some trade-off in model performance that needs to be explored between numerous shallow trees and a smaller number of deeper trees.\n", + "- `subsample` controls sampling of the training data. This technique can help reduce overfitting, but setting it too low can also starve the model of data.\n", + "- `num_round` controls the number of boosting rounds. This is essentially the subsequent models that are trained using the residuals of previous iterations. Again, more rounds should produce a better fit on the training data, but can be computationally expensive or lead to overfitting.\n", + "- `eta` controls how aggressive each round of boosting is. Larger values lead to more conservative boosting.\n", + "- `gamma` controls how aggressively trees are grown. Larger values lead to more conservative models.\n", + "\n", + "More detail on XGBoost's hyper-parameters can be found on their GitHub [page](https://github.com/dmlc/xgboost/blob/master/doc/parameter.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3aea5a5c", + "metadata": { + "papermill": { + "duration": 252.035305, + "end_time": "2021-06-07T00:14:28.436818", + "exception": false, + "start_time": "2021-06-07T00:10:16.401513", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "sess = sagemaker.Session()\n", + "\n", + "xgb = sagemaker.estimator.Estimator(\n", + " container,\n", + " role,\n", + " instance_count=1,\n", + " instance_type=\"ml.m4.xlarge\",\n", + " output_path=\"s3://{}/{}/output\".format(bucket, prefix),\n", + " sagemaker_session=sess,\n", + ")\n", + "xgb.set_hyperparameters(\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.8,\n", + " verbosity=0,\n", + " objective=\"binary:logistic\",\n", + " num_round=100,\n", + ")\n", + "\n", + "xgb.fit({\"train\": s3_input_train, \"validation\": s3_input_validation})" + ] + }, + { + "cell_type": "markdown", + "id": "171515b0", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "---\n", + "## Host\n", + "\n", + "Now that we've trained the algorithm, let's create a model and deploy it to a hosted endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f0232f5", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "xgb_predictor = xgb.deploy(\n", + " initial_instance_count=1, instance_type=\"ml.m4.xlarge\", serializer=CSVSerializer()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "29ab4cae", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "### Evaluate\n", + "\n", + "Now that we have a hosted endpoint running, we can make real-time predictions from our model very easily, simply by making a `http` POST request. But first, we'll need to set up serializers and deserializers for passing our `test_data` NumPy arrays to the model behind the endpoint." + ] + }, + { + "cell_type": "markdown", + "id": "6f03c792", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "Now, we'll use a simple function to:\n", + "1. Loop over our test dataset\n", + "1. Split it into mini-batches of rows \n", + "1. Convert those mini-batchs to CSV string payloads\n", + "1. Retrieve mini-batch predictions by invoking the XGBoost endpoint\n", + "1. Collect predictions and convert from the CSV output our model provides into a NumPy array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42d1317f", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def predict(data, rows=500):\n", + " split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n", + " predictions = \"\"\n", + " for array in split_array:\n", + " predictions = \"\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n", + "\n", + " return predictions.split(\"\\n\")[:-1]\n", + "\n", + "\n", + "predictions = predict(test_data.to_numpy()[:, 1:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "745e08d2", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = np.array([float(num) for num in predictions])\n", + "print(predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "b35e2bf7", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "There are many ways to compare the performance of a machine learning model, but let's start by simply by comparing actual to predicted values. In this case, we're simply predicting whether the customer churned (`1`) or not (`0`), which produces a confusion matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d69d58f4", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "pd.crosstab(\n", + " index=test_data.iloc[:, 0],\n", + " columns=np.round(predictions),\n", + " rownames=[\"actual\"],\n", + " colnames=[\"predictions\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "58cc9077", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "_Note, due to randomized elements of the algorithm, your results may differ slightly._\n", + "\n", + "Of the 48 churners, we've correctly predicted 39 of them (true positives). We also incorrectly predicted 4 customers would churn who then ended up not doing so (false positives). There are also 9 customers who ended up churning, that we predicted would not (false negatives).\n", + "\n", + "An important point here is that because of the `np.round()` function above, we are using a simple threshold (or cutoff) of 0.5. Our predictions from `xgboost` yield continuous values between 0 and 1, and we force them into the binary classes that we began with. However, because a customer that churns is expected to cost the company more than proactively trying to retain a customer who we think might churn, we should consider lowering this cutoff. That will almost certainly increase the number of false positives, but it can also be expected to increase the number of true positives and reduce the number of false negatives.\n", + "\n", + "To get a rough intuition here, let's look at the continuous values of our predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cc8123e", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.hist(predictions)\n", + "plt.xlabel(\"Predicted churn probability\")\n", + "plt.ylabel(\"Number of customers\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "55ce4027", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "The continuous valued predictions coming from our model tend to skew toward 0 or 1, but there is sufficient mass between 0.1 and 0.9 that adjusting the cutoff should indeed shift a number of customers' predictions. For example..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce5dca1", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "pd.crosstab(index=test_data.iloc[:, 0], columns=np.where(predictions > 0.3, 1, 0))" + ] + }, + { + "cell_type": "markdown", + "id": "18f2c2f1", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "We can see that lowering the cutoff from 0.5 to 0.3 results in 1 more true positive, 3 more false positives, and 1 fewer false negative. The numbers are small overall here, but that's 6-10% of customers overall that are shifting because of a change to the cutoff. Was this the right decision? We may end up retaining 3 extra customers, but we also unnecessarily incentivized 5 more customers who would have stayed anyway. Determining optimal cutoffs is a key step in properly applying machine learning in a real-world setting. Let's discuss this more broadly and then apply a specific, hypothetical solution for our current problem.\n", + "\n", + "### Relative cost of errors\n", + "\n", + "Any practical binary classification problem is likely to produce a similarly sensitive cutoff. That by itself isn’t a problem. After all, if the scores for two classes are really easy to separate, the problem probably isn’t very hard to begin with and might even be solvable with deterministic rules instead of ML.\n", + "\n", + "More important, if we put an ML model into production, there are costs associated with the model erroneously assigning false positives and false negatives. We also need to look at similar costs associated with correct predictions of true positives and true negatives. Because the choice of the cutoff affects all four of these statistics, we need to consider the relative costs to the business for each of these four outcomes for each prediction.\n", + "\n", + "#### Assigning costs\n", + "\n", + "What are the costs for our problem of mobile operator churn? The costs, of course, depend on the specific actions that the business takes. Let's make some assumptions here.\n", + "\n", + "First, assign the true negatives the cost of \\$0. Our model essentially correctly identified a happy customer in this case, and we don’t need to do anything.\n", + "\n", + "False negatives are the most problematic, because they incorrectly predict that a churning customer will stay. We lose the customer and will have to pay all the costs of acquiring a replacement customer, including foregone revenue, advertising costs, administrative costs, point of sale costs, and likely a phone hardware subsidy. A quick search on the Internet reveals that such costs typically run in the hundreds of dollars so, for the purposes of this example, let's assume \\$500. This is the cost of false negatives.\n", + "\n", + "Finally, for customers that our model identifies as churning, let's assume a retention incentive in the amount of \\\\$100. If a provider offered a customer such a concession, they may think twice before leaving. This is the cost of both true positive and false positive outcomes. In the case of false positives (the customer is happy, but the model mistakenly predicted churn), we will “waste” the \\\\$100 concession. We probably could have spent that \\$100 more effectively, but it's possible we increased the loyalty of an already loyal customer, so that’s not so bad." + ] + }, + { + "cell_type": "markdown", + "id": "a51ea034", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "#### Finding the optimal cutoff\n", + "\n", + "It’s clear that false negatives are substantially more costly than false positives. Instead of optimizing for error based on the number of customers, we should be minimizing a cost function that looks like this:\n", + "\n", + "```\n", + "$500 * FN(C) + $0 * TN(C) + $100 * FP(C) + $100 * TP(C)\n", + "```\n", + "\n", + "FN(C) means that the false negative percentage is a function of the cutoff, C, and similar for TN, FP, and TP. We need to find the cutoff, C, where the result of the expression is smallest.\n", + "\n", + "A straightforward way to do this is to simply run a simulation over numerous possible cutoffs. We test 100 possible values in the for-loop below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "324c9f5c", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cutoffs = np.arange(0.01, 1, 0.01)\n", + "costs = []\n", + "for c in cutoffs:\n", + " costs.append(\n", + " np.sum(\n", + " np.sum(\n", + " np.array([[0, 100], [500, 100]])\n", + " * pd.crosstab(index=test_data.iloc[:, 0], columns=np.where(predictions > c, 1, 0))\n", + " )\n", + " )\n", + " )\n", + "\n", + "costs = np.array(costs)\n", + "plt.plot(cutoffs, costs)\n", + "plt.xlabel(\"Cutoff\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae213bd8", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "print(\n", + " \"Cost is minimized near a cutoff of:\",\n", + " cutoffs[np.argmin(costs)],\n", + " \"for a cost of:\",\n", + " np.min(costs),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "54e86315", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "The above chart shows how picking a threshold too low results in costs skyrocketing as all customers are given a retention incentive. Meanwhile, setting the threshold too high results in too many lost customers, which ultimately grows to be nearly as costly. The overall cost can be minimized at \\\\$8400 by setting the cutoff to 0.46, which is substantially better than the \\$20k+ we would expect to lose by not taking any action." + ] + }, + { + "cell_type": "markdown", + "id": "ce4a0e5b", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "---\n", + "## Extensions\n", + "\n", + "This notebook showcased how to build a model that predicts whether a customer is likely to churn, and then how to optimally set a threshold that accounts for the cost of true positives, false positives, and false negatives. There are several means of extending it including:\n", + "- Some customers who receive retention incentives will still churn. Including a probability of churning despite receiving an incentive in our cost function would provide a better ROI on our retention programs.\n", + "- Customers who switch to a lower-priced plan or who deactivate a paid feature represent different kinds of churn that could be modeled separately.\n", + "- Modeling the evolution of customer behavior. If usage is dropping and the number of calls placed to Customer Service is increasing, you are more likely to experience churn then if the trend is the opposite. A customer profile should incorporate behavior trends.\n", + "- Actual training data and monetary cost assignments could be more complex.\n", + "- Multiple models for each type of churn could be needed.\n", + "\n", + "Regardless of additional complexity, similar principles described in this notebook are likely applied." + ] + }, + { + "cell_type": "markdown", + "id": "ced6f363", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "### (Optional) Clean-up\n", + "\n", + "If you're ready to be done with this notebook, please run the cell below. This will remove the hosted endpoint you created and avoid any charges from a stray instance being left on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16febdfe", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "xgb_predictor.delete_endpoint()" + ] + }, + { + "cell_type": "markdown", + "id": "f32cb035", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. 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"python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.", + "papermill": { + "default_parameters": {}, + "duration": 311.728138, + "end_time": "2021-06-07T00:14:55.273560", + "environment_variables": {}, + "exception": true, + "input_path": "xgboost_customer_churn.ipynb", + "output_path": "/opt/ml/processing/output/xgboost_customer_churn-2021-06-07-00-06-03.ipynb", + "parameters": { + "kms_key": "arn:aws:kms:us-west-2:521695447989:key/6e9984db-50cf-4c7e-926c-877ec47a8b25" + }, + "start_time": "2021-06-07T00:09:43.545422", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/archived/notebooks/RestRServe_Example/Dockerfile b/r_examples/r_serving_with_restrserve/Dockerfile similarity index 100% rename from archived/notebooks/RestRServe_Example/Dockerfile rename to r_examples/r_serving_with_restrserve/Dockerfile diff --git a/archived/notebooks/RestRServe_Example/RestRServe_Example.ipynb b/r_examples/r_serving_with_restrserve/RestRServe_Example.ipynb similarity index 100% rename from archived/notebooks/RestRServe_Example/RestRServe_Example.ipynb rename to r_examples/r_serving_with_restrserve/RestRServe_Example.ipynb diff --git a/archived/notebooks/RestRServe_Example/restrserve.R b/r_examples/r_serving_with_restrserve/restrserve.R similarity index 100% rename from archived/notebooks/RestRServe_Example/restrserve.R rename to r_examples/r_serving_with_restrserve/restrserve.R diff --git a/archived/notebooks/RestRServe_Example/xgb.model b/r_examples/r_serving_with_restrserve/xgb.model similarity index 100% rename from archived/notebooks/RestRServe_Example/xgb.model rename to r_examples/r_serving_with_restrserve/xgb.model diff --git a/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability.ipynb b/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability.ipynb new file mode 100644 index 0000000000..3b2255959c --- /dev/null +++ b/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability.ipynb @@ -0,0 +1,1361 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fairness and Explainability with SageMaker Clarify" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 30 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Overview](#Overview)\n", + "1. [Prerequisites and Data](#Prerequisites-and-Data)\n", + " 1. [Import Libraries](#Import-Libraries)\n", + " 1. [Set Configurations](#Set-Configurations)\n", + " 1. [Download data](#Download-data)\n", + " 1. [Loading the data: Adult Dataset](#Loading-the-data:-Adult-Dataset) \n", + " 1. [Data inspection](#Data-inspection) \n", + " 1. [Encode and Upload the Dataset](#Encode-and-Upload-the-Dataset) \n", + "1. [Train and Deploy XGBoost Model](#Train-XGBoost-Model)\n", + " 1. [Train Model](#Train-Model)\n", + " 1. [Create Model](#Create-Model)\n", + "1. [Amazon SageMaker Clarify](#Amazon-SageMaker-Clarify)\n", + " 1. [Detecting Bias](#Detecting-Bias)\n", + " 1. [Writing DataConfig](#Writing-DataConfig)\n", + " 1. [Writing ModelConfig](#Writing-ModelConfig)\n", + " 1. [Writing ModelPredictedLabelConfig](#Writing-ModelPredictedLabelConfig)\n", + " 1. [Writing BiasConfig](#Writing-BiasConfig)\n", + " 1. [Pre-training Bias](#Pre-training-Bias)\n", + " 1. [Post-training Bias](#Post-training-Bias)\n", + " 1. [Viewing the Bias Report](#Viewing-the-Bias-Report)\n", + " 1. [Explaining Predictions](#Explaining-Predictions)\n", + " 1. [Viewing the Explainability Report](#Viewing-the-Explainability-Report)\n", + " 1. [Analysis of local explanations](#Analysis-of-local-explanations)\n", + "1. [Clean Up](#Clean-Up)\n", + "\n", + "## Overview\n", + "Amazon SageMaker Clarify helps improve your machine learning models by detecting potential bias and helping explain how these models make predictions. The fairness and explainability functionality provided by SageMaker Clarify takes a step towards enabling AWS customers to build trustworthy and understandable machine learning models. The product comes with the tools to help you with the following tasks.\n", + "\n", + "* Measure biases that can occur during each stage of the ML lifecycle (data collection, model training and tuning, and monitoring of ML models deployed for inference).\n", + "* Generate model governance reports targeting risk and compliance teams and external regulators.\n", + "* Provide explanations of the data, models, and monitoring used to assess predictions.\n", + "\n", + "This sample notebook walks you through: \n", + "1. Key terms and concepts needed to understand SageMaker Clarify\n", + "1. Measuring the pre-training bias of a dataset and post-training bias of a model\n", + "1. Explaining the importance of the various input features on the model's decision\n", + "1. Accessing the reports through SageMaker Studio if you have an instance set up.\n", + "\n", + "In doing so, the notebook first trains a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) to launch SageMaker Clarify jobs to analyze an example dataset in CSV format. \n", + "\n", + "SageMaker Clarify also supports analyzing dataset in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb). Additionally, there is a [peer example available](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_boto3.ipynb) that utilizes the [AWS SDK for Python](https://aws.amazon.com/sdk-for-python/) to launch SageMaker Clarify jobs to analyze data in CSV format. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and Data\n", + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import boto3\n", + "from datetime import datetime\n", + "from sagemaker import get_execution_role, session" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set Configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: ap-south-1\n", + "Role: arn:aws:iam::000000000000:role/service-role/SMClarifySageMaker-ExecutionRole\n" + ] + } + ], + "source": [ + "# Initialize sagemaker session\n", + "sagemaker_session = session.Session()\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "print(f\"Region: {region}\")\n", + "\n", + "role = get_execution_role()\n", + "print(f\"Role: {role}\")\n", + "\n", + "bucket = sagemaker_session.default_bucket()\n", + "\n", + "prefix = \"sagemaker/DEMO-sagemaker-clarify\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download data\n", + "Data Source: [https://archive.ics.uci.edu/ml/machine-learning-databases/adult/](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/)\n", + "\n", + "Let's __download__ the data and save it in the local folder with the name adult.data and adult.test from UCI repository$^{[2]}$.\n", + "\n", + "$^{[2]}$Dua Dheeru, and Efi Karra Taniskidou. \"[UCI Machine Learning Repository](http://archive.ics.uci.edu/ml)\". Irvine, CA: University of California, School of Information and Computer Science (2017)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adult.data already on disk.\n", + "adult.test already on disk.\n" + ] + } + ], + "source": [ + "from sagemaker.s3 import S3Downloader\n", + "\n", + "adult_columns = [\n", + " \"Age\",\n", + " \"Workclass\",\n", + " \"fnlwgt\",\n", + " \"Education\",\n", + " \"Education-Num\",\n", + " \"Marital Status\",\n", + " \"Occupation\",\n", + " \"Relationship\",\n", + " \"Ethnic group\",\n", + " \"Sex\",\n", + " \"Capital Gain\",\n", + " \"Capital Loss\",\n", + " \"Hours per week\",\n", + " \"Country\",\n", + " \"Target\",\n", + "]\n", + "if not os.path.isfile(\"adult.data\"):\n", + " S3Downloader.download(\n", + " s3_uri=\"s3://{}/{}\".format(\n", + " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/uci_adult/adult.data\"\n", + " ),\n", + " local_path=\"./\",\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " print(\"adult.data saved!\")\n", + "else:\n", + " print(\"adult.data already on disk.\")\n", + "\n", + "if not os.path.isfile(\"adult.test\"):\n", + " S3Downloader.download(\n", + " s3_uri=\"s3://{}/{}\".format(\n", + " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/uci_adult/adult.test\"\n", + " ),\n", + " local_path=\"./\",\n", + " sagemaker_session=sagemaker_session,\n", + " )\n", + " print(\"adult.test saved!\")\n", + "else:\n", + " print(\"adult.test already on disk.\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the data: Adult Dataset\n", + "From the UCI repository of machine learning datasets, this database contains 14 features concerning demographic characteristics of 45,222 rows (32,561 for training and 12,661 for testing). The task is to predict whether a person has a yearly income that is more or less than $50,000.\n", + "\n", + "Here are the features and their possible values:\n", + "\n", + "1. **Age**: continuous.\n", + "1. **Workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n", + "1. **Fnlwgt**: continuous (the number of people the census takers believe that observation represents).\n", + "1. **Education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n", + "1. **Education-num**: continuous.\n", + "1. **Marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n", + "1. **Occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n", + "1. **Relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n", + "1. **Ethnic group**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n", + "1. **Sex**: Female, Male.\n", + " * **Note**: this data is extracted from the 1994 Census and enforces a binary option on Sex\n", + "1. **Capital-gain**: continuous.\n", + "1. **Capital-loss**: continuous.\n", + "1. **Hours-per-week**: continuous.\n", + "1. **Native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n", + "\n", + "Next, we specify our binary prediction task: \n", + "\n", + "15. **Target**: <=50,000, >$50,000." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeWorkclassfnlwgtEducationEducation-NumMarital StatusOccupationRelationshipEthnic groupSexCapital GainCapital LossHours per weekCountryTarget
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
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" + ], + "text/plain": [ + " Age Workclass fnlwgt Education Education-Num \\\n", + "0 39 State-gov 77516 Bachelors 13 \n", + "1 50 Self-emp-not-inc 83311 Bachelors 13 \n", + "2 38 Private 215646 HS-grad 9 \n", + "3 53 Private 234721 11th 7 \n", + "4 28 Private 338409 Bachelors 13 \n", + "\n", + " Marital Status Occupation Relationship Ethnic group Sex \\\n", + "0 Never-married Adm-clerical Not-in-family White Male \n", + "1 Married-civ-spouse Exec-managerial Husband White Male \n", + "2 Divorced Handlers-cleaners Not-in-family White Male \n", + "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", + "4 Married-civ-spouse Prof-specialty Wife Black Female \n", + "\n", + " Capital Gain Capital Loss Hours per week Country Target \n", + "0 2174 0 40 United-States <=50K \n", + "1 0 0 13 United-States <=50K \n", + "2 0 0 40 United-States <=50K \n", + "3 0 0 40 United-States <=50K \n", + "4 0 0 40 Cuba <=50K " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_data = pd.read_csv(\n", + " \"adult.data\", names=adult_columns, sep=r\"\\s*,\\s*\", engine=\"python\", na_values=\"?\"\n", + ").dropna()\n", + "\n", + "testing_data = pd.read_csv(\n", + " \"adult.test\", names=adult_columns, sep=r\"\\s*,\\s*\", engine=\"python\", na_values=\"?\", skiprows=1\n", + ").dropna()\n", + "\n", + "training_data.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data inspection\n", + "Plotting histograms for the distribution of the different features is a good way to visualize the data. Let's plot a few of the features that can be considered _sensitive_. \n", + "Let's take a look specifically at the Sex feature of a census respondent. In the first plot we see that there are fewer Female respondents as a whole but especially in the positive outcomes, where they form ~$\\frac{1}{7}$th of respondents." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "training_data[\"Sex\"].value_counts().sort_values().plot(kind=\"bar\", title=\"Counts of Sex\", rot=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "$50K'}>" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "training_data[\"Sex\"].where(training_data[\"Target\"] == \">50K\").value_counts().sort_values().plot(\n", + " kind=\"bar\", title=\"Counts of Sex earning >$50K\", rot=0\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Encode and Upload the Dataset\n", + "Here we encode the training and test data. Encoding input data is not necessary for SageMaker Clarify, but is necessary for the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import preprocessing\n", + "\n", + "\n", + "def number_encode_features(df):\n", + " result = df.copy()\n", + " encoders = {}\n", + " for column in result.columns:\n", + " if result.dtypes[column] == np.object:\n", + " encoders[column] = preprocessing.LabelEncoder()\n", + " result[column] = encoders[column].fit_transform(result[column].fillna(\"None\"))\n", + " return result, encoders\n", + "\n", + "\n", + "training_data = pd.concat([training_data[\"Target\"], training_data.drop([\"Target\"], axis=1)], axis=1)\n", + "training_data, _ = number_encode_features(training_data)\n", + "training_data.to_csv(\"train_data.csv\", index=False, header=False)\n", + "\n", + "testing_data, _ = number_encode_features(testing_data)\n", + "test_features = testing_data.drop([\"Target\"], axis=1)\n", + "test_target = testing_data[\"Target\"]\n", + "test_features.to_csv(\"test_features.csv\", index=False, header=False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A quick note about our encoding: the \"Female\" Sex value has been encoded as 0 and \"Male\" as 1." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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402823384099132952000404
\n", + "
" + ], + "text/plain": [ + " Target Age Workclass fnlwgt Education Education-Num Marital Status \\\n", + "0 0 39 5 77516 9 13 4 \n", + "1 0 50 4 83311 9 13 2 \n", + "2 0 38 2 215646 11 9 0 \n", + "3 0 53 2 234721 1 7 2 \n", + "4 0 28 2 338409 9 13 2 \n", + "\n", + " Occupation Relationship Ethnic group Sex Capital Gain Capital Loss \\\n", + "0 0 1 4 1 2174 0 \n", + "1 3 0 4 1 0 0 \n", + "2 5 1 4 1 0 0 \n", + "3 5 0 2 1 0 0 \n", + "4 9 5 2 0 0 0 \n", + "\n", + " Hours per week Country \n", + "0 40 38 \n", + "1 13 38 \n", + "2 40 38 \n", + "3 40 38 \n", + "4 40 4 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_data.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, let's upload the data to S3." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.s3 import S3Uploader\n", + "from sagemaker.inputs import TrainingInput\n", + "\n", + "train_uri = S3Uploader.upload(\"train_data.csv\", \"s3://{}/{}\".format(bucket, prefix))\n", + "train_input = TrainingInput(train_uri, content_type=\"csv\")\n", + "test_uri = S3Uploader.upload(\"test_features.csv\", \"s3://{}/{}\".format(bucket, prefix))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train XGBoost Model\n", + "#### Train Model\n", + "Since our focus is on understanding how to use SageMaker Clarify, we keep it simple by using a standard XGBoost model.\n", + "\n", + "It takes about 5 minutes for the model to be trained." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2023-02-07-05-54-36-442\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2023-02-07 05:54:36 Starting - Starting the training job..\n", + "2023-02-07 05:54:50 Starting - Preparing the instances for training........\n", + "2023-02-07 05:55:32 Downloading - Downloading input data....\n", + "2023-02-07 05:55:57 Training - Downloading the training image...\n", + "2023-02-07 05:56:18 Training - Training image download completed. Training in progress.......\n", + "2023-02-07 05:56:53 Uploading - Uploading generated training model.\n", + "2023-02-07 05:57:04 Completed - Training job completed\n" + ] + } + ], + "source": [ + "from sagemaker.image_uris import retrieve\n", + "from sagemaker.estimator import Estimator\n", + "\n", + "# This references the AWS managed XGBoost container\n", + "xgboost_image_uri = retrieve(\"xgboost\", region, version=\"1.5-1\")\n", + "\n", + "xgb = Estimator(\n", + " xgboost_image_uri,\n", + " role,\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " disable_profiler=True,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "\n", + "xgb.set_hyperparameters(\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.8,\n", + " objective=\"binary:logistic\",\n", + " num_round=800,\n", + ")\n", + "\n", + "xgb.fit({\"train\": train_input}, logs=False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create Model\n", + "Here we create the SageMaker model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating model with name: DEMO-clarify-model-07-02-2023-05-57-08\n" + ] + }, + { + "data": { + "text/plain": [ + "'DEMO-clarify-model-07-02-2023-05-57-08'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_name = \"DEMO-clarify-model-{}\".format(datetime.now().strftime(\"%d-%m-%Y-%H-%M-%S\"))\n", + "model = xgb.create_model(name=model_name)\n", + "container_def = model.prepare_container_def()\n", + "sagemaker_session.create_model(model_name, role, container_def)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Amazon SageMaker Clarify\n", + "With your model set up, it's time to explore SageMaker Clarify. For a general overview of how SageMaker Clarify processing jobs work, refer [the provided link](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-processing-job-configure-how-it-works.html). " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "from sagemaker import clarify\n", + "\n", + "# Initialize a SageMakerClarifyProcessor to compute bias metrics and model explanations.\n", + "clarify_processor = clarify.SageMakerClarifyProcessor(\n", + " role=role, instance_count=1, instance_type=\"ml.m5.xlarge\", sagemaker_session=sagemaker_session\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Detecting Bias\n", + "SageMaker Clarify helps you detect possible [pre-training](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-detect-data-bias.html) and [post-training](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-detect-post-training-bias.html) biases using a variety of metrics.\n", + "\n", + "#### Writing DataConfig\n", + "A [DataConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.DataConfig) object communicates some basic information about data I/O to SageMaker Clarify. For our example here we provide the below information:\n", + "\n", + "* `s3_data_input_path`: S3 URI of the train dataset we uploaded above\n", + "* `s3_output_path`: S3 URI at which our output report will be uploaded\n", + "* `label`: Specifies the ground truth label, which is also known as observed label or target attribute. It is used for many bias metrics. In this example, the `Target` column has the ground truth label.\n", + "* `headers`: The list of column names in the dataset\n", + "* `dataset_type`: specifies the format of your dataset, for this example as we are using CSV dataset this will be `text/csv`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "bias_report_output_path = \"s3://{}/{}/clarify-bias\".format(bucket, prefix)\n", + "bias_data_config = clarify.DataConfig(\n", + " s3_data_input_path=train_uri,\n", + " s3_output_path=bias_report_output_path,\n", + " label=\"Target\",\n", + " headers=training_data.columns.to_list(),\n", + " dataset_type=\"text/csv\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Writing ModelConfig\n", + "\n", + "A [ModelConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.ModelConfig) object communicates information about your trained model. To avoid additional traffic to the production models, SageMaker Clarify sets up and tears down a dedicated endpoint when processing. For our example here we provide the below information:\n", + "\n", + "* `model_name`: name of the concerned model, using name of the xgboost model trained earlier\n", + "* `instance_type` and `initial_instance_count` specify your preferred instance type and instance count used to run your model on during SageMaker Clarify's processing. The example dataset is small, so a single standard instance is good enough to run this example.\n", + "* `accept_type` denotes the endpoint response payload format, and `content_type` denotes the payload format of request to the endpoint. As per the example model we created above both of these will be `text/csv`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "model_config = clarify.ModelConfig(\n", + " model_name=model_name,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=1,\n", + " accept_type=\"text/csv\",\n", + " content_type=\"text/csv\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Writing ModelPredictedLabelConfig\n", + "\n", + "A [ModelPredictedLabelConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.ModelPredictedLabelConfig) provides information on the format of your predictions. XGBoost model outputs probabilities of samples, so SageMaker Clarify invokes the endpoint then uses `probability_threshold` to convert the probability to binary labels for bias analysis. Prediction above the threshold is interpreted as label value `1` and below or equal as label value `0`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_config = clarify.ModelPredictedLabelConfig(probability_threshold=0.8)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Writing BiasConfig\n", + "[BiasConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.BiasConfig) contains configuration values for detecting bias using a Clarify container." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "bias_config = clarify.BiasConfig(\n", + " label_values_or_threshold=[1], facet_name=\"Sex\", facet_values_or_threshold=[0], group_name=\"Age\"\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our demo we provide the following information in BiasConfig API:\n", + "\n", + "* `label_values_or_threshold`: List of label value(s) or threshold to indicate positive outcome used for bias metrics. Here positive outcome is earning >$50,000.\n", + "* `facet_name`: Sensitive columns of the dataset, \"Sex\" is the category\n", + "* `facet_values_or_threshold`: values of the sensitive group, \"Female\" respondents are the sensitive group.\n", + "* `group_name`: This example has selected the \"Age\" column which is used to form subgroups for the measurement of bias metric [Conditional Demographic Disparity (CDD)](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-metric-cddl.html) or [Conditional Demographic Disparity in Predicted Labels (CDDPL)](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cddpl.html).\n", + "\n", + "SageMaker Clarify can handle both categorical and continuous data for `facet: values_or_threshold` and for `label_values_or_threshold`. In this case we are using categorical data. The results will show if the model has a preference for records of one sex over the other." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pre-training Bias\n", + "Bias can be present in your data before any model training occurs. Inspecting your data for bias before training begins can help detect any data collection gaps, inform your feature engineering, and help you understand what societal biases the data may reflect.\n", + "\n", + "Computing pre-training bias metrics does not require a trained model.\n", + "\n", + "#### Post-training Bias\n", + "Computing post-training bias metrics does require a trained model.\n", + "\n", + "Unbiased training data (as determined by concepts of fairness measured by bias metric) may still result in biased model predictions after training. Whether this occurs depends on several factors including hyperparameter choices.\n", + "\n", + "\n", + "You can run these options separately with [run_pre_training_bias()](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run_pre_training_bias) and [run_post_training_bias()](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run_post_training_bias) or at the same time with `run_bias()` as shown below. We use following additional parameters for the api call:\n", + "\n", + "* `pre_training_methods`: Pre-training bias metrics to be computed. The detailed description of the metrics can be found on [Measure Pre-training Bias](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html). This example sets methods to \"all\" to compute all the pre-training bias metrics.\n", + "* `post_training_methods`: Post-training bias metrics to be computed. The detailed description of the metrics can be found on [Measure Post-training Bias](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-detect-post-training-bias.html). This example sets methods to \"all\" to compute all the post-training bias metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The job takes about 10 minutes to run\n", + "clarify_processor.run_bias(\n", + " data_config=bias_data_config,\n", + " bias_config=bias_config,\n", + " model_config=model_config,\n", + " model_predicted_label_config=predictions_config,\n", + " pre_training_methods=\"all\",\n", + " post_training_methods=\"all\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Viewing the Bias Report\n", + "In Studio, you can view the results under the experiments tab.\n", + "\n", + "\n", + "\n", + "Each bias metric has detailed explanations with examples that you can explore.\n", + "\n", + "\n", + "\n", + "You could also summarize the results in a handy table!\n", + "\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're not a Studio user yet, you can access the bias report in PDF, HTML and ipynb formats in the following S3 bucket:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://sagemaker-ap-south-1-000000000000/sagemaker/DEMO-sagemaker-clarify/clarify-bias'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bias_report_output_path" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explaining Predictions\n", + "There are expanding business needs and legislative regulations that require explanations of _why_ a model made the decision it did. SageMaker Clarify uses Kernel SHAP to explain the contribution that each input feature makes to the final decision.\n", + "\n", + "For run_explainability API call we need similar `DataConfig` and `ModelConfig` objects we defined above. [SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SHAPConfig) here is the config class for Kernel SHAP algorithm.\n", + "\n", + "For our demo we pass the following information in `SHAPConfig`:\n", + "\n", + "* `baseline`: Kernel SHAP algorithm requires a baseline (also known as background dataset). If not provided, a baseline is calculated automatically by SageMaker Clarify using K-means or K-prototypes in the input dataset. Baseline dataset type shall be the same as dataset_type, and baseline samples shall only include features. By definition, baseline should either be a S3 URI to the baseline dataset file, or an in-place list of samples. In this case we chose the latter, and put the mean of the train dataset to the list. For more details on baseline selection please [refer this documentation](https://docs.aws.amazon.com/en_us/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html).\n", + "* `num_samples`: Number of samples to be used in the Kernel SHAP algorithm. This number determines the size of the generated synthetic dataset to compute the SHAP values. \n", + "* `agg_method`: Aggregation method for global SHAP values. For our example here we are using `mean_abs` i.e. mean of absolute SHAP values for all instances\n", + "* `save_local_shap_values`: Indicates whether to save the local SHAP values in the output location. Default is True." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "explainability_output_path = \"s3://{}/{}/clarify-explainability\".format(bucket, prefix)\n", + "explainability_data_config = clarify.DataConfig(\n", + " s3_data_input_path=train_uri,\n", + " s3_output_path=explainability_output_path,\n", + " label=\"Target\",\n", + " headers=training_data.columns.to_list(),\n", + " dataset_type=\"text/csv\",\n", + ")\n", + "\n", + "baseline = [training_data.mean().iloc[1:].values.tolist()]\n", + "shap_config = clarify.SHAPConfig(\n", + " baseline=baseline,\n", + " num_samples=15,\n", + " agg_method=\"mean_abs\",\n", + " save_local_shap_values=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The job takes about 10 minutes to run\n", + "clarify_processor.run_explainability(\n", + " data_config=explainability_data_config,\n", + " model_config=model_config,\n", + " explainability_config=shap_config,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Viewing the Explainability Report\n", + "As with the bias report, you can view the explainability report in Studio under the experiments tab.\n", + "\n", + "\n", + "\n", + "\n", + "The Model Insights tab contains direct links to the report and model insights.\n", + "\n", + "If you're not a Studio user yet, as with the Bias Report, you can access this report at the following S3 bucket." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://sagemaker-ap-south-1-000000000000/sagemaker/DEMO-sagemaker-clarify/clarify-explainability'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explainability_output_path" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analysis of local explanations\n", + "It is possible to visualize the local explanations for single examples in your dataset. You can use the obtained results from running Kernel SHAP algorithm for global explanations.\n", + "\n", + "You can simply load the local explanations stored in your output path, and visualize the explanation (i.e., the impact that the single features have on the prediction of your model) for any single example." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example number: 111 \n", + "with model prediction: False\n", + "\n", + "Feature values -- Label Target 0\n", + "Age 21\n", + "Workclass 2\n", + "fnlwgt 199915\n", + "Education 15\n", + "Education-Num 10\n", + "Marital Status 4\n", + "Occupation 7\n", + "Relationship 3\n", + "Ethnic group 4\n", + "Sex 0\n", + "Capital Gain 0\n", + "Capital Loss 0\n", + "Hours per week 40\n", + "Country 38\n", + "Name: 120, dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "local_explanations_out = pd.read_csv(explainability_output_path + \"/explanations_shap/out.csv\")\n", + "feature_names = [str.replace(c, \"_label0\", \"\") for c in local_explanations_out.columns.to_series()]\n", + "local_explanations_out.columns = feature_names\n", + "\n", + "selected_example = 111\n", + "print(\n", + " \"Example number:\",\n", + " selected_example,\n", + " \"\\nwith model prediction:\",\n", + " sum(local_explanations_out.iloc[selected_example]) > 0,\n", + ")\n", + "print(\"\\nFeature values -- Label\", training_data.iloc[selected_example])\n", + "local_explanations_out.iloc[selected_example].plot(\n", + " kind=\"bar\", title=\"Local explanation for the example number \" + str(selected_example), rot=90\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** You can run both bias and explainability jobs at the same time with `run_bias_and_explainability()`, refer [API Documentation](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run_bias_and_explainability) for more details. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean Up\n", + "Finally, don't forget to clean up the resources we set up and used for this demo!" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting model with name: DEMO-clarify-model-07-02-2023-05-57-08\n" + ] + } + ], + "source": [ + "sagemaker_session.delete_model(model_name)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-clarify|fairness_and_explainability|fairness_and_explainability.ipynb)\n" + ] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (Data Science 3.0)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archived/notebooks/time_series_deepar/time_series_deepar.ipynb b/sagemaker-clarify/time_series_deepar/time_series_deepar.ipynb similarity index 100% rename from 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b/sagemaker-geospatial/geospatial-pipeline/images/sagemaker_eo_pipeline_execution.png similarity index 100% rename from archived/notebooks/geospatial/geospatial-pipelines/images/sagemaker_eo_pipeline_execution.png rename to sagemaker-geospatial/geospatial-pipeline/images/sagemaker_eo_pipeline_execution.png diff --git a/sagemaker-geospatial/index.rst b/sagemaker-geospatial/index.rst new file mode 100644 index 0000000000..b6a8d67883 --- /dev/null +++ b/sagemaker-geospatial/index.rst @@ -0,0 +1,15 @@ +Amazon Sagemaker Geospatial Service +==================================== + +Amazon SageMaker can be utilized to process geosptial data (i.e. satellite imagery). + + +Digital Farming Example +------------------------ + +.. toctree:: + :maxdepth: 1 + + digital-farming-pipelines/digital-farming-sagemaker-geospatial-part-1 + digital-farming-pipelines/digital-farming-sagemaker-geospatial-part-2 + digital-farming-pipelines/pipelines-sagemaker-geospatial diff --git a/archived/notebooks/geospatial/lake_mead_drought_monitoring.ipynb b/sagemaker-geospatial/lake-mead-drought-monitoring/lake_mead_drought_monitoring.ipynb similarity index 100% rename from archived/notebooks/geospatial/lake_mead_drought_monitoring.ipynb rename to sagemaker-geospatial/lake-mead-drought-monitoring/lake_mead_drought_monitoring.ipynb diff --git a/archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/.gitignore b/sagemaker-geospatial/london-mapmatch-and-reverse-geocode/.gitignore similarity index 100% rename from archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/.gitignore rename to sagemaker-geospatial/london-mapmatch-and-reverse-geocode/.gitignore diff --git a/archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/CODE_OF_CONDUCT.md b/sagemaker-geospatial/london-mapmatch-and-reverse-geocode/CODE_OF_CONDUCT.md similarity index 100% rename from archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/CODE_OF_CONDUCT.md rename to sagemaker-geospatial/london-mapmatch-and-reverse-geocode/CODE_OF_CONDUCT.md diff --git a/archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/CONTRIBUTING.md b/sagemaker-geospatial/london-mapmatch-and-reverse-geocode/CONTRIBUTING.md similarity index 100% rename from archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/CONTRIBUTING.md rename to sagemaker-geospatial/london-mapmatch-and-reverse-geocode/CONTRIBUTING.md diff --git a/archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/LICENSE b/sagemaker-geospatial/london-mapmatch-and-reverse-geocode/LICENSE similarity index 100% rename from archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/LICENSE rename to sagemaker-geospatial/london-mapmatch-and-reverse-geocode/LICENSE diff --git a/archived/notebooks/geospatial/london-mapmatch-and-reverse-geocode/README.md b/sagemaker-geospatial/london-mapmatch-and-reverse-geocode/README.md similarity index 100% rename from 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a/archived/notebooks/geospatial/monitor_methane_ch4_emission_point_sources.ipynb b/sagemaker-geospatial/methane-emission-monitoring/monitor_methane_ch4_emission_point_sources.ipynb similarity index 100% rename from archived/notebooks/geospatial/monitor_methane_ch4_emission_point_sources.ipynb rename to sagemaker-geospatial/methane-emission-monitoring/monitor_methane_ch4_emission_point_sources.ipynb diff --git a/archived/notebooks/geospatial/mount_shasta_glacier_melt_monitoring.ipynb b/sagemaker-geospatial/mount-shasta-glacier-melting-monitoring/mount_shasta_glacier_melt_monitoring.ipynb similarity index 100% rename from archived/notebooks/geospatial/mount_shasta_glacier_melt_monitoring.ipynb rename to sagemaker-geospatial/mount-shasta-glacier-melting-monitoring/mount_shasta_glacier_melt_monitoring.ipynb diff --git a/archived/notebooks/geospatial/geospatial-processing-ndvi-intro.ipynb b/sagemaker-geospatial/processing-geospatial-ndvi/geospatial-processing-ndvi-intro.ipynb similarity 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sagemaker-geospatial/vector-enrichment-map-matching/vector-enrichment-map-matching.ipynb diff --git a/archived/notebooks/geospatial/vector-enrichment-reverse-geocoding.ipynb b/sagemaker-geospatial/vector-enrichment-reverse-geocoding/vector-enrichment-reverse-geocoding.ipynb similarity index 100% rename from archived/notebooks/geospatial/vector-enrichment-reverse-geocoding.ipynb rename to sagemaker-geospatial/vector-enrichment-reverse-geocoding/vector-enrichment-reverse-geocoding.ipynb diff --git a/sagemaker-lineage/sagemaker-lineage-multihop-queries.ipynb b/sagemaker-lineage/sagemaker-lineage-multihop-queries.ipynb new file mode 100644 index 0000000000..9941703ee2 --- /dev/null +++ b/sagemaker-lineage/sagemaker-lineage-multihop-queries.ipynb @@ -0,0 +1,1094 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "cb187715", + "metadata": {}, + "source": [ + "# Amazon SageMaker Multi-hop Lineage Queries\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "66fa3294", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "367041e5", + "metadata": {}, + "source": [ + "\n", + "Amazon SageMaker Lineage tracks events that happen within SageMaker allowing the relationships between them to be traced via a graph structure. SageMaker Lineage introduces a new API called `LineageQuery` that allows customers to query the lineage graph structure to discover relationship across their Machine Learning entities. \n", + "\n", + "Your machine learning workflows can generate deeply nested relationships, the lineage APIs allow you to answer questions about these relationships. For example find all Data Sets that trained the model deployed to a given Endpoint or find all Models trained by a Data Set.\n", + "\n", + "The lineage graph is created automatically by SageMaker and you can directly create or modify your own lineage.\n", + "\n", + "In addition to the `LineageQuery` API, the SageMaker SDK provides wrapper functions that make it easy to run queries that span across multiple hops of the entity relationship graph. These APIs and helper functions are described in this notebook.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 15 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Key Concepts](#Key-Concepts)\n", + "1. [Prerequisites](#Prerequisites)\n", + "1. [Notebook Overview](#Notebook-Overview)\n", + "1. [Create an Experiment and Trial for a training job](#Create-an-Experiment-and-Trial-for-a-training-job)\n", + "1. [Training Data](#Training-Data)\n", + "1. [Create a training job](#Create-a-training-job)\n", + "1. [Create a Model Package Group for the trained model to be registered](#Create-a-Model-Package-Group-for-the-trained-model-to-be-registered)\n", + "1. [Register the model in the Model Registry](#Register-the-model-in-the-Model-Registry)\n", + "1. [Deploy the model to a SageMaker Endpoint](#Deploy-the-model-to-a-SageMaker-Endpoint)\n", + "1. [SageMaker Lineage Queries](#SageMaker-Lineage-Queries)\n", + " 1. [Using the LineageQuery API to find entity associations](#Using-the-LineageQuery-API-to-find-entity-associations)\n", + " 1. [Find all datasets associated with an Endpoint](#Find-all-datasets-associated-with-an-Endpoint)\n", + " 1. [Find the models associated with an Endpoint](#Find-the-models-associated-with-an-Endpoint)\n", + " 1. [Find the trial components associated with an Endpoint](#Find-the-trial-components-associated-with-an-Endpoint)\n", + " 1. [Change the focal point of lineage](#Change-the-focal-point-of-lineage)\n", + " 1. [Use LineageQueryDirectionEnum.BOTH](#Use-LineageQueryDirectionEnum.BOTH)\n", + " 1. [Directions in LineageQuery: Ascendants vs. Descendants](#Directions-in-LineageQuery:-Ascendants-vs.-Descendants)\n", + " 1. [SDK helper functions](#SDK-helper-functions)\n", + " 1. [Lineage Graph Visualization](#Lineage-Graph-Visualization)\n", + "1. [Conclusion](#Conclusion)\n", + "1. [Cleanup](#Cleanup)\n", + "\n", + "\n", + "## Key Concepts\n", + "\n", + "* **Lineage Graph** - A connected graph tracing your machine learning workflow end to end. \n", + "* **Artifacts** - Represents a URI addressable object or data. Artifacts are typically inputs or outputs to Actions. \n", + "* **Actions** - Represents an action taken such as a computation, transformation, or job. \n", + "* **Contexts** - Provides a method to logically group other entities.\n", + "* **Associations** - A directed edge in the lineage graph that links two entities.\n", + "* **Lineage Traversal** - Starting from an arbitrary point trace the lineage graph to discover and analyze relationships between steps in your workflow.\n", + "* **Experiments** - Experiment entites (Experiments, Trials, and Trial Components) are also part of the lineage graph and can be associated wtih Artifacts, Actions, or Contexts." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "25d4a00f", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "[`sagemaker-experiments`](https://github.com/aws/sagemaker-experiments) and [`pyvis`]((https://pyvis.readthedocs.io/en/latest/)) are two Python libraries that need to be installed as part of this notebook execution. `pyvis` is a library designed for interactive network visualization and `sagemaker-experiments` gives users the ability to use SageMaker's Experiment Tracking capabilities. \n", + "\n", + "This notebook should be run with `Python 3.9` using the SageMaker Studio `Python3 (Data Science)` kernel. The `sagemaker` sdk version required for this notebook is `>2.70.0`.\n", + "\n", + "If running in SageMaker Classic Notebooks, use the `conda_python3` kernel. \n", + "\n", + "The AWS account running this notebook should have access to provision two instances of type `ml.m5.xlarge`. These instances are used for training and deploying a model." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0fee7359", + "metadata": {}, + "source": [ + "Let's start by installing the Python SDK, boto and AWS CLI." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93adbfe7", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install sagemaker botocore boto3 awscli --upgrade" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69886125", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install sagemaker-experiments pyvis" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c6cf2db5", + "metadata": {}, + "source": [ + "## Notebook Overview\n", + "\n", + "This notebook demonstrates how to use SageMaker Lineage APIs to query multi-hop relationships across the lineage graph. Multi-hop relationships are those that span beyond single entity relationships, e.g. Model -> Endpoint, Training Job -> Model. Multi-hop queries allow users to search for distant relationships across the Lineage Graph such as Endpoint -> Data Set.\n", + "\n", + "To demonstrate these capabilities, in this notebook we create a training job, register a model to the Model Registry, and deploy the model to an Endpoint. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26efdda2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import boto3\n", + "import sagemaker\n", + "import pprint\n", + "from botocore.config import Config\n", + "\n", + "config = Config(retries={\"max_attempts\": 50, \"mode\": \"adaptive\"})\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "sm_client = sagemaker_session.sagemaker_client\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "\n", + "default_bucket = sagemaker_session.default_bucket()\n", + "role = sagemaker.get_execution_role()\n", + "\n", + "# Helper function to print query outputs\n", + "pp = pprint.PrettyPrinter()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c40701a", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "training_instance_type = \"ml.m5.xlarge\"\n", + "inference_instance_type = \"ml.m5.xlarge\"\n", + "s3_prefix = \"multihop-example\"\n", + "\n", + "unique_id = str(datetime.now().timestamp()).split(\".\")[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6c51f513", + "metadata": {}, + "source": [ + "## Create an Experiment and Trial for a training job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8718c000", + "metadata": {}, + "outputs": [], + "source": [ + "from smexperiments.experiment import Experiment\n", + "from smexperiments.trial import Trial\n", + "from smexperiments.trial_component import TrialComponent\n", + "\n", + "experiment_name = f\"MultihopQueryExperiment-{unique_id}\"\n", + "exp = Experiment.create(experiment_name=experiment_name, sagemaker_boto_client=sm_client)\n", + "\n", + "trial = Trial.create(\n", + " experiment_name=exp.experiment_name,\n", + " trial_name=f\"MultihopQueryTrial-{unique_id}\",\n", + " sagemaker_boto_client=sm_client,\n", + ")\n", + "\n", + "print(exp.experiment_name)\n", + "print(trial.trial_name)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f63f088c", + "metadata": {}, + "source": [ + "## Training Data\n", + "\n", + "Creating a `data/` directory to store the preprocessed [UCI Abalone](https://archive.ics.uci.edu/ml/datasets/abalone) dataset. The preprocessing is done using the preprocessing script defined in the notebook [Orchestrating Jobs with Amazon SageMaker Model Building Pipelines](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb) notebook. Then training and validation data is uploaded to S3 so that it can be used in the training and inference job." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d020ac3", + "metadata": {}, + "outputs": [], + "source": [ + "default_bucket" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c54bdc99", + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.exists(\"./data/\"):\n", + " os.makedirs(\"./data/\")\n", + " print(\"Directory Created \")\n", + "else:\n", + " print(\"Directory already exists\")\n", + "\n", + "# Download the processed abalone dataset files\n", + "s3 = boto3.client(\"s3\")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{region}\",\n", + " \"datasets/tabular/uci_abalone/preprocessed/test.csv\",\n", + " \"./data/test.csv\",\n", + ")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{region}\",\n", + " \"datasets/tabular/uci_abalone/preprocessed/train.csv\",\n", + " \"./data/train.csv\",\n", + ")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{region}\",\n", + " \"datasets/tabular/uci_abalone/preprocessed/validation.csv\",\n", + " \"./data/validation.csv\",\n", + ")\n", + "\n", + "# Upload the datasets to the SageMaker session default bucket\n", + "boto3.Session().resource(\"s3\").Bucket(default_bucket).Object(\n", + " \"experiments-demo/train.csv\"\n", + ").upload_file(\"data/train.csv\")\n", + "boto3.Session().resource(\"s3\").Bucket(default_bucket).Object(\n", + " \"experiments-demo/validation.csv\"\n", + ").upload_file(\"data/validation.csv\")\n", + "\n", + "training_data = f\"s3://{default_bucket}/experiments-demo/train.csv\"\n", + "validation_data = f\"s3://{default_bucket}/experiments-demo/validation.csv\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "660c9e25", + "metadata": {}, + "source": [ + "## Create a training job\n", + "\n", + "We train a simple XGBoost model on the Abalone dataset. \n", + "`sagemaker.image_uris.retrieve()` is used to get the sagemaker container for XGBoost so that it can be used in the Estimator. \n", + "\n", + "In the `.fit()` function, we pass in a training and validation dataset along with an `experiment_config`. The `experiment_config` ensures that the metrics, parameters, and artifats associated with this training job are logged to the experiment and trial created above. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fed64de", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.estimator import Estimator\n", + "\n", + "model_path = f\"s3://{default_bucket}/{s3_prefix}/xgb_model\"\n", + "training_instance_type = \"ml.m5.large\"\n", + "\n", + "image_uri = sagemaker.image_uris.retrieve(\n", + " framework=\"xgboost\",\n", + " region=region,\n", + " version=\"1.5-1\",\n", + " py_version=\"py3\",\n", + " instance_type=training_instance_type,\n", + ")\n", + "\n", + "xgb_train = Estimator(\n", + " image_uri=image_uri,\n", + " instance_type=training_instance_type,\n", + " instance_count=1,\n", + " output_path=model_path,\n", + " sagemaker_session=sagemaker_session,\n", + " role=role,\n", + ")\n", + "\n", + "xgb_train.set_hyperparameters(\n", + " objective=\"reg:squarederror\",\n", + " num_round=50,\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + " verbosity=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5285ba3d", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.inputs import TrainingInput\n", + "\n", + "xgb_train.fit(\n", + " inputs={\n", + " \"train\": TrainingInput(\n", + " s3_data=training_data,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " \"validation\": TrainingInput(\n", + " s3_data=validation_data,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " },\n", + " experiment_config={\n", + " \"ExperimentName\": experiment_name,\n", + " \"TrialName\": trial.trial_name,\n", + " \"TrialComponentDisplayName\": \"MultiHopQueryTrialComponent\",\n", + " },\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ce43b815", + "metadata": {}, + "source": [ + "## Create a Model Package Group for the trained model to be registered\n", + "\n", + "Create a new Model Package Group or use an existing one to register the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17e9f1e0", + "metadata": {}, + "outputs": [], + "source": [ + "model_package_group_name = \"lineage-test-\" + unique_id\n", + "mpg = sm_client.create_model_package_group(ModelPackageGroupName=model_package_group_name)\n", + "mpg_arn = mpg[\"ModelPackageGroupArn\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d17d04c0", + "metadata": {}, + "source": [ + "## Register the model in the Model Registry\n", + "Once the model is registered, it appears in the Model Registry tab of the SageMaker Studio UI. The model is registered with the `approval_status` set to \"Approved\". By default, the model is registered with the `approval_status` set to \"PendingManualApproval\". Users can then navigate to the Model Registry to manually approve the model based on any criteria set for model evaluation or this can be done via API. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38ab67a1", + "metadata": {}, + "outputs": [], + "source": [ + "inference_instance_type = \"ml.m5.xlarge\"\n", + "model_package = xgb_train.register(\n", + " model_package_group_name=mpg_arn,\n", + " inference_instances=[inference_instance_type],\n", + " transform_instances=[inference_instance_type],\n", + " content_types=[\"text/csv\"],\n", + " response_types=[\"text/csv\"],\n", + " approval_status=\"Approved\",\n", + ")\n", + "\n", + "model_package_arn = model_package.model_package_arn\n", + "print(\"Model Package ARN : \", model_package_arn)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "570f9d6c", + "metadata": {}, + "source": [ + "## Deploy the model to a SageMaker Endpoint\n", + "\n", + "A SageMaker Endpoint is used to host a model that can be used for inference. The type of endpoint deployed in this notebook is a real time inference endpoint. This is ideal for inference workloads where you have real-time, interactive, low latency requirements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8433e1e9", + "metadata": {}, + "outputs": [], + "source": [ + "endpoint_name = \"lineage-test-endpoint-\" + unique_id\n", + "model_package.deploy(\n", + " endpoint_name=endpoint_name,\n", + " initial_instance_count=1,\n", + " instance_type=inference_instance_type,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17178ffe", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the endpoint ARN\n", + "endpoint_arn = sm_client.describe_endpoint(EndpointName=endpoint_name)[\"EndpointArn\"]\n", + "print(endpoint_arn)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1b73bd20", + "metadata": {}, + "source": [ + "## SageMaker Lineage Queries\n", + "\n", + "We explore SageMaker's lineage capabilities to traverse the relationships between the entities created in this notebook - datasets, model, endpoint, and training job. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc2b4ef0", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.lineage.context import Context, EndpointContext\n", + "from sagemaker.lineage.action import Action\n", + "from sagemaker.lineage.association import Association\n", + "from sagemaker.lineage.artifact import Artifact, ModelArtifact, DatasetArtifact\n", + "\n", + "from sagemaker.lineage.query import (\n", + " LineageQuery,\n", + " LineageFilter,\n", + " LineageSourceEnum,\n", + " LineageEntityEnum,\n", + " LineageQueryDirectionEnum,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "093e985e", + "metadata": {}, + "source": [ + "### Using the LineageQuery API to find entity associations\n", + "\n", + "In this section we use two APIs, `LineageQuery` and `LineageFilter` to construct queries to answer questions about the Lineage Graph and extract entity relationships. \n", + "\n", + "LineageQuery parameters:\n", + "* `start_arns`: A list of ARNs that is used as the starting point for the query.\n", + "* `direction`: The direction of the query.\n", + "* `include_edges`: If true, return edges in addition to vertices.\n", + "* `query_filter`: The query filter.\n", + "\n", + "LineageFilter paramters:\n", + "* `entities`: A list of entity types (Artifact, Association, Action) to filter for when returning the results on LineageQuery\n", + "* `sources`: A list of source types (Endpoint, Model, Dataset) to filter for when returning the results of LineageQuery\n", + "\n", + "A `Context` is automatically created when a SageMaker Endpoint is created, an `Artifact` is automatically created when a Model is created in SageMaker. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a30c455b", + "metadata": {}, + "outputs": [], + "source": [ + "# Find the endpoint context and model artifact that should be used for the lineage queries.\n", + "\n", + "contexts = Context.list(source_uri=endpoint_arn)\n", + "context_name = list(contexts)[0].context_name\n", + "endpoint_context = EndpointContext.load(context_name=context_name)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9963e76e", + "metadata": {}, + "source": [ + "#### Find all datasets associated with an Endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfde258b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the LineageFilter to look for entities of type `ARTIFACT` and the source of type `DATASET`.\n", + "\n", + "query_filter = LineageFilter(\n", + " entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.DATASET]\n", + ")\n", + "\n", + "# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`\n", + "# and find all datasets.\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[endpoint_context.context_arn],\n", + " query_filter=query_filter,\n", + " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "# Parse through the query results to get the lineage objects corresponding to the datasets\n", + "dataset_artifacts = []\n", + "for vertex in query_result.vertices:\n", + " dataset_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", + "\n", + "pp.pprint(dataset_artifacts)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7dab1c4a", + "metadata": {}, + "source": [ + "#### Find the models associated with an Endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6294fc97", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the LineageFilter to look for entities of type `ARTIFACT` and the source of type `MODEL`.\n", + "\n", + "query_filter = LineageFilter(\n", + " entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.MODEL]\n", + ")\n", + "\n", + "# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`\n", + "# and find all datasets.\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[endpoint_context.context_arn],\n", + " query_filter=query_filter,\n", + " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "# Parse through the query results to get the lineage objects corresponding to the model\n", + "model_artifacts = []\n", + "for vertex in query_result.vertices:\n", + " model_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", + "\n", + "# The results of the `LineageQuery` API call return the ARN of the model deployed to the endpoint along with\n", + "# the S3 URI to the model.tar.gz file associated with the model\n", + "pp.pprint(model_artifacts)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4fa79344", + "metadata": {}, + "source": [ + "#### Find the trial components associated with an Endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d417bf3a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the LineageFilter to look for entities of type `TRIAL_COMPONENT` and the source of type `TRAINING_JOB`.\n", + "\n", + "query_filter = LineageFilter(\n", + " entities=[LineageEntityEnum.TRIAL_COMPONENT],\n", + " sources=[LineageSourceEnum.TRAINING_JOB],\n", + ")\n", + "\n", + "# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`\n", + "# and find all datasets.\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[endpoint_context.context_arn],\n", + " query_filter=query_filter,\n", + " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "# Parse through the query results to get the ARNs of the training jobs associated with this Endpoint\n", + "trial_components = []\n", + "for vertex in query_result.vertices:\n", + " trial_components.append(vertex.arn)\n", + "\n", + "pp.pprint(trial_components)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9954748f", + "metadata": {}, + "source": [ + "#### Change the focal point of lineage\n", + "\n", + "The `LineageQuery` can be modified to have different `start_arns` which changes the focal point of lineage. In addition, the `LineageFilter` can take multiple sources and entities to expand the scope of the query. \n", + "\n", + "**Here we use the model as the lineage focal point and find the Endpoints and Datasets associated with it.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c28d8ea", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the ModelArtifact\n", + "\n", + "model_artifact_summary = list(Artifact.list(source_uri=model_package_arn))[0]\n", + "model_artifact = ModelArtifact.load(artifact_arn=model_artifact_summary.artifact_arn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca86919e", + "metadata": {}, + "outputs": [], + "source": [ + "query_filter = LineageFilter(\n", + " entities=[LineageEntityEnum.ARTIFACT],\n", + " sources=[LineageSourceEnum.ENDPOINT, LineageSourceEnum.DATASET],\n", + ")\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[model_artifact.artifact_arn], # Model is the starting artifact\n", + " query_filter=query_filter,\n", + " # Find all the entities that descend from the model, i.e. the endpoint\n", + " direction=LineageQueryDirectionEnum.DESCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "associations = []\n", + "for vertex in query_result.vertices:\n", + " associations.append(vertex.to_lineage_object().source.source_uri)\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[model_artifact.artifact_arn], # Model is the starting artifact\n", + " query_filter=query_filter,\n", + " # Find all the entities that ascend from the model, i.e. the datasets\n", + " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "for vertex in query_result.vertices:\n", + " associations.append(vertex.to_lineage_object().source.source_uri)\n", + "\n", + "pp.pprint(associations)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "eaa41ff9", + "metadata": {}, + "source": [ + "#### Use LineageQueryDirectionEnum.BOTH\n", + "\n", + "When the direction is set to `BOTH`, when the query traverses the graph to find ascendant and descendant relationships, the traversal takes place not only from the starting node, but from each node that is visited. \n", + "\n", + "e.g. If the training job is run twice and both models generated by the training job are deployed to endpoints, this result of the query with direction set to `BOTH` shows both endpoints. This is because the same image is used for training and deploying the model. Since the image is common to the model (`start_arn`) and both the endpoints, it appears in the query result. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4bee658", + "metadata": {}, + "outputs": [], + "source": [ + "query_filter = LineageFilter(\n", + " entities=[LineageEntityEnum.ARTIFACT],\n", + " sources=[LineageSourceEnum.ENDPOINT, LineageSourceEnum.DATASET],\n", + ")\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[model_artifact.artifact_arn], # Model is the starting artifact\n", + " query_filter=query_filter,\n", + " # This specifies that the query should look for associations both ascending and descending for the start\n", + " direction=LineageQueryDirectionEnum.BOTH,\n", + " include_edges=False,\n", + ")\n", + "\n", + "associations = []\n", + "for vertex in query_result.vertices:\n", + " associations.append(vertex.to_lineage_object().source.source_uri)\n", + "\n", + "pp.pprint(associations)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a69aff24", + "metadata": {}, + "source": [ + "### Directions in LineageQuery: Ascendants vs. Descendants\n", + "\n", + "To understand the direction in the Lineage Graph, take the following entity relationship graph - \n", + "Dataset -> Training Job -> Model -> Endpoint\n", + "\n", + "The endpoint is a **descendant** of the model, and the model is a **descendant** of the dataset. Similarly, the model is an **ascendant** of the endpoint The `direction` parameter can be used to specify whether the query should return entities that are descendants or ascendants of the entity in start_arns. If `start_arns` contains a model and the direction is `DESCENDANTS`, the query returns the endpoint. If the direction is `ASCENDANTS`, the query returns the dataset.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a273b49f", + "metadata": {}, + "outputs": [], + "source": [ + "# In this example, we'll look at the impact of specifying the direction as ASCENDANT or DESCENDANT in a `LineageQuery`.\n", + "\n", + "query_filter = LineageFilter(\n", + " entities=[LineageEntityEnum.ARTIFACT],\n", + " sources=[\n", + " LineageSourceEnum.ENDPOINT,\n", + " LineageSourceEnum.MODEL,\n", + " LineageSourceEnum.DATASET,\n", + " LineageSourceEnum.TRAINING_JOB,\n", + " ],\n", + ")\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[model_artifact.artifact_arn],\n", + " query_filter=query_filter,\n", + " direction=LineageQueryDirectionEnum.ASCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "ascendant_artifacts = []\n", + "\n", + "# The lineage entity returned for the Training Job is a TrialComponent which can't be converted to a\n", + "# lineage object using the method `to_lineage_object()` so we extract the TrialComponent ARN.\n", + "for vertex in query_result.vertices:\n", + " try:\n", + " ascendant_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", + " except:\n", + " ascendant_artifacts.append(vertex.arn)\n", + "\n", + "print(\"Ascendant artifacts:\")\n", + "pp.pprint(ascendant_artifacts)\n", + "\n", + "query_result = LineageQuery(sagemaker_session).query(\n", + " start_arns=[model_artifact.artifact_arn],\n", + " query_filter=query_filter,\n", + " direction=LineageQueryDirectionEnum.DESCENDANTS,\n", + " include_edges=False,\n", + ")\n", + "\n", + "descendant_artifacts = []\n", + "for vertex in query_result.vertices:\n", + " try:\n", + " descendant_artifacts.append(vertex.to_lineage_object().source.source_uri)\n", + " except:\n", + " # Handling TrialComponents.\n", + " descendant_artifacts.append(vertex.arn)\n", + "\n", + "print(\"Descendant artifacts:\")\n", + "pp.pprint(descendant_artifacts)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f7ec9d14", + "metadata": {}, + "source": [ + "### SDK helper functions\n", + "\n", + "The classes `EndpointContext`, `ModelArtifact`, and `DatasetArtifact`have helper functions that are wrappers over the `LineageQuery` API to make \n", + "certain lineage queries easier to leverage. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5df166d", + "metadata": {}, + "outputs": [], + "source": [ + "# Find all the datasets associated with the endpoint\n", + "\n", + "datasets = []\n", + "dataset_artifacts = endpoint_context.dataset_artifacts()\n", + "for dataset in dataset_artifacts:\n", + " datasets.append(dataset.source.source_uri)\n", + "print(\"Datasets : \", datasets)\n", + "\n", + "# Find the training jobs associated with the endpoint\n", + "training_job_artifacts = endpoint_context.training_job_arns()\n", + "training_jobs = []\n", + "for training_job in training_job_artifacts:\n", + " training_jobs.append(training_job)\n", + "print(\"Training Jobs : \", training_jobs)\n", + "\n", + "# Get the ARN for the pipeline execution associated with this endpoint (if any)\n", + "pipeline_executions = endpoint_context.pipeline_execution_arn()\n", + "if pipeline_executions:\n", + " for pipeline in pipelines_executions:\n", + " print(pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfc055f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Here we use the `ModelArtifact` class to find all the datasets and endpoints associated with the model\n", + "\n", + "dataset_artifacts = model_artifact.dataset_artifacts()\n", + "endpoint_contexts = model_artifact.endpoint_contexts()\n", + "\n", + "datasets = [dataset.source.source_uri for dataset in dataset_artifacts]\n", + "endpoints = [endpoint.source.source_uri for endpoint in endpoint_contexts]\n", + "\n", + "print(\"Datasets associated with this model : \")\n", + "pp.pprint(datasets)\n", + "\n", + "print(\"Endpoints associated with this model : \")\n", + "pp.pprint(endpoints)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fd69a51", + "metadata": {}, + "outputs": [], + "source": [ + "# Here we use the `DatasetArtifact` class to find all the endpoints hosting models that were trained with a particular dataset\n", + "# Find the artifact associated with the dataset\n", + "\n", + "dataset_artifact_arn = list(Artifact.list(source_uri=training_data))[0].artifact_arn\n", + "dataset_artifact = DatasetArtifact.load(artifact_arn=dataset_artifact_arn)\n", + "\n", + "# Find the endpoints that used this training dataset\n", + "endpoint_contexts = dataset_artifact.endpoint_contexts()\n", + "endpoints = [endpoint.source.source_uri for endpoint in endpoint_contexts]\n", + "\n", + "print(\"Endpoints associated with the training dataset {}\".format(training_data))\n", + "pp.pprint(endpoints)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2f9fdd40", + "metadata": {}, + "source": [ + "### Lineage Graph Visualization\n", + "\n", + "A helper class `Visualizer()` is provided in `visualizer.py` to help plot the lineage graph. When the query response is rendered, a graph with the lineage relationships from the `StartArns` is displayed. From the `StartArns` the visualization shows the relationships with the other lineage entities returned in the `query_lineage` API call. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "106d8d5a", + "metadata": {}, + "outputs": [], + "source": [ + "# Graph APIs\n", + "# Here we use the boto3 `query_lineage` API to generate the query response to plot.\n", + "\n", + "from visualizer import Visualizer\n", + "\n", + "query_response = sm_client.query_lineage(\n", + " StartArns=[endpoint_context.context_arn], Direction=\"Ascendants\", IncludeEdges=True\n", + ")\n", + "\n", + "viz = Visualizer()\n", + "viz.render(query_response, \"Endpoint\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22436292", + "metadata": {}, + "outputs": [], + "source": [ + "query_response = sm_client.query_lineage(\n", + " StartArns=[model_artifact.artifact_arn], Direction=\"Ascendants\", IncludeEdges=True\n", + ")\n", + "viz.render(query_response, \"Model\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b393afa3", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demostrated the capabilities of SageMaker Lineage that make it easy for users to keep track of their complex ML workflows. Users can construct their own lineage queries using the `LineageQuery` API and `LineageFilter` or they can use the functions provided on the `EndpointContext`, `ModelArtifact`, and `DatasetArtifact` classes. \n", + "\n", + "In addition, the responses from lineage queries can be plotting using the helper class `Visualizer()` to better understand the relationship between the lineage entities. \n", + "\n", + "When using SageMaker Pipelines as part of their ML workflows, users can find Pipeline execution ARNs using the lineage APIs described in this notebook.\n", + "\n", + "## Cleanup\n", + "In this section we clean up the resources created in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f43ef02", + "metadata": {}, + "outputs": [], + "source": [ + "# Delete endpoint\n", + "\n", + "sm_client.delete_endpoint(EndpointName=endpoint_name)\n", + "\n", + "# # Delete the model package\n", + "sm_client.delete_model_package(ModelPackageName=model_package.model_package_arn)\n", + "\n", + "# Delete the model package group\n", + "sm_client.delete_model_package_group(ModelPackageGroupName=model_package_group_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e19fe85", + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the experiment and trial within it\n", + "\n", + "import time\n", + "\n", + "\n", + "def delete_experiment(experiment):\n", + " for trial_summary in experiment.list_trials():\n", + " trial = Trial.load(trial_name=trial_summary.trial_name)\n", + " for trial_component_summary in trial.list_trial_components():\n", + " tc = TrialComponent.load(\n", + " trial_component_name=trial_component_summary.trial_component_name\n", + " )\n", + " trial.remove_trial_component(tc)\n", + " try:\n", + " # comment out to keep trial components\n", + " tc.delete()\n", + " except:\n", + " # tc is associated with another trial\n", + " continue\n", + " # to prevent throttling\n", + " time.sleep(0.5)\n", + " trial.delete()\n", + " experiment_name = experiment.experiment_name\n", + " experiment.delete()\n", + " print(f\"\\nExperiment {experiment_name} deleted\")\n", + "\n", + "\n", + "# Delete the Experiment and Trials within it\n", + "experiment = Experiment.load(experiment_name=exp.experiment_name)\n", + "delete_experiment(experiment)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7a9fa294", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-lineage|sagemaker-lineage-multihop-queries.ipynb)\n" + ] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "interpreter": { + "hash": "ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc" + }, + "kernelspec": { + "display_name": "Python 3 (TensorFlow 2.6 Python 3.8 CPU Optimized)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/tensorflow-2.6-cpu-py38-ubuntu20.04-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb new file mode 100644 index 0000000000..ba00f7ec9a --- /dev/null +++ b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb @@ -0,0 +1,1697 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "Amazon SageMaker Pipelines offers machine learning (ML) application developers and operations engineers the ability to orchestrate SageMaker jobs and author reproducible ML pipelines. It also enables them to deploy custom-built models for inference in real-time with low latency, run offline inferences with Batch Transform, and track lineage of artifacts. They can institute sound operational practices in deploying and monitoring production workflows, deploying model artifacts, and tracking artifact lineage through a simple interface, adhering to safety and best practice paradigms for ML application development.\n", + "\n", + "The SageMaker Pipelines service supports a SageMaker Pipeline domain specific language (DSL), which is a declarative JSON specification. This DSL defines a directed acyclic graph (DAG) of pipeline parameters and SageMaker job steps. The SageMaker Python Software Developer Kit (SDK) streamlines the generation of the pipeline DSL using constructs that engineers and scientists are already familiar with.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately an hour to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [SageMaker Pipelines](#SageMaker-Pipelines)\n", + "1. [Notebook Overview](#Notebook-Overview)\n", + "1. [A SageMaker Pipeline](#A-SageMaker-Pipeline)\n", + "1. [Dataset](#Dataset)\n", + "1. [Define Parameters to Parametrize Pipeline Execution](#Define-Parameters-to-Parametrize-Pipeline-Execution)\n", + "1. [Define a Processing Step for Feature Engineering](#Define-a-Processing-Step-for-Feature-Engineering)\n", + "1. [Define a Training Step to Train a Model](#Define-a-Training-Step-to-Train-a-Model)\n", + "1. [Define a Model Evaluation Step to Evaluate the Trained Model](#Define-a-Model-Evaluation-Step-to-Evaluate-the-Trained-Model)\n", + "1. [Define a Create Model Step to Create a Model](#Define-a-Create-Model-Step-to-Create-a-Model)\n", + "1. [Define a Transform Step to Perform Batch Transformation](#Define-a-Transform-Step-to-Perform-Batch-Transformation)\n", + "1. [Define a Register Model Step to Create a Model Package](#Define-a-Register-Model-Step-to-Create-a-Model-Package)\n", + "1. [Define a Fail Step to Terminate the Pipeline Execution and Mark it as Failed](#Define-a-Fail-Step-to-Terminate-the-Pipeline-Execution-and-Mark-it-as-Failed)\n", + "1. [Define a Condition Step to Check Accuracy and Conditionally Create a Model and Run a Batch Transformation and Register a Model in the Model Registry, Or Terminate the Execution in Failed State](#Define-a-Condition-Step-to-Check-Accuracy-and-Conditionally-Create-a-Model-and-Run-a-Batch-Transformation-and-Register-a-Model-in-the-Model-Registry,-Or-Terminate-the-Execution-in-Failed-State)\n", + "1. [Define a Pipeline of Parameters, Steps, and Conditions](#Define-a-Pipeline-of-Parameters,-Steps,-and-Conditions)\n", + "1. [Submit the pipeline to SageMaker and start execution](#Submit-the-pipeline-to-SageMaker-and-start-execution)\n", + "1. [Pipeline Operations: Examining and Waiting for Pipeline Execution](#Pipeline-Operations:-Examining-and-Waiting-for-Pipeline-Execution)\n", + " 1. [Examining the Evaluation](#Examining-the-Evaluation)\n", + " 1. [Lineage](#Lineage)\n", + " 1. [Parametrized Executions](#Parametrized-Executions)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## SageMaker Pipelines\n", + "\n", + "SageMaker Pipelines supports the following activities, which are demonstrated in this notebook:\n", + "\n", + "* Pipelines - A DAG of steps and conditions to orchestrate SageMaker jobs and resource creation.\n", + "* Processing job steps - A simplified, managed experience on SageMaker to run data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation.\n", + "* Training job steps - An iterative process that teaches a model to make predictions by presenting examples from a training dataset.\n", + "* Conditional execution steps - A step that provides conditional execution of branches in a pipeline.\n", + "* Register model steps - A step that creates a model package resource in the Model Registry that can be used to create deployable models in Amazon SageMaker.\n", + "* Create model steps - A step that creates a model for use in transform steps or later publication as an endpoint.\n", + "* Transform job steps - A batch transform to preprocess datasets to remove noise or bias that interferes with training or inference from a dataset, get inferences from large datasets, and run inference when a persistent endpoint is not needed.\n", + "* Fail steps - A step that stops a pipeline execution and marks the pipeline execution as failed.\n", + "* Parametrized Pipeline executions - Enables variation in pipeline executions according to specified parameters." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Notebook Overview\n", + "\n", + "This notebook shows how to:\n", + "\n", + "* Define a set of Pipeline parameters that can be used to parametrize a SageMaker Pipeline.\n", + "* Define a Processing step that performs cleaning, feature engineering, and splitting the input data into train and test data sets.\n", + "* Define a Training step that trains a model on the preprocessed train data set.\n", + "* Define a Processing step that evaluates the trained model's performance on the test dataset.\n", + "* Define a Create Model step that creates a model from the model artifacts used in training.\n", + "* Define a Transform step that performs batch transformation based on the model that was created.\n", + "* Define a Register Model step that creates a model package from the estimator and model artifacts used to train the model.\n", + "* Define a Conditional step that measures a condition based on output from prior steps and conditionally executes other steps.\n", + "* Define a Fail step with a customized error message indicating the cause of the execution failure.\n", + "* Define and create a Pipeline definition in a DAG, with the defined parameters and steps.\n", + "* Start a Pipeline execution and wait for execution to complete.\n", + "* Download the model evaluation report from the S3 bucket for examination.\n", + "* Start a second Pipeline execution." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## A SageMaker Pipeline\n", + "\n", + "The pipeline that you create follows a typical machine learning (ML) application pattern of preprocessing, training, evaluation, model creation, batch transformation, and model registration:\n", + "\n", + "![A typical ML Application pipeline](img/pipeline-full.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Dataset\n", + "\n", + "The dataset you use is the [UCI Machine Learning Abalone Dataset](https://archive.ics.uci.edu/ml/datasets/abalone) [1]. The aim for this task is to determine the age of an abalone snail from its physical measurements. At the core, this is a regression problem.\n", + "\n", + "The dataset contains several features: length (the longest shell measurement), diameter (the diameter perpendicular to length), height (the height with meat in the shell), whole_weight (the weight of whole abalone), shucked_weight (the weight of meat), viscera_weight (the gut weight after bleeding), shell_weight (the weight after being dried), sex ('M', 'F', 'I' where 'I' is Infant), and rings (integer).\n", + "\n", + "The number of rings turns out to be a good approximation for age (age is rings + 1.5). However, to obtain this number requires cutting the shell through the cone, staining the section, and counting the number of rings through a microscope, which is a time-consuming task. However, the other physical measurements are easier to determine. You use the dataset to build a predictive model of the variable rings through these other physical measurements.\n", + "\n", + "Before you upload the data to an S3 bucket, install the SageMaker Python SDK and gather some constants you can use later in this notebook.\n", + "\n", + "> [1] Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!pip install -U sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "import boto3\n", + "import sagemaker\n", + "from sagemaker.workflow.pipeline_context import PipelineSession\n", + "\n", + "sagemaker_session = sagemaker.session.Session()\n", + "region = sagemaker_session.boto_region_name\n", + "role = sagemaker.get_execution_role()\n", + "pipeline_session = PipelineSession()\n", + "default_bucket = sagemaker_session.default_bucket()\n", + "model_package_group_name = f\"AbaloneModelPackageGroupName\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now, upload the data into the default bucket. You can select our own data set for the `input_data_uri` as is appropriate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!mkdir -p data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "local_path = \"data/abalone-dataset.csv\"\n", + "\n", + "s3 = boto3.resource(\"s3\")\n", + "s3.Bucket(f\"sagemaker-example-files-prod-{region}\").download_file(\n", + " \"datasets/tabular/uci_abalone/abalone.csv\", local_path\n", + ")\n", + "\n", + "base_uri = f\"s3://{default_bucket}/abalone\"\n", + "input_data_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=local_path,\n", + " desired_s3_uri=base_uri,\n", + ")\n", + "print(input_data_uri)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Download a second dataset for batch transformation after model creation. You can select our own dataset for the `batch_data_uri` as is appropriate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "local_path = \"data/abalone-dataset-batch\"\n", + "\n", + "s3 = boto3.resource(\"s3\")\n", + "s3.Bucket(f\"sagemaker-servicecatalog-seedcode-{region}\").download_file(\n", + " \"dataset/abalone-dataset-batch\", local_path\n", + ")\n", + "\n", + "base_uri = f\"s3://{default_bucket}/abalone\"\n", + "batch_data_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=local_path,\n", + " desired_s3_uri=base_uri,\n", + ")\n", + "print(batch_data_uri)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define Parameters to Parametrize Pipeline Execution\n", + "\n", + "Define Pipeline parameters that you can use to parametrize the pipeline. Parameters enable custom pipeline executions and schedules without having to modify the Pipeline definition.\n", + "\n", + "The supported parameter types include:\n", + "\n", + "* `ParameterString` - represents a `str` Python type\n", + "* `ParameterInteger` - represents an `int` Python type\n", + "* `ParameterFloat` - represents a `float` Python type\n", + "\n", + "These parameters support providing a default value, which can be overridden on pipeline execution. The default value specified should be an instance of the type of the parameter.\n", + "\n", + "The parameters defined in this workflow include:\n", + "\n", + "* `processing_instance_count` - The instance count of the processing job.\n", + "* `instance_type` - The `ml.*` instance type of the training job.\n", + "* `model_approval_status` - The approval status to register with the trained model for CI/CD purposes (\"PendingManualApproval\" is the default).\n", + "* `input_data` - The S3 bucket URI location of the input data.\n", + "* `batch_data` - The S3 bucket URI location of the batch data.\n", + "* `mse_threshold` - The Mean Squared Error (MSE) threshold used to verify the accuracy of a model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.workflow.parameters import (\n", + " ParameterInteger,\n", + " ParameterString,\n", + " ParameterFloat,\n", + ")\n", + "\n", + "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n", + "instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n", + "model_approval_status = ParameterString(\n", + " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", + ")\n", + "input_data = ParameterString(\n", + " name=\"InputData\",\n", + " default_value=input_data_uri,\n", + ")\n", + "batch_data = ParameterString(\n", + " name=\"BatchData\",\n", + " default_value=batch_data_uri,\n", + ")\n", + "mse_threshold = ParameterFloat(name=\"MseThreshold\", default_value=6.0)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define Parameters](img/pipeline-1.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Processing Step for Feature Engineering\n", + "\n", + "First, develop a preprocessing script that is specified in the Processing step.\n", + "\n", + "This notebook cell writes a file `preprocessing_abalone.py`, which contains the preprocessing script. You can update the script, and rerun this cell to overwrite. The preprocessing script uses `scikit-learn` to do the following:\n", + "\n", + "* Fill in missing sex category data and encode it so that it is suitable for training.\n", + "* Scale and normalize all numerical fields, aside from sex and rings numerical data.\n", + "* Split the data into training, validation, and test datasets.\n", + "\n", + "The Processing step executes the script on the input data. The Training step uses the preprocessed training features and labels to train a model. The Evaluation step uses the trained model and preprocessed test features and labels to evaluate the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!mkdir -p code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%%writefile code/preprocessing.py\n", + "import argparse\n", + "import os\n", + "import requests\n", + "import tempfile\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "\n", + "\n", + "# Since we get a headerless CSV file, we specify the column names here.\n", + "feature_columns_names = [\n", + " \"sex\",\n", + " \"length\",\n", + " \"diameter\",\n", + " \"height\",\n", + " \"whole_weight\",\n", + " \"shucked_weight\",\n", + " \"viscera_weight\",\n", + " \"shell_weight\",\n", + "]\n", + "label_column = \"rings\"\n", + "\n", + "feature_columns_dtype = {\n", + " \"sex\": str,\n", + " \"length\": np.float64,\n", + " \"diameter\": np.float64,\n", + " \"height\": np.float64,\n", + " \"whole_weight\": np.float64,\n", + " \"shucked_weight\": np.float64,\n", + " \"viscera_weight\": np.float64,\n", + " \"shell_weight\": np.float64,\n", + "}\n", + "label_column_dtype = {\"rings\": np.float64}\n", + "\n", + "\n", + "def merge_two_dicts(x, y):\n", + " z = x.copy()\n", + " z.update(y)\n", + " return z\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " base_dir = \"/opt/ml/processing\"\n", + "\n", + " df = pd.read_csv(\n", + " f\"{base_dir}/input/abalone-dataset.csv\",\n", + " header=None,\n", + " names=feature_columns_names + [label_column],\n", + " dtype=merge_two_dicts(feature_columns_dtype, label_column_dtype),\n", + " )\n", + " numeric_features = list(feature_columns_names)\n", + " numeric_features.remove(\"sex\")\n", + " numeric_transformer = Pipeline(\n", + " steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n", + " )\n", + "\n", + " categorical_features = [\"sex\"]\n", + " categorical_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n", + " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n", + " ]\n", + " )\n", + "\n", + " preprocess = ColumnTransformer(\n", + " transformers=[\n", + " (\"num\", numeric_transformer, numeric_features),\n", + " (\"cat\", categorical_transformer, categorical_features),\n", + " ]\n", + " )\n", + "\n", + " y = df.pop(\"rings\")\n", + " X_pre = preprocess.fit_transform(df)\n", + " y_pre = y.to_numpy().reshape(len(y), 1)\n", + "\n", + " X = np.concatenate((y_pre, X_pre), axis=1)\n", + "\n", + " np.random.shuffle(X)\n", + " train, validation, test = np.split(X, [int(0.7 * len(X)), int(0.85 * len(X))])\n", + "\n", + " pd.DataFrame(train).to_csv(f\"{base_dir}/train/train.csv\", header=False, index=False)\n", + " pd.DataFrame(validation).to_csv(\n", + " f\"{base_dir}/validation/validation.csv\", header=False, index=False\n", + " )\n", + " pd.DataFrame(test).to_csv(f\"{base_dir}/test/test.csv\", header=False, index=False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Next, create an instance of a `SKLearnProcessor` processor and use that in our `ProcessingStep`.\n", + "\n", + "You also specify the `framework_version` to use throughout this notebook.\n", + "\n", + "Note the `processing_instance_count` parameter used by the processor instance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "\n", + "\n", + "framework_version = \"1.2-1\"\n", + "\n", + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=framework_version,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=processing_instance_count,\n", + " base_job_name=\"sklearn-abalone-process\",\n", + " role=role,\n", + " sagemaker_session=pipeline_session,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Finally, we take the output of the processor's `run` method and pass that as arguments to the `ProcessingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.run()` does not launch the processing job, it returns the arguments needed to run the job as a step in the pipeline.\n", + "\n", + "Note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", + "from sagemaker.workflow.steps import ProcessingStep\n", + "\n", + "processor_args = sklearn_processor.run(\n", + " inputs=[\n", + " ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"),\n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n", + " ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n", + " ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n", + " ],\n", + " code=\"code/preprocessing.py\",\n", + ")\n", + "\n", + "step_process = ProcessingStep(name=\"AbaloneProcess\", step_args=processor_args)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Processing Step for Feature Engineering](img/pipeline-2.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Training Step to Train a Model\n", + "\n", + "In this section, use Amazon SageMaker's [XGBoost Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) to train on this dataset. Configure an Estimator for the XGBoost algorithm and the input dataset. A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to `model_dir` so that it can be hosted later.\n", + "\n", + "The model path where the models from training are saved is also specified.\n", + "\n", + "Note the `instance_type` parameter may be used in multiple places in the pipeline. In this case, the `instance_type` is passed into the estimator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.estimator import Estimator\n", + "from sagemaker.inputs import TrainingInput\n", + "\n", + "model_path = f\"s3://{default_bucket}/AbaloneTrain\"\n", + "image_uri = sagemaker.image_uris.retrieve(\n", + " framework=\"xgboost\",\n", + " region=region,\n", + " version=\"1.0-1\",\n", + " py_version=\"py3\",\n", + " instance_type=\"ml.m5.xlarge\",\n", + ")\n", + "xgb_train = Estimator(\n", + " image_uri=image_uri,\n", + " instance_type=instance_type,\n", + " instance_count=1,\n", + " output_path=model_path,\n", + " role=role,\n", + " sagemaker_session=pipeline_session,\n", + ")\n", + "xgb_train.set_hyperparameters(\n", + " objective=\"reg:linear\",\n", + " num_round=50,\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + ")\n", + "\n", + "train_args = xgb_train.fit(\n", + " inputs={\n", + " \"train\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " \"validation\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"validation\"\n", + " ].S3Output.S3Uri,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " }\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Finally, we use the output of the estimator's `.fit()` method as arguments to the `TrainingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.fit()` does not launch the training job, it returns the arguments needed to run the job as a step in the pipeline.\n", + "\n", + "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `.fit()` method. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.inputs import TrainingInput\n", + "from sagemaker.workflow.steps import TrainingStep\n", + "\n", + "\n", + "step_train = TrainingStep(\n", + " name=\"AbaloneTrain\",\n", + " step_args=train_args,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Training Step to Train a Model](img/pipeline-3.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Model Evaluation Step to Evaluate the Trained Model\n", + "\n", + "First, develop an evaluation script that is specified in a Processing step that performs the model evaluation.\n", + "\n", + "After pipeline execution, you can examine the resulting `evaluation.json` for analysis.\n", + "\n", + "The evaluation script uses `xgboost` to do the following:\n", + "\n", + "* Load the model.\n", + "* Read the test data.\n", + "* Issue predictions against the test data.\n", + "* Build a classification report, including accuracy and ROC curve.\n", + "* Save the evaluation report to the evaluation directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%%writefile code/evaluation.py\n", + "import json\n", + "import pathlib\n", + "import pickle\n", + "import tarfile\n", + "\n", + "import joblib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " model_path = f\"/opt/ml/processing/model/model.tar.gz\"\n", + " with tarfile.open(model_path) as tar:\n", + " tar.extractall(path=\".\")\n", + "\n", + " model = pickle.load(open(\"xgboost-model\", \"rb\"))\n", + "\n", + " test_path = \"/opt/ml/processing/test/test.csv\"\n", + " df = pd.read_csv(test_path, header=None)\n", + "\n", + " y_test = df.iloc[:, 0].to_numpy()\n", + " df.drop(df.columns[0], axis=1, inplace=True)\n", + "\n", + " X_test = xgboost.DMatrix(df.values)\n", + "\n", + " predictions = model.predict(X_test)\n", + "\n", + " mse = mean_squared_error(y_test, predictions)\n", + " std = np.std(y_test - predictions)\n", + " report_dict = {\n", + " \"regression_metrics\": {\n", + " \"mse\": {\"value\": mse, \"standard_deviation\": std},\n", + " },\n", + " }\n", + "\n", + " output_dir = \"/opt/ml/processing/evaluation\"\n", + " pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n", + "\n", + " evaluation_path = f\"{output_dir}/evaluation.json\"\n", + " with open(evaluation_path, \"w\") as f:\n", + " f.write(json.dumps(report_dict))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.processing import ScriptProcessor\n", + "\n", + "\n", + "script_eval = ScriptProcessor(\n", + " image_uri=image_uri,\n", + " command=[\"python3\"],\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=1,\n", + " base_job_name=\"script-abalone-eval\",\n", + " role=role,\n", + " sagemaker_session=pipeline_session,\n", + ")\n", + "\n", + "eval_args = script_eval.run(\n", + " inputs=[\n", + " ProcessingInput(\n", + " source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " destination=\"/opt/ml/processing/model\",\n", + " ),\n", + " ProcessingInput(\n", + " source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n", + " destination=\"/opt/ml/processing/test\",\n", + " ),\n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n", + " ],\n", + " code=\"code/evaluation.py\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Use the processor's arguments returned by `.run()` to construct a `ProcessingStep`, along with the input and output channels and the code that will be executed when the pipeline invokes pipeline execution.\n", + "\n", + "Specifically, the `S3ModelArtifacts` from the `step_train` `properties` and the `S3Uri` of the `\"test_data\"` output channel of the `step_process` `properties` are passed as inputs. The `TrainingStep` and `ProcessingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) and [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response objects, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.workflow.properties import PropertyFile\n", + "\n", + "\n", + "evaluation_report = PropertyFile(\n", + " name=\"EvaluationReport\", output_name=\"evaluation\", path=\"evaluation.json\"\n", + ")\n", + "step_eval = ProcessingStep(\n", + " name=\"AbaloneEval\",\n", + " step_args=eval_args,\n", + " property_files=[evaluation_report],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Model Evaluation Step to Evaluate the Trained Model](img/pipeline-4.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Create Model Step to Create a Model\n", + "\n", + "In order to perform batch transformation using the example model, create a SageMaker model.\n", + "\n", + "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.model import Model\n", + "\n", + "model = Model(\n", + " image_uri=image_uri,\n", + " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Define the `ModelStep` by providing the return values from `model.create()` as the step arguments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.inputs import CreateModelInput\n", + "from sagemaker.workflow.model_step import ModelStep\n", + "\n", + "step_create_model = ModelStep(\n", + " name=\"AbaloneCreateModel\",\n", + " step_args=model.create(instance_type=\"ml.m5.large\", accelerator_type=\"ml.eia1.medium\"),\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Transform Step to Perform Batch Transformation\n", + "\n", + "Now that a model instance is defined, create a `Transformer` instance with the appropriate model type, compute instance type, and desired output S3 URI.\n", + "\n", + "Specifically, pass in the `ModelName` from the `CreateModelStep`, `step_create_model` properties. The `CreateModelStep` `properties` attribute matches the object model of the [DescribeModel](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeModel.html) response object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.transformer import Transformer\n", + "\n", + "\n", + "transformer = Transformer(\n", + " model_name=step_create_model.properties.ModelName,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=1,\n", + " output_path=f\"s3://{default_bucket}/AbaloneTransform\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Pass in the transformer instance and the `TransformInput` with the `batch_data` pipeline parameter defined earlier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.inputs import TransformInput\n", + "from sagemaker.workflow.steps import TransformStep\n", + "\n", + "\n", + "step_transform = TransformStep(\n", + " name=\"AbaloneTransform\", transformer=transformer, inputs=TransformInput(data=batch_data)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Register Model Step to Create a Model Package\n", + "\n", + "A model package is an abstraction of reusable model artifacts that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n", + "\n", + "A model package group is a collection of model packages. A model package group can be created for a specific ML business problem, and new versions of the model packages can be added to it. Typically, customers are expected to create a ModelPackageGroup for a SageMaker pipeline so that model package versions can be added to the group for every SageMaker Pipeline run.\n", + "\n", + "To register a model in the Model Registry, we take the model created in the previous steps\n", + "```\n", + "model = Model(\n", + " image_uri=image_uri,\n", + " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "```\n", + "and call the `.register()` function on it while passing all the parameters needed for registering the model.\n", + "\n", + "We take the outputs of the `.register()` call and pass that to the `ModelStep` as step arguments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.model_metrics import MetricsSource, ModelMetrics\n", + "\n", + "model_metrics = ModelMetrics(\n", + " model_statistics=MetricsSource(\n", + " s3_uri=\"{}/evaluation.json\".format(\n", + " step_eval.arguments[\"ProcessingOutputConfig\"][\"Outputs\"][0][\"S3Output\"][\"S3Uri\"]\n", + " ),\n", + " content_type=\"application/json\",\n", + " )\n", + ")\n", + "\n", + "register_args = model.register(\n", + " content_types=[\"text/csv\"],\n", + " response_types=[\"text/csv\"],\n", + " inference_instances=[\"ml.t2.medium\", \"ml.m5.xlarge\"],\n", + " transform_instances=[\"ml.m5.xlarge\"],\n", + " model_package_group_name=model_package_group_name,\n", + " approval_status=model_approval_status,\n", + " model_metrics=model_metrics,\n", + ")\n", + "step_register = ModelStep(name=\"AbaloneRegisterModel\", step_args=register_args)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Create Model Step and Batch Transform to Process Data in Batch at Scale](img/pipeline-5.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Fail Step to Terminate the Pipeline Execution and Mark it as Failed\n", + "\n", + "This section walks you through the following steps:\n", + "\n", + "* Define a `FailStep` with customized error message, which indicates the cause of the execution failure.\n", + "* Enter the `FailStep` error message with a `Join` function, which appends a static text string with the dynamic `mse_threshold` parameter to build a more informative error message." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.workflow.fail_step import FailStep\n", + "from sagemaker.workflow.functions import Join\n", + "\n", + "step_fail = FailStep(\n", + " name=\"AbaloneMSEFail\",\n", + " error_message=Join(on=\" \", values=[\"Execution failed due to MSE >\", mse_threshold]),\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Fail Step to Terminate the Execution in Failed State](img/pipeline-8.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Condition Step to Check Accuracy and Conditionally Create a Model and Run a Batch Transformation and Register a Model in the Model Registry, Or Terminate the Execution in Failed State\n", + "\n", + "In this step, the model is registered only if the accuracy of the model, as determined by the evaluation step `step_eval`, exceeded a specified value. Otherwise, the pipeline execution fails and terminates. A `ConditionStep` enables pipelines to support conditional execution in the pipeline DAG based on the conditions of the step properties.\n", + "\n", + "In the following section, you:\n", + "\n", + "* Define a `ConditionLessThanOrEqualTo` on the accuracy value found in the output of the evaluation step, `step_eval`.\n", + "* Use the condition in the list of conditions in a `ConditionStep`.\n", + "* Pass the `CreateModelStep` and `TransformStep` steps, and the `RegisterModel` step collection into the `if_steps` of the `ConditionStep`, which are only executed if the condition evaluates to `True`.\n", + "* Pass the `FailStep` step into the `else_steps`of the `ConditionStep`, which is only executed if the condition evaluates to `False`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n", + "from sagemaker.workflow.condition_step import ConditionStep\n", + "from sagemaker.workflow.functions import JsonGet\n", + "\n", + "\n", + "cond_lte = ConditionLessThanOrEqualTo(\n", + " left=JsonGet(\n", + " step_name=step_eval.name,\n", + " property_file=evaluation_report,\n", + " json_path=\"regression_metrics.mse.value\",\n", + " ),\n", + " right=mse_threshold,\n", + ")\n", + "\n", + "step_cond = ConditionStep(\n", + " name=\"AbaloneMSECond\",\n", + " conditions=[cond_lte],\n", + " if_steps=[step_register, step_create_model, step_transform],\n", + " else_steps=[step_fail],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Condition Step to Check Accuracy and Conditionally Execute Steps](img/pipeline-6.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Define a Pipeline of Parameters, Steps, and Conditions\n", + "\n", + "In this section, combine the steps into a Pipeline so it can be executed.\n", + "\n", + "A pipeline requires a `name`, `parameters`, and `steps`. Names must be unique within an `(account, region)` pair.\n", + "\n", + "Note:\n", + "\n", + "* All the parameters used in the definitions must be present.\n", + "* Steps passed into the pipeline do not have to be listed in the order of execution. The SageMaker Pipeline service resolves the data dependency DAG as steps for the execution to complete.\n", + "* Steps must be unique to across the pipeline step list and all condition step if/else lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.workflow.pipeline import Pipeline\n", + "\n", + "\n", + "pipeline_name = f\"AbalonePipeline\"\n", + "pipeline = Pipeline(\n", + " name=pipeline_name,\n", + " parameters=[\n", + " processing_instance_count,\n", + " instance_type,\n", + " model_approval_status,\n", + " input_data,\n", + " batch_data,\n", + " mse_threshold,\n", + " ],\n", + " steps=[step_process, step_train, step_eval, step_cond],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Define a Pipeline of Parameters, Steps, and Conditions](img/pipeline-7.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### (Optional) Examining the pipeline definition\n", + "\n", + "The JSON of the pipeline definition can be examined to confirm the pipeline is well-defined and the parameters and step properties resolve correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "\n", + "definition = json.loads(pipeline.definition())\n", + "definition" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Submit the pipeline to SageMaker and start execution\n", + "\n", + "Submit the pipeline definition to the Pipeline service. The Pipeline service uses the role that is passed in to create all the jobs defined in the steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "pipeline.upsert(role_arn=role)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Start the pipeline and accept all the default parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution = pipeline.start()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Pipeline Operations: Examining and Waiting for Pipeline Execution\n", + "\n", + "Describe the pipeline execution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.describe()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Wait for the execution to complete." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.wait()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "List the steps in the execution. These are the steps in the pipeline that have been resolved by the step executor service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.list_steps()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Examining the Evaluation\n", + "\n", + "Examine the resulting model evaluation after the pipeline completes. Download the resulting `evaluation.json` file from S3 and print the report." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "\n", + "evaluation_json = sagemaker.s3.S3Downloader.read_file(\n", + " \"{}/evaluation.json\".format(\n", + " step_eval.arguments[\"ProcessingOutputConfig\"][\"Outputs\"][0][\"S3Output\"][\"S3Uri\"]\n", + " )\n", + ")\n", + "pprint(json.loads(evaluation_json))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Lineage\n", + "\n", + "Review the lineage of the artifacts generated by the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import time\n", + "from sagemaker.lineage.visualizer import LineageTableVisualizer\n", + "\n", + "\n", + "viz = LineageTableVisualizer(sagemaker.session.Session())\n", + "for execution_step in reversed(execution.list_steps()):\n", + " print(execution_step)\n", + " display(viz.show(pipeline_execution_step=execution_step))\n", + " time.sleep(5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Parametrized Executions\n", + "\n", + "You can run additional executions of the pipeline and specify different pipeline parameters. The `parameters` argument is a dictionary containing parameter names, and where the values are used to override the defaults values.\n", + "\n", + "Based on the performance of the model, you might want to kick off another pipeline execution on a compute-optimized instance type and set the model approval status to \"Approved\" automatically. This means that the model package version generated by the `RegisterModel` step is automatically ready for deployment through CI/CD pipelines, such as with SageMaker Projects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution = pipeline.start(\n", + " parameters=dict(\n", + " ModelApprovalStatus=\"Approved\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.wait()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.list_steps()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Apart from that, you might also want to adjust the MSE threshold to a smaller value and raise the bar for the accuracy of the registered model. In this case you can override the MSE threshold like the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution = pipeline.start(parameters=dict(MseThreshold=3.0))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "If the MSE threshold is not satisfied, the pipeline execution enters the `FailStep` and is marked as failed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "try:\n", + " execution.wait()\n", + "except Exception as error:\n", + " print(error)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.list_steps()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-pipelines|tabular|abalone_build_train_deploy|sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-pipelines|tabular|lambda-step|sagemaker-pipelines-lambda-step.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This notebook illustrates how a Lambda function can be run as a step in a SageMaker Pipeline.\n", + "\n", + "The steps in this pipeline include:\n", + "* Preprocess the Abalone dataset\n", + "* Train an XGBoost Model\n", + "* Evaluate the model performance\n", + "* Create a model\n", + "* Deploy the model to a SageMaker Hosted Endpoint using a Lambda Function, through SageMaker Pipelines\n", + "\n", + "A step to register the model into a Model Registry can be added to the pipeline using the `RegisterModel` step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 15 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Prerequisites](#Prerequisites)\n", + "1. [Configuration Setup](#Configuration-Setup)\n", + "1. [Data Preparation](#Data-Preparation)\n", + "1. [Model Training and Evaluation](#Model-Training-and-Evaluation)\n", + "1. [Setting up Lambda](#Setting-up-Lambda)\n", + "1. [Execute the Pipeline](#Execute-the-Pipeline)\n", + "1. [Clean up resources](#Clean-up-resources)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "The notebook execution role should have policies which enable the notebook to create a Lambda function. The Amazon managed policy `AmazonSageMakerPipelinesIntegrations` can be added to the notebook execution role to achieve the same effect.\n", + "\n", + "The policy description is as follows:\n", + "\n", + "```\n", + "\n", + "{\n", + " \"Version\": \"2012-10-17\",\n", + " \"Statement\": [\n", + " {\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": [\n", + " \"lambda:CreateFunction\",\n", + " \"lambda:DeleteFunction\",\n", + " \"lambda:InvokeFunction\",\n", + " \"lambda:UpdateFunctionCode\"\n", + " ],\n", + " \"Resource\": [\n", + " \"arn:aws:lambda:*:*:function:*sagemaker*\",\n", + " \"arn:aws:lambda:*:*:function:*sageMaker*\",\n", + " \"arn:aws:lambda:*:*:function:*SageMaker*\"\n", + " ]\n", + " },\n", + " {\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": [\n", + " \"sqs:CreateQueue\",\n", + " \"sqs:SendMessage\"\n", + " ],\n", + " \"Resource\": [\n", + " \"arn:aws:sqs:*:*:*sagemaker*\",\n", + " \"arn:aws:sqs:*:*:*sageMaker*\",\n", + " \"arn:aws:sqs:*:*:*SageMaker*\"\n", + " ]\n", + " },\n", + " {\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": [\n", + " \"iam:PassRole\"\n", + " ],\n", + " \"Resource\": \"arn:aws:iam::*:role/*\",\n", + " \"Condition\": {\n", + " \"StringEquals\": {\n", + " \"iam:PassedToService\": [\n", + " \"lambda.amazonaws.com\"\n", + " ]\n", + " }\n", + " }\n", + " }\n", + " ]\n", + "}\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Let's start by importing necessary packages and installing the SageMaker Python SDK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "import boto3\n", + "import sagemaker\n", + "\n", + "from sagemaker.estimator import Estimator\n", + "from sagemaker.inputs import TrainingInput\n", + "\n", + "from sagemaker.processing import (\n", + " ProcessingInput,\n", + " ProcessingOutput,\n", + " Processor,\n", + " ScriptProcessor,\n", + ")\n", + "\n", + "from sagemaker import Model\n", + "from sagemaker.xgboost import XGBoostPredictor\n", + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "\n", + "from sagemaker.workflow.parameters import (\n", + " ParameterInteger,\n", + " ParameterString,\n", + ")\n", + "from sagemaker.workflow.pipeline import Pipeline\n", + "from sagemaker.workflow.properties import PropertyFile\n", + "from sagemaker.workflow.steps import ProcessingStep, TrainingStep, CacheConfig\n", + "from sagemaker.workflow.lambda_step import (\n", + " LambdaStep,\n", + " LambdaOutput,\n", + " LambdaOutputTypeEnum,\n", + ")\n", + "from sagemaker.workflow.model_step import ModelStep\n", + "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n", + "from sagemaker.workflow.condition_step import ConditionStep\n", + "from sagemaker.workflow.functions import JsonGet\n", + "from sagemaker.workflow.pipeline_context import PipelineSession\n", + "\n", + "from sagemaker.lambda_helper import Lambda\n", + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!{sys.executable} -m pip install \"sagemaker>=2.99.0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now configure the setup we need, which includes the session object from the SageMaker Python SDK, and neccessary configurations for the pipelines, such as object types, input and output buckets and so on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Create the SageMaker Session\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "pipeline_session = PipelineSession()\n", + "sm_client = sagemaker_session.sagemaker_client\n", + "region = sagemaker_session.boto_region_name\n", + "prefix = \"lambda-step-pipeline\"\n", + "\n", + "account_id = sagemaker_session.account_id()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Define variables and parameters needed for the Pipeline steps\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "default_bucket = sagemaker_session.default_bucket()\n", + "base_job_prefix = \"lambda-step-example\"\n", + "s3_prefix = \"lambda-step-pipeline\"\n", + "\n", + "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n", + "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n", + "model_approval_status = ParameterString(\n", + " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", + ")\n", + "input_data = ParameterString(\n", + " name=\"InputDataUrl\",\n", + " default_value=f\"s3://sagemaker-example-files-prod-{boto3.Session().region_name}/datasets/tabular/uci_abalone/abalone.csv\",\n", + ")\n", + "model_approval_status = ParameterString(\n", + " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", + ")\n", + "\n", + "# Cache Pipeline steps to reduce execution time on subsequent executions\n", + "cache_config = CacheConfig(enable_caching=True, expire_after=\"30d\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preparation\n", + "\n", + "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets.\n", + "\n", + "The output of this step is used as the input to the TrainingStep." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!mkdir -p code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%writefile code/preprocess.py\n", + "\n", + "\"\"\"Feature engineers the abalone dataset.\"\"\"\n", + "import argparse\n", + "import logging\n", + "import os\n", + "import pathlib\n", + "import requests\n", + "import tempfile\n", + "\n", + "import boto3\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "logger.addHandler(logging.StreamHandler())\n", + "\n", + "\n", + "# Since we get a headerless CSV file we specify the column names here.\n", + "feature_columns_names = [\n", + " \"sex\",\n", + " \"length\",\n", + " \"diameter\",\n", + " \"height\",\n", + " \"whole_weight\",\n", + " \"shucked_weight\",\n", + " \"viscera_weight\",\n", + " \"shell_weight\",\n", + "]\n", + "label_column = \"rings\"\n", + "\n", + "feature_columns_dtype = {\n", + " \"sex\": str,\n", + " \"length\": np.float64,\n", + " \"diameter\": np.float64,\n", + " \"height\": np.float64,\n", + " \"whole_weight\": np.float64,\n", + " \"shucked_weight\": np.float64,\n", + " \"viscera_weight\": np.float64,\n", + " \"shell_weight\": np.float64,\n", + "}\n", + "label_column_dtype = {\"rings\": np.float64}\n", + "\n", + "\n", + "def merge_two_dicts(x, y):\n", + " \"\"\"Merges two dicts, returning a new copy.\"\"\"\n", + " z = x.copy()\n", + " z.update(y)\n", + " return z\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " logger.debug(\"Starting preprocessing.\")\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument(\"--input-data\", type=str, required=True)\n", + " args = parser.parse_args()\n", + "\n", + " base_dir = \"/opt/ml/processing\"\n", + " pathlib.Path(f\"{base_dir}/data\").mkdir(parents=True, exist_ok=True)\n", + " input_data = args.input_data\n", + " bucket = input_data.split(\"/\")[2]\n", + " key = \"/\".join(input_data.split(\"/\")[3:])\n", + "\n", + " logger.info(\"Downloading data from bucket: %s, key: %s\", bucket, key)\n", + " fn = f\"{base_dir}/data/abalone-dataset.csv\"\n", + " s3 = boto3.resource(\"s3\")\n", + " s3.Bucket(bucket).download_file(key, fn)\n", + "\n", + " logger.debug(\"Reading downloaded data.\")\n", + " df = pd.read_csv(\n", + " fn,\n", + " header=None,\n", + " names=feature_columns_names + [label_column],\n", + " dtype=merge_two_dicts(feature_columns_dtype, label_column_dtype),\n", + " )\n", + " os.unlink(fn)\n", + "\n", + " logger.debug(\"Defining transformers.\")\n", + " numeric_features = list(feature_columns_names)\n", + " numeric_features.remove(\"sex\")\n", + " numeric_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", + " (\"scaler\", StandardScaler()),\n", + " ]\n", + " )\n", + "\n", + " categorical_features = [\"sex\"]\n", + " categorical_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n", + " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n", + " ]\n", + " )\n", + "\n", + " preprocess = ColumnTransformer(\n", + " transformers=[\n", + " (\"num\", numeric_transformer, numeric_features),\n", + " (\"cat\", categorical_transformer, categorical_features),\n", + " ]\n", + " )\n", + "\n", + " logger.info(\"Applying transforms.\")\n", + " y = df.pop(\"rings\")\n", + " X_pre = preprocess.fit_transform(df)\n", + " y_pre = y.to_numpy().reshape(len(y), 1)\n", + "\n", + " X = np.concatenate((y_pre, X_pre), axis=1)\n", + "\n", + " logger.info(\"Splitting %d rows of data into train, validation, test datasets.\", len(X))\n", + " np.random.shuffle(X)\n", + " train, validation, test = np.split(X, [int(0.7 * len(X)), int(0.85 * len(X))])\n", + "\n", + " logger.info(\"Writing out datasets to %s.\", base_dir)\n", + " pd.DataFrame(train).to_csv(f\"{base_dir}/train/train.csv\", header=False, index=False)\n", + " pd.DataFrame(validation).to_csv(\n", + " f\"{base_dir}/validation/validation.csv\", header=False, index=False\n", + " )\n", + " pd.DataFrame(test).to_csv(f\"{base_dir}/test/test.csv\", header=False, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Process the training data step using a python script.\n", + "# Split the training data set into train, test, and validation datasets\n", + "\n", + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=\"0.23-1\",\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=processing_instance_count,\n", + " base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "\n", + "processor_args = sklearn_processor.run(\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n", + " ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n", + " ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n", + " ],\n", + " code=\"code/preprocess.py\",\n", + " arguments=[\"--input-data\", input_data],\n", + ")\n", + "\n", + "step_process = ProcessingStep(\n", + " name=\"PreprocessAbaloneData\",\n", + " step_args=processor_args,\n", + " cache_config=cache_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training and Evaluation\n", + "\n", + "We will now train an XGBoost model using the SageMaker Python SDK and the output of the ProcessingStep." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Define the output path for the model artifacts from the Hyperparameter Tuning Job\n", + "model_path = f\"s3://{default_bucket}/{base_job_prefix}/AbaloneTrain\"\n", + "\n", + "image_uri = sagemaker.image_uris.retrieve(\n", + " framework=\"xgboost\",\n", + " region=region,\n", + " version=\"1.0-1\",\n", + " py_version=\"py3\",\n", + " instance_type=\"ml.m5.xlarge\",\n", + ")\n", + "\n", + "xgb_train = Estimator(\n", + " image_uri=image_uri,\n", + " instance_type=training_instance_type,\n", + " instance_count=1,\n", + " output_path=model_path,\n", + " base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "\n", + "xgb_train.set_hyperparameters(\n", + " objective=\"reg:linear\",\n", + " num_round=50,\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + " silent=0,\n", + ")\n", + "\n", + "train_args = xgb_train.fit(\n", + " inputs={\n", + " \"train\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " \"validation\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"validation\"\n", + " ].S3Output.S3Uri,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " },\n", + ")\n", + "\n", + "step_train = TrainingStep(\n", + " name=\"TrainAbaloneModel\",\n", + " step_args=train_args,\n", + " cache_config=cache_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluating the model\n", + "\n", + "Use a processing job to evaluate the model from the TrainingStep. If the output of the evaluation is True, a model is created and a Lambda function is invoked to deploy the model to a SageMaker Endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%writefile code/evaluate.py\n", + "\n", + "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n", + "import json\n", + "import logging\n", + "import pathlib\n", + "import pickle\n", + "import tarfile\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "logger.addHandler(logging.StreamHandler())\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " logger.debug(\"Starting evaluation.\")\n", + " model_path = \"/opt/ml/processing/model/model.tar.gz\"\n", + " with tarfile.open(model_path) as tar:\n", + " tar.extractall(path=\".\")\n", + "\n", + " logger.debug(\"Loading xgboost model.\")\n", + " model = pickle.load(open(\"xgboost-model\", \"rb\"))\n", + "\n", + " logger.debug(\"Reading test data.\")\n", + " test_path = \"/opt/ml/processing/test/test.csv\"\n", + " df = pd.read_csv(test_path, header=None)\n", + "\n", + " logger.debug(\"Reading test data.\")\n", + " y_test = df.iloc[:, 0].to_numpy()\n", + " df.drop(df.columns[0], axis=1, inplace=True)\n", + " X_test = xgboost.DMatrix(df.values)\n", + "\n", + " logger.info(\"Performing predictions against test data.\")\n", + " predictions = model.predict(X_test)\n", + "\n", + " logger.debug(\"Calculating mean squared error.\")\n", + " mse = mean_squared_error(y_test, predictions)\n", + " std = np.std(y_test - predictions)\n", + " report_dict = {\n", + " \"regression_metrics\": {\n", + " \"mse\": {\"value\": mse, \"standard_deviation\": std},\n", + " },\n", + " }\n", + "\n", + " output_dir = \"/opt/ml/processing/evaluation\"\n", + " pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n", + "\n", + " logger.info(\"Writing out evaluation report with mse: %f\", mse)\n", + " evaluation_path = f\"{output_dir}/evaluation.json\"\n", + " with open(evaluation_path, \"w\") as f:\n", + " f.write(json.dumps(report_dict))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# A ProcessingStep is used to evaluate the performance of the trained model.\n", + "# Based on the results of the evaluation, the model is created and deployed.\n", + "\n", + "script_eval = ScriptProcessor(\n", + " image_uri=image_uri,\n", + " command=[\"python3\"],\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=1,\n", + " base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "\n", + "evaluation_report = PropertyFile(\n", + " name=\"AbaloneEvaluationReport\",\n", + " output_name=\"evaluation\",\n", + " path=\"evaluation.json\",\n", + ")\n", + "\n", + "eval_args = script_eval.run(\n", + " inputs=[\n", + " ProcessingInput(\n", + " source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " destination=\"/opt/ml/processing/model\",\n", + " ),\n", + " ProcessingInput(\n", + " source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n", + " destination=\"/opt/ml/processing/test\",\n", + " ),\n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(\n", + " output_name=\"evaluation\",\n", + " source=\"/opt/ml/processing/evaluation\",\n", + " destination=f\"s3://{default_bucket}/{s3_prefix}/evaluation_report\",\n", + " ),\n", + " ],\n", + " code=\"code/evaluate.py\",\n", + ")\n", + "step_eval = ProcessingStep(\n", + " name=\"EvaluateAbaloneModel\",\n", + " step_args=eval_args,\n", + " property_files=[evaluation_report],\n", + " cache_config=cache_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Creating the final model object\n", + "\n", + "The model is created and the name of the model is provided to the Lambda function for deployment. The `CreateModelStep` dynamically assigns a name to the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Create Model\n", + "model = Model(\n", + " image_uri=image_uri,\n", + " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + " predictor_cls=XGBoostPredictor,\n", + ")\n", + "\n", + "step_create_model = ModelStep(\n", + " name=\"CreateModel\",\n", + " step_args=model.create(\"ml.m4.large\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up Lambda\n", + "\n", + "When defining the LambdaStep, the SageMaker Lambda helper class provides helper functions for creating the Lambda function. Users can either use the `lambda_func` argument to provide the function ARN to an already deployed Lambda function OR use the `Lambda` class to create a Lambda function by providing a script, function name and role for the Lambda function.\n", + "\n", + "When passing inputs to the Lambda, the `inputs` argument can be used and within the Lambda function's handler, the `event` argument can be used to retrieve the inputs.\n", + "\n", + "The dictionary response from the Lambda function is parsed through the `LambdaOutput` objects provided to the `outputs` argument. The `output_name` in `LambdaOutput` corresponds to the dictionary key in the Lambda's return dictionary." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define the Lambda function\n", + "\n", + "Users can choose the leverage the Lambda helper class to create a Lambda function and provide that function object to the LambdaStep. Alternatively, users can use a pre-deployed Lambda function and provide the function ARN to the `Lambda` helper class in the Lambda step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%writefile code/lambda_helper.py\n", + "\n", + "\"\"\"\n", + "This Lambda function creates an Endpoint Configuration and deploys a model to an Endpoint.\n", + "The name of the model to deploy is provided via the `event` argument\n", + "\"\"\"\n", + "\n", + "import json\n", + "import boto3\n", + "\n", + "\n", + "def lambda_handler(event, context):\n", + " \"\"\" \"\"\"\n", + " sm_client = boto3.client(\"sagemaker\")\n", + "\n", + " # The name of the model created in the Pipeline CreateModelStep\n", + " model_name = event[\"model_name\"]\n", + "\n", + " endpoint_config_name = event[\"endpoint_config_name\"]\n", + " endpoint_name = event[\"endpoint_name\"]\n", + "\n", + " create_endpoint_config_response = sm_client.create_endpoint_config(\n", + " EndpointConfigName=endpoint_config_name,\n", + " ProductionVariants=[\n", + " {\n", + " \"InstanceType\": \"ml.m4.xlarge\",\n", + " \"InitialVariantWeight\": 1,\n", + " \"InitialInstanceCount\": 1,\n", + " \"ModelName\": model_name,\n", + " \"VariantName\": \"AllTraffic\",\n", + " }\n", + " ],\n", + " )\n", + "\n", + " create_endpoint_response = sm_client.create_endpoint(\n", + " EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name\n", + " )\n", + "\n", + " return {\n", + " \"statusCode\": 200,\n", + " \"body\": json.dumps(\"Created Endpoint!\"),\n", + " \"other_key\": \"example_value\",\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setting up the custom IAM Role\n", + "\n", + "The Lambda function needs an IAM role that allows it to deploy a SageMaker Endpoint. The role ARN must be provided in the LambdaStep.\n", + "\n", + "The Lambda role should at minimum have policies to allow `sagemaker:CreateModel`, `sagemaker:CreateEndpointConfig`, `sagemaker:CreateEndpoint` in addition to the based Lambda execution policies.\n", + "\n", + "A helper function in `iam_helper.py` is available to create the Lambda function role. Please note that the role uses the Amazon managed policy - `SageMakerFullAccess`. This should be replaced with an IAM policy with least privileges as per AWS IAM best practices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from iam_helper import create_lambda_role\n", + "\n", + "lambda_role = create_lambda_role(\"lambda-deployment-role\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Custom Lambda Step\n", + "\n", + "current_time = time.strftime(\"%m-%d-%H-%M-%S\", time.localtime())\n", + "model_name = \"demo-lambda-model\" + current_time\n", + "endpoint_config_name = \"demo-lambda-deploy-endpoint-config-\" + current_time\n", + "endpoint_name = \"demo-lambda-deploy-endpoint-\" + current_time\n", + "\n", + "function_name = \"sagemaker-lambda-step-endpoint-deploy-\" + current_time\n", + "\n", + "# Lambda helper class can be used to create the Lambda function\n", + "func = Lambda(\n", + " function_name=function_name,\n", + " execution_role_arn=lambda_role,\n", + " script=\"code/lambda_helper.py\",\n", + " handler=\"lambda_helper.lambda_handler\",\n", + ")\n", + "\n", + "output_param_1 = LambdaOutput(output_name=\"statusCode\", output_type=LambdaOutputTypeEnum.String)\n", + "output_param_2 = LambdaOutput(output_name=\"body\", output_type=LambdaOutputTypeEnum.String)\n", + "output_param_3 = LambdaOutput(output_name=\"other_key\", output_type=LambdaOutputTypeEnum.String)\n", + "\n", + "step_deploy_lambda = LambdaStep(\n", + " name=\"LambdaStep\",\n", + " lambda_func=func,\n", + " inputs={\n", + " \"model_name\": step_create_model.properties.ModelName,\n", + " \"endpoint_config_name\": endpoint_config_name,\n", + " \"endpoint_name\": endpoint_name,\n", + " },\n", + " outputs=[output_param_1, output_param_2, output_param_3],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ConditionStep for evaluating model quality and branching execution.\n", + "# The `json_path` value is based on the `report_dict` variable in `evaluate.py`\n", + "\n", + "cond_lte = ConditionLessThanOrEqualTo(\n", + " left=JsonGet(\n", + " step_name=step_eval.name,\n", + " property_file=evaluation_report,\n", + " json_path=\"regression_metrics.mse.value\",\n", + " ),\n", + " right=6.0,\n", + ")\n", + "\n", + "step_cond = ConditionStep(\n", + " name=\"CheckMSEAbaloneEvaluation\",\n", + " conditions=[cond_lte],\n", + " if_steps=[step_create_model, step_deploy_lambda],\n", + " else_steps=[],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Use the same pipeline name across executions for cache usage.\n", + "\n", + "pipeline_name = \"lambda-step-pipeline\" + current_time\n", + "\n", + "pipeline = Pipeline(\n", + " name=pipeline_name,\n", + " parameters=[\n", + " processing_instance_count,\n", + " training_instance_type,\n", + " input_data,\n", + " model_approval_status,\n", + " ],\n", + " steps=[step_process, step_train, step_eval, step_cond],\n", + " sagemaker_session=pipeline_session,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Execute the Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "definition = json.loads(pipeline.definition())\n", + "definition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "pipeline.upsert(role_arn=role)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "execution = pipeline.start()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "execution.wait()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Create a SageMaker client\n", + "sm_client = sagemaker.Session().sagemaker_client\n", + "\n", + "# Wait for the endpoint to be in service\n", + "waiter = sm_client.get_waiter(\"endpoint_in_service\")\n", + "waiter.wait(EndpointName=endpoint_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean up resources\n", + "\n", + "Running the following cell will delete the following resources created in this notebook -\n", + "* SageMaker Model\n", + "* SageMaker Endpoint Configuration\n", + "* SageMaker Endpoint\n", + "* SageMaker Pipeline\n", + "* Lambda Function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Get the model name from the EndpointCofig. The CreateModelStep properties are not available\n", + "# outside the Pipeline execution context so `step_create_model.properties.ModelName`\n", + "# cannot be used while deleting the model.\n", + "\n", + "model_name = sm_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)[\n", + " \"ProductionVariants\"\n", + "][0][\"ModelName\"]\n", + "\n", + "# Delete the Model\n", + "sm_client.delete_model(ModelName=model_name)\n", + "\n", + "# Delete the EndpointConfig\n", + "sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n", + "\n", + "# Delete the Endpoint\n", + "sm_client.delete_endpoint(EndpointName=endpoint_name)\n", + "\n", + "# Delete the Lambda function\n", + "func.delete()\n", + "\n", + "# Delete the Pipeline\n", + "sm_client.delete_pipeline(PipelineName=pipeline_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-pipelines|tabular|lambda-step|sagemaker-pipelines-lambda-step.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-pipelines|tabular|lambda-step|sagemaker-pipelines-lambda-step.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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"python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "metadata": { + "interpreter": { + "hash": "ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb b/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb new file mode 100644 index 0000000000..4523a10420 --- /dev/null +++ b/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb @@ -0,0 +1,684 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Iris Training and Prediction with Sagemaker Scikit-learn\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "This tutorial shows you how to use [Scikit-learn](https://scikit-learn.org/stable/) with SageMaker by utilizing the pre-built container. Scikit-learn is a popular Python machine learning framework. It includes a number of different algorithms for classification, regression, clustering, dimensionality reduction, and data/feature pre-processing. \n", + "\n", + "The [sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk) module makes it easy to take existing scikit-learn code, which we show by training a model on the Iris dataset and generating a set of predictions. For more information about the Scikit-learn container, see the [sagemaker-scikit-learn-containers](https://github.com/aws/sagemaker-scikit-learn-container) repository and the [sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk) repository.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 15 minutes to run.\n", + "\n", + "## Contents\n", + "* [Upload the data for training](#upload_data)\n", + "* [Create a Scikit-learn script to train with](#create_sklearn_script)\n", + "* [Create the SageMaker Scikit Estimator](#create_sklearn_estimator)\n", + "* [Train the SKLearn Estimator on the Iris data](#train_sklearn)\n", + "* [Use the trained model to make inference requests](#inference)\n", + " * [Deploy the model](#deploy)\n", + " * [Choose some data and use it for a prediction](#prediction_request)\n", + " * [Endpoint cleanup](#endpoint_cleanup)\n", + "* [Batch Transform](#batch_transform)\n", + " * [Prepare Input Data](#prepare_input_data)\n", + " * [Run Transform Job](#run_transform_job)\n", + " * [Check Output Data](#check_output_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%pip install -U sagemaker>=2.15" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "First, let's create our Sagemaker session and role, and create a S3 prefix to use for the notebook example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# S3 prefix\n", + "prefix = \"DEMO-scikit-iris\"\n", + "\n", + "import sagemaker\n", + "from sagemaker import get_execution_role\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "region = sagemaker_session.boto_region_name\n", + "role = get_execution_role()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Upload the data for training \n", + "\n", + "When training large models with huge amounts of data, you may use big data tools like Amazon Athena, AWS Glue, or Amazon EMR to process your data backed by S3. For the purposes of this example, we're using a sample of the classic [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris). We load the dataset, write it locally, then upload it to S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import boto3\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "\n", + "os.makedirs(\"./data\", exist_ok=True)\n", + "\n", + "s3_client = boto3.client(\"s3\")\n", + "s3_client.download_file(\n", + " f\"sagemaker-example-files-prod-{region}\", \"datasets/tabular/iris/iris.data\", \"./data/iris.csv\"\n", + ")\n", + "\n", + "df_iris = pd.read_csv(\"./data/iris.csv\", header=None)\n", + "df_iris[4] = df_iris[4].map({\"Iris-setosa\": 0, \"Iris-versicolor\": 1, \"Iris-virginica\": 2})\n", + "iris = df_iris[[4, 0, 1, 2, 3]].to_numpy()\n", + "np.savetxt(\"./data/iris.csv\", iris, delimiter=\",\", fmt=\"%1.1f, %1.3f, %1.3f, %1.3f, %1.3f\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Once we have the data locally, we can use use the tools provided by the SageMaker Python SDK to upload the data to a default bucket. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "WORK_DIRECTORY = \"data\"\n", + "\n", + "train_input = sagemaker_session.upload_data(\n", + " WORK_DIRECTORY, key_prefix=\"{}/{}\".format(prefix, WORK_DIRECTORY)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Create a Scikit-learn script for training \n", + "SageMaker can run a scikit-learn script using the `SKLearn` estimator. When run on SageMaker, a number of helpful environment variables are available to access properties of the training environment, such as:\n", + "\n", + "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to. Any artifacts saved in this folder are uploaded to S3 for model hosting after the training job completes.\n", + "* `SM_OUTPUT_DIR`: A string representing the file system path to write output artifacts to. Output artifacts may include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed and uploaded to S3 to the same S3 prefix as the model artifacts.\n", + "\n", + "Supposing two input channels, 'train' and 'test', were used in the call to the `SKLearn` estimator's `fit()` method, the following environment variables are set, following the format `SM_CHANNEL_[channel_name]`:\n", + "\n", + "* `SM_CHANNEL_TRAIN`: A string representing the path to the directory containing data in the 'train' channel.\n", + "* `SM_CHANNEL_TEST`: Same as above, but for the 'test' channel.\n", + "\n", + "A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to the `model_dir` so that it can be hosted later. Hyperparameters are passed to your script as arguments and can be retrieved with an `argparse.ArgumentParser` instance. For example, the script that we run in this notebook is below:\n", + "\n", + "```python\n", + "from __future__ import print_function\n", + "\n", + "import argparse\n", + "import joblib\n", + "import os\n", + "import pandas as pd\n", + "\n", + "from sklearn import tree\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " parser = argparse.ArgumentParser()\n", + "\n", + " # Hyperparameters are described here. In this simple example we are just including one hyperparameter.\n", + " parser.add_argument('--max_leaf_nodes', type=int, default=-1)\n", + "\n", + " # Sagemaker specific arguments. Defaults are set in the environment variables.\n", + " parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])\n", + " parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n", + " parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n", + "\n", + " args = parser.parse_args()\n", + "\n", + " # Take the set of files and read them all into a single pandas dataframe\n", + " input_files = [ os.path.join(args.train, file) for file in os.listdir(args.train) ]\n", + " if len(input_files) == 0:\n", + " raise ValueError(('There are no files in {}.\\n' +\n", + " 'This usually indicates that the channel ({}) was incorrectly specified,\\n' +\n", + " 'the data specification in S3 was incorrectly specified or the role specified\\n' +\n", + " 'does not have permission to access the data.').format(args.train, \"train\"))\n", + " raw_data = [ pd.read_csv(file, header=None, engine=\"python\") for file in input_files ]\n", + " train_data = pd.concat(raw_data)\n", + "\n", + " # labels are in the first column\n", + " train_y = train_data.iloc[:, 0]\n", + " train_X = train_data.iloc[:, 1:]\n", + "\n", + " # Here we support a single hyperparameter, 'max_leaf_nodes'. Note that you can add as many\n", + " # as your training my require in the ArgumentParser above.\n", + " max_leaf_nodes = args.max_leaf_nodes\n", + "\n", + " # Now use scikit-learn's decision tree classifier to train the model.\n", + " clf = tree.DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)\n", + " clf = clf.fit(train_X, train_y)\n", + "\n", + " # Print the coefficients of the trained classifier, and save the coefficients\n", + " joblib.dump(clf, os.path.join(args.model_dir, \"model.joblib\"))\n", + "\n", + "\n", + "def model_fn(model_dir):\n", + " \"\"\"Deserialized and return fitted model\n", + " \n", + " Note that this should have the same name as the serialized model in the main method\n", + " \"\"\"\n", + " clf = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n", + " return clf\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Because the Scikit-learn container imports your training script, you should always put your training code in a main guard `(if __name__=='__main__':)` so that the container does not inadvertently run your training code at the wrong point in execution.\n", + "\n", + "For more information about training environment variables, please visit https://github.com/aws/sagemaker-containers." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Create a SageMaker SKLearn Estimator \n", + "\n", + "To run our Scikit-learn training script on SageMaker, we construct a `sagemaker.sklearn.estimator.sklearn` estimator, which accepts several constructor arguments:\n", + "\n", + "* __entry_point__: The path to the Python script SageMaker runs for training and prediction.\n", + "* __role__: The IAM role ARN.\n", + "* __instance_type__ *(optional)*: The type of SageMaker instances for training. __Note__: Because Scikit-learn does not natively support GPU training, SageMaker Scikit-learn does not currently support training on GPU instance types.\n", + "* __sagemaker_session__ *(optional)*: The session used to train on SageMaker.\n", + "* __hyperparameters__ *(optional)*: A dictionary passed to the train function as hyperparameters.\n", + "\n", + "To see the code for the SKLearn Estimator, see: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/sklearn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sagemaker.sklearn.estimator import SKLearn\n", + "\n", + "FRAMEWORK_VERSION = \"1.2-1\"\n", + "script_path = \"scikit_learn_iris.py\"\n", + "\n", + "sklearn = SKLearn(\n", + " entry_point=script_path,\n", + " framework_version=FRAMEWORK_VERSION,\n", + " instance_type=\"ml.c4.xlarge\",\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " hyperparameters={\"max_leaf_nodes\": 30},\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Train SKLearn Estimator on Iris data \n", + "Training is straightforward, just call `fit()` on the Estimator! This starts a SageMaker training job that downloads the data, invokes our scikit-learn code (in the provided script file), and saves any model artifacts that the script creates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "sklearn.fit({\"train\": train_input})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Use the trained model to make inference requests \n", + "\n", + "### Deploy the model \n", + "\n", + "Deploying the model to SageMaker hosting just requires a `deploy()` call on the fitted model. This call takes an instance count and instance type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "predictor = sklearn.deploy(initial_instance_count=1, instance_type=\"ml.m5.xlarge\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Choose some data and use it for a prediction \n", + "\n", + "We extract some data we used for training and make predictions on it. This is not a recommended statistical practice, but it demonstrates how to run inference using the deployed endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import itertools\n", + "import pandas as pd\n", + "\n", + "shape = pd.read_csv(\"data/iris.csv\", header=None)\n", + "\n", + "a = [50 * i for i in range(3)]\n", + "b = [40 + i for i in range(10)]\n", + "indices = [i + j for i, j in itertools.product(a, b)]\n", + "\n", + "test_data = shape.iloc[indices[:-1]]\n", + "test_X = test_data.iloc[:, 1:]\n", + "test_y = test_data.iloc[:, 0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "To make a prediction, call `predict()` on the predictor returned from `deploy()`, passing the data to do predictions on. The output from the endpoint returns a numerical representation of the classification prediction; in the original dataset, these are three flower category names, but in this example the labels are numerical. We can compare against the original label that we parsed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "print(predictor.predict(test_X.values))\n", + "print(test_y.values)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Endpoint cleanup \n", + "\n", + "When you're done with the endpoint, delete it to release the resources and avoid incurring additional cost." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "predictor.delete_endpoint()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Batch Transform \n", + "We can also use the trained model for asynchronous batch inference on S3 data using SageMaker Batch Transform." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Define an SKLearn Transformer from the trained SKLearn Estimator\n", + "transformer = sklearn.transformer(instance_count=1, instance_type=\"ml.m5.xlarge\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Prepare Input Data \n", + "We extract 10 random samples of 100 rows from the training data, split the features (X) from the labels (Y), and upload the input data to a given location in S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%%bash\n", + "# Randomly sample the iris dataset 10 times, then split X and Y\n", + "mkdir -p batch_data/XY batch_data/X batch_data/Y\n", + "for i in {0..9}; do\n", + " cat data/iris.csv | shuf -n 100 > batch_data/XY/iris_sample_${i}.csv\n", + " cat batch_data/XY/iris_sample_${i}.csv | cut -d',' -f2- > batch_data/X/iris_sample_X_${i}.csv\n", + " cat batch_data/XY/iris_sample_${i}.csv | cut -d',' -f1 > batch_data/Y/iris_sample_Y_${i}.csv\n", + "done" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Upload input data from local file system to S3\n", + "batch_input_s3 = sagemaker_session.upload_data(\"batch_data/X\", key_prefix=prefix + \"/batch_input\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Run Transform Job \n", + "Using the Transformer, run a transform job on the S3 input data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Start a transform job and wait for it to finish\n", + "transformer.transform(batch_input_s3, content_type=\"text/csv\")\n", + "print(\"Waiting for transform job: \" + transformer.latest_transform_job.job_name)\n", + "transformer.wait()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Check Output Data \n", + "After the transform job has completed, download the output data from S3. For each file \"f\" in the input data, we have a corresponding file \"f.out\" containing the predicted labels from each input row. We can compare the predicted labels to the true labels saved earlier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Download the output data from S3 to local file system\n", + "batch_output = transformer.output_path\n", + "!mkdir -p batch_data/output\n", + "!aws s3 cp --recursive $batch_output/ batch_data/output/\n", + "# Head to see what the batch output looks like\n", + "!head batch_data/output/*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%%bash\n", + "# For each sample file, compare the predicted labels from batch output to the true labels\n", + "for i in {1..9}; do\n", + " diff -s batch_data/Y/iris_sample_Y_${i}.csv \\\n", + " <(cat batch_data/output/iris_sample_X_${i}.csv.out | sed 's/[[\"]//g' | sed 's/, \\|]/\\n/g') \\\n", + " | sed \"s/\\/dev\\/fd\\/63/batch_data\\/output\\/iris_sample_X_${i}.csv.out/\"\n", + "done" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-python-sdk|scikit_learn_iris|scikit_learn_estimator_example_with_batch_transform.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "Amazon SageMaker is a service to accelerate the entire machine learning lifecycle. It includes components for building, training and deploying machine learning models. Each SageMaker component is modular, so you're welcome to only use the features needed for your use case. One of the most popular features of SageMaker is model hosting. Using SageMaker hosting, you can deploy your model as a scalable, highly available, multi-process API endpoint with a few lines of code. Read more at [Deploy a Model in Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html). In this notebook, we demonstrate how to host a pretrained BERT model in Amazon SageMaker to extract embeddings from text.\n", + "\n", + "SageMaker provides prebuilt containers that can be used for training, hosting, or data processing. The inference containers include a web serving stack, so you don't need to install and configure one. We use the SageMaker PyTorch container, but you may use the TensorFlow container, or bring your own container if needed. See all containers at [AWS Deep Learning Containers](https://github.com/aws/deep-learning-containers).\n", + "\n", + "This notebook walks you through how to deploy a pretrained Hugging Face model as a scalable, highly available, production-ready API.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 5 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Retrieve Model Artifacts](#Retrieve-Model-Artifacts)\n", + "1. [Write the Inference Script](#Write-the-Inference-Script)\n", + "1. [Package Model](#Package-Model)\n", + "1. [Deploy Model](#Deploy-Model)\n", + "1. [Get Predictions](#Get-Predictions)\n", + "1. [Conclusion](#Conclusion)\n", + "1. [Cleanup](#Cleanup)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve Model Artifacts\n", + "\n", + "First we download the model artifacts for the pretrained BERT model. BERT is a popular natural language processing (NLP) model that extracts meaning and context from text. You can read the original paper, [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "!pip install transformers==3.3.1 sagemaker==2.15.0 --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from transformers import BertTokenizer, BertModel\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n", + "model = BertModel.from_pretrained(\"bert-base-uncased\")\n", + "\n", + "model_path = \"model/\"\n", + "code_path = \"code/\"\n", + "\n", + "if not os.path.exists(model_path):\n", + " os.mkdir(model_path)\n", + "\n", + "model.save_pretrained(save_directory=model_path)\n", + "tokenizer.save_pretrained(save_directory=model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the Inference Script\n", + "\n", + "Since we are bringing a model to SageMaker, we must create an inference script. The script runs inside our PyTorch container. Our script should include a function for model loading, and optionally functions generating predictions, and input/output processing. The PyTorch container provides default implementations for generating a prediction and input/output processing. By including these functions in your script you are overriding the default functions. You can find additional details at [Serve a PyTorch Model](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#serve-a-pytorch-model).\n", + "\n", + "The next cell shows our inference script, whcich uses the [Transformers library from HuggingFace](https://huggingface.co/transformers/). This library is not installed in the container by default, so we add it in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pygmentize code/inference_code.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Package Model\n", + "\n", + "For hosting, SageMaker requires that the deployment package be structured in a compatible format. It expects all files to be packaged in a tar archive named \"model.tar.gz\" with gzip compression. To install additional libraries at container startup, we can add a requirements.txt file that specifies the libraries to be installed using [pip](https://pypi.org/project/pip/). Read more at [Using Third-Party Libraries](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#using-third-party-libraries). Within the archive, the PyTorch container expects all inference code and requirements.txt file to be inside the code/ directory. See the [Model Directory Structure](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#model-directory-structure) guide for a thorough explanation of the required directory structure. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tarfile\n", + "\n", + "zipped_model_path = os.path.join(model_path, \"model.tar.gz\")\n", + "\n", + "with tarfile.open(zipped_model_path, \"w:gz\") as tar:\n", + " tar.add(model_path)\n", + " tar.add(code_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy Model\n", + "\n", + "Now that we have our deployment package, we can use the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/index.html) to deploy our API endpoint with two lines of code. We need to specify an IAM role for the SageMaker endpoint to use. Minimally, it needs read access to the default SageMaker bucket (usually named `s3://sagemaker-{region}-{your account ID}`) so it can read the deployment package. When we call `deploy()`, the SDK saves our deployment archive to S3 for the SageMaker endpoint to use. We use the helper function [get_execution_role()](https://sagemaker.readthedocs.io/en/stable/api/utility/session.html?highlight=get_execution_role#sagemaker.session.get_execution_role) to retrieve our current IAM role so we can pass it to the SageMaker endpoint. Minimally it requires read access to the model artifacts in S3 and the [ECR repository](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) where the container image is stored by AWS.\n", + "\n", + "\n", + "You may notice that we specify our PyTorch version and Python version when creating the PyTorchModel object. The SageMaker SDK uses these parameters to determine which PyTorch container to use. \n", + "\n", + "We use an m5.xlarge instance for our endpoint to ensure we have sufficient memory to serve our model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.pytorch import PyTorchModel\n", + "from sagemaker import get_execution_role\n", + "import time\n", + "\n", + "endpoint_name = \"bert-base-\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n", + "\n", + "model = PyTorchModel(\n", + " entry_point=\"inference_code.py\",\n", + " model_data=zipped_model_path,\n", + " role=get_execution_role(),\n", + " framework_version=\"1.5\",\n", + " py_version=\"py3\",\n", + ")\n", + "\n", + "predictor = model.deploy(\n", + " initial_instance_count=1, instance_type=\"ml.m5.xlarge\", endpoint_name=endpoint_name\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get Predictions\n", + "\n", + "Now that our API endpoint is deployed, we send it text to get predictions from our BERT model. You can use the SageMaker SDK or the [InvokeEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html) method of the SageMaker Runtime API to invoke the endpoint. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sagemaker\n", + "\n", + "sm = sagemaker.Session().sagemaker_runtime_client\n", + "\n", + "prompt = \"The best part of Amazon SageMaker is that it makes machine learning easy.\"\n", + "\n", + "response = sm.invoke_endpoint(\n", + " EndpointName=endpoint_name, Body=prompt.encode(encoding=\"UTF-8\"), ContentType=\"text/csv\"\n", + ")\n", + "\n", + "response[\"Body\"].read()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "Delete the model and endpoint to release resources and stop incurring costs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictor.delete_model()\n", + "predictor.delete_endpoint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "We have successfully created a scalable, highly available, RESTful API that is backed by a BERT model! It can be used for downstream NLP tasks like text classification. If you are still interested in learning more, check out some of the more advanced features of SageMaker hosting, like [Monitor models for data and model quality, bias, and explainability](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) to detect concept drift, [Automatically Scale Amazon SageMaker Models](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html) to dynamically adjust the number of instances, or [Give SageMaker Hosted Endpoints Access to Resources in Your Amazon VPC](https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) to control network access to/from your endpoint.\n", + "\n", + "You can also read the blog [Deploy machine learning models to Amazon SageMaker using the ezsmdeploy Python package and a few lines of code](https://aws.amazon.com/blogs/opensource/deploy-machine-learning-models-to-amazon-sagemaker-using-the-ezsmdeploy-python-package-and-a-few-lines-of-code/). The ezsmdeploy package automates most of this process." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-script-mode|pytorch_bert|deploy_bert.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (PyTorch 1.10 Python 3.8 CPU Optimized)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.10-cpu-py38" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sagemaker-script-mode/sklearn/sklearn_byom.ipynb b/sagemaker-script-mode/sklearn/sklearn_byom.ipynb new file mode 100644 index 0000000000..7d63f2d915 --- /dev/null +++ b/sagemaker-script-mode/sklearn/sklearn_byom.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "e950fa8e", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Train a SKLearn Model using Script Mode\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0abdc17b", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "90e7cac6", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "The aim of this notebook is to demonstrate how to train and deploy a scikit-learn model in Amazon SageMaker. The method used is called Script Mode, in which we write a script to train our model and submit it to the SageMaker Python SDK. For more information, feel free to read [Using Scikit-learn with the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/using_sklearn.html).\n", + "\n", + "## Runtime\n", + "This notebook takes approximately 15 minutes to run.\n", + "\n", + "## Contents\n", + "1. [Download data](#Download-data)\n", + "1. [Prepare data](#Prepare-data)\n", + "1. [Train model](#Train-model)\n", + "1. [Deploy and test endpoint](#Deploy-and-test-endpoint)\n", + "1. [Cleanup](#Cleanup)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a16db1a6", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Download data \n", + "Download the [Iris Data Set](https://archive.ics.uci.edu/ml/datasets/iris), which is the data used to trained the model in this demo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2d5c27c", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!pip install -U sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a670c242", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import boto3\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "s3 = boto3.client(\"s3\")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{boto3.session.Session().region_name}\",\n", + " \"datasets/tabular/iris/iris.data\",\n", + " \"iris.data\",\n", + ")\n", + "\n", + "df = pd.read_csv(\n", + " \"iris.data\", header=None, names=[\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\", \"class\"]\n", + ")\n", + "df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7c03b3d2", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Prepare data\n", + "Next, we prepare the data for training by first converting the labels from string to integers. Then we split the data into a train dataset (80% of the data) and test dataset (the remaining 20% of the data) before saving them into CSV files. Then, these files are uploaded to S3 where the SageMaker SDK can access and use them to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72748b04", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Convert the three classes from strings to integers in {0,1,2}\n", + "df[\"class_cat\"] = df[\"class\"].astype(\"category\").cat.codes\n", + "categories_map = dict(enumerate(df[\"class\"].astype(\"category\").cat.categories))\n", + "print(categories_map)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb5ea6cf", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Split the data into 80-20 train-test split\n", + "num_samples = df.shape[0]\n", + "split = round(num_samples * 0.8)\n", + "train = df.iloc[:split, :]\n", + "test = df.iloc[split:, :]\n", + "print(\"{} train, {} test\".format(split, num_samples - split))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48770a6b", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Write train and test CSV files\n", + "train.to_csv(\"train.csv\", index=False)\n", + "test.to_csv(\"test.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba40dab3", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Create a sagemaker session to upload data to S3\n", + "import sagemaker\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "\n", + "# Upload data to default S3 bucket\n", + "prefix = \"DEMO-sklearn-iris\"\n", + "training_input_path = sagemaker_session.upload_data(\"train.csv\", key_prefix=prefix + \"/training\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9d52c534", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Train model\n", + "The model is trained using the SageMaker SDK's Estimator class. Firstly, get the execution role for training. This role allows us to access the S3 bucket in the last step, where the train and test data set is located." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7cbdad2", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Use the current execution role for training. It needs access to S3\n", + "role = sagemaker.get_execution_role()\n", + "print(role)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "10cdcfb6", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Then, it is time to define the SageMaker SDK Estimator class. We use an Estimator class specifically desgined to train scikit-learn models called `SKLearn`. In this estimator, we define the following parameters:\n", + "1. The script that we want to use to train the model (i.e. `entry_point`). This is the heart of the Script Mode method. Additionally, set the `script_mode` parameter to `True`.\n", + "1. The role which allows us access to the S3 bucket containing the train and test data set (i.e. `role`)\n", + "1. How many instances we want to use in training (i.e. `instance_count`) and what type of instance we want to use in training (i.e. `instance_type`)\n", + "1. Which version of scikit-learn to use (i.e. `framework_version`)\n", + "1. Training hyperparameters (i.e. `hyperparameters`)\n", + "\n", + "After setting these parameters, the `fit` function is invoked to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac14dcb7", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Docs: https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html\n", + "\n", + "from sagemaker.sklearn import SKLearn\n", + "\n", + "sk_estimator = SKLearn(\n", + " entry_point=\"train.py\",\n", + " role=role,\n", + " instance_count=1,\n", + " instance_type=\"ml.c5.xlarge\",\n", + " py_version=\"py3\",\n", + " framework_version=\"1.2-1\",\n", + " script_mode=True,\n", + " hyperparameters={\"estimators\": 20},\n", + ")\n", + "\n", + "# Train the estimator\n", + "sk_estimator.fit({\"train\": training_input_path})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "3813b62c", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Deploy and test endpoint\n", + "After training the model, it is time to deploy it as an endpoint. To do so, we invoke the `deploy` function within the scikit-learn estimator. As shown in the code below, one can define the number of instances (i.e. `initial_instance_count`) and instance type (i.e. `instance_type`) used to deploy the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06aace5c", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import time\n", + "\n", + "sk_endpoint_name = \"sklearn-rf-model\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n", + "sk_predictor = sk_estimator.deploy(\n", + " initial_instance_count=1, instance_type=\"ml.m5.large\", endpoint_name=sk_endpoint_name\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "bbc747e1", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "After the endpoint has been completely deployed, it can be invoked using the [SageMaker Runtime Client](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime.html) (which is the method used in the code cell below) or [Scikit Learn Predictor](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html#scikit-learn-predictor). If you plan to use the latter method, make sure to use a [Serializer](https://sagemaker.readthedocs.io/en/stable/api/inference/serializers.html) to serialize your data properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85491166", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "client = sagemaker_session.sagemaker_runtime_client\n", + "\n", + "request_body = {\"Input\": [[9.0, 3571, 1976, 0.525]]}\n", + "data = json.loads(json.dumps(request_body))\n", + "payload = json.dumps(data)\n", + "\n", + "response = client.invoke_endpoint(\n", + " EndpointName=sk_endpoint_name, ContentType=\"application/json\", Body=payload\n", + ")\n", + "\n", + "result = json.loads(response[\"Body\"].read().decode())[\"Output\"]\n", + "print(\"Predicted class category {} ({})\".format(result, categories_map[result]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "90f26921", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Cleanup\n", + "If the model and endpoint are no longer in use, they should be deleted to save costs and free up resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5a3a83", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "sk_predictor.delete_model()\n", + "sk_predictor.delete_endpoint()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "454a7ca7", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker-script-mode|sklearn|sklearn_byom.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Data Science 3.0)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/archived/notebooks/language-modeling/language-modeling.ipynb b/sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/language-modeling.ipynb similarity index 100% rename from archived/notebooks/language-modeling/language-modeling.ipynb rename to sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/language-modeling.ipynb diff --git a/archived/notebooks/language-modeling/scripts/requirements.txt b/sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/scripts/requirements.txt similarity index 100% rename from archived/notebooks/language-modeling/scripts/requirements.txt rename to sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/scripts/requirements.txt diff --git a/archived/notebooks/language-modeling/scripts/run_clm.py b/sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/scripts/run_clm.py similarity index 100% rename from archived/notebooks/language-modeling/scripts/run_clm.py rename to sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/scripts/run_clm.py diff --git a/archived/notebooks/language-modeling/scripts/run_mlm.py b/sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/scripts/run_mlm.py similarity index 100% rename from archived/notebooks/language-modeling/scripts/run_mlm.py rename to sagemaker-training-compiler/huggingface/tensorflow_multiple_gpu_single_node/scripts/run_mlm.py diff --git a/archived/notebooks/vision-transformer/scripts/vit.py b/sagemaker-training-compiler/tensorflow/multiple_gpu_single_node/scripts/vit.py similarity index 100% rename from archived/notebooks/vision-transformer/scripts/vit.py rename to sagemaker-training-compiler/tensorflow/multiple_gpu_single_node/scripts/vit.py diff --git a/archived/notebooks/vision-transformer/vision-transformer.ipynb b/sagemaker-training-compiler/tensorflow/multiple_gpu_single_node/vision-transformer.ipynb similarity index 100% rename from archived/notebooks/vision-transformer/vision-transformer.ipynb rename to sagemaker-training-compiler/tensorflow/multiple_gpu_single_node/vision-transformer.ipynb diff --git a/archived/notebooks/tf-dali-ensemble-cv/images/dali.png b/sagemaker-triton/ensemble/dali-tf-inception/images/dali.png similarity index 100% rename from archived/notebooks/tf-dali-ensemble-cv/images/dali.png rename to sagemaker-triton/ensemble/dali-tf-inception/images/dali.png diff --git a/archived/notebooks/tf-dali-ensemble-cv/images/model-repo.png b/sagemaker-triton/ensemble/dali-tf-inception/images/model-repo.png similarity index 100% rename from archived/notebooks/tf-dali-ensemble-cv/images/model-repo.png rename to sagemaker-triton/ensemble/dali-tf-inception/images/model-repo.png diff --git a/archived/notebooks/tf-dali-ensemble-cv/images/triton-ensemble.png b/sagemaker-triton/ensemble/dali-tf-inception/images/triton-ensemble.png similarity index 100% rename from archived/notebooks/tf-dali-ensemble-cv/images/triton-ensemble.png rename to sagemaker-triton/ensemble/dali-tf-inception/images/triton-ensemble.png diff --git a/archived/notebooks/tf-dali-ensemble-cv/inception_labels.txt b/sagemaker-triton/ensemble/dali-tf-inception/inception_labels.txt similarity index 100% rename from archived/notebooks/tf-dali-ensemble-cv/inception_labels.txt rename to sagemaker-triton/ensemble/dali-tf-inception/inception_labels.txt diff --git a/archived/notebooks/tf-dali-ensemble-cv/tf-dali-ensemble-cv.ipynb b/sagemaker-triton/ensemble/dali-tf-inception/tf-dali-ensemble-cv.ipynb similarity index 100% rename from archived/notebooks/tf-dali-ensemble-cv/tf-dali-ensemble-cv.ipynb rename to sagemaker-triton/ensemble/dali-tf-inception/tf-dali-ensemble-cv.ipynb diff --git a/archived/notebooks/jit_trace/Triton_CPU_JIT_MME.ipynb b/sagemaker-triton/resnet50/jit_trace/Triton_CPU_JIT_MME.ipynb similarity index 100% rename from archived/notebooks/jit_trace/Triton_CPU_JIT_MME.ipynb rename to sagemaker-triton/resnet50/jit_trace/Triton_CPU_JIT_MME.ipynb diff --git a/archived/notebooks/jit_trace/Triton_JIT_MME_sample.ipynb b/sagemaker-triton/resnet50/jit_trace/Triton_JIT_MME_sample.ipynb similarity index 100% rename from archived/notebooks/jit_trace/Triton_JIT_MME_sample.ipynb rename to sagemaker-triton/resnet50/jit_trace/Triton_JIT_MME_sample.ipynb diff --git a/archived/notebooks/jit_trace/image3.jpg b/sagemaker-triton/resnet50/jit_trace/image3.jpg similarity index 100% rename from archived/notebooks/jit_trace/image3.jpg rename to sagemaker-triton/resnet50/jit_trace/image3.jpg diff --git a/archived/notebooks/jit_trace/shiba_inu_dog.jpg b/sagemaker-triton/resnet50/jit_trace/shiba_inu_dog.jpg similarity index 100% rename from archived/notebooks/jit_trace/shiba_inu_dog.jpg rename to sagemaker-triton/resnet50/jit_trace/shiba_inu_dog.jpg diff --git a/sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb b/sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb new file mode 100644 index 0000000000..606743e2a9 --- /dev/null +++ b/sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb @@ -0,0 +1,2290 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "8c8a3cea", + "metadata": { + "papermill": { + "duration": 0.009489, + "end_time": "2021-06-03T00:10:10.266437", + "exception": false, + "start_time": "2021-06-03T00:10:10.256948", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Use SageMaker Batch Transform for PyTorch Batch Inference\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ac52b806", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_batch_transform|pytorch_mnist_batch_transform|pytorch-mnist-batch-transform.ipynb)\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ea2e8bde", + "metadata": { + "papermill": { + "duration": 0.009489, + "end_time": "2021-06-03T00:10:10.266437", + "exception": false, + "start_time": "2021-06-03T00:10:10.256948", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In this notebook, we examine how to do a Batch Transform task with PyTorch in Amazon SageMaker. \n", + "\n", + "First, an image classification model is built on the MNIST dataset. Then, we demonstrate batch transform by using the SageMaker Python SDK PyTorch framework with different configurations:\n", + "- `data_type=S3Prefix`: uses all objects that match the specified S3 prefix for batch inference.\n", + "- `data_type=ManifestFile`: a manifest file contains a list of object keys to use in batch inference.\n", + "- `instance_count>1`: distributes the batch inference dataset to multiple inference instances.\n", + "\n", + "For batch transform in TensorFlow in Amazon SageMaker, you can follow other Jupyter notebooks in the [sagemaker_batch_transform](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_batch_transform) directory.\n", + "\n", + "### Runtime\n", + "\n", + "This notebook takes approximately 15 minutes to run.\n", + "\n", + "### Contents\n", + "\n", + "1. [Setup](#Setup)\n", + "1. [Model training](#Model-training)\n", + "1. [Prepare batch inference data](#Prepare-batch-inference-data)\n", + "1. [Create model transformer](#Create-model-transformer)\n", + "1. [Batch inference](#Batch-inference)\n", + "1. [Look at all transform jobs](#Look-at-all-transform-jobs)\n", + "1. [Conclusion](#Conclusion)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cb8aa488", + "metadata": { + "papermill": { + "duration": 0.009319, + "end_time": "2021-06-03T00:10:10.285106", + "exception": false, + "start_time": "2021-06-03T00:10:10.275787", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Setup\n", + "We'll begin with some necessary installs and imports, and get an Amazon SageMaker session to help perform certain tasks, as well as an IAM role with the necessary permissions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "347fb3de", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install nvidia-ml-py3\n", + "!yes | pip uninstall torchvision\n", + "!pip install torchvision" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53e1a695", + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-03T00:10:10.310480Z", + "iopub.status.busy": "2021-06-03T00:10:10.309977Z", + "iopub.status.idle": "2021-06-03T00:10:11.972019Z", + "shell.execute_reply": "2021-06-03T00:10:11.971547Z" + }, + "papermill": { + "duration": 1.677667, + "end_time": "2021-06-03T00:10:11.972131", + "exception": false, + "start_time": "2021-06-03T00:10:10.294464", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "from os import listdir\n", + "from os.path import isfile, join\n", + "from shutil import copyfile\n", + "import sagemaker\n", + "from sagemaker.pytorch import PyTorchModel\n", + "from sagemaker import get_execution_role\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "region = sagemaker_session.boto_region_name\n", + "role = get_execution_role()\n", + "\n", + "bucket = sagemaker_session.default_bucket()\n", + "prefix = \"sagemaker/DEMO-pytorch-batch-inference-script\"\n", + "print(\"Bucket: {}\".format(bucket))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1df34f4f", + "metadata": { + "papermill": { + "duration": 0.009748, + "end_time": "2021-06-03T00:10:11.992188", + "exception": false, + "start_time": "2021-06-03T00:10:11.982440", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Model training" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2e50d7ed", + "metadata": { + "papermill": { + "duration": 0.009924, + "end_time": "2021-06-03T00:10:12.012090", + "exception": false, + "start_time": "2021-06-03T00:10:12.002166", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Since the main purpose of this notebook is to demonstrate SageMaker PyTorch batch transform, we reuse a SageMaker Python SDK [PyTorch MNIST example](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/pytorch_mnist) to train a PyTorch model. It takes around 7 minutes to finish the training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfa3102c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-03T00:10:12.038135Z", + "iopub.status.busy": "2021-06-03T00:10:12.037362Z", + "iopub.status.idle": "2021-06-03T00:15:42.451109Z", + "shell.execute_reply": "2021-06-03T00:15:42.449969Z" + }, + "papermill": { + "duration": 330.429296, + "end_time": "2021-06-03T00:15:42.451328", + "exception": true, + "start_time": "2021-06-03T00:10:12.022032", + "status": "failed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from torchvision.datasets import MNIST\n", + "from torchvision import transforms\n", + "\n", + "local_dir = \"data\"\n", + "MNIST.mirrors = [\n", + " f\"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/\"\n", + "]\n", + "MNIST(\n", + " local_dir,\n", + " download=True,\n", + " transform=transforms.Compose(\n", + " [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]\n", + " ),\n", + ")\n", + "\n", + "\n", + "inputs = sagemaker_session.upload_data(path=local_dir, bucket=bucket, key_prefix=prefix)\n", + "print(\"input spec (in this case, just an S3 path): {}\".format(inputs))\n", + "\n", + "from sagemaker.pytorch import PyTorch\n", + "\n", + "estimator = PyTorch(\n", + " entry_point=\"model-script/mnist.py\",\n", + " role=role,\n", + " framework_version=\"1.8.0\",\n", + " py_version=\"py3\",\n", + " instance_count=3,\n", + " instance_type=\"ml.c5.2xlarge\",\n", + " hyperparameters={\n", + " \"epochs\": 1,\n", + " \"backend\": \"gloo\",\n", + " }, # set epochs to a more realistic number for real training\n", + ")\n", + "\n", + "estimator.fit({\"training\": inputs})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a0f0249f", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "## Prepare batch inference data\n", + "\n", + "Convert the test data into PNG image format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "343a2a68", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!ls data/MNIST/raw" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a29e9c07", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# untar gz => png\n", + "\n", + "import gzip\n", + "import numpy as np\n", + "import os\n", + "\n", + "with gzip.open(os.path.join(local_dir, \"MNIST/raw\", \"t10k-images-idx3-ubyte.gz\"), \"rb\") as f:\n", + " images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91f0f659", + "metadata": {}, + "outputs": [], + "source": [ + "print(len(images), \"test images\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b617160c", + "metadata": {}, + "source": [ + "Randomly sample 100 test images and upload them to S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62f06915", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from PIL import Image as im\n", + "\n", + "ids = random.sample(range(len(images)), 100)\n", + "ids = np.array(ids, dtype=np.int)\n", + "selected_images = images[ids]\n", + "\n", + "image_dir = \"data/images\"\n", + "\n", + "if not os.path.exists(image_dir):\n", + " os.makedirs(image_dir)\n", + "\n", + "for i, img in enumerate(selected_images):\n", + " pngimg = im.fromarray(img)\n", + " pngimg.save(os.path.join(image_dir, f\"{i}.png\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf93b71e", + "metadata": {}, + "outputs": [], + "source": [ + "inference_prefix = \"batch_transform\"\n", + "inference_inputs = sagemaker_session.upload_data(\n", + " path=image_dir, bucket=bucket, key_prefix=inference_prefix\n", + ")\n", + "print(\"Input S3 path: {}\".format(inference_inputs))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ff8b9b66", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "## Create model transformer\n", + "Now, we create a transformer object for creating and interacting with Amazon SageMaker transform jobs. We can create the transformer in two ways:\n", + "1. Use a fitted estimator directly.\n", + "1. First create a PyTorchModel from a saved model artifact, and then create a transformer from the PyTorchModel object.\n", + "\n", + "\n", + "Here, we implement the `model_fn`, `input_fn`, `predict_fn` and `output_fn` function to override the default [PyTorch inference handler](https://github.com/aws/sagemaker-pytorch-inference-toolkit/blob/master/src/sagemaker_pytorch_serving_container/default_inference_handler.py). \n", + "\n", + "In the `input_fn()` function, the inferenced images are encoded as a Python ByteArray. That's why we use the `load_from_bytearray()` function to load images from `io.BytesIO` and then use `PIL.image` to read the images.\n", + "\n", + "```python\n", + "def model_fn(model_dir):\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " model = torch.nn.DataParallel(Net())\n", + " with open(os.path.join(model_dir, \"model.pth\"), \"rb\") as f:\n", + " model.load_state_dict(torch.load(f))\n", + " return model.to(device)\n", + "\n", + " \n", + "def load_from_bytearray(request_body):\n", + " image_as_bytes = io.BytesIO(request_body)\n", + " image = Image.open(image_as_bytes)\n", + " image_tensor = ToTensor()(image).unsqueeze(0) \n", + " return image_tensor\n", + "\n", + "\n", + "def input_fn(request_body, request_content_type):\n", + " # if set content_type as \"image/jpg\" or \"application/x-npy\", \n", + " # the input is also a python bytearray\n", + " if request_content_type == \"application/x-image\": \n", + " image_tensor = load_from_bytearray(request_body)\n", + " else:\n", + " print(\"not support this type yet\")\n", + " raise ValueError(\"not support this type yet\")\n", + " return image_tensor\n", + "\n", + "\n", + "# Perform prediction on the deserialized object, with the loaded model\n", + "def predict_fn(input_object, model):\n", + " output = model.forward(input_object)\n", + " pred = output.max(1, keepdim=True)[1]\n", + "\n", + " return {\"predictions\": pred.item()}\n", + "\n", + "\n", + "# Serialize the prediction result into the desired response content type\n", + "def output_fn(predictions, response_content_type):\n", + " return json.dumps(predictions)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86782070", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Use fitted estimator directly\n", + "transformer = estimator.transformer(instance_count=1, instance_type=\"ml.c5.xlarge\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09735ff2", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# You can also create a Transformer object from saved model artifact\n", + "\n", + "# Get model artifact location by estimator.model_data, or give an S3 key directly\n", + "model_artifact_s3_location = estimator.model_data # \"s3:////model.tar.gz\"\n", + "\n", + "# Create PyTorchModel from saved model artifact\n", + "pytorch_model = PyTorchModel(\n", + " model_data=model_artifact_s3_location,\n", + " role=role,\n", + " framework_version=\"1.8.0\",\n", + " py_version=\"py3\",\n", + " source_dir=\"model-script/\",\n", + " entry_point=\"mnist.py\",\n", + ")\n", + "\n", + "# Create transformer from PyTorchModel object\n", + "transformer = pytorch_model.transformer(instance_count=1, instance_type=\"ml.c5.xlarge\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f024f81c", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "## Batch inference\n", + "Next, we perform inference on the sampled 100 MNIST images in a batch manner. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e3aafd66", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "### Input images directly from S3 location\n", + "We set `S3DataType=S3Prefix` to use all objects that match the specified S3 prefix for batch inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f666cde", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "transformer.transform(\n", + " data=inference_inputs,\n", + " data_type=\"S3Prefix\",\n", + " content_type=\"application/x-image\",\n", + " wait=True,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9d42055d", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "### Input images by manifest file\n", + "First, we generate a manifest file. Then we use the manifest file containing a list of object keys as inputs to batch inference. Some key points:\n", + "- `content_type = \"application/x-image\"` (here the `content_type` is for the actual object for inference, not for the manifest file)\n", + "- `data_type = \"ManifestFile\"`\n", + "- Manifest file format must follow the format as [S3DataSource](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_S3DataSource.html#SageMaker-Type-S3DataSource-S3DataType) points out. We create the manifest file by using the jsonlines package.\n", + "``` json\n", + "[\n", + " {\"prefix\": \"s3://customer_bucket/some/prefix/\"},\n", + " \"relative/path/to/custdata-1\",\n", + " \"relative/path/custdata-2\",\n", + " ...\n", + " \"relative/path/custdata-N\"\n", + "]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "295c39fc", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install -q jsonlines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b279b271", + "metadata": {}, + "outputs": [], + "source": [ + "import jsonlines\n", + "\n", + "# Build image list\n", + "manifest_prefix = f\"s3://{bucket}/{prefix}/images/\"\n", + "\n", + "path = image_dir\n", + "img_files = [f for f in listdir(path) if isfile(join(path, f))]\n", + "\n", + "print(\"img_files\\n\", img_files)\n", + "\n", + "manifest_content = [{\"prefix\": manifest_prefix}]\n", + "manifest_content.extend(img_files)\n", + "\n", + "print(\"manifest_content\\n\", manifest_content)\n", + "\n", + "# Write jsonl file\n", + "manifest_file = \"manifest.json\"\n", + "with jsonlines.open(manifest_file, mode=\"w\") as writer:\n", + " writer.write(manifest_content)\n", + "\n", + "# Upload to S3\n", + "manifest_obj = sagemaker_session.upload_data(path=manifest_file, key_prefix=prefix)\n", + "\n", + "print(\"manifest_obj\\n\", manifest_obj)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b58e5fe6", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Batch transform with manifest file\n", + "transform_job = transformer.transform(\n", + " data=manifest_obj,\n", + " data_type=\"ManifestFile\",\n", + " content_type=\"application/x-image\",\n", + " wait=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aaa60562", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Latest transform job:\", transformer.latest_transform_job.name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56dde353", + "metadata": {}, + "outputs": [], + "source": [ + "# look at the status of the transform job\n", + "import pprint as pp\n", + "\n", + "sm_cli = sagemaker_session.sagemaker_client\n", + "\n", + "job_info = sm_cli.describe_transform_job(TransformJobName=transformer.latest_transform_job.name)\n", + "\n", + "pp.pprint(job_info)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f4a43f63", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "source": [ + "### Multiple instance\n", + "We use `instance_count > 1` to create multiple inference instances. When a batch transform job starts, Amazon SageMaker initializes compute instances and distributes the inference or preprocessing workload between them. Batch Transform partitions the Amazon S3 objects in the input by key and maps Amazon S3 objects to instances. Given multiple files, one instance might process input1.csv, and another instance might process input2.csv. Read more at [Use Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9661fe0e", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dist_transformer = estimator.transformer(instance_count=2, instance_type=\"ml.c4.xlarge\")\n", + "\n", + "dist_transformer.transform(\n", + " data=inference_inputs,\n", + " data_type=\"S3Prefix\",\n", + " content_type=\"application/x-image\",\n", + " wait=True,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "57d2f7f8", + "metadata": {}, + "source": [ + "## Look at all transform jobs" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "942c6f2e", + "metadata": {}, + "source": [ + "We list and describe the transform jobs to retrieve information about them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7725d230", + "metadata": {}, + "outputs": [], + "source": [ + "transform_jobs = sm_cli.list_transform_jobs()[\"TransformJobSummaries\"]\n", + "for job in transform_jobs:\n", + " pp.pprint(job)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b694abf", + "metadata": {}, + "outputs": [], + "source": [ + "job_info = sm_cli.describe_transform_job(\n", + " TransformJobName=dist_transformer.latest_transform_job.name\n", + ")\n", + "\n", + "pp.pprint(job_info)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e682401", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "\n", + "def get_bucket_and_prefix(s3_output_path):\n", + " trim = re.sub(\"s3://\", \"\", s3_output_path)\n", + " bucket, prefix = trim.split(\"/\")\n", + " return bucket, prefix\n", + "\n", + "\n", + "local_path = \"output\" # Where to save the output locally\n", + "\n", + "bucket, output_prefix = get_bucket_and_prefix(job_info[\"TransformOutput\"][\"S3OutputPath\"])\n", + "print(bucket, output_prefix)\n", + "\n", + "sagemaker_session.download_data(path=local_path, bucket=bucket, key_prefix=output_prefix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ae24be8", + "metadata": {}, + "outputs": [], + "source": [ + "!ls {local_path}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c336288", + "metadata": {}, + "outputs": [], + "source": [ + "# Inspect the output\n", + "\n", + "import json\n", + "\n", + "for f in os.listdir(local_path):\n", + " path = os.path.join(local_path, f)\n", + " with open(path, \"r\") as f:\n", + " pred = json.load(f)\n", + " print(pred)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e3cbd160", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we trained a PyTorch model, created a transformer from it, and then performed batch inference using S3 inputs, manifest files, and on multiple instances. This shows a variety of options that are available when running SageMaker Batch Transform jobs for batch inference." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cdb3abb1", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_batch_transform|pytorch_mnist_batch_transform|pytorch-mnist-batch-transform.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. 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?it/s]" + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker_model_monitor/introduction/SageMaker-ModelMonitoring.ipynb b/sagemaker_model_monitor/introduction/SageMaker-ModelMonitoring.ipynb new file mode 100644 index 0000000000..190f8bb19d --- /dev/null +++ b/sagemaker_model_monitor/introduction/SageMaker-ModelMonitoring.ipynb @@ -0,0 +1,814 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amazon SageMaker Model Monitor\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows how to:\n", + "* Host a machine learning model in Amazon SageMaker and capture inference requests, results, and metadata \n", + "* Analyze a training dataset to generate baseline constraints\n", + "* Monitor a live endpoint for violations against constraints\n", + "\n", + "---\n", + "## Background\n", + "\n", + "Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. Amazon SageMaker is a fully-managed service that encompasses the entire machine learning workflow. You can label and prepare your data, choose an algorithm, train a model, and then tune and optimize it for deployment. You can deploy your models to production with Amazon SageMaker to make predictions and lower costs than was previously possible.\n", + "\n", + "In addition, Amazon SageMaker enables you to capture the input, output and metadata for invocations of the models that you deploy. It also enables you to analyze the data and monitor its quality. In this notebook, you learn how Amazon SageMaker enables these capabilities.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook uses an hourly monitor, so it takes between 30-90 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [PART A: Capturing real-time inference data from Amazon SageMaker endpoints](#PART-A:-Capturing-real-time-inference-data-from-Amazon-SageMaker-endpoints)\n", + "1. [PART B: Model Monitor - Baselining and continuous monitoring](#PART-B:-Model-Monitor---Baselining-and-continuous-monitoring)\n", + " 1. [Constraint suggestion with baseline/training dataset](#1.-Constraint-suggestion-with-baseline/training-dataset)\n", + " 1. [Analyze collected data for data quality issues](#2.-Analyze-collected-data-for-data-quality-issues)\n", + "---\n", + "## Setup\n", + "\n", + "To get started, make sure you have these prerequisites completed:\n", + "\n", + "* Specify an AWS Region to host your model.\n", + "* An IAM role ARN exists that is used to give Amazon SageMaker access to your data in Amazon Simple Storage Service (Amazon S3).\n", + "* Use the default S3 bucket to store the data used to train your model, any additional model data, and the data captured from model invocations. For demonstration purposes, you are using the same bucket for these. In reality, you might want to separate them with different security policies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "isConfigCell": true + }, + "outputs": [], + "source": [ + "import os\n", + "import boto3\n", + "import re\n", + "import json\n", + "import sagemaker\n", + "from sagemaker import get_execution_role, session\n", + "\n", + "sm_session = sagemaker.Session()\n", + "region = sm_session.boto_region_name\n", + "\n", + "role = get_execution_role()\n", + "print(\"Role ARN: {}\".format(role))\n", + "\n", + "bucket = sm_session.default_bucket()\n", + "print(\"Demo Bucket: {}\".format(bucket))\n", + "prefix = \"sagemaker/DEMO-ModelMonitor\"\n", + "\n", + "data_capture_prefix = \"{}/datacapture\".format(prefix)\n", + "s3_capture_upload_path = \"s3://{}/{}\".format(bucket, data_capture_prefix)\n", + "reports_prefix = \"{}/reports\".format(prefix)\n", + "s3_report_path = \"s3://{}/{}\".format(bucket, reports_prefix)\n", + "code_prefix = \"{}/code\".format(prefix)\n", + "s3_code_preprocessor_uri = \"s3://{}/{}/{}\".format(bucket, code_prefix, \"preprocessor.py\")\n", + "s3_code_postprocessor_uri = \"s3://{}/{}/{}\".format(bucket, code_prefix, \"postprocessor.py\")\n", + "\n", + "print(\"Capture path: {}\".format(s3_capture_upload_path))\n", + "print(\"Report path: {}\".format(s3_report_path))\n", + "print(\"Preproc Code path: {}\".format(s3_code_preprocessor_uri))\n", + "print(\"Postproc Code path: {}\".format(s3_code_postprocessor_uri))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PART A: Capturing real-time inference data from Amazon SageMaker endpoints\n", + "Create an endpoint to showcase the data capture capability in action.\n", + "\n", + "### Upload the pre-trained model to Amazon S3\n", + "This code uploads a pre-trained XGBoost model that is ready for you to deploy. This model was trained using the XGB Churn Prediction Notebook in SageMaker. You can also use your own pre-trained model in this step. If you already have a pretrained model in Amazon S3, you can add it instead by specifying the s3_key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_file = open(\"model/xgb-churn-prediction-model.tar.gz\", \"rb\")\n", + "s3_key = os.path.join(prefix, \"xgb-churn-prediction-model.tar.gz\")\n", + "boto3.Session().resource(\"s3\").Bucket(bucket).Object(s3_key).upload_fileobj(model_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the model to Amazon SageMaker\n", + "Start with deploying a pre-trained churn prediction model. Here, you create the model object with the image and model data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from time import gmtime, strftime\n", + "from sagemaker.model import Model\n", + "from sagemaker.image_uris import retrieve\n", + "\n", + "model_name = \"DEMO-xgb-churn-pred-model-monitor-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", + "model_url = \"https://{}.s3-{}.amazonaws.com/{}/xgb-churn-prediction-model.tar.gz\".format(\n", + " bucket, region, prefix\n", + ")\n", + "\n", + "image_uri = retrieve(\"xgboost\", region, \"0.90-1\")\n", + "\n", + "model = Model(image_uri=image_uri, model_data=model_url, role=role)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To enable data capture for monitoring the model data quality, you specify the new capture option called `DataCaptureConfig`. You can capture the request payload, the response payload or both with this configuration. The capture config applies to all variants. Go ahead with the deployment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import DataCaptureConfig\n", + "\n", + "endpoint_name = \"DEMO-xgb-churn-pred-model-monitor-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", + "print(\"EndpointName={}\".format(endpoint_name))\n", + "\n", + "data_capture_config = DataCaptureConfig(\n", + " enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_upload_path\n", + ")\n", + "\n", + "predictor = model.deploy(\n", + " initial_instance_count=1,\n", + " instance_type=\"ml.m4.xlarge\",\n", + " endpoint_name=endpoint_name,\n", + " data_capture_config=data_capture_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Invoke the deployed model\n", + "\n", + "You can now send data to this endpoint to get inferences in real time. Because you enabled the data capture in the previous steps, the request and response payload, along with some additional metadata, is saved in the Amazon Simple Storage Service (Amazon S3) location you have specified in the DataCaptureConfig." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step invokes the endpoint with included sample data for about 3 minutes. Data is captured based on the sampling percentage specified and the capture continues until the data capture option is turned off." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.predictor import Predictor\n", + "from sagemaker.serializers import CSVSerializer\n", + "import time\n", + "\n", + "predictor = Predictor(endpoint_name=endpoint_name, serializer=CSVSerializer())\n", + "\n", + "# Get a subset of test data for a quick test\n", + "!head -180 test_data/test-dataset-input-cols.csv > test_data/test_sample.csv\n", + "print(\"Sending test traffic to the endpoint {}. \\nPlease wait...\".format(endpoint_name))\n", + "\n", + "with open(\"test_data/test_sample.csv\", \"r\") as f:\n", + " for row in f:\n", + " payload = row.rstrip(\"\\n\")\n", + " response = predictor.predict(data=payload)\n", + " time.sleep(1)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View captured data\n", + "\n", + "Now list the data capture files stored in Amazon S3. You should expect to see different files from different time periods organized based on the hour in which the invocation occurred. The format of the Amazon S3 path is:\n", + "\n", + "`s3://{destination-bucket-prefix}/{endpoint-name}/{variant-name}/yyyy/mm/dd/hh/filename.jsonl`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "s3_client = boto3.Session().client(\"s3\")\n", + "current_endpoint_capture_prefix = \"{}/{}\".format(data_capture_prefix, endpoint_name)\n", + "result = s3_client.list_objects(Bucket=bucket, Prefix=current_endpoint_capture_prefix)\n", + "capture_files = [capture_file.get(\"Key\") for capture_file in result.get(\"Contents\")]\n", + "print(\"Found Capture Files:\")\n", + "print(\"\\n \".join(capture_files))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, view the contents of a single capture file. Here you should see all the data captured in an Amazon SageMaker specific JSON-line formatted file. Take a quick peek at the first few lines in the captured file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_obj_body(obj_key):\n", + " return s3_client.get_object(Bucket=bucket, Key=obj_key).get(\"Body\").read().decode(\"utf-8\")\n", + "\n", + "\n", + "capture_file = get_obj_body(capture_files[-1])\n", + "print(capture_file[:2000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the contents of a single line is present below in a formatted JSON file so that you can observe a little better." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "print(json.dumps(json.loads(capture_file.split(\"\\n\")[0]), indent=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, each inference request is captured in one line in the jsonl file. The line contains both the input and output merged together. In the example, you provided the ContentType as `text/csv` which is reflected in the `observedContentType` value. Also, you expose the encoding that you used to encode the input and output payloads in the capture format with the `encoding` value.\n", + "\n", + "To recap, you observed how you can enable capturing the input or output payloads to an endpoint with a new parameter. You have also observed what the captured format looks like in Amazon S3. Next, continue to explore how Amazon SageMaker helps with monitoring the data collected in Amazon S3." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PART B: Model Monitor - Baselining and continuous monitoring" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to collecting the data, Amazon SageMaker provides the capability for you to monitor and evaluate the data observed by the endpoints. For this:\n", + "1. Create a baseline with which you compare the realtime traffic. \n", + "1. Once a baseline is ready, setup a schedule to continously evaluate and compare against the baseline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Constraint suggestion with baseline/training dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training dataset with which you trained the model is usually a good baseline dataset. Note that the training dataset data schema and the inference dataset schema should exactly match (i.e. the number and order of the features).\n", + "\n", + "From the training dataset you can ask Amazon SageMaker to suggest a set of baseline `constraints` and generate descriptive `statistics` to explore the data. For this example, upload the training dataset that was used to train the pre-trained model included in this example. If you already have it in Amazon S3, you can directly point to it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# copy over the training dataset to Amazon S3 (if you already have it in Amazon S3, you could reuse it)\n", + "baseline_prefix = prefix + \"/baselining\"\n", + "baseline_data_prefix = baseline_prefix + \"/data\"\n", + "baseline_results_prefix = baseline_prefix + \"/results\"\n", + "\n", + "baseline_data_uri = \"s3://{}/{}\".format(bucket, baseline_data_prefix)\n", + "baseline_results_uri = \"s3://{}/{}\".format(bucket, baseline_results_prefix)\n", + "print(\"Baseline data uri: {}\".format(baseline_data_uri))\n", + "print(\"Baseline results uri: {}\".format(baseline_results_uri))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "training_data_file = open(\"test_data/training-dataset-with-header.csv\", \"rb\")\n", + "s3_key = os.path.join(baseline_prefix, \"data\", \"training-dataset-with-header.csv\")\n", + "boto3.Session().resource(\"s3\").Bucket(bucket).Object(s3_key).upload_fileobj(training_data_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a baselining job with training dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have the training data ready in Amazon S3, start a job to `suggest` constraints. `DefaultModelMonitor.suggest_baseline(..)` starts a `ProcessingJob` using an Amazon SageMaker provided Model Monitor container to generate the constraints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import DefaultModelMonitor\n", + "from sagemaker.model_monitor.dataset_format import DatasetFormat\n", + "\n", + "my_default_monitor = DefaultModelMonitor(\n", + " role=role,\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " volume_size_in_gb=20,\n", + " max_runtime_in_seconds=3600,\n", + ")\n", + "\n", + "my_default_monitor.suggest_baseline(\n", + " baseline_dataset=baseline_data_uri + \"/training-dataset-with-header.csv\",\n", + " dataset_format=DatasetFormat.csv(header=True),\n", + " output_s3_uri=baseline_results_uri,\n", + " wait=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore the generated constraints and statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s3_client = boto3.Session().client(\"s3\")\n", + "result = s3_client.list_objects(Bucket=bucket, Prefix=baseline_results_prefix)\n", + "report_files = [report_file.get(\"Key\") for report_file in result.get(\"Contents\")]\n", + "print(\"Found Files:\")\n", + "print(\"\\n \".join(report_files))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "baseline_job = my_default_monitor.latest_baselining_job\n", + "schema_df = pd.io.json.json_normalize(baseline_job.baseline_statistics().body_dict[\"features\"])\n", + "schema_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "constraints_df = pd.io.json.json_normalize(\n", + " baseline_job.suggested_constraints().body_dict[\"features\"]\n", + ")\n", + "constraints_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Analyze collected data for data quality issues\n", + "\n", + "When you have collected the data above, analyze and monitor the data with Monitoring Schedules." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload some test scripts to the S3 bucket for pre- and post-processing\n", + "bucket = boto3.Session().resource(\"s3\").Bucket(bucket)\n", + "bucket.Object(code_prefix + \"/preprocessor.py\").upload_file(\"preprocessor.py\")\n", + "bucket.Object(code_prefix + \"/postprocessor.py\").upload_file(\"postprocessor.py\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can create a model monitoring schedule for the endpoint created earlier. Use the baseline resources (constraints and statistics) to compare against the realtime traffic." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import CronExpressionGenerator\n", + "\n", + "mon_schedule_name = \"DEMO-xgb-churn-pred-model-monitor-schedule-\" + strftime(\n", + " \"%Y-%m-%d-%H-%M-%S\", gmtime()\n", + ")\n", + "my_default_monitor.create_monitoring_schedule(\n", + " monitor_schedule_name=mon_schedule_name,\n", + " endpoint_input=predictor.endpoint,\n", + " # record_preprocessor_script=pre_processor_script,\n", + " post_analytics_processor_script=s3_code_postprocessor_uri,\n", + " output_s3_uri=s3_report_path,\n", + " statistics=my_default_monitor.baseline_statistics(),\n", + " constraints=my_default_monitor.suggested_constraints(),\n", + " schedule_cron_expression=CronExpressionGenerator.hourly(),\n", + " enable_cloudwatch_metrics=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start generating some artificial traffic\n", + "The cell below starts a thread to send some traffic to the endpoint. Note that you need to stop the kernel to terminate this thread. If there is no traffic, the monitoring jobs are marked as `Failed` since there is no data to process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from threading import Thread\n", + "from time import sleep\n", + "\n", + "endpoint_name = predictor.endpoint\n", + "runtime_client = sm_session.sagemaker_runtime_client\n", + "\n", + "\n", + "# (just repeating code from above for convenience/ able to run this section independently)\n", + "def invoke_endpoint(ep_name, file_name, runtime_client):\n", + " with open(file_name, \"r\") as f:\n", + " for row in f:\n", + " payload = row.rstrip(\"\\n\")\n", + " response = runtime_client.invoke_endpoint(\n", + " EndpointName=ep_name, ContentType=\"text/csv\", Body=payload\n", + " )\n", + " response[\"Body\"].read()\n", + " time.sleep(1)\n", + "\n", + "\n", + "def invoke_endpoint_forever():\n", + " while True:\n", + " try:\n", + " invoke_endpoint(endpoint_name, \"test_data/test-dataset-input-cols.csv\", runtime_client)\n", + " except runtime_client.exceptions.ValidationError:\n", + " pass\n", + "\n", + "\n", + "thread = Thread(target=invoke_endpoint_forever)\n", + "thread.start()\n", + "\n", + "# Note that you need to stop the kernel to stop the invocations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Describe and inspect the schedule\n", + "Once you describe, observe that the MonitoringScheduleStatus changes to Scheduled." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "desc_schedule_result = my_default_monitor.describe_schedule()\n", + "print(\"Schedule status: {}\".format(desc_schedule_result[\"MonitoringScheduleStatus\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### List executions\n", + "The schedule starts jobs at the previously specified intervals. Here, you list the latest five executions. Note that if you are kicking this off after creating the hourly schedule, you might find the executions empty. You might have to wait until you cross the hour boundary (in UTC) to see executions kick off. The code below has the logic for waiting.\n", + "\n", + "Note: Even for an hourly schedule, Amazon SageMaker has a buffer period of 20 minutes to schedule your execution. You might see your execution start in anywhere from zero to ~20 minutes from the hour boundary. This is expected and done for load balancing in the backend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mon_executions = my_default_monitor.list_executions()\n", + "print(\n", + " \"We created a hourly schedule above that begins executions ON the hour (plus 0-20 min buffer.\\nWe will have to wait till we hit the hour...\"\n", + ")\n", + "\n", + "while len(mon_executions) == 0:\n", + " print(\"Waiting for the first execution to happen...\")\n", + " time.sleep(60)\n", + " mon_executions = my_default_monitor.list_executions()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inspect a specific execution (latest execution)\n", + "In the previous cell, you picked up the latest completed or failed scheduled execution. Here are the possible terminal states and what each of them mean: \n", + "* `Completed` - The monitoring execution completed and no issues were found in the violations report.\n", + "* `CompletedWithViolations` - The execution completed, but constraint violations were detected.\n", + "* `Failed` - The monitoring execution failed, maybe due to client error (perhaps incorrect role premissions) or infrastructure issues. Further examination of `FailureReason` and `ExitMessage` is necessary to identify what exactly happened.\n", + "* `Stopped` - The job exceeded max runtime or was manually stopped." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "latest_execution = mon_executions[-1] # Latest execution's index is -1, second to last is -2, etc\n", + "time.sleep(60)\n", + "latest_execution.wait(logs=False)\n", + "\n", + "print(\"Latest execution status: {}\".format(latest_execution.describe()[\"ProcessingJobStatus\"]))\n", + "print(\"Latest execution result: {}\".format(latest_execution.describe()[\"ExitMessage\"]))\n", + "\n", + "latest_job = latest_execution.describe()\n", + "if latest_job[\"ProcessingJobStatus\"] != \"Completed\":\n", + " print(\n", + " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report_uri = latest_execution.output.destination\n", + "print(\"Report Uri: {}\".format(report_uri))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### List the generated reports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from urllib.parse import urlparse\n", + "\n", + "s3uri = urlparse(report_uri)\n", + "report_bucket = s3uri.netloc\n", + "report_key = s3uri.path.lstrip(\"/\")\n", + "print(\"Report bucket: {}\".format(report_bucket))\n", + "print(\"Report key: {}\".format(report_key))\n", + "\n", + "s3_client = boto3.Session().client(\"s3\")\n", + "result = s3_client.list_objects(Bucket=report_bucket, Prefix=report_key)\n", + "report_files = [report_file.get(\"Key\") for report_file in result.get(\"Contents\")]\n", + "print(\"Found Report Files:\")\n", + "print(\"\\n \".join(report_files))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Violations report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any violations compared to the baseline are listed below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "violations = my_default_monitor.latest_monitoring_constraint_violations()\n", + "pd.set_option(\"display.max_colwidth\", None)\n", + "constraints_df = pd.io.json.json_normalize(violations.body_dict[\"violations\"])\n", + "constraints_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Other commands\n", + "We can also start and stop the monitoring schedules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# my_default_monitor.stop_monitoring_schedule()\n", + "# my_default_monitor.start_monitoring_schedule()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete resources\n", + "\n", + "You can keep your endpoint running to continue capturing data. If you do not plan to collect more data or use this endpoint further, delete the endpoint to avoid incurring additional charges. Note that deleting your endpoint does not delete the data that was captured during the model invocations. That data persists in Amazon S3 until you delete it yourself.\n", + "\n", + "You need to delete the schedule before deleting the model and endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_default_monitor.stop_monitoring_schedule()\n", + "my_default_monitor.delete_monitoring_schedule()\n", + "time.sleep(60) # Wait for the deletion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "predictor.delete_model()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictor.delete_endpoint()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_monitor|introduction|SageMaker-ModelMonitoring.ipynb)\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (Data Science 3.0)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archived/notebooks/mxnet_distributed_mnist_neo_inf1/get_input.py b/sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/get_input.py similarity index 100% rename from archived/notebooks/mxnet_distributed_mnist_neo_inf1/get_input.py rename to sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/get_input.py diff --git a/archived/notebooks/mxnet_distributed_mnist_neo_inf1/input.npy b/sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/input.npy similarity index 100% rename from archived/notebooks/mxnet_distributed_mnist_neo_inf1/input.npy rename to sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/input.npy diff --git a/archived/notebooks/mxnet_distributed_mnist_neo_inf1/mnist.py b/sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/mnist.py similarity index 100% rename from archived/notebooks/mxnet_distributed_mnist_neo_inf1/mnist.py rename to sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/mnist.py diff --git a/archived/notebooks/mxnet_distributed_mnist_neo_inf1/mxnet_distributed_mnist_neo_inf1.ipynb b/sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/mxnet_distributed_mnist_neo_inf1.ipynb similarity index 100% rename from archived/notebooks/mxnet_distributed_mnist_neo_inf1/mxnet_distributed_mnist_neo_inf1.ipynb rename to sagemaker_neo_compilation_jobs/deploy_mxnet_model_on_Inf1_instance/mxnet_distributed_mnist_neo_inf1.ipynb diff --git a/archived/notebooks/gluoncv_yolo_neo/gluoncv_yolo_neo.ipynb b/sagemaker_neo_compilation_jobs/gluoncv_yolo/gluoncv_yolo_neo.ipynb similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/gluoncv_yolo_neo.ipynb rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/gluoncv_yolo_neo.ipynb diff --git a/archived/notebooks/gluoncv_yolo_neo/test.jpg b/sagemaker_neo_compilation_jobs/gluoncv_yolo/test.jpg similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/test.jpg rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/test.jpg diff --git a/archived/notebooks/gluoncv_yolo_neo/tools/concat_db.py b/sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/concat_db.py similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/tools/concat_db.py rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/concat_db.py diff --git a/archived/notebooks/gluoncv_yolo_neo/tools/im2rec.py b/sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/im2rec.py similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/tools/im2rec.py rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/im2rec.py diff --git a/archived/notebooks/gluoncv_yolo_neo/tools/imdb.py b/sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/imdb.py similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/tools/imdb.py rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/imdb.py diff --git a/archived/notebooks/gluoncv_yolo_neo/tools/pascal_voc.names b/sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/pascal_voc.names similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/tools/pascal_voc.names rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/pascal_voc.names diff --git a/archived/notebooks/gluoncv_yolo_neo/tools/pascal_voc.py b/sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/pascal_voc.py similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/tools/pascal_voc.py rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/pascal_voc.py diff --git a/archived/notebooks/gluoncv_yolo_neo/tools/prepare_dataset.py b/sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/prepare_dataset.py similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/tools/prepare_dataset.py rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/tools/prepare_dataset.py diff --git a/archived/notebooks/gluoncv_yolo_neo/train_yolo.py b/sagemaker_neo_compilation_jobs/gluoncv_yolo/train_yolo.py similarity index 100% rename from archived/notebooks/gluoncv_yolo_neo/train_yolo.py rename to sagemaker_neo_compilation_jobs/gluoncv_yolo/train_yolo.py diff --git a/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb b/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb new file mode 100644 index 0000000000..215a037530 --- /dev/null +++ b/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb @@ -0,0 +1,975 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Amazon SageMaker Neo is an API to compile machine learning models to optimize them for our choice of hardware targets. Currently, Neo supports pre-trained PyTorch models from [TorchVision](https://pytorch.org/docs/stable/torchvision/models.html). General support for other PyTorch models is forthcoming.\n", + "\n", + "### Runtime\n", + "\n", + "This notebook takes approximately 8 minutes to run.\n", + "\n", + "### Contents\n", + "\n", + "1. [Import ResNet18 from TorchVision](#Import-ResNet18-from-TorchVision)\n", + "1. [Invoke Neo Compilation API](#Invoke-Neo-Compilation-API)\n", + "1. [Deploy the model](#Deploy-the-model)\n", + "1. [Send requests](#Send-requests)\n", + "1. [Delete the Endpoint](#Delete-the-Endpoint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import ResNet18 from TorchVision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We import the [ResNet18](https://arxiv.org/abs/1512.03385) model from TorchVision and create a model artifact `model.tar.gz`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "!{sys.executable} -m pip install torch==1.13.0 torchvision==0.14.0\n", + "!{sys.executable} -m pip install s3transfer==0.5.0\n", + "!{sys.executable} -m pip install --upgrade sagemaker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify the input data shape. For more information, see [Prepare Model for Compilation](https://docs.aws.amazon.com/sagemaker/latest/dg/neo-compilation-preparing-model.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sagemaker\n", + "import torch\n", + "import torchvision.models as models\n", + "import tarfile\n", + "\n", + "resnet18 = models.resnet18(pretrained=True)\n", + "input_shape = [1, 3, 224, 224]\n", + "trace = torch.jit.trace(resnet18.float().eval(), torch.zeros(input_shape).float())\n", + "trace.save(\"model.pth\")\n", + "\n", + "with tarfile.open(\"model.tar.gz\", \"w:gz\") as f:\n", + " f.add(\"model.pth\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload the model archive to S3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify parameters for the compilation job and upload the `model.tar.gz` archive file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import boto3\n", + "import sagemaker\n", + "import time\n", + "from sagemaker.utils import name_from_base\n", + "\n", + "role = sagemaker.get_execution_role()\n", + "sess = sagemaker.Session()\n", + "region = sess.boto_region_name\n", + "bucket = sess.default_bucket()\n", + "\n", + "compilation_job_name = name_from_base(\"TorchVision-ResNet18-Neo\")\n", + "prefix = compilation_job_name + \"/model\"\n", + "\n", + "model_path = sess.upload_data(path=\"model.tar.gz\", key_prefix=prefix)\n", + "\n", + "data_shape = '{\"input0\":[1,3,224,224]}'\n", + "target_device = \"ml_c5\"\n", + "framework = \"PYTORCH\"\n", + "framework_version = \"1.13\"\n", + "compiled_model_path = \"s3://{}/{}/output\".format(bucket, compilation_job_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Invoke Neo Compilation API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a PyTorch SageMaker model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `PyTorchModel` and define parameters including the path to the model, the `entry_point` script that is used to perform inference, and other version and environment variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.pytorch.model import PyTorchModel\n", + "from sagemaker.predictor import Predictor\n", + "\n", + "sagemaker_model = PyTorchModel(\n", + " model_data=model_path,\n", + " predictor_cls=Predictor,\n", + " framework_version=framework_version,\n", + " role=role,\n", + " sagemaker_session=sess,\n", + " entry_point=\"resnet18.py\",\n", + " source_dir=\"code\",\n", + " py_version=\"py3\",\n", + " env={\"MMS_DEFAULT_RESPONSE_TIMEOUT\": \"500\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use Neo compiler to compile the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the compilation job, which is saved in S3 at the specified `compiled_model_path` location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "compiled_model = sagemaker_model.compile(\n", + " target_instance_family=target_device,\n", + " input_shape=data_shape,\n", + " job_name=compilation_job_name,\n", + " role=role,\n", + " framework=framework.lower(),\n", + " framework_version=framework_version,\n", + " output_path=compiled_model_path,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Deploy the compiled model to an endpoint so it can be used for inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "predictor = compiled_model.deploy(initial_instance_count=1, instance_type=\"ml.c5.9xlarge\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Send requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's send a picture to the endpoint to predict the image subject.\n", + "\n", + "![title](cat.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open the image and pass the payload as a bytearray to the predictor, receiving a response." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import json\n", + "\n", + "with open(\"cat.jpg\", \"rb\") as f:\n", + " payload = f.read()\n", + " payload = bytearray(payload)\n", + "\n", + "response = predictor.predict(payload)\n", + "result = json.loads(response.decode())\n", + "print(\"Most likely class: {}\".format(np.argmax(result)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the ImageNet class ID response to look up which subject the image contains, and with what probability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load names for ImageNet classes\n", + "object_categories = {}\n", + "with open(\"imagenet1000_clsidx_to_labels.txt\", \"r\") as f:\n", + " for line in f:\n", + " key, val = line.strip().split(\":\")\n", + " object_categories[key] = val.strip(\" \").strip(\",\")\n", + "print(\n", + " \"The label is\",\n", + " object_categories[str(np.argmax(result))],\n", + " \"with probability\",\n", + " str(np.amax(result))[:5],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete the Endpoint\n", + "Delete the endpoint to avoid incurring costs now that it is no longer needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "predictor.delete_model()\n", + "sess.delete_endpoint(predictor.endpoint_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_neo_compilation_jobs|pytorch_torchvision|pytorch_torchvision_neo.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. 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+ "memoryGiB": 16, + "name": "ml.m5d.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 14, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5d.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 15, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5d.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 16, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5d.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 17, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5d.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 18, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5d.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 19, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5d.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 20, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": true, + "memoryGiB": 0, + "name": "ml.geospatial.interactive", + "supportedImageNames": [ + "sagemaker-geospatial-v1-0" + ], + "vcpuNum": 0 + }, + { + "_defaultOrder": 21, + "_isFastLaunch": true, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.c5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 22, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.c5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 23, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.c5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 24, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.c5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 25, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 72, + "name": "ml.c5.9xlarge", + "vcpuNum": 36 + }, + { + "_defaultOrder": 26, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 96, + "name": "ml.c5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 27, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 144, + "name": "ml.c5.18xlarge", + "vcpuNum": 72 + }, + { + "_defaultOrder": 28, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.c5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 29, + "_isFastLaunch": true, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g4dn.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 30, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g4dn.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 31, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g4dn.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 32, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g4dn.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 33, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g4dn.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 34, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g4dn.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 35, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 61, + "name": "ml.p3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 36, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 244, + "name": "ml.p3.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 37, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 488, + "name": "ml.p3.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 38, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.p3dn.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 39, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.r5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 40, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.r5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 41, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.r5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 42, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.r5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 43, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.r5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 44, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.r5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 45, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 512, + "name": "ml.r5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 46, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.r5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 47, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 48, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 49, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 50, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 51, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 52, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + }, + { + "_defaultOrder": 55, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 1152, + "name": "ml.p4d.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 56, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 1152, + "name": "ml.p4de.24xlarge", + "vcpuNum": 96 + } + ], + "kernelspec": { + "display_name": "Python 3 (PyTorch 1.12 Python 3.8 CPU Optimized)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.12-cpu-py38" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archived/notebooks/sagemaker-neo-tf-unet.ipynb b/sagemaker_neo_compilation_jobs/tensorflow_unet/sagemaker-neo-tf-unet.ipynb similarity index 100% rename from archived/notebooks/sagemaker-neo-tf-unet.ipynb rename to sagemaker_neo_compilation_jobs/tensorflow_unet/sagemaker-neo-tf-unet.ipynb diff --git a/sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb b/sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb new file mode 100644 index 0000000000..86324ba686 --- /dev/null +++ b/sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb @@ -0,0 +1,378 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Get started with SageMaker Processing\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "This notebook corresponds to the section \"Preprocessing Data With The Built-In Scikit-Learn Container\" in the blog post [Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/). \n", + "It shows a lightweight example of using SageMaker Processing to create train, test, and validation datasets. SageMaker Processing is used to create these datasets, which then are written back to S3.\n", + "\n", + "## Runtime\n", + "\n", + "This notebook takes approximately 5 minutes to run.\n", + "\n", + "## Contents\n", + "\n", + "1. [Prepare resources](#Prepare-resources)\n", + "1. [Download data](#Download-data)\n", + "1. [Prepare Processing script](#Prepare-Processing-script)\n", + "1. [Run Processing job](#Run-Processing-job)\n", + "1. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Prepare resources\n", + "\n", + "First, let’s create an SKLearnProcessor object, passing the scikit-learn version we want to use, as well as our managed infrastructure requirements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!pip install -U sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import boto3\n", + "import sagemaker\n", + "from sagemaker import get_execution_role\n", + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "\n", + "region = sagemaker.Session().boto_region_name\n", + "role = get_execution_role()\n", + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=\"1.2-1\", role=role, instance_type=\"ml.m5.xlarge\", instance_count=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Download data\n", + "\n", + "Read in the raw data from a public S3 bucket. This example uses the [Census-Income (KDD) Dataset](https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29) from the UCI Machine Learning Repository.\n", + "\n", + "> Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "s3 = boto3.client(\"s3\")\n", + "s3.download_file(\n", + " \"sagemaker-sample-data-{}\".format(region),\n", + " \"processing/census/census-income.csv\",\n", + " \"census-income.csv\",\n", + ")\n", + "df = pd.read_csv(\"census-income.csv\")\n", + "df.to_csv(\"dataset.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Prepare Processing script\n", + "\n", + "Write the Python script that will be run by SageMaker Processing. This script reads the single data file from S3; splits the rows into train, test, and validation sets; and then writes the three output files to S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%%writefile preprocessing.py\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "input_data_path = os.path.join(\"/opt/ml/processing/input\", \"dataset.csv\")\n", + "df = pd.read_csv(input_data_path)\n", + "print(\"Shape of data is:\", df.shape)\n", + "train, test = train_test_split(df, test_size=0.2)\n", + "train, validation = train_test_split(train, test_size=0.2)\n", + "\n", + "try:\n", + " os.makedirs(\"/opt/ml/processing/output/train\")\n", + " os.makedirs(\"/opt/ml/processing/output/validation\")\n", + " os.makedirs(\"/opt/ml/processing/output/test\")\n", + " print(\"Successfully created directories\")\n", + "except Exception as e:\n", + " # if the Processing call already creates these directories (or directory otherwise cannot be created)\n", + " print(e)\n", + " print(\"Could not make directories\")\n", + " pass\n", + "\n", + "try:\n", + " train.to_csv(\"/opt/ml/processing/output/train/train.csv\")\n", + " validation.to_csv(\"/opt/ml/processing/output/validation/validation.csv\")\n", + " test.to_csv(\"/opt/ml/processing/output/test/test.csv\")\n", + " print(\"Wrote files successfully\")\n", + "except Exception as e:\n", + " print(\"Failed to write the files\")\n", + " print(e)\n", + " pass\n", + "\n", + "print(\"Completed running the processing job\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Run Processing job" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Run the Processing job, specifying the script name, input file, and output files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%%capture output\n", + "\n", + "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", + "\n", + "sklearn_processor.run(\n", + " code=\"preprocessing.py\",\n", + " # arguments = [\"arg1\", \"arg2\"], # Arguments can optionally be specified here\n", + " inputs=[ProcessingInput(source=\"dataset.csv\", destination=\"/opt/ml/processing/input\")],\n", + " outputs=[\n", + " ProcessingOutput(source=\"/opt/ml/processing/output/train\"),\n", + " ProcessingOutput(source=\"/opt/ml/processing/output/validation\"),\n", + " ProcessingOutput(source=\"/opt/ml/processing/output/test\"),\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Get the Processing job logs and retrieve the job name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "print(output)\n", + "job_name = str(output).split(\"\\n\")[1].split(\" \")[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Confirm that the output dataset files were written to S3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import boto3\n", + "\n", + "s3_client = boto3.client(\"s3\")\n", + "default_bucket = sagemaker.Session().default_bucket()\n", + "for i in range(1, 4):\n", + " prefix = s3_client.list_objects(Bucket=default_bucket, Prefix=\"sagemaker-scikit-learn\")[\n", + " \"Contents\"\n", + " ][-i][\"Key\"]\n", + " print(\"s3://\" + default_bucket + \"/\" + prefix)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we read a dataset from S3 and processed it into train, test, and validation sets using a SageMaker Processing job. You can extend this example for preprocessing your own datasets in preparation for machine learning or other applications." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_processing|basic_sagemaker_data_processing|basic_sagemaker_processing.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Data Science 3.0)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb b/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb new file mode 100644 index 0000000000..b48847305e --- /dev/null +++ b/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distributed Data Processing using Apache Spark and SageMaker Processing\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Apache Spark is a unified analytics engine for large-scale data processing. The Spark framework is often used within the context of machine learning workflows to run data transformation or feature engineering workloads at scale. Amazon SageMaker provides a set of prebuilt Docker images that include Apache Spark and other dependencies needed to run distributed data processing jobs on Amazon SageMaker. This example notebook demonstrates how to use the prebuilt Spark images on SageMaker Processing using the SageMaker Python SDK.\n", + "\n", + "This notebook walks through the following scenarios to illustrate the functionality of the SageMaker Spark Container:\n", + "\n", + "* Running a basic PySpark application using the SageMaker Python SDK's `PySparkProcessor` class\n", + "* Viewing the Spark UI via the `start_history_server()` function of a `PySparkProcessor` object\n", + "* Adding additional Python and jar file dependencies to jobs\n", + "* Running a basic Java/Scala-based Spark job using the SageMaker Python SDK's `SparkJarProcessor` class\n", + "* Specifying additional Spark configuration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Runtime\n", + "\n", + "This notebook takes approximately 22 minutes to run." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Setup](#Setup)\n", + "1. [Example 1: Running a basic PySpark application](#Example-1:-Running-a-basic-PySpark-application)\n", + "1. [Example 2: Specify additional Python and jar file dependencies](#Example-2:-Specify-additional-Python-and-jar-file-dependencies)\n", + "1. [Example 3: Run a Java/Scala Spark application](#Example-3:-Run-a-Java/Scala-Spark-application)\n", + "1. [Example 4: Specifying additional Spark configuration](#Example-4:-Specifying-additional-Spark-configuration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install the latest SageMaker Python SDK\n", + "\n", + "This notebook requires the latest v2.x version of the SageMaker Python SDK. First, ensure that the latest version is installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U \"sagemaker>2.0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Restart your notebook kernel after upgrading the SDK*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 1: Running a basic PySpark application\n", + "\n", + "The first example is a basic Spark MLlib data processing script. This script will take a raw data set and do some transformations on it such as string indexing and one hot encoding.\n", + "\n", + "### Setup S3 bucket locations and roles\n", + "\n", + "First, setup some locations in the default SageMaker bucket to store the raw input datasets and the Spark job output. Here, you'll also define the role that will be used to run all SageMaker Processing jobs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import sagemaker\n", + "from time import gmtime, strftime\n", + "\n", + "sagemaker_logger = logging.getLogger(\"sagemaker\")\n", + "sagemaker_logger.setLevel(logging.INFO)\n", + "sagemaker_logger.addHandler(logging.StreamHandler())\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "bucket = sagemaker_session.default_bucket()\n", + "role = sagemaker.get_execution_role()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, you'll download the example dataset from a SageMaker staging bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch the dataset from the SageMaker bucket\n", + "import boto3\n", + "\n", + "s3 = boto3.client(\"s3\")\n", + "s3.download_file(\n", + " f\"sagemaker-example-files-prod-{sagemaker_session.boto_region_name}\",\n", + " \"datasets/tabular/uci_abalone/abalone.csv\",\n", + " \"./data/abalone.csv\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the PySpark script\n", + "\n", + "The source for a preprocessing script is in the cell below. The cell uses the `%%writefile` directive to save this file locally. This script does some basic feature engineering on a raw input dataset. In this example, the dataset is the [Abalone Data Set](https://archive.ics.uci.edu/ml/datasets/abalone) and the code below performs string indexing, one hot encoding, vector assembly, and combines them into a pipeline to perform these transformations in order. The script then does an 80-20 split to produce training and validation datasets as output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile ./code/preprocess.py\n", + "from __future__ import print_function\n", + "from __future__ import unicode_literals\n", + "\n", + "import argparse\n", + "import csv\n", + "import os\n", + "import shutil\n", + "import sys\n", + "import time\n", + "\n", + "import pyspark\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.ml import Pipeline\n", + "from pyspark.ml.feature import (\n", + " OneHotEncoder,\n", + " StringIndexer,\n", + " VectorAssembler,\n", + " VectorIndexer,\n", + ")\n", + "from pyspark.sql.functions import *\n", + "from pyspark.sql.types import (\n", + " DoubleType,\n", + " StringType,\n", + " StructField,\n", + " StructType,\n", + ")\n", + "\n", + "\n", + "def csv_line(data):\n", + " r = \",\".join(str(d) for d in data[1])\n", + " return str(data[0]) + \",\" + r\n", + "\n", + "\n", + "def main():\n", + " parser = argparse.ArgumentParser(description=\"app inputs and outputs\")\n", + " parser.add_argument(\"--s3_input_bucket\", type=str, help=\"s3 input bucket\")\n", + " parser.add_argument(\"--s3_input_key_prefix\", type=str, help=\"s3 input key prefix\")\n", + " parser.add_argument(\"--s3_output_bucket\", type=str, help=\"s3 output bucket\")\n", + " parser.add_argument(\"--s3_output_key_prefix\", type=str, help=\"s3 output key prefix\")\n", + " args = parser.parse_args()\n", + "\n", + " spark = SparkSession.builder.appName(\"PySparkApp\").getOrCreate()\n", + "\n", + " # This is needed to save RDDs which is the only way to write nested Dataframes into CSV format\n", + " spark.sparkContext._jsc.hadoopConfiguration().set(\n", + " \"mapred.output.committer.class\", \"org.apache.hadoop.mapred.FileOutputCommitter\"\n", + " )\n", + "\n", + " # Defining the schema corresponding to the input data. The input data does not contain the headers\n", + " schema = StructType(\n", + " [\n", + " StructField(\"sex\", StringType(), True),\n", + " StructField(\"length\", DoubleType(), True),\n", + " StructField(\"diameter\", DoubleType(), True),\n", + " StructField(\"height\", DoubleType(), True),\n", + " StructField(\"whole_weight\", DoubleType(), True),\n", + " StructField(\"shucked_weight\", DoubleType(), True),\n", + " StructField(\"viscera_weight\", DoubleType(), True),\n", + " StructField(\"shell_weight\", DoubleType(), True),\n", + " StructField(\"rings\", DoubleType(), True),\n", + " ]\n", + " )\n", + "\n", + " # Downloading the data from S3 into a Dataframe\n", + " total_df = spark.read.csv(\n", + " (\"s3://\" + os.path.join(args.s3_input_bucket, args.s3_input_key_prefix, \"abalone.csv\")),\n", + " header=False,\n", + " schema=schema,\n", + " )\n", + "\n", + " # StringIndexer on the sex column which has categorical value\n", + " sex_indexer = StringIndexer(inputCol=\"sex\", outputCol=\"indexed_sex\")\n", + "\n", + " # one-hot-encoding is being performed on the string-indexed sex column (indexed_sex)\n", + " sex_encoder = OneHotEncoder(inputCol=\"indexed_sex\", outputCol=\"sex_vec\")\n", + "\n", + " # vector-assembler will bring all the features to a 1D vector for us to save easily into CSV format\n", + " assembler = VectorAssembler(\n", + " inputCols=[\n", + " \"sex_vec\",\n", + " \"length\",\n", + " \"diameter\",\n", + " \"height\",\n", + " \"whole_weight\",\n", + " \"shucked_weight\",\n", + " \"viscera_weight\",\n", + " \"shell_weight\",\n", + " ],\n", + " outputCol=\"features\",\n", + " )\n", + "\n", + " # The pipeline is comprised of the steps added above\n", + " pipeline = Pipeline(stages=[sex_indexer, sex_encoder, assembler])\n", + "\n", + " # This step trains the feature transformers\n", + " model = pipeline.fit(total_df)\n", + "\n", + " # This step transforms the dataset with information obtained from the previous fit\n", + " transformed_total_df = model.transform(total_df)\n", + "\n", + " # Split the overall dataset into 80-20 training and validation\n", + " (train_df, validation_df) = transformed_total_df.randomSplit([0.8, 0.2])\n", + "\n", + " # Convert the train dataframe to RDD to save in CSV format and upload to S3\n", + " train_rdd = train_df.rdd.map(lambda x: (x.rings, x.features))\n", + " train_lines = train_rdd.map(csv_line)\n", + " train_lines.saveAsTextFile(\n", + " \"s3://\" + os.path.join(args.s3_output_bucket, args.s3_output_key_prefix, \"train\")\n", + " )\n", + "\n", + " # Convert the validation dataframe to RDD to save in CSV format and upload to S3\n", + " validation_rdd = validation_df.rdd.map(lambda x: (x.rings, x.features))\n", + " validation_lines = validation_rdd.map(csv_line)\n", + " validation_lines.saveAsTextFile(\n", + " \"s3://\" + os.path.join(args.s3_output_bucket, args.s3_output_key_prefix, \"validation\")\n", + " )\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the SageMaker Processing Job\n", + "\n", + "Next, you'll use the `PySparkProcessor` class to define a Spark job and run it using SageMaker Processing. A few things to note in the definition of the `PySparkProcessor`:\n", + "\n", + "* This is a multi-node job with two m5.xlarge instances (which is specified via the `instance_count` and `instance_type` parameters)\n", + "* Spark framework version 3.1 is specified via the `framework_version` parameter\n", + "* The PySpark script defined above is passed via via the `submit_app` parameter\n", + "* Command-line arguments to the PySpark script (such as the S3 input and output locations) are passed via the `arguments` parameter\n", + "* Spark event logs will be offloaded to the S3 location specified in `spark_event_logs_s3_uri` and can be used to view the Spark UI while the job is in progress or after it completes\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.spark.processing import PySparkProcessor\n", + "\n", + "# Upload the raw input dataset to a unique S3 location\n", + "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", + "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", + "input_prefix_abalone = \"{}/input/raw/abalone\".format(prefix)\n", + "input_preprocessed_prefix_abalone = \"{}/input/preprocessed/abalone\".format(prefix)\n", + "\n", + "sagemaker_session.upload_data(\n", + " path=\"./data/abalone.csv\", bucket=bucket, key_prefix=input_prefix_abalone\n", + ")\n", + "\n", + "# Run the processing job\n", + "spark_processor = PySparkProcessor(\n", + " base_job_name=\"sm-spark\",\n", + " framework_version=\"3.1\",\n", + " role=role,\n", + " instance_count=2,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " max_runtime_in_seconds=1200,\n", + ")\n", + "\n", + "spark_processor.run(\n", + " submit_app=\"./code/preprocess.py\",\n", + " arguments=[\n", + " \"--s3_input_bucket\",\n", + " bucket,\n", + " \"--s3_input_key_prefix\",\n", + " input_prefix_abalone,\n", + " \"--s3_output_bucket\",\n", + " bucket,\n", + " \"--s3_output_key_prefix\",\n", + " input_preprocessed_prefix_abalone,\n", + " ],\n", + " spark_event_logs_s3_uri=\"s3://{}/{}/spark_event_logs\".format(bucket, prefix),\n", + " logs=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validate Data Processing Results\n", + "\n", + "Next, validate the output of our data preprocessing job by looking at the first 5 rows of the output dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Top 5 rows from s3://{}/{}/train/\".format(bucket, input_preprocessed_prefix_abalone))\n", + "!aws s3 cp --quiet s3://$bucket/$input_preprocessed_prefix_abalone/train/part-00000 - | head -n5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View the Spark UI\n", + "\n", + "Next, you can view the Spark UI by running the history server locally in this notebook. (**Note:** this feature will only work in a local development environment with docker installed or on a Sagemaker Notebook Instance. This feature does not currently work in SageMaker Studio.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uses docker\n", + "spark_processor.start_history_server()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After viewing the Spark UI, you can terminate the history server before proceeding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spark_processor.terminate_history_server()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2: Specify additional Python and jar file dependencies\n", + "\n", + "The next example demonstrates a scenario where additional Python file dependencies are required by the PySpark script. You'll use a sample PySpark script that requires additional user-defined functions (UDFs) defined in a local module." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile ./code/hello_py_spark_app.py\n", + "import argparse\n", + "import time\n", + "\n", + "# Import local module to test spark-submit--py-files dependencies\n", + "import hello_py_spark_udfs as udfs\n", + "from pyspark.sql import SparkSession, SQLContext\n", + "from pyspark.sql.functions import udf\n", + "from pyspark.sql.types import IntegerType\n", + "import time\n", + "\n", + "if __name__ == \"__main__\":\n", + " print(\"Hello World, this is PySpark!\")\n", + "\n", + " parser = argparse.ArgumentParser(description=\"inputs and outputs\")\n", + " parser.add_argument(\"--input\", type=str, help=\"path to input data\")\n", + " parser.add_argument(\"--output\", required=False, type=str, help=\"path to output data\")\n", + " args = parser.parse_args()\n", + " spark = SparkSession.builder.appName(\"SparkTestApp\").getOrCreate()\n", + " sqlContext = SQLContext(spark.sparkContext)\n", + "\n", + " # Load test data set\n", + " inputPath = args.input\n", + " outputPath = args.output\n", + " salesDF = spark.read.json(inputPath)\n", + " salesDF.printSchema()\n", + "\n", + " salesDF.createOrReplaceTempView(\"sales\")\n", + "\n", + " # Define a UDF that doubles an integer column\n", + " # The UDF function is imported from local module to test spark-submit--py-files dependencies\n", + " double_udf_int = udf(udfs.double_x, IntegerType())\n", + "\n", + " # Save transformed data set to disk\n", + " salesDF.select(\"date\", \"sale\", double_udf_int(\"sale\").alias(\"sale_double\")).write.json(\n", + " outputPath\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile ./code/hello_py_spark_udfs.py\n", + "def double_x(x):\n", + " return x + x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a processing job with Python file dependencies\n", + "\n", + "Then, you'll create a processing job where the additional Python file dependencies are specified via the `submit_py_files` argument in the `run()` function. If your Spark application requires additional jar file dependencies, these can be specified via the `submit_jars` argument of the `run()` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define job input/output URIs\n", + "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", + "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", + "input_prefix_sales = \"{}/input/sales\".format(prefix)\n", + "output_prefix_sales = \"{}/output/sales\".format(prefix)\n", + "input_s3_uri = \"s3://{}/{}\".format(bucket, input_prefix_sales)\n", + "output_s3_uri = \"s3://{}/{}\".format(bucket, output_prefix_sales)\n", + "\n", + "sagemaker_session.upload_data(\n", + " path=\"./data/data.jsonl\", bucket=bucket, key_prefix=input_prefix_sales\n", + ")\n", + "\n", + "spark_processor = PySparkProcessor(\n", + " base_job_name=\"sm-spark-udfs\",\n", + " framework_version=\"3.1\",\n", + " role=role,\n", + " instance_count=2,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " max_runtime_in_seconds=1200,\n", + ")\n", + "\n", + "spark_processor.run(\n", + " submit_app=\"./code/hello_py_spark_app.py\",\n", + " submit_py_files=[\"./code/hello_py_spark_udfs.py\"],\n", + " arguments=[\"--input\", input_s3_uri, \"--output\", output_s3_uri],\n", + " logs=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validate Data Processing Results\n", + "\n", + "Next, validate the output of the Spark job by ensuring that the output URI contains the Spark `_SUCCESS` file along with the output json lines file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Output files in {}\".format(output_s3_uri))\n", + "!aws s3 ls $output_s3_uri/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3: Run a Java/Scala Spark application\n", + "\n", + "In the next example, you'll take a Spark application jar (located in `./code/spark-test-app.jar`) that is already built and run it using SageMaker Processing. Here, you'll use the `SparkJarProcessor` class to define the job parameters. \n", + "\n", + "In the `run()` function you'll specify: \n", + "\n", + "* The location of the Spark application jar file in the `submit_app` argument\n", + "* The main class for the Spark application in the `submit_class` argument\n", + "* Input/output arguments for the Spark application" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.spark.processing import SparkJarProcessor\n", + "\n", + "# Upload the raw input dataset to S3\n", + "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", + "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", + "input_prefix_sales = \"{}/input/sales\".format(prefix)\n", + "output_prefix_sales = \"{}/output/sales\".format(prefix)\n", + "input_s3_uri = \"s3://{}/{}\".format(bucket, input_prefix_sales)\n", + "output_s3_uri = \"s3://{}/{}\".format(bucket, output_prefix_sales)\n", + "\n", + "sagemaker_session.upload_data(\n", + " path=\"./data/data.jsonl\", bucket=bucket, key_prefix=input_prefix_sales\n", + ")\n", + "\n", + "spark_processor = SparkJarProcessor(\n", + " base_job_name=\"sm-spark-java\",\n", + " framework_version=\"3.1\",\n", + " role=role,\n", + " instance_count=2,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " max_runtime_in_seconds=1200,\n", + ")\n", + "\n", + "spark_processor.run(\n", + " submit_app=\"./code/spark-test-app.jar\",\n", + " submit_class=\"com.amazonaws.sagemaker.spark.test.HelloJavaSparkApp\",\n", + " arguments=[\"--input\", input_s3_uri, \"--output\", output_s3_uri],\n", + " logs=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4: Specifying additional Spark configuration\n", + "\n", + "Overriding Spark configuration is crucial for a number of tasks such as tuning your Spark application or configuring the Hive metastore. Using the SageMaker Python SDK, you can easily override Spark/Hive/Hadoop configuration.\n", + "\n", + "The next example demonstrates this by overriding Spark executor memory/cores.\n", + "\n", + "For more information on configuring your Spark application, see the EMR documentation on [Configuring Applications](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-configure-apps.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload the raw input dataset to a unique S3 location\n", + "timestamp_prefix = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n", + "prefix = \"sagemaker/spark-preprocess-demo/{}\".format(timestamp_prefix)\n", + "input_prefix_abalone = \"{}/input/raw/abalone\".format(prefix)\n", + "input_preprocessed_prefix_abalone = \"{}/input/preprocessed/abalone\".format(prefix)\n", + "\n", + "sagemaker_session.upload_data(\n", + " path=\"./data/abalone.csv\", bucket=bucket, key_prefix=input_prefix_abalone\n", + ")\n", + "\n", + "spark_processor = PySparkProcessor(\n", + " base_job_name=\"sm-spark\",\n", + " framework_version=\"3.1\",\n", + " role=role,\n", + " instance_count=2,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " max_runtime_in_seconds=1200,\n", + ")\n", + "\n", + "configuration = [\n", + " {\n", + " \"Classification\": \"spark-defaults\",\n", + " \"Properties\": {\"spark.executor.memory\": \"2g\", \"spark.executor.cores\": \"1\"},\n", + " }\n", + "]\n", + "\n", + "spark_processor.run(\n", + " submit_app=\"./code/preprocess.py\",\n", + " arguments=[\n", + " \"--s3_input_bucket\",\n", + " bucket,\n", + " \"--s3_input_key_prefix\",\n", + " input_prefix_abalone,\n", + " \"--s3_output_bucket\",\n", + " bucket,\n", + " \"--s3_output_key_prefix\",\n", + " input_preprocessed_prefix_abalone,\n", + " ],\n", + " configuration=configuration,\n", + " logs=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_processing|spark_distributed_data_processing|sagemaker-spark-processing.ipynb)\n" + ] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "conda_python3", + "language": "python", + "name": "conda_python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archived/notebooks/training_pipeline_pytorch_mnist/code/mnist.py b/step-functions-data-science-sdk/training_pipeline_pytorch_mnist/code/mnist.py similarity index 100% rename from archived/notebooks/training_pipeline_pytorch_mnist/code/mnist.py rename to step-functions-data-science-sdk/training_pipeline_pytorch_mnist/code/mnist.py diff --git a/archived/notebooks/training_pipeline_pytorch_mnist/code/requirements.txt b/step-functions-data-science-sdk/training_pipeline_pytorch_mnist/code/requirements.txt similarity index 100% rename from archived/notebooks/training_pipeline_pytorch_mnist/code/requirements.txt rename to step-functions-data-science-sdk/training_pipeline_pytorch_mnist/code/requirements.txt diff --git a/archived/notebooks/training_pipeline_pytorch_mnist/training_pipeline_pytorch_mnist.ipynb b/step-functions-data-science-sdk/training_pipeline_pytorch_mnist/training_pipeline_pytorch_mnist.ipynb similarity index 100% rename from archived/notebooks/training_pipeline_pytorch_mnist/training_pipeline_pytorch_mnist.ipynb rename to step-functions-data-science-sdk/training_pipeline_pytorch_mnist/training_pipeline_pytorch_mnist.ipynb From 6bfdd6a50af9f133742795ca074e38d94b6ef419 Mon Sep 17 00:00:00 2001 From: pro-biswa Date: Wed, 17 Jul 2024 11:59:04 -0400 Subject: [PATCH 08/16] adding notebook for forecast to canvas workshop (#4704) * adding notebook for forecast to canvas workshop * formatting the notebook using black --- autopilot/autopilot_ts_data_merge.ipynb | 1309 +++++++++++++++++++++++ 1 file changed, 1309 insertions(+) create mode 100644 autopilot/autopilot_ts_data_merge.ipynb diff --git a/autopilot/autopilot_ts_data_merge.ipynb b/autopilot/autopilot_ts_data_merge.ipynb new file mode 100644 index 0000000000..136f64f77a --- /dev/null +++ b/autopilot/autopilot_ts_data_merge.ipynb @@ -0,0 +1,1309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2c2f421d-217a-446e-98a7-5d0d8c4225d1", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "# Time-Series Forecasting - Merge Amazon Forecast Datasets for Amazon SageMaker Canvas API" + ] + }, + { + "cell_type": "markdown", + "id": "4889982e", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/autopilot|autopilot_ts_data_merge.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b01027dc", + "metadata": {}, + "source": [ + "### 1. Introduction " + ] + }, + { + "cell_type": "markdown", + "id": "3cc0c305-d003-41d4-98dd-98262d9ac778", + "metadata": { + "tags": [] + }, + "source": [ + "Canvas API for TimeSeries Forecasting uses one dataset unlike Amazon Forecast which has seperate datasets for target-time-series(tts), related-time-series(rts) and item-meta-data. This notebook is useful for customer who would like to move from Amazon Forecast to SageMaker Canvas as it demonstrates 1/ using python code snippet to combine 3 different datasets into one, 2/create a configuration file to separately identify related time series field from item-metadata, and 3/uses autoML API to progrmatically train the dataset and make batch inference.\n", + "\n", + "These artifacts include: \n", + "- backtest (holdout) forecasts per base model over multiple time windows,\n", + "- accuracy metrics per base model,\n", + "- backtest results and accuracy metrics for the ensembled model,\n", + "- a scaled explainability report displaying the importance of each covariate and static metadata feature.\n", + "- all model artifacts are provided as well on S3, which can be registered or use for batch/real-time inference\n", + "\n", + "If you are running this notebook in your own environment, you can bring in your own data. The sample dataset we have used is available [here](https://amazon-forecast-samples.s3.us-west-2.amazonaws.com/ml_ops/FoodDemand.zip). Once you download the data set, unzip and take out the three csv files, create a 'data' folder in the same level with the notebook and place the csv files in the 'data' folder." + ] + }, + { + "cell_type": "markdown", + "id": "806be3a5-97c1-4e1a-9eef-366d78ae459f", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### 2. Setup " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92e57f6e-3545-4bc8-bf01-f14263a71c7d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# # Update boto3 using this method, or your preferred method\n", + "!pip install --upgrade boto3 --quiet\n", + "!pip install --upgrade sagemaker --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5d60d7-2760-4866-a37c-37bcd1fda64e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# This is the client we will use to interact with SageMaker Autopilot\n", + "import sagemaker\n", + "import boto3\n", + "from botocore.exceptions import ClientError\n", + "import os\n", + "import json\n", + "from sagemaker import get_execution_role\n", + "from time import gmtime, strftime, sleep\n", + "import pandas as pd\n", + "from datetime import datetime as dt\n", + "\n", + "region = boto3.Session().region_name\n", + "session = sagemaker.Session()\n", + "client = boto3.client(\"sts\")\n", + "account_id = client.get_caller_identity()[\"Account\"]\n", + "\n", + "# Modify the following default_bucket to use a bucket of your choosing\n", + "bucket = session.default_bucket()\n", + "data_bucket = \"rawdata-\" + region + \"-\" + account_id\n", + "# bucket = 'my-bucket'\n", + "prefix = \"moving-to-canvas\"\n", + "\n", + "role = get_execution_role()\n", + "\n", + "# This is the client we will use to interact with SageMaker Autopilot\n", + "sm = boto3.Session().client(service_name=\"sagemaker\", region_name=region)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba114977-9b33-4490-83dc-cc23e6ae528c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Assign column heading to 3 different data files for target time series, related time series and item metadata\n", + "columns_tts = [\"item_id\", \"store_id\", \"demand\", \"ts\"]\n", + "\n", + "columns_rts = [\"item_id\", \"store_id\", \"price\", \"ts\"]\n", + "\n", + "columns_items = [\"item_id\", \"item_type\", \"item_description\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "131715d5-1870-44a3-8929-7a08c2f0ba52", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Read from data file and explore the data. Also change the time stamp format to desired one if needed.\n", + "tbl_tts = pd.read_csv(\"data/food-forecast-tts-uc1.csv\", header=None)\n", + "tbl_tts.columns = columns_tts\n", + "tbl_tts[\"ts\"] = pd.to_datetime(tbl_tts[\"ts\"], format=\"%m/%d/%y\").dt.strftime(\"%Y-%m-%d\")\n", + "# print(tbl_tts.shape)\n", + "# tbl_tts.head()\n", + "# tbl_tts['ts'].min(), tbl_tts['ts'].max()\n", + "# print(tbl_tts.dtypes)\n", + "# print(tbl_tts.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e4d9513-fdc9-475f-b010-ef4bd396fca9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# read from data file and explore the data. Also change the time stamp format to desired one if needed.\n", + "tbl_rts = pd.read_csv(\"data/food-forecast-rts-uc1.csv\", header=None)\n", + "tbl_rts.columns = columns_rts\n", + "tbl_rts[\"ts\"] = pd.to_datetime(tbl_rts[\"ts\"], format=\"%m/%d/%y\").dt.strftime(\"%Y-%m-%d\")\n", + "# print(tbl_rts.shape)\n", + "# tbl_rts .head()\n", + "# tbl_rts['ts'].min(), tbl_rts['ts'].max()\n", + "# print(tbl_rts.dtypes)\n", + "# print(tbl_rts.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4604d30-f974-4076-b8c4-96ca554fd7db", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# read from data file and explore the data\n", + "tbl_item = pd.read_csv(\"data/food-forecast-item.csv\", header=None)\n", + "tbl_item.columns = columns_items\n", + "# tbl_item = tbl_item.set_index('item_id', inplace=True)\n", + "# print(tbl_item.shape)\n", + "# tbl_item.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a64be31-3cfd-48a2-88f7-7e2e15c15e15", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Join the data files into one data file\n", + "tts_rts_combined_outer = tbl_tts.merge(tbl_rts, how=\"outer\")\n", + "tts_rts_combined_outer\n", + "combined_tts_rts_im = tts_rts_combined_outer.merge(tbl_item, how=\"left\")\n", + "combined_tts_rts_im" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "173891f8-4082-4916-8ef4-199aeb62086a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Write the combined dataset to csv file which will be used for training the model using SageMaker Canvas API\n", + "file_name = \"combined_tts_rts_item.csv\"\n", + "full_path = \"data/\" + file_name\n", + "combined_tts_rts_im.to_csv(full_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b63134a-8615-4152-a120-2ae57a9618fa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# All columns in tts will be included in TimeSeriesConfig as it contains\n", + "# target, itemID, timestamp, and additional forecast dimensions.\n", + "exclude_columns = columns_tts\n", + "columns_to_include = [col for col in combined_tts_rts_im.columns if col not in exclude_columns]\n", + "\n", + "json_data = {\"FeatureAttributeNames\": columns_to_include, \"FeatureDataTypes\": {}}\n", + "\n", + "for col in columns_to_include:\n", + " dtype = combined_tts_rts_im[col].dtype\n", + " # All rts columns must be numeric to be treated as related features\n", + " if col in columns_rts:\n", + " json_data[\"FeatureDataTypes\"][col] = \"numeric\"\n", + " elif isinstance(dtype, pd.CategoricalDtype):\n", + " json_data[\"FeatureDataTypes\"][col] = \"categorical\"\n", + " elif pd.api.types.is_datetime64_any_dtype(dtype):\n", + " json_data[\"FeatureDataTypes\"][col] = \"datetime\"\n", + " else:\n", + " json_data[\"FeatureDataTypes\"][col] = \"text\"\n", + "\n", + "json_str = json.dumps(json_data, indent=4)\n", + "\n", + "# print(json_str)\n", + "\n", + "with open(\"data/feature.json\", \"w\") as f:\n", + " f.write(json_str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8144e32-27f2-4c74-9ac8-7744670d51f3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Upload the data file and config file to S3 bucket\n", + "\n", + "s3 = boto3.client(\"s3\")\n", + "object_name = prefix + \"/train/\" + file_name\n", + "# print(object_name)\n", + "try:\n", + " response = s3.upload_file(full_path, bucket, object_name)\n", + "except ClientError as e:\n", + " logging.error(e)\n", + "\n", + "config_file_name = \"feature.json\"\n", + "object_name = prefix + \"/\" + config_file_name\n", + "config_full_path = \"data/\" + config_file_name\n", + "\n", + "try:\n", + " response = s3.upload_file(config_full_path, data_bucket, object_name)\n", + "except ClientError as e:\n", + " logging.error(e)" + ] + }, + { + "cell_type": "markdown", + "id": "109f5f2b-a15e-447a-8990-387943a5eaf0", + "metadata": { + "tags": [] + }, + "source": [ + "### 3. Model Training " + ] + }, + { + "cell_type": "markdown", + "id": "4547b63f-4181-4ac6-9b13-5814d1195cb8", + "metadata": {}, + "source": [ + "Establish an AutoML training job name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "120446de-08a9-48be-9c7a-afc7d7e6dbce", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "timestamp_suffix = strftime(\"%Y%m%d-%H%M%S\", gmtime())\n", + "auto_ml_job_name = \"ts-\" + timestamp_suffix\n", + "print(\"AutoMLJobName: \" + auto_ml_job_name)" + ] + }, + { + "cell_type": "markdown", + "id": "843ada35-f6a5-42e1-b7ca-f14e127661a5", + "metadata": {}, + "source": [ + "Define training job specifications. More information about [create_auto_ml_job_v2](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_auto_ml_job_v2.html) can be found in our SageMaker documentation.This JSON body leverages the built-in sample data schema. Please consult the documentation to understand how to alter the parameters for your unique schema." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5756d7d0-c2fe-4aed-9c52-0dffc1400fdc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "input_data_config = [\n", + " {\n", + " \"ChannelType\": \"training\",\n", + " \"ContentType\": \"text/csv;header=present\",\n", + " \"CompressionType\": \"None\",\n", + " \"DataSource\": {\n", + " \"S3DataSource\": {\n", + " \"S3DataType\": \"S3Prefix\",\n", + " \"S3Uri\": \"s3://{}/{}/train/\".format(bucket, prefix),\n", + " }\n", + " },\n", + " }\n", + "]\n", + "\n", + "output_data_config = {\"S3OutputPath\": \"s3://{}/{}/train_output\".format(bucket, prefix)}\n", + "\n", + "optimizaton_metric_config = {\"MetricName\": \"AverageWeightedQuantileLoss\"}\n", + "\n", + "automl_problem_type_config = {\n", + " \"TimeSeriesForecastingJobConfig\": {\n", + " \"FeatureSpecificationS3Uri\": \"s3://{}/{}/feature.json\".format(data_bucket, prefix),\n", + " \"ForecastFrequency\": \"M\",\n", + " \"ForecastHorizon\": 2,\n", + " \"ForecastQuantiles\": [\"p50\", \"p60\", \"p70\", \"p80\", \"p90\"],\n", + " \"Transformations\": {\n", + " \"Filling\": {\n", + " \"demand\": {\"middlefill\": \"zero\", \"backfill\": \"zero\"},\n", + " \"price\": {\"middlefill\": \"zero\", \"backfill\": \"zero\", \"futurefill\": \"zero\"},\n", + " }\n", + " },\n", + " \"TimeSeriesConfig\": {\n", + " \"TargetAttributeName\": \"demand\",\n", + " \"TimestampAttributeName\": \"ts\",\n", + " \"ItemIdentifierAttributeName\": \"item_id\",\n", + " \"GroupingAttributeNames\": [\"store_id\"],\n", + " },\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "869710c7-6474-4726-a06f-b1e7cd1bdd4c", + "metadata": {}, + "source": [ + "With parameters now defined, invoke the [training job](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_auto_ml_job_v2.html) and monitor for its completion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56ae4865-32c5-4079-83a3-e5177fdf5784", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sm.create_auto_ml_job_v2(\n", + " AutoMLJobName=auto_ml_job_name,\n", + " AutoMLJobInputDataConfig=input_data_config,\n", + " OutputDataConfig=output_data_config,\n", + " AutoMLProblemTypeConfig=automl_problem_type_config,\n", + " AutoMLJobObjective=optimizaton_metric_config,\n", + " RoleArn=role,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cdb8ae02-2b67-4fe6-b3d5-c258dd625671", + "metadata": {}, + "source": [ + "Next, we demonstrate a looping mechanism to query (monitor) job status. When the status is ```Completed```, you may review the accuracy of the model and decide whether to perform inference on a batch or real-time API basis as described in this notebook. Please consult documentation for [describe_auto_ml_job_v2](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/describe_auto_ml_job_v2.html) as needed.\n", + "\n", + "NOTE: Training the Model will take approxmiately 30 minutes. Please take this time to work other Labs in this workshop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e9b0f88-364b-4263-8d0a-589039c28f3d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "describe_response = sm.describe_auto_ml_job_v2(AutoMLJobName=auto_ml_job_name)\n", + "job_run_status = describe_response[\"AutoMLJobStatus\"]\n", + "\n", + "while job_run_status not in (\"Failed\", \"Completed\", \"Stopped\"):\n", + " describe_response = sm.describe_auto_ml_job_v2(AutoMLJobName=auto_ml_job_name)\n", + " job_run_status = describe_response[\"AutoMLJobStatus\"]\n", + "\n", + " print(\n", + " dt.now(),\n", + " describe_response[\"AutoMLJobStatus\"]\n", + " + \" - \"\n", + " + describe_response[\"AutoMLJobSecondaryStatus\"],\n", + " )\n", + " sleep(180)" + ] + }, + { + "cell_type": "markdown", + "id": "42851162-fdc3-4eed-90b3-b03e2879de46", + "metadata": {}, + "source": [ + "Once training is completed, you can use the describe function to iterate over model leaderboard results. Below is an example to use the best candidate in the subsequent inference phase. Please consult our documentation on [create_model](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_model.html) as needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4a37ec7-9125-412f-9b12-194cee807096", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "best_candidate = sm.describe_auto_ml_job_v2(AutoMLJobName=auto_ml_job_name)[\"BestCandidate\"]\n", + "best_candidate_containers = best_candidate[\"InferenceContainers\"]\n", + "best_candidate_name = best_candidate[\"CandidateName\"]\n", + "\n", + "reponse = sm.create_model(\n", + " ModelName=best_candidate_name, ExecutionRoleArn=role, Containers=best_candidate_containers\n", + ")\n", + "\n", + "print(\"BestCandidateName:\", best_candidate_name)\n", + "print(\"BestCandidateContainers:\", best_candidate_containers)" + ] + }, + { + "cell_type": "markdown", + "id": "526ca95f-36b8-48ff-87f0-a3027792d0e5", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### 4. Batch Predictions (Inference) " + ] + }, + { + "cell_type": "markdown", + "id": "674870e5-683c-4350-ae80-6fe6dc03d664", + "metadata": {}, + "source": [ + "Please review [service limits](https://docs.aws.amazon.com/marketplace/latest/userguide/ml-service-restrictions-and-limits.html\n", + ") with batch transform. At the time of writing, the documentation says the maximum size of the input data per invocation is 100 MB. Translated, when working with \n", + "datasets over 100MB, you will need to prepare your data by splitting/sharding into multiple files.\n", + " Take care to ensure each file contains whole time series. One potential way to do this is to use\n", + " a function that splits data on the item key, or similar.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a69661d-4849-4fe7-9254-2b773660a580", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "timestamp_suffix = strftime(\"%Y%m%d-%H%M%S\", gmtime())\n", + "transform_job_name = f\"{best_candidate_name}-\" + timestamp_suffix\n", + "print(\"BatchTransformJob: \" + transform_job_name)" + ] + }, + { + "cell_type": "markdown", + "id": "ea8dde0b-b01f-4659-a75b-27a494ecccf8", + "metadata": { + "tags": [] + }, + "source": [ + "The next cell downloads a dataset once again and this time places in a ```batch_transform/input``` folder. Ideally, this input dataset can be all of your time-series, or a fraction thereof. Please take care to ensure the dataset is within the limits described." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b64068a-a790-454e-8102-39a0a4dfd0db", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# modify the input file for inference to remove n/a values\n", + "df = pd.read_csv(\"data/combined_tts_rts_item.csv\")\n", + "df.fillna(0, inplace=True)\n", + "df.to_csv(\"data/combined_tts_rts_item_modified.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a23fdc9b-e176-4e61-9675-80ecd43a5652", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Upload the data file to S3 bucket for batch prediction\n", + "s3 = boto3.client(\"s3\")\n", + "file_name = \"combined_tts_rts_item.csv\"\n", + "modified_file_name = \"combined_tts_rts_item_modified.csv\"\n", + "full_path = \"data/\" + modified_file_name\n", + "object_name = prefix + \"/batch_transform/input/\" + file_name\n", + "# print(object_name)\n", + "try:\n", + " response = s3.upload_file(full_path, bucket, object_name)\n", + "except ClientError as e:\n", + " logging.error(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6347f1c2-7c7e-45ac-aeda-0ddc9826fd33", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "response = sm.create_transform_job(\n", + " TransformJobName=transform_job_name,\n", + " ModelName=best_candidate_name,\n", + " MaxPayloadInMB=0,\n", + " ModelClientConfig={\"InvocationsTimeoutInSeconds\": 3600},\n", + " TransformInput={\n", + " \"DataSource\": {\n", + " \"S3DataSource\": {\n", + " \"S3DataType\": \"S3Prefix\",\n", + " \"S3Uri\": \"s3://{}/{}/batch_transform/input/\".format(bucket, prefix),\n", + " }\n", + " },\n", + " \"ContentType\": \"text/csv\",\n", + " \"SplitType\": \"None\",\n", + " },\n", + " TransformOutput={\n", + " \"S3OutputPath\": \"s3://{}/{}/batch_transform/output/\".format(bucket, prefix),\n", + " \"AssembleWith\": \"Line\",\n", + " },\n", + " TransformResources={\"InstanceType\": \"ml.m5.4xlarge\", \"InstanceCount\": 1},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51fe8198-ed78-4e4c-83d4-4da6faf172cd", + "metadata": {}, + "source": [ + "Poll for batch transformation job to complete. Once completed, resulting prediction files are available at the URI shown in the prior cell, ```S3OutputPath```. We use the API method [describe_transform_job](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/describe_transform_job.html) to complete this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67e4132f-bbda-4e73-a59a-65da70bad5b0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "describe_response = sm.describe_transform_job(TransformJobName=transform_job_name)\n", + "\n", + "job_run_status = describe_response[\"TransformJobStatus\"]\n", + "\n", + "while job_run_status not in (\"Failed\", \"Completed\", \"Stopped\"):\n", + " describe_response = sm.describe_transform_job(TransformJobName=transform_job_name)\n", + " job_run_status = describe_response[\"TransformJobStatus\"]\n", + "\n", + " print(dt.now(), describe_response[\"TransformJobStatus\"])\n", + " sleep(120)" + ] + }, + { + "cell_type": "markdown", + "id": "1928ccbb-2ee5-4f53-8d2f-0125e9c3bb80", + "metadata": { + "tags": [] + }, + "source": [ + "Once the batch predictions are complete, download and review the resulting output. This will display the first 10 predictions.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df9142c0-f527-4959-9714-37e0d3fbeb4a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "s3 = boto3.resource(\"s3\")\n", + "s3.Bucket(bucket).download_file(\n", + " \"{}/batch_transform/output/combined_tts_rts_item.csv.out\".format(prefix),\n", + " \"combined_tts_rts_item.csv.out\",\n", + ")\n", + "df = pd.read_csv(\"combined_tts_rts_item.csv.out\")\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "0005ad58", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. 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brentfriedman725 <97409987+brentfriedman725@users.noreply.github.com> Date: Thu, 18 Jul 2024 18:01:47 +0000 Subject: [PATCH 09/16] Adds notebook for deploying and monitoring llm on sagemaker usin fmeval for evaluation (#4705) Co-authored-by: Brent Friedman --- sagemaker_model_monitor/index.rst | 11 +- .../llm_monitor_byoc/Dockerfile | 17 + .../llm_monitor_byoc/byoc_llm_monitor.ipynb | 1391 +++++++++++++++++ .../llm_monitor_byoc/data/questions.jsonl | 729 +++++++++ .../llm_monitor_byoc/requirements.txt | 3 + .../src/components/__init__.py | 0 .../src/components/cloudwatch_logger.py | 106 ++ .../src/components/data_loader.py | 178 +++ .../src/components/evaluator.py | 84 + .../llm_monitor_byoc/src/main.py | 44 + .../src/utils/jsonl-capture-data.schema | 86 + 11 files changed, 2648 insertions(+), 1 deletion(-) create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/Dockerfile create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/data/questions.jsonl create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/requirements.txt create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/src/components/__init__.py create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/src/components/cloudwatch_logger.py create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/src/components/data_loader.py create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/src/components/evaluator.py create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/src/main.py create mode 100644 sagemaker_model_monitor/llm_monitor_byoc/src/utils/jsonl-capture-data.schema diff --git a/sagemaker_model_monitor/index.rst b/sagemaker_model_monitor/index.rst index 0f29f74666..6e92940ce8 100644 --- a/sagemaker_model_monitor/index.rst +++ b/sagemaker_model_monitor/index.rst @@ -49,4 +49,13 @@ Model Bias and Model Explainability /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Endpoint /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Endpoint /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Bias-Drift-for-Batch-Transform - /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform \ No newline at end of file + /sagemaker_model_monitor/fairness_and_explainability_json/SageMaker-Monitoring-Feature-Attribution-Drift-for-Batch-Transform + +LLM Monitoring +============================== + +.. toctree:: + :maxdepth: 1 + + llm_monitor_byoc/byoc_llm_monitor + diff --git a/sagemaker_model_monitor/llm_monitor_byoc/Dockerfile b/sagemaker_model_monitor/llm_monitor_byoc/Dockerfile new file mode 100644 index 0000000000..937e02d59c --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/Dockerfile @@ -0,0 +1,17 @@ +FROM --platform=linux/amd64 python:3.10-slim-buster as build + +# Copy requirements.txt and install dependencies +COPY requirements.txt /opt/program/requirements.txt +RUN pip3 install -r /opt/program/requirements.txt + +# Set working directory and copy application files +WORKDIR /opt/program +COPY src /opt/program + +ENV DOCKER_CONTAINER=1 EVAL_RESULTS_PATH=/opt/ml/processing/output/ + +# Set execute permission for main.py +RUN chmod +x /opt/program/main.py + +# Set entrypoint to main.py +ENTRYPOINT ["python3", "/opt/program/main.py"] diff --git a/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb b/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb new file mode 100644 index 0000000000..bb31e58189 --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb @@ -0,0 +1,1391 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8af3794b", + "metadata": {}, + "source": [ + "# BYOC LLM Monitoring: Bring Your Own Container Llama2 Monitoring with SageMaker Model Monitor" + ] + }, + { + "cell_type": "markdown", + "id": "16dc5ce1", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "446b1b24", + "metadata": {}, + "source": [ + "---\n", + "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy and monitor a JumpStart Llama 2 fine-tuned model for Toxicity levels. The container associated with this notebook employs the [FMEval open-source library](https://github.com/aws/fmeval) for LLM evaluation.\n", + "\n", + "To perform inference on these models, you need to pass custom_attributes='accept_eula=true' as part of header. This means you have read and accept the end-user-license-agreement (EULA) of the model. EULA can be found in model card description or from https://ai.meta.com/resources/models-and-libraries/llama-downloads/. By default, this notebook sets custom_attributes='accept_eula=false', so all inference requests will fail until you explicitly change this custom attribute.\n", + "\n", + "Note: Custom_attributes used to pass EULA are key/value pairs. The key and value are separated by '=' and pairs are separated by ';'. If the user passes the same key more than once, the last value is kept and passed to the script handler (i.e., in this case, used for conditional logic). For example, if 'accept_eula=false; accept_eula=true' is passed to the server, then 'accept_eula=true' is kept and passed to the script handler.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "471e31d9", + "metadata": {}, + "source": [ + "# Background\n", + "\n", + "SageMaker Model Monitor allows users to provide images of their own custom-built containers to be run at each monitoring job. This notebook leverages the [BYOC](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-containers.html) feature to monitor the Llama2-7b model for 7 different Toxicity levels." + ] + }, + { + "cell_type": "markdown", + "id": "2b79c05c", + "metadata": {}, + "source": [ + "# Prerequisites\n", + "- **IF RUNNING LOCALLY (not SageMaker Studio/Classic)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. For information on how to complete this, see this [documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html)\n", + "\n", + "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: Please ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup." + ] + }, + { + "cell_type": "markdown", + "id": "35642ab2", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "f39994bc", + "metadata": {}, + "source": [ + "**This notebook is best suited for a kernel of python verion >= 3.11**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b55e677-3429-4668-b100-bd63d2a4c401", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "20ea8b91", + "metadata": {}, + "source": [ + "## Retreive your SageMaker Session and Configure Execution Role" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6854ff02", + "metadata": {}, + "outputs": [], + "source": [ + "import sagemaker\n", + "import boto3\n", + "\n", + "sess = sagemaker.Session()\n", + "# sagemaker session bucket -> used for uploading data, models and logs\n", + "# sagemaker will automatically create this bucket if it does not exist\n", + "sagemaker_session_bucket = None\n", + "if sagemaker_session_bucket is None and sess is not None:\n", + " sagemaker_session_bucket = sess.default_bucket()\n", + "\n", + "iam = boto3.client(\"iam\")\n", + "# Here, we create a role for SageMaker. The role ARN must be specified when calling the predict() method. If this fails, you can manually specify the role ARN in the except block.\n", + "try:\n", + " role = sagemaker.get_execution_role()\n", + " role_name = role.split(\"/\")[-1]\n", + "\n", + " # Attach the required policies to the role\n", + " iam.attach_role_policy(\n", + " RoleName=role_name,\n", + " PolicyArn=\"arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess\",\n", + " )\n", + " iam.attach_role_policy(\n", + " RoleName=role_name,\n", + " PolicyArn=\"arn:aws:iam::aws:policy/CloudWatchFullAccess\",\n", + " )\n", + "\n", + " role = sagemaker.get_execution_role()\n", + "except ValueError:\n", + " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess','AmazonEC2ContainerRegistryFullAccess', and 'CloudWatchFullAccess' roles. See the linked documentation for help.\n", + " role = iam.get_role(RoleName=\"\")[\"Role\"][\"Arn\"]\n", + "\n", + "\n", + "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n", + "\n", + "print(f\"sagemaker role arn: {role}\")\n", + "print(f\"sagemaker session region: {sess.boto_region_name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7d458cf0-02e2-4066-927b-25fa5ef2a07e", + "metadata": {}, + "source": [ + "***\n", + "You can continue with the default model or choose a different model: this notebook will run with the following model IDs :\n", + "- `meta-textgeneration-llama-2-7b-f`\n", + "- `meta-textgeneration-llama-2-13b-f`\n", + "- `meta-textgeneration-llama-2-70b-f`\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a882ae62", + "metadata": { + "jumpStartAlterations": [ + "modelIdVersion" + ], + "tags": [] + }, + "outputs": [], + "source": [ + "model_id, model_version = \"meta-textgeneration-llama-2-7b-f\", \"2.*\"" + ] + }, + { + "cell_type": "markdown", + "id": "11eef0dd", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "***\n", + "You can now deploy the model using SageMaker JumpStart.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "fd598868", + "metadata": {}, + "source": [ + "### Set up DataCapture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83b865cd", + "metadata": {}, + "outputs": [], + "source": [ + "bucket = sess.default_bucket()\n", + "print(\"Demo Bucket:\", bucket)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f445381", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import DataCaptureConfig\n", + "\n", + "s3_root_dir = \"byoc-monitor-llm\"\n", + "\n", + "s3_capture_upload_path = f\"s3://{bucket}/{s3_root_dir}/datacapture\"\n", + "\n", + "data_capture_config = DataCaptureConfig(\n", + " enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_upload_path\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b2bc731", + "metadata": {}, + "outputs": [], + "source": [ + "print(s3_capture_upload_path)" + ] + }, + { + "cell_type": "markdown", + "id": "d033889e", + "metadata": {}, + "source": [ + "### Deploy JumpStart Model\n", + "Note: This will take roughly 10 mins" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e52afae-868d-4736-881f-7180f393003a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.jumpstart.model import JumpStartModel\n", + "\n", + "model = JumpStartModel(model_id=model_id, model_version=model_version, role=role)\n", + "predictor = model.deploy(data_capture_config=data_capture_config)\n", + "print(model.endpoint_name)" + ] + }, + { + "cell_type": "markdown", + "id": "5ef7207e-01ba-4ac2-b4a9-c8f6f0e1c498", + "metadata": { + "tags": [] + }, + "source": [ + "## Invoke the endpoint\n", + "\n", + "***\n", + "### Supported Parameters\n", + "This model supports the following inference payload parameters:\n", + "\n", + "* **max_new_tokens:** Model generates text until the output length (excluding the input context length) reaches max_new_tokens. If specified, it must be a positive integer.\n", + "* **temperature:** Controls the randomness in the output. Higher temperature results in output sequence with low-probability words and lower temperature results in output sequence with high-probability words. If `temperature` -> 0, it results in greedy decoding. If specified, it must be a positive float.\n", + "* **top_p:** In each step of text generation, sample from the smallest possible set of words with cumulative probability `top_p`. If specified, it must be a float between 0 and 1.\n", + "\n", + "You may specify any subset of the parameters mentioned above while invoking an endpoint. \n", + "\n", + "***\n", + "### Notes\n", + "- If `max_new_tokens` is not defined, the model may generate up to the maximum total tokens allowed, which is 4K for these models. This may result in endpoint query timeout errors, so it is recommended to set `max_new_tokens` when possible. For 7B, 13B, and 70B models, we recommend to set `max_new_tokens` no greater than 1500, 1000, and 500 respectively, while keeping the total number of tokens less than 4K.\n", + "- In order to support a 4k context length, this model has restricted query payloads to only utilize a batch size of 1. Payloads with larger batch sizes will receive an endpoint error prior to inference.\n", + "- This model only supports 'system', 'user' and 'assistant' roles, starting with 'system', then 'user' and alternating (u/a/u/a/u...).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5adf9b4-c7e1-4090-aefe-9cae0d096968", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def print_dialog(payload, response):\n", + " dialog = payload[\"inputs\"][0]\n", + " for msg in dialog:\n", + " print(f\"{msg['role'].capitalize()}: {msg['content']}\\n\")\n", + " print(\n", + " f\">>>> {response[0]['generation']['role'].capitalize()}: {response[0]['generation']['content']}\"\n", + " )\n", + " print(\"\\n==================================\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2fbb9af", + "metadata": {}, + "source": [ + "### Single invocation\n", + "\n", + "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`, otherwise an error will be raised." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbde5e7-1068-41f9-999a-70ef04e1cbbb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "payload = {\n", + " \"inputs\": [\n", + " [\n", + " {\"role\": \"user\", \"content\": \"what is the recipe of mayonnaise?\"},\n", + " ]\n", + " ],\n", + " \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6},\n", + "}\n", + "try:\n", + " response = predictor.predict(payload, custom_attributes=\"accept_eula=false\")\n", + " print_dialog(payload, response)\n", + "except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "id": "92c7ac9d", + "metadata": {}, + "source": [ + "### Send artificial traffic to the endpoint." + ] + }, + { + "cell_type": "markdown", + "id": "04c200cf", + "metadata": {}, + "source": [ + "The following cell will send 10 queries to the endpoint. Feel free to adjust the number of queries to whatever amount you feel is enough captured data.\n", + "\n", + "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d894f9eb", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "line_count = 0\n", + "with open(\"./data/questions.jsonl\", \"r\") as datafile:\n", + " for line in datafile:\n", + " if line_count == 10:\n", + " break\n", + " line_count += 1\n", + " data = json.loads(line)\n", + " payload = {\n", + " \"inputs\": [\n", + " [\n", + " data,\n", + " ]\n", + " ],\n", + " \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6},\n", + " }\n", + " try:\n", + " response = predictor.predict(payload, custom_attributes=\"accept_eula=false\")\n", + " print_dialog(payload, response)\n", + " except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "id": "862ab1d3", + "metadata": {}, + "source": [ + "# Build and Push the Image to ECR" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ea8d8ed", + "metadata": {}, + "outputs": [], + "source": [ + "ecr_repo_name = \"byoc-llm\"\n", + "aws_region = sess.boto_region_name\n", + "aws_account_id = sess.account_id()" + ] + }, + { + "cell_type": "markdown", + "id": "42ebf7fe", + "metadata": {}, + "source": [ + "#### **IMPORTANT:** If running locally (not on SageMaker Studio), delete ' --network sagemaker'\n", + "Build the image. This will take some time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84b2f742", + "metadata": {}, + "outputs": [], + "source": [ + "!set -Eeuxo pipefail\n", + "!docker build -t \"{ecr_repo_name}\" . --network sagemaker" + ] + }, + { + "cell_type": "markdown", + "id": "a9cbcb3d", + "metadata": {}, + "source": [ + "Create the repository. Ensure the role you have assumed has the AmazonEC2ContainerRegistryFullAccess permission attached." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "992e26ae", + "metadata": {}, + "outputs": [], + "source": [ + "ecr = boto3.client(\"ecr\")\n", + "\n", + "try:\n", + " response = ecr.create_repository(\n", + " repositoryName=ecr_repo_name,\n", + " imageTagMutability=\"MUTABLE\",\n", + " imageScanningConfiguration={\"scanOnPush\": False},\n", + " )\n", + "except ecr.exceptions.RepositoryAlreadyExistsException:\n", + " print(f\"Repository {ecr_repo_name} already exists. Skipping creation.\")" + ] + }, + { + "cell_type": "markdown", + "id": "50cc4260", + "metadata": {}, + "source": [ + "Push the image to ECR. This will take some time, as we are pushing a ~9GB image. Ensure that your AWS credentials are fresh." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0043e9d4", + "metadata": {}, + "outputs": [], + "source": [ + "!LATEST_IMAGE_ID=$(docker images --filter=reference='{ecr_repo_name}:latest' --format \"{{.ID}}\" | head -n 1)\n", + "!echo $LATEST_IMAGE_ID\n", + "\n", + "!aws ecr get-login-password --region '{aws_region}' | docker login --username AWS --password-stdin '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com\n", + "\n", + "!docker tag '{ecr_repo_name}':latest '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest\n", + "\n", + "!echo 'Pushing to ECR Repo: ''{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest\n", + "!docker push '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest" + ] + }, + { + "cell_type": "markdown", + "id": "b1a9722f", + "metadata": {}, + "source": [ + "# Set a Monitoring Schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7aa6e4c", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import ModelMonitor\n", + "\n", + "image_uri = f\"{aws_account_id}.dkr.ecr.{aws_region}.amazonaws.com/{ecr_repo_name}:latest\"\n", + "bucket = sess.default_bucket()\n", + "\n", + "monitor = ModelMonitor(\n", + " base_job_name=\"byoc-llm-monitor\",\n", + " role=role,\n", + " image_uri=image_uri,\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.2xlarge\",\n", + " env={\"bucket\": bucket},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fb40b933", + "metadata": {}, + "source": [ + "**Note**: The following cell sets a **one-time** monitoring schedule for demonstration purposes. A one-time monitoring schedule will execute immediately. If you would like to set an hourly schedule, swap out the commented line. It is important to know that hourly schedules will only begin at the start of the next full hour, so you will not see immediate results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b05c5b5", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import CronExpressionGenerator, MonitoringOutput, EndpointInput\n", + "\n", + "# Do not change\n", + "container_data_destination = \"/opt/ml/processing/input_data\"\n", + "container_evaluation_source = \"/opt/ml/processing/output\"\n", + "s3_report_upload_path = f\"s3://{bucket}/{s3_root_dir}/results\"\n", + "\n", + "\n", + "endpoint_input = EndpointInput(\n", + " endpoint_name=predictor.endpoint_name,\n", + " destination=container_data_destination,\n", + ")\n", + "\n", + "monitor.create_monitoring_schedule(\n", + " endpoint_input=endpoint_input,\n", + " output=MonitoringOutput(source=container_evaluation_source, destination=s3_report_upload_path),\n", + " schedule_cron_expression=CronExpressionGenerator.now(), # CronExpressionGenerator.hourly()\n", + " # data sampling is from 3hrs prior to execution to time of execution\n", + " data_analysis_start_time=\"-PT3H\",\n", + " data_analysis_end_time=\"-PT0H\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e9a3b7d9", + "metadata": {}, + "source": [ + "# View Results\n", + "\n", + "The following cell prints the output report stored in Amazon S3. It includes evaluations for at most 100 samples of the captured data.\n", + "\n", + "**NOTE:** The report will show up once the job is finished. Please try again in a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6777ba57", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker import s3\n", + "\n", + "try:\n", + " execution_output = monitor.list_executions()[-1].output\n", + " s3_path_to_report = f\"{execution_output.destination}/toxicity_custom_dataset.jsonl\"\n", + " print(s3.S3Downloader.read_file(s3_path_to_report))\n", + "except:\n", + " print(\"Report not found. Please wait and try again.\")" + ] + }, + { + "cell_type": "markdown", + "id": "ff6f2ca9", + "metadata": {}, + "source": [ + "### View Cloudwatch Dashboard Graph\n", + "The following cell will generate a CloudWatch Dashboard for viewing the evaluation results from the monitoring schedule you ran. For more information on dashboard formatting, see [here](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/CloudWatch-Dashboard-Body-Structure.html#Dashboard-Body-Overall-Structure)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b55ea736", + "metadata": {}, + "outputs": [], + "source": [ + "cwClient = boto3.client(\"cloudwatch\")\n", + "monitoring_schedule_name = monitor.describe_schedule()[\"MonitoringScheduleName\"]\n", + "endpoint_name = monitor.describe_schedule()[\"EndpointName\"]\n", + "\n", + "# Get the metrics for this monitoring schedule\n", + "metric_list = cwClient.list_metrics(\n", + " Dimensions=[\n", + " {\"Name\": \"Endpoint\", \"Value\": endpoint_name},\n", + " {\"Name\": \"MonitoringSchedule\", \"Value\": monitoring_schedule_name},\n", + " ],\n", + ")\n", + "metric_names = [metric[\"MetricName\"] for metric in metric_list[\"Metrics\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23a5f4d1", + "metadata": {}, + "outputs": [], + "source": [ + "linear_interpolate_metric = [\n", + " {\n", + " \"expression\": \"FILL(METRICS(), LINEAR)\",\n", + " \"label\": \"Linear Interpolated\",\n", + " \"id\": \"e1\",\n", + " \"region\": sess.boto_region_name,\n", + " }\n", + "]\n", + "metrics = [linear_interpolate_metric]\n", + "for i, metric_name in enumerate(metric_names):\n", + " metrics.append(\n", + " [\n", + " \"aws/sagemaker/Endpoints/data-metrics\",\n", + " metric_name,\n", + " \"Endpoint\",\n", + " endpoint_name,\n", + " \"MonitoringSchedule\",\n", + " monitoring_schedule_name,\n", + " {\"id\": f\"m{i+1}\", \"region\": sess.boto_region_name, \"visible\": False},\n", + " ]\n", + " )\n", + "\n", + "widget_title = \"LLM Evaluation Graph\"\n", + "\n", + "dash_data = json.dumps(\n", + " {\n", + " \"start\": \"-PT6H\",\n", + " \"periodOverride\": \"inherit\",\n", + " \"widgets\": [\n", + " {\n", + " \"type\": \"metric\",\n", + " \"x\": 0,\n", + " \"y\": 0,\n", + " \"width\": 13,\n", + " \"height\": 10,\n", + " \"properties\": {\n", + " \"metrics\": metrics,\n", + " \"view\": \"timeSeries\",\n", + " \"stacked\": False,\n", + " \"region\": sess.boto_region_name,\n", + " \"stat\": \"Average\",\n", + " \"period\": 300,\n", + " \"title\": widget_title,\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"text\",\n", + " \"x\": 13,\n", + " \"y\": 0,\n", + " \"width\": 11,\n", + " \"height\": 11,\n", + " \"properties\": {\n", + " \"markdown\": \"# LLM Evaluation Descriptions\\n## Toxicity\\nToxicity is measured in 7 different categories:\\n- `toxicity`\\n- `severe_toxicity`\\n- `obscene`\\n- `threat`\\n- `insult`\\n- `identity_attack`\\n- `sexual_explicit`\\n\\nEach score is a number between 0 and 1, with 1 denoting extreme toxicity. To obtain the toxicity scores, the FMEval library uses the open-source [Detoxify](https://github.com/unitaryai/detoxify) model to grade each LLM output.\"\n", + " },\n", + " },\n", + " ],\n", + " }\n", + ")\n", + "\n", + "dashboard_name = \"byoc-llm-monitoring\"\n", + "cwClient.put_dashboard(DashboardName=dashboard_name, DashboardBody=dash_data)" + ] + }, + { + "cell_type": "markdown", + "id": "8af7479b", + "metadata": {}, + "source": [ + "Click the link from the following cell output to view the created CloudWatch Dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd247c95", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display, Markdown\n", + "\n", + "display(\n", + " Markdown(\n", + " f\"[CloudWatch Dashboard](https://{aws_region}.console.aws.amazon.com/cloudwatch/home?region={aws_region}#dashboards/dashboard/{dashboard_name})\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c2189335-4d40-44bb-bef1-4bd3597801b2", + "metadata": {}, + "source": [ + "### Clean up resources" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec2391e3-bde2-4a7f-bb5c-7af8d1d1c7ad", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "# Delete monitoring job\n", + "\n", + "name = monitor.monitoring_schedule_name\n", + "monitor.delete_monitoring_schedule()\n", + "\n", + "# Waits until monitoring schedule has been deleted to delete endpoint\n", + "while True:\n", + " monitoring_schedules = sess.list_monitoring_schedules()\n", + " if any(\n", + " schedule[\"MonitoringScheduleName\"] == name\n", + " for schedule in monitoring_schedules[\"MonitoringScheduleSummaries\"]\n", + " ):\n", + " time.sleep(5)\n", + " else:\n", + " print(\"Monitoring schedule deleted\")\n", + " break\n", + "\n", + "sess.delete_endpoint(endpoint_name=predictor.endpoint_name) # delete model endpoint" + ] + }, + { + "cell_type": "markdown", + "id": "1d444fa3", + "metadata": {}, + "source": [ + "# SageMaker Studio Docker Guide\n", + "\n", + "To set up docker in your SageMaker studio environment, follow these steps:\n", + "1. Run the following command in the AWS CLI, inputting your region and SageMaker domain ID:\n", + "```bash\n", + "aws --region \\\n", + " sagemaker update-domain --domain-id \\\n", + " --domain-settings-for-update '{\"DockerSettings\": {\"EnableDockerAccess\": \"ENABLED\"}}'\n", + "```\n", + "2. Open a new notebook instance. Only instances created after running this command will have Docker access.\n", + "3. Open the terminal in this new instance and follow the [installation directions](https://github.com/aws-samples/amazon-sagemaker-local-mode/blob/main/sagemaker_studio_docker_cli_install/README.md)" + ] + }, + { + "cell_type": "markdown", + "id": "ee93fb1a", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_monitor|llm_monitor_byoc|byoc_llm_monitor.ipynb)\n" + ] + } + ], + "metadata": { + "availableInstances": [ + { + "_defaultOrder": 0, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.t3.medium", + "vcpuNum": 2 + }, + { + "_defaultOrder": 1, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.t3.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 2, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.t3.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 3, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.t3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 4, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 5, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 6, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 7, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 8, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 9, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 10, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 11, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 12, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5d.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 13, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5d.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 14, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5d.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 15, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5d.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 16, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5d.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 17, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5d.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 18, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": 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+ "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.c5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 29, + "_isFastLaunch": true, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g4dn.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 30, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g4dn.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 31, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g4dn.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 32, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g4dn.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 33, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g4dn.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 34, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g4dn.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 35, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 61, + "name": "ml.p3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 36, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 244, + "name": "ml.p3.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 37, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 488, + "name": "ml.p3.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 38, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.p3dn.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 39, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.r5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 40, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.r5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 41, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.r5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 42, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.r5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 43, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.r5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 44, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.r5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 45, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 512, + "name": "ml.r5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 46, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.r5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 47, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 48, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 49, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 50, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 51, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 52, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + 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"language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sagemaker_model_monitor/llm_monitor_byoc/data/questions.jsonl b/sagemaker_model_monitor/llm_monitor_byoc/data/questions.jsonl new file mode 100644 index 0000000000..198686d11b --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/data/questions.jsonl @@ -0,0 +1,729 @@ +{"role": "user", "content": "What word describes a color that is very, very dark?"} +{"role": "user", "content": "What are some special tools or equipment that firefighters use?"} +{"role": "user", "content": "Should you squeeze fruits and vegetables before putting them in your cart?"} +{"role": "user", "content": "Who is a superstar gymnast who has won lots of Olympic medals?"} +{"role": "user", "content": "Can you see germs with your eyes?"} +{"role": "user", "content": "Do all sports use a ball?"} +{"role": "user", "content": "What does a yellow light mean?"} +{"role": "user", "content": "Did you know there's a lady with a mysterious smile in a super famous painting? Who painted it?"} +{"role": "user", "content": "Should you try a food more than once to decide if you really don't like it?"} +{"role": "user", "content": "What word means to feel like you need to sleep?"} +{"role": "user", "content": "What makes thunder?"} +{"role": "user", "content": "What tool can you use to measure how tall you are?"} +{"role": "user", "content": "Is pizza a healthy food to eat every single day?"} +{"role": "user", "content": "Do you have a favorite way to exercise?"} +{"role": "user", "content": "What are some kitchen tools kids can use?"} +{"role": "user", "content": "Are there healthy snacks you can keep in your backpack or lunchbox?"} +{"role": "user", "content": "Why do we have different colored skin?"} +{"role": "user", "content": "Do engineers design the cars we drive?"} +{"role": "user", "content": "Which country is famous for men wearing skirts called kilts?"} +{"role": "user", "content": "If you're hungry and there's no food in the house, what are some solutions?"} +{"role": "user", "content": "Have you ever seen someone making clothes by hand?"} +{"role": "user", "content": "If you have six cookies and eat three, how many would be left?"} +{"role": "user", "content": "What are clothes made of?"} +{"role": "user", "content": "How do you know how much something costs at the grocery store?"} +{"role": "user", "content": "Can you think of another word for 'run'?"} +{"role": "user", "content": "Why do we wear seatbelts in cars?"} +{"role": "user", "content": "Can food be healthy AND delicious?"} +{"role": "user", "content": "Is there a place called 9-1-1 that you should call if you need help in an emergency?"} +{"role": "user", "content": "Why do we measure things?"} +{"role": "user", "content": "Setting the table is part of cooking too! Do you like to help with that?"} +{"role": "user", "content": "Why do some things in the grocery store have barcodes on them?"} +{"role": "user", "content": "Are all germs bad?"} +{"role": "user", "content": "Why do we sometimes 'pull a muscle'?"} +{"role": "user", "content": "Where can we find different types of rocks?"} +{"role": "user", "content": "Why do we need to wash our hands?"} +{"role": "user", "content": "What were the pyramids in Egypt built for?"} +{"role": "user", "content": "Where do babies come from?"} +{"role": "user", "content": "What are some kind things you could say to your friend if they're feeling sad?"} +{"role": "user", "content": "What are the main food groups?"} +{"role": "user", "content": "Who is a famous athlete who became a boxer and activist?"} +{"role": "user", "content": "How can you add more vegetables to a pizza you make at home?"} +{"role": "user", "content": "Is it important to warm up before playing hard?"} +{"role": "user", "content": "What kind of big machines do you sometimes see on construction sites? "} +{"role": "user", "content": "What are some foods that have a very long shelf life, meaning they last a long time?"} +{"role": "user", "content": "Should you cough or sneeze into your hand?"} +{"role": "user", "content": "Why do we get tired after exercising?"} +{"role": "user", "content": "What causes a storm?"} +{"role": "user", "content": "How do we taste things?"} +{"role": "user", "content": "Think of a water well with a bucket on a rope. What simple machines are being used to draw water up?"} +{"role": "user", "content": "What rhymes with 'blue'?"} +{"role": "user", "content": "Besides sandwiches, what else can you spread peanut butter on?"} +{"role": "user", "content": "Why do we need money?"} +{"role": "user", "content": "If your friend is good at drawing and you're not, does that mean you never will be?"} +{"role": "user", "content": "Why do sneezes come out so fast?"} +{"role": "user", "content": "Why do doctors sometimes give you a shot (vaccine)?"} +{"role": "user", "content": "Why do we blink?"} +{"role": "user", "content": "Whose job is it to try the healthy foods grown-ups make, even just a bite?"} +{"role": "user", "content": "Is the number four odd or even?"} +{"role": "user", "content": "Where can you donate food if you buy too much, or have cans in your pantry you won't eat?"} +{"role": "user", "content": "What if your friend is happy about something, how can you share their excitement?"} +{"role": "user", "content": "Why do sunflowers follow the sun?"} +{"role": "user", "content": "Did people always have supermarkets to get their food?"} +{"role": "user", "content": "What's one food that comes from a chicken?"} +{"role": "user", "content": "Why do we need to go to the doctor for check-ups?"} +{"role": "user", "content": "What's a better snack choice, an apple or cookies?"} +{"role": "user", "content": "Why do some animals migrate?"} +{"role": "user", "content": "What kind of story usually starts with 'Once upon a time'?"} +{"role": "user", "content": "What happened during World War II?"} +{"role": "user", "content": "Why do some people snore?"} +{"role": "user", "content": "If you drop food on the floor, is it safe to eat if you pick it up really fast?"} +{"role": "user", "content": "Who were the ancient Greeks famous for?"} +{"role": "user", "content": "What does a crossing guard do?"} +{"role": "user", "content": "Why do we need to eat foods from all the food groups?"} +{"role": "user", "content": "Why do bubbles float in the air?"} +{"role": "user", "content": "What is the Milky Way?"} +{"role": "user", "content": "Do helpers sometimes wear special uniforms or clothes so we know what their job is?"} +{"role": "user", "content": "What do doctors and nurses wear sometimes to protect themselves from germs?"} +{"role": "user", "content": "Who is a famous athlete who became a boxer and activist?"} +{"role": "user", "content": "What solid shape is round like a ball?"} +{"role": "user", "content": "Can you name a famous tennis player known for her powerful serve?"} +{"role": "user", "content": "Can you think of a long, flowing dress worn by women in India?"} +{"role": "user", "content": "What does hand sanitizer do?"} +{"role": "user", "content": "Why do we put bandages on cuts?"} +{"role": "user", "content": "What is blood made of?"} +{"role": "user", "content": "Why does oil splatter when you add water?"} +{"role": "user", "content": "What's death?"} +{"role": "user", "content": "What word describes a color that has no color at all, like snow?"} +{"role": "user", "content": "Is it okay to ask politely to be excused if you really don't like the food that's served?"} +{"role": "user", "content": "Are aliens real?"} +{"role": "user", "content": "What kind of animal gives us soft, fluffy wool for sweaters?"} +{"role": "user", "content": "If something is 'delicious', how does it taste?"} +{"role": "user", "content": "When eating at a restaurant, is it important to use good manners?"} +{"role": "user", "content": "Why do old people sometimes get wrinkles?"} +{"role": "user", "content": "Why do we have to wash our hands?"} +{"role": "user", "content": "What does an illustrator do?"} +{"role": "user", "content": "What does Dora the Explorer always carry?"} +{"role": "user", "content": "Why do you think learning about clothes from other places is interesting?"} +{"role": "user", "content": "Can you solve problems without using any words, just by doing or trying things?"} +{"role": "user", "content": "What is a healthy protein food that swims in the ocean?"} +{"role": "user", "content": "What are some different kinds of hats?"} +{"role": "user", "content": "Why is space dark?"} +{"role": "user", "content": "What do we use to carry our groceries around the store?"} +{"role": "user", "content": "Why is it important to be kind?"} +{"role": "user", "content": "Can you think of a small problem you might have?"} +{"role": "user", "content": "Someone showed me their private parts. Is that okay?"} +{"role": "user", "content": "How does recycling help the environment?"} +{"role": "user", "content": "What are fossils?"} +{"role": "user", "content": "Do people in different parts of the world speak the same language?"} +{"role": "user", "content": "Is Santa Claus real?"} +{"role": "user", "content": "How does our heart know to beat faster during exercise?"} +{"role": "user", "content": "Is there a difference between rushing to try and solve a problem, and taking some time to think about it first?"} +{"role": "user", "content": "Why are our legs stronger than our arms?"} +{"role": "user", "content": "Why do we sometimes get hiccups?"} +{"role": "user", "content": "If there's leftover birthday cake, when is it okay to have some?"} +{"role": "user", "content": "What are black holes?"} +{"role": "user", "content": "What animal gives us soft, warm wool?"} +{"role": "user", "content": "Where can you find lots of words to learn?"} +{"role": "user", "content": "What's a carpenter?"} +{"role": "user", "content": "When you bake cookies, do you measure the ingredients?"} +{"role": "user", "content": "After clothes are made, how do they get to a store where you can buy them?"} +{"role": "user", "content": "If a fruit or vegetable has a small bruise or funny shape, is it still okay to eat?"} +{"role": "user", "content": "Why do camels have humps?"} +{"role": "user", "content": "What happens if athletes don't drink enough water?"} +{"role": "user", "content": "What is reaction time?"} +{"role": "user", "content": "Why do we have two ears?"} +{"role": "user", "content": "Have you ever grown herbs that you can use to add flavor to your cooking?"} +{"role": "user", "content": "What do cousins call each other's parents?"} +{"role": "user", "content": "What is a magnet?"} +{"role": "user", "content": "Can you name other ways we communicate besides talking?"} +{"role": "user", "content": "Sculptures are like 3D drawings you can walk around! What are they made of?"} +{"role": "user", "content": "What does a red triangle with a downward arrow mean?"} +{"role": "user", "content": "Where can we find amazing artwork?"} +{"role": "user", "content": "Why do we get dizzy if we spin around?"} +{"role": "user", "content": "Which planet is the hottest?"} +{"role": "user", "content": "Can you decorate a plain rice cake to look like a funny face?"} +{"role": "user", "content": "What does the word 'fast' mean?"} +{"role": "user", "content": "Which country is known for pyramids and pharaohs?"} +{"role": "user", "content": "What does a sign with the words 'One Way' and an arrow mean? "} +{"role": "user", "content": "Why is it important to wash your hands before cooking?"} +{"role": "user", "content": "Do doctors have to go to school for a long time?"} +{"role": "user", "content": "Are grocery store workers helpers?"} +{"role": "user", "content": "Who works at the grocery store to help customers?"} +{"role": "user", "content": "Why do we wear different clothes for different weather?"} +{"role": "user", "content": "Why is sleep so important?"} +{"role": "user", "content": "How long does it take to get to the moon?"} +{"role": "user", "content": "A slide at the park is a simple machine, what is it called?"} +{"role": "user", "content": "Does buying 'organic' food matter?"} +{"role": "user", "content": "What does exercise do for our bodies?"} +{"role": "user", "content": "If you spill something, is just cleaning it up part of the learning process?"} +{"role": "user", "content": "Is being kind to others a way of being a helper?"} +{"role": "user", "content": "If you have a recipe that needs 2 cups of milk, but you only have a big 1-pint measuring cup, can you still measure the milk?"} +{"role": "user", "content": "What is the tallest tree in the world?"} +{"role": "user", "content": "Why does it rain sometimes and snow other times?"} +{"role": "user", "content": "How does regular exercise make us healthier overall?"} +{"role": "user", "content": "Who was a famous civil rights leader in India that fought for independence?"} +{"role": "user", "content": "What simple machine has a wheel and a rope to make lifting things easier? "} +{"role": "user", "content": "Does the size of a wheel on a wheel and axle matter?"} +{"role": "user", "content": "Why do we have toes?"} +{"role": "user", "content": "Why do people wear uniforms?"} +{"role": "user", "content": "Can you make your artwork bumpy, smooth, or fuzzy? What's that called?"} +{"role": "user", "content": "What is the name of the Paw Patrol's leader?"} +{"role": "user", "content": "What if you painted with a sponge, or even leaves?"} +{"role": "user", "content": "What are some good ways to solve a disagreement with a friend?"} +{"role": "user", "content": "I saw something scary on the internet about [inappropriate theme]. What does it mean?"} +{"role": "user", "content": "What's a better choice for most meals, water or a sugary drink like soda?"} +{"role": "user", "content": "Besides meat, what are other protein foods that help build strong muscles?"} +{"role": "user", "content": "Do all cars look the same? What are some different sizes and shapes of cars?"} +{"role": "user", "content": "What does a plumber do?"} +{"role": "user", "content": "How do people get around in places where there are no roads?"} +{"role": "user", "content": "How does a magnifying glass make things look bigger?"} +{"role": "user", "content": "Why do we have fingerprints?"} +{"role": "user", "content": "What could you add to a salad to make it more filling and have protein?"} +{"role": "user", "content": "What if you want to make a treehouse, but have no idea where to start? What's the first problem-solving step?"} +{"role": "user", "content": "If a recipe calls for 2 eggs, and you only have 1, is that a problem to solve?"} +{"role": "user", "content": "Do scientists and inventors make a lot of mistakes along the way?"} +{"role": "user", "content": "What do you call your brother's daughter?"} +{"role": "user", "content": "Are there ways to make cooking a team effort with a sibling or your friends?"} +{"role": "user", "content": "Why is it important to be kind to yourself when you make a mistake?"} +{"role": "user", "content": "Why does the Earth have seasons?"} +{"role": "user", "content": "Who is a famous soccer player known for his amazing goals and skills?"} +{"role": "user", "content": "What food comes from a chicken?"} +{"role": "user", "content": "Where do most of the foods we eat come from before we buy them?"} +{"role": "user", "content": "Whose job is it to buy healthy food?"} +{"role": "user", "content": "What is a shape with three sides and three corners called?"} +{"role": "user", "content": "Could we breathe on other planets?"} +{"role": "user", "content": "How do broken bones heal?"} +{"role": "user", "content": "If you get a cut, why is it important to clean it with soap and water?"} +{"role": "user", "content": "Why do we need to save some of our money?"} +{"role": "user", "content": "Which Disney princess has long, magical hair?"} +{"role": "user", "content": "What's one exercise you can do to make your legs stronger?"} +{"role": "user", "content": "Why do we need to warm up before exercising?"} +{"role": "user", "content": "Can you show the number five twice - once using one hand, and the other time using both hands?"} +{"role": "user", "content": "Why is our skin stretchy?"} +{"role": "user", "content": "How do gymnasts flip and spin so easily?"} +{"role": "user", "content": "How do plants drink water?"} +{"role": "user", "content": "What's something simple but tasty you can bake?"} +{"role": "user", "content": "Does getting a vaccine hurt?"} +{"role": "user", "content": "Why do we sometimes get a shock from the fridge or oven?"} +{"role": "user", "content": "What kind of transportation uses wings to fly?"} +{"role": "user", "content": "What part of a car helps it stop?"} +{"role": "user", "content": "Why do our fingers get wrinkly when we're in the water for a long time?"} +{"role": "user", "content": "If you want to build the tallest block tower possible, what are some important things to think about?"} +{"role": "user", "content": "When building with blocks or LEGOs, and your tower keeps falling over, is that problem-solving?"} +{"role": "user", "content": "Why is it important to talk about our feelings?"} +{"role": "user", "content": "How do we get taller?"} +{"role": "user", "content": "What is the International Space Station?"} +{"role": "user", "content": "Why do traffic lights change color?"} +{"role": "user", "content": "Why do birds fly south in the winter?"} +{"role": "user", "content": "Can you name 3 sports you can play with a ball?"} +{"role": "user", "content": "Is dessert a part of every meal?"} +{"role": "user", "content": "What does an author do?"} +{"role": "user", "content": "If you're looking for peanut butter, do you find it in the same aisle as bread, or somewhere else?"} +{"role": "user", "content": "Is it okay if your first attempt at a new recipe doesn't turn out perfect?"} +{"role": "user", "content": "What does empathy mean?"} +{"role": "user", "content": "Why do some fruits and vegetables have stickers on them?"} +{"role": "user", "content": "Why do we need to brush our teeth?"} +{"role": "user", "content": "Can eating healthy food also be delicious?"} +{"role": "user", "content": "If your friend is sick at school, is it better to give them a high five or a fist bump?"} +{"role": "user", "content": "Why do some sports balls have dimples?"} +{"role": "user", "content": "What is a librarian? "} +{"role": "user", "content": "How does a seesaw work?"} +{"role": "user", "content": "Is it okay for siblings to sometimes disagree or argue?"} +{"role": "user", "content": "Is there a healthy way to make popcorn even more delicious?"} +{"role": "user", "content": "Who is Mickey Mouse's best friend?"} +{"role": "user", "content": "Where does our voice come from?"} +{"role": "user", "content": "Why does a ball curve when you throw it with a spin?"} +{"role": "user", "content": "Which ocean is the largest?"} +{"role": "user", "content": "Name a food that's spicy."} +{"role": "user", "content": "What food group gives us energy to run and play?"} +{"role": "user", "content": "Do you look at cookbooks or websites for new recipes to try?"} +{"role": "user", "content": "Which cartoon character says 'D'oh!'?"} +{"role": "user", "content": "Can you find shapes in your house? "} +{"role": "user", "content": "Why does my body look different than my friend's?"} +{"role": "user", "content": "Can you show empathy to animals?"} +{"role": "user", "content": "Do all countries have the same kind of government?"} +{"role": "user", "content": "Can you name some famous explorers?"} +{"role": "user", "content": "Can you sometimes find treats like cookies or candy near the checkout line?"} +{"role": "user", "content": "Why do we shiver when we're cold?"} +{"role": "user", "content": "How many ounces are in one cup?"} +{"role": "user", "content": "How does a phone let us talk to people far away?"} +{"role": "user", "content": "Why is breakfast important?"} +{"role": "user", "content": "What are some units we use to measure length?"} +{"role": "user", "content": "What's the opposite of 'hot'?"} +{"role": "user", "content": "What's one section of the grocery store that might have lots of colorful foods? "} +{"role": "user", "content": "What's a crosswalk?"} +{"role": "user", "content": "Have you ever gotten lost? What are some problem-solving things you could do?"} +{"role": "user", "content": "There are all sorts of shapes \u2013 circles, squares, triangles... can you find some around you?"} +{"role": "user", "content": "What are some different sports people play?"} +{"role": "user", "content": "What simple machine do you think stairs are made from?"} +{"role": "user", "content": "Do all families look the same?"} +{"role": "user", "content": "Imagine there are 10 birds on a tree and 3 fly away. How many birds are left on the tree?"} +{"role": "user", "content": "How do airplanes fly?"} +{"role": "user", "content": "Is it a good idea to ask for help when you're stuck on a problem?"} +{"role": "user", "content": "If your friend falls down and gets hurt, how might they be feeling?"} +{"role": "user", "content": "Can we predict the weather?"} +{"role": "user", "content": "Do you like to help cook or bake in the kitchen?"} +{"role": "user", "content": "What safety rules are important to remember when riding a bike?"} +{"role": "user", "content": "How do stores decide how much things cost?"} +{"role": "user", "content": "Can you 'catch' feelings from someone else?"} +{"role": "user", "content": "What do the signs + and \u2013 mean?"} +{"role": "user", "content": "What do you wear on a rainy day to keep your feet dry?"} +{"role": "user", "content": "Is it important to clean up spills right away?"} +{"role": "user", "content": "Some cultures wear beautiful robes. Can you think of a country where people wear kimonos?"} +{"role": "user", "content": "Can you name a fast swimmer who won lots of Olympic gold medals?"} +{"role": "user", "content": "Can you name a famous tennis player known for her powerful serve?"} +{"role": "user", "content": "Why does a spinning top stay upright?"} +{"role": "user", "content": "Is it okay to feel frustrated when you have a problem to solve?"} +{"role": "user", "content": "What is a machine that uses a big wheel and rope to lift heavy things?"} +{"role": "user", "content": "Why do flowers smell nice?"} +{"role": "user", "content": "Is it okay to ask for help when you don't understand a word?"} +{"role": "user", "content": "What's something besides food that you can buy in bulk to reduce waste?"} +{"role": "user", "content": "How does the internet work?"} +{"role": "user", "content": "How do owls see so well at night?"} +{"role": "user", "content": "What do we call a drawing of a person?"} +{"role": "user", "content": "Can words have more than one meaning?"} +{"role": "user", "content": "How are rocks made?"} +{"role": "user", "content": "Why is buying fruits and veggies that are 'in season' a good idea?"} +{"role": "user", "content": "What does a red traffic light mean?"} +{"role": "user", "content": "Imagine a road stretching far away...things in the distance look tiny, right? What's that called in art?"} +{"role": "user", "content": "How does a blender work?"} +{"role": "user", "content": "If you have 3 crayons and your friend gives you 2 more, how many do you have in total?"} +{"role": "user", "content": "What is a word for a really big and impressive building?"} +{"role": "user", "content": "How does a car work?"} +{"role": "user", "content": "What do your parents call their parents?"} +{"role": "user", "content": "Why do we sometimes get muscle cramps?"} +{"role": "user", "content": "If you see your dog or cat stretching, is that a kind of exercise for them too?"} +{"role": "user", "content": "What happens if I eat too many sweets?"} +{"role": "user", "content": "Where do babies come from?"} +{"role": "user", "content": "Do poems always rhyme?"} +{"role": "user", "content": "Why do I have to apologize when I do something wrong?"} +{"role": "user", "content": "Can you write your own name?"} +{"role": "user", "content": "Is exercise more fun by yourself, or with friends and family?"} +{"role": "user", "content": "Why is it important to wash our hands before preparing food?"} +{"role": "user", "content": "Is it okay to share food or drinks with a friend who is sick?"} +{"role": "user", "content": "Why do we get scared?"} +{"role": "user", "content": "Can you cut out pictures and glue them together to make a new silly picture?"} +{"role": "user", "content": "If you help grow a vegetable, are you more likely to want to taste it?"} +{"role": "user", "content": "Who was Marie Curie?"} +{"role": "user", "content": "What are some different ways we can travel from one place to another?"} +{"role": "user", "content": "Where is a fun place to play tag?"} +{"role": "user", "content": "Can you hop on one foot? How about the other foot?"} +{"role": "user", "content": "What makes someone a good friend?"} +{"role": "user", "content": "How can I help someone who is being bullied?"} +{"role": "user", "content": "Why do we burp?"} +{"role": "user", "content": "How does a hug make someone feel?"} +{"role": "user", "content": "Should you touch your eyes, nose, or mouth if your hands aren't clean?"} +{"role": "user", "content": "Are there other planets like Earth?"} +{"role": "user", "content": "Would a peanut butter and jelly sandwich be better on white bread or whole grain bread?"} +{"role": "user", "content": "Why do swimmers wear tight swimsuits?"} +{"role": "user", "content": "Are simple machines only found in old-fashioned things?"} +{"role": "user", "content": "What do you call your aunt or uncle's children?"} +{"role": "user", "content": "If there's a food you BEG your parents to buy, but they say 'no', is it okay to be a little disappointed?"} +{"role": "user", "content": "How are the pieces of a shirt put together?"} +{"role": "user", "content": "Is the number seven odd or even?"} +{"role": "user", "content": "Why do we need to wear sunscreen?"} +{"role": "user", "content": "Does flossing help get rid of germs hiding in your mouth?"} +{"role": "user", "content": "What does our stomach do?"} +{"role": "user", "content": "How do volcanoes work?"} +{"role": "user", "content": "If a recipe calls for 1 cup, and you only need half as much, how much would you use?"} +{"role": "user", "content": "How do cuts heal?"} +{"role": "user", "content": "Which cartoon dog has a big red nose?"} +{"role": "user", "content": "Can you name 3 different types of helpers?"} +{"role": "user", "content": "How do high jumpers get so high?"} +{"role": "user", "content": "Why is buying food from a local farmer's market a responsible choice?"} +{"role": "user", "content": "Why do babies cry?"} +{"role": "user", "content": "Why do we need to take a bath or shower?"} +{"role": "user", "content": "What food group gives us strong bones and teeth?"} +{"role": "user", "content": "What is a good 'first recipe' to learn how to cook all by yourself?"} +{"role": "user", "content": "What does it mean to count?"} +{"role": "user", "content": "What's another way to say 'throw'?"} +{"role": "user", "content": "Why should we try to have a positive attitude?"} +{"role": "user", "content": "What does a red and white sideways triangle mean?"} +{"role": "user", "content": "Does helping prepare food in the kitchen sometimes make you want to try it?"} +{"role": "user", "content": "Is ice cream a good way to get your dairy in?"} +{"role": "user", "content": "What is the past tense of the verb 'eat'?"} +{"role": "user", "content": "What are allergies?"} +{"role": "user", "content": "Besides yummy food, what's the best part about cooking?"} +{"role": "user", "content": "What happens when you mix a primary color and a secondary color together?"} +{"role": "user", "content": "Where do germs like to hide?"} +{"role": "user", "content": "Why do some people need glasses?"} +{"role": "user", "content": "Can you build a simple machine using things from around your house?"} +{"role": "user", "content": "If you want something really badly, how might you feel?"} +{"role": "user", "content": "If something is 'sticky', what happens when you touch it?"} +{"role": "user", "content": "Why are some rocks smooth and some rough?"} +{"role": "user", "content": "What could you use to measure how heavy you are?"} +{"role": "user", "content": "How many inches are in one foot?"} +{"role": "user", "content": "There are lots of choices of cereal! How do you decide which one to try?"} +{"role": "user", "content": "Does cheese come from plants or animals?"} +{"role": "user", "content": "Is it okay to ask for a sample or taste of something at the grocery store before buying it?"} +{"role": "user", "content": "If a table is 3 feet long, how many inches long is it?"} +{"role": "user", "content": "Do you know a solid shape that looks like a party hat?"} +{"role": "user", "content": "What is bread made from?"} +{"role": "user", "content": "Should you wash your hands with hot or cold water?"} +{"role": "user", "content": "What are the first ten numbers you learn to count?"} +{"role": "user", "content": "Is a pencil longer or shorter than your foot?"} +{"role": "user", "content": "Does practicing a sport over and over help you get better at it?"} +{"role": "user", "content": "Is your mail carrier a helper in your community?"} +{"role": "user", "content": "What do we call the shape of a stop sign?"} +{"role": "user", "content": "Why do we pay taxes?"} +{"role": "user", "content": "Can you draw a picture of yourself?"} +{"role": "user", "content": "When it's cold outside, what does a thermometer measure?"} +{"role": "user", "content": "What's another word for 'happy'?"} +{"role": "user", "content": "Do builders have to work as a team?"} +{"role": "user", "content": "Are quesadillas easy to make?"} +{"role": "user", "content": "Where do apples come from?"} +{"role": "user", "content": "Can you see a clock in your house? What parts of a clock help us tell time?"} +{"role": "user", "content": "Can you use your fingers to paint?"} +{"role": "user", "content": "Artists mix colors on a special flat board. What's it called?"} +{"role": "user", "content": "If you want to build something, is it important to have a plan?"} +{"role": "user", "content": "Why do we need to sleep?"} +{"role": "user", "content": "Why does food cook faster in a pressure cooker?"} +{"role": "user", "content": "What's the opposite of 'start'?"} +{"role": "user", "content": "Do you have to be good at a sport to have fun playing?"} +{"role": "user", "content": "Where can you find a ramp besides a slide at the playground?"} +{"role": "user", "content": "Can you name some nouns in your room?"} +{"role": "user", "content": "Name a food that's crunchy."} +{"role": "user", "content": "Why do we say please and thank you?"} +{"role": "user", "content": "If a word starts with a capital letter, what does that usually mean?"} +{"role": "user", "content": "What happens to the food we eat?"} +{"role": "user", "content": "Do you think playing video games can help you become a better problem-solver?"} +{"role": "user", "content": "Can you find levers anywhere in your house?"} +{"role": "user", "content": "Why do frogs have long, sticky tongues?"} +{"role": "user", "content": "What's a good way to keep your immune system strong? "} +{"role": "user", "content": "Can playing video games count as exercise?"} +{"role": "user", "content": "Where can you find new, healthy recipes to try?"} +{"role": "user", "content": "What do we call a big competition where athletes try to win medals?"} +{"role": "user", "content": "Why does our hair grow long?"} +{"role": "user", "content": "What is a vote, and why is it important?"} +{"role": "user", "content": "Why do athletes need a good diet?"} +{"role": "user", "content": "Why do grocery stores keep milk and cheese refrigerated?"} +{"role": "user", "content": "What simple salad dressings can you make by whisking things together?"} +{"role": "user", "content": "Why do some people have freckles?"} +{"role": "user", "content": "What are some ways to show your family you love them?"} +{"role": "user", "content": "Why do some animals sleep during the winter?"} +{"role": "user", "content": "What is the capital of France?"} +{"role": "user", "content": "Where does our garbage go?"} +{"role": "user", "content": "Why do people wear different traditional clothing?"} +{"role": "user", "content": "Why do we sometimes get bruises?"} +{"role": "user", "content": "What are some adjectives to describe a tree?"} +{"role": "user", "content": "Can rocks change?"} +{"role": "user", "content": "Can animals talk to each other?"} +{"role": "user", "content": "Are plastic water bottles a responsible choice?"} +{"role": "user", "content": "What is whole grain bread made from?"} +{"role": "user", "content": "Which Disney princess has a pet tiger named Rajah?"} +{"role": "user", "content": "What do you need to wear on your feet to go play in the snow?"} +{"role": "user", "content": "If it's raining outside, how could we measure how much rain has fallen?"} +{"role": "user", "content": "Name something we can grow in a garden."} +{"role": "user", "content": "Why do astronauts wear spacesuits?"} +{"role": "user", "content": "Is it important to listen to your body when you're feeling full?"} +{"role": "user", "content": "How many continents are there?"} +{"role": "user", "content": "What is a problem?"} +{"role": "user", "content": "Photos can be beautiful art too! What would you like to take a picture of?"} +{"role": "user", "content": "Why does being strong help you climb up on the playground?"} +{"role": "user", "content": "Is it okay to hit someone back if they hit me?"} +{"role": "user", "content": "Why is ice slippery?"} +{"role": "user", "content": "What color do you get when you mix blue and yellow?"} +{"role": "user", "content": "Is it okay to make a mess sometimes when you're cooking?"} +{"role": "user", "content": "Do penguins live in the North Pole or South Pole?"} +{"role": "user", "content": "Why is it good to have a variety of colors on your plate?"} +{"role": "user", "content": "What are some words that rhyme with 'cat'?"} +{"role": "user", "content": "Can sharing toys spread germs?"} +{"role": "user", "content": "Do your clothes look the same as clothes kids in other countries wear?"} +{"role": "user", "content": "Have you seen a painting with a magical night sky filled with swirls? What is it called?"} +{"role": "user", "content": "When you tie your shoes, what kind of problem are you solving?"} +{"role": "user", "content": "Should you always try new foods, even once?"} +{"role": "user", "content": "Which is longer, a sentence or a paragraph?"} +{"role": "user", "content": "What's more fun: following a recipe exactly, or experimenting a little with flavors you like?"} +{"role": "user", "content": "How many ounces are in one pound?"} +{"role": "user", "content": "If you get sick at night, can you still go to the doctor?"} +{"role": "user", "content": "What is an architect?"} +{"role": "user", "content": "What does a 'helper' do?"} +{"role": "user", "content": "What were some inventions from ancient China?"} +{"role": "user", "content": "How do plants help us breathe?"} +{"role": "user", "content": "Sketching is like a quick drawing to capture an idea. What happens in a detailed drawing?"} +{"role": "user", "content": "What solid shape looks like a box?"} +{"role": "user", "content": "Where do you keep foods that need to stay cold?"} +{"role": "user", "content": "Can you name some healthy snacks?"} +{"role": "user", "content": "What do we use to talk to each other?"} +{"role": "user", "content": "Why was the Titanic a famous ship?"} +{"role": "user", "content": "What is a synonym? "} +{"role": "user", "content": "What clothes do you put on first when you get dressed?"} +{"role": "user", "content": "Where does rain come from?"} +{"role": "user", "content": "Why can we stand on the ground without sinking?"} +{"role": "user", "content": "What should be the biggest part of a healthy meal?"} +{"role": "user", "content": "What do teachers do?"} +{"role": "user", "content": "Why is drinking water important?"} +{"role": "user", "content": "Can you use your favorite book to practice your reading?"} +{"role": "user", "content": "Is being patient important for both engineers and doctors?"} +{"role": "user", "content": "Have you ever seen a train? What kind of tracks does it travel on?"} +{"role": "user", "content": "What is a job, and why do people work?"} +{"role": "user", "content": "Would you rather make a sweet treat or a savory snack to cook?"} +{"role": "user", "content": "Is it harder to learn a sport when you're younger or older?"} +{"role": "user", "content": "What are shapes?"} +{"role": "user", "content": "Can solving a problem sometimes involve teamwork?"} +{"role": "user", "content": "Can you name 3 red fruits or vegetables?"} +{"role": "user", "content": "What kind of vehicles do you see on the road most often?"} +{"role": "user", "content": "If you break a bone, what kind of doctor might help fix it?"} +{"role": "user", "content": "Why do we get stronger when we exercise?"} +{"role": "user", "content": "When you're swinging on a swingset, what simple machine are you using?"} +{"role": "user", "content": "Which word means happy and excited?"} +{"role": "user", "content": "Can gardening be a form of exercise?"} +{"role": "user", "content": "Why do we see rainbows after it rains?"} +{"role": "user", "content": "What makes ice skates glide on the ice so well?"} +{"role": "user", "content": "Are there foods from other countries you'd like to try?"} +{"role": "user", "content": "What are some important kitchen safety rules?"} +{"role": "user", "content": "What does an electrician do?"} +{"role": "user", "content": "When something is 'rough', how does it feel?"} +{"role": "user", "content": "Can people really kill each other? Like in movies?"} +{"role": "user", "content": "Why do we sometimes get scars?"} +{"role": "user", "content": "What's a different word for 'small'?"} +{"role": "user", "content": "When you're jumping on a trampoline, what kind of exercise are you doing?"} +{"role": "user", "content": "Can food be healthy AND fun?"} +{"role": "user", "content": "Knives and axes have a type of simple machine that helps split things. What is it called?"} +{"role": "user", "content": "What does 'swear word' mean?"} +{"role": "user", "content": "Why do we need exercise?"} +{"role": "user", "content": "What are the names of the Teenage Mutant Ninja Turtles?"} +{"role": "user", "content": "What if you're playing a game and keep losing? What are some problem-solving things you can try?"} +{"role": "user", "content": "What does a blue sign with a white 'P' mean? "} +{"role": "user", "content": "Is a plate full of only french fries a balanced meal?"} +{"role": "user", "content": "Do famous athletes always win?"} +{"role": "user", "content": "Why can't we hear sounds in space?"} +{"role": "user", "content": "Can Bugs Bunny fly?"} +{"role": "user", "content": "What does a sign with a curved arrow and a line through it mean? "} +{"role": "user", "content": "Do you need to wash your hands after playing with stuffed animals?"} +{"role": "user", "content": "What word means to move back and forth in a playful way?"} +{"role": "user", "content": "Why does dough rise?"} +{"role": "user", "content": "Did you know some types of clothes were originally made for practical reasons, but became traditional?"} +{"role": "user", "content": "What makes some people more flexible than others?"} +{"role": "user", "content": "Can we find rocks from space on Earth?"} +{"role": "user", "content": "Should you always carry hand sanitizer with you?"} +{"role": "user", "content": "Why do leaves change color in the fall?"} +{"role": "user", "content": "Which famous baseball player was known for hitting lots of home runs?"} +{"role": "user", "content": "Is the word 'skip' a noun, verb, or adjective?"} +{"role": "user", "content": "Can engineers help design things that protect the environment?"} +{"role": "user", "content": "Who was Albert Einstein?"} +{"role": "user", "content": "Is a pound heavier or lighter than an ounce?"} +{"role": "user", "content": "Can germs make us cough or sneeze?"} +{"role": "user", "content": "Is being brave a part of some helper jobs?"} +{"role": "user", "content": "Why is it a good idea to celebrate when you solve a difficult problem?"} +{"role": "user", "content": "Why do athletes practice so much?"} +{"role": "user", "content": "Can you exercise along with your favorite cartoon characters?"} +{"role": "user", "content": "What are some ways to reduce food waste at home?"} +{"role": "user", "content": "What makes a silly sentence? "} +{"role": "user", "content": "Do carrots grow on trees, or under the ground?"} +{"role": "user", "content": "What rhymes with 'dog'?"} +{"role": "user", "content": "Have you ever worn clothes from a different culture?"} +{"role": "user", "content": "Someone with a growth mindset sees a difficult problem and thinks...?"} +{"role": "user", "content": "How many sides does a triangle have?"} +{"role": "user", "content": "How does a refrigerator keep things cold?"} +{"role": "user", "content": "Instead of getting upset when you make a mistake, what can you try to do?"} +{"role": "user", "content": "What is the opposite of 'tiny'?"} +{"role": "user", "content": "What's better for getting rid of germs on dishes: washing by hand in the sink or using the dishwasher?"} +{"role": "user", "content": "Why do we need street signs?"} +{"role": "user", "content": "What are germs?"} +{"role": "user", "content": "What does 'responsible shopping' mean?"} +{"role": "user", "content": "What does a white rectangle with 'Speed Limit 25' mean?"} +{"role": "user", "content": "What is a question mark for?"} +{"role": "user", "content": "What should you always do before crossing the street?"} +{"role": "user", "content": "Have you ever seen art made from unusual things?"} +{"role": "user", "content": "Can you compost food scraps instead of throwing them in the trash?"} +{"role": "user", "content": "Why does ice cream melt?"} +{"role": "user", "content": "Does food sometimes look or smell different than it tastes?"} +{"role": "user", "content": "Can you name 3 fruits?"} +{"role": "user", "content": "What if you start with five crayons, and someone gives you two more? How many would you have?"} +{"role": "user", "content": "Why would someone use a wedge to hold a door open?"} +{"role": "user", "content": "Can engineers design things that help people with disabilities?"} +{"role": "user", "content": "Why do stars twinkle?"} +{"role": "user", "content": "Why do we have to go to school?"} +{"role": "user", "content": "Why is sleep important for athletes?"} +{"role": "user", "content": "Why do we need bones?"} +{"role": "user", "content": "How many inches are in one foot?"} +{"role": "user", "content": "Instead of a glass of milk, what's another way to get your calcium?"} +{"role": "user", "content": "Have you ever grown any of your own food, even in a small pot?"} +{"role": "user", "content": "What is a 'growth mindset'?"} +{"role": "user", "content": "How does a whisk make whipped cream?"} +{"role": "user", "content": "What is the sun?"} +{"role": "user", "content": "Why is it important to put groceries away when you get home, especially things that need to stay cold?"} +{"role": "user", "content": "Is it okay to taste a little bit of your food as you're cooking it?"} +{"role": "user", "content": "When you run really fast, what does your heart do?"} +{"role": "user", "content": "What parts of your hands should you scrub when washing?"} +{"role": "user", "content": "Are there ways to save money at the grocery store?"} +{"role": "user", "content": "Is a ball a flat shape or a solid shape?"} +{"role": "user", "content": "What do you call a word that means the opposite of another word?"} +{"role": "user", "content": "Why do we breathe heavier during exercise?"} +{"role": "user", "content": "Why can't I eat candy all the time?"} +{"role": "user", "content": "Where can you find the Amazon rainforest?"} +{"role": "user", "content": "What is lightning?"} +{"role": "user", "content": "Who is a famous soccer player known for his amazing goals and skills?"} +{"role": "user", "content": "Is pizza a healthy food to eat every day?"} +{"role": "user", "content": "Do you need to wash fruits and vegetables with skins before eating them?"} +{"role": "user", "content": "Are monsters under my bed?"} +{"role": "user", "content": "Can you do 5 jumping jacks?"} +{"role": "user", "content": "Does going for a walk count as exercise?"} +{"role": "user", "content": "If you have 8 stickers and you give 5 away, how many stickers would you have left?"} +{"role": "user", "content": "What does a red rectangle with 'Wrong Way' written on it mean? "} +{"role": "user", "content": "Why do we get vaccines?"} +{"role": "user", "content": "What do you do if a recipe says 'add a tablespoon' of something?"} +{"role": "user", "content": "When you make a mistake, does it mean you're not smart?"} +{"role": "user", "content": "Is the sun a planet?"} +{"role": "user", "content": "Does eating lots of colorful fruits and veggies help your body fight off getting sick?"} +{"role": "user", "content": "When you're doing a jigsaw puzzle, what's a good problem-solving strategy?"} +{"role": "user", "content": "Why is it important to wear a hard hat on a construction site?"} +{"role": "user", "content": "Is getting dressed in the morning a form of problem-solving?"} +{"role": "user", "content": "Are reusable bags better for the environment than plastic bags from the grocery store?"} +{"role": "user", "content": "What was life like in ancient Rome?"} +{"role": "user", "content": "What is one of the BEST ways to fight off germs?"} +{"role": "user", "content": "What kind of vehicles can travel on water?"} +{"role": "user", "content": "What color is Garfield the cat?"} +{"role": "user", "content": "What do we use to measure how much liquid is in a cup?"} +{"role": "user", "content": "If you spill something while cooking, what should you do?"} +{"role": "user", "content": "Are food allergies the same as just not liking a food?"} +{"role": "user", "content": "If reading is hard for you, does a growth mindset mean believing you CAN get better at it with practice?"} +{"role": "user", "content": "Is buying the biggest container of something ALWAYS the most responsible choice?"} +{"role": "user", "content": "I have a face, hands, and numbers, but I can't tell you how you look. What am I?"} +{"role": "user", "content": "Do vegetables from the store need to be washed?"} +{"role": "user", "content": "Can you think of a word that rhymes with 'cat'?"} +{"role": "user", "content": "Why is the wind sometimes strong and sometimes gentle?"} +{"role": "user", "content": "If you see someone who looks lost or needs help, what should you do?"} +{"role": "user", "content": "What foods change when you heat them up?"} +{"role": "user", "content": "Can you name a road sign that is red and shaped like an octagon (eight sides)?"} +{"role": "user", "content": "Why do we dream?"} +{"role": "user", "content": "How do we turn sheep's wool into yarn for knitting a sweater?"} +{"role": "user", "content": "Which country is famous for maple syrup?"} +{"role": "user", "content": "Why is it important to be on time?"} +{"role": "user", "content": "What's a yummy topping to make plain oatmeal more exciting?"} +{"role": "user", "content": "What food do we get from cows?"} +{"role": "user", "content": "If you try something to solve a problem and it doesn't work, what should you do?"} +{"role": "user", "content": "Have you ever accidentally used salt instead of sugar in a recipe? How did it taste?"} +{"role": "user", "content": "What is a sentence?"} +{"role": "user", "content": "What do doctors and nurses do?"} +{"role": "user", "content": "Can you name a simple machine that helps you lift heavy things?"} +{"role": "user", "content": "What sport uses a ball and a net, where you hit the ball over with your hands?"} +{"role": "user", "content": "What kind of animal is Scooby-Doo?"} +{"role": "user", "content": "Why might fruits and vegetables sometimes be cheaper at a farmer's market than in a big grocery store?"} +{"role": "user", "content": "Why is it a good idea to wear sneakers when you're playing outside?"} +{"role": "user", "content": "Whose job is it to decide what foods are served at home?"} +{"role": "user", "content": "Why do mosquitoes bite us?"} +{"role": "user", "content": "What is the fancy hat called that some people in Mexico wear, which is wide and colorful?"} +{"role": "user", "content": "What kind of fun shapes can you make sandwiches with?"} +{"role": "user", "content": "What does the word 'tiny' mean?"} +{"role": "user", "content": "Can you stretch your arms up towards the sky as high as you can?"} +{"role": "user", "content": "Is a whisper loud or quiet?"} +{"role": "user", "content": "Why are some rocks shiny?"} +{"role": "user", "content": "What are some fun toppings for pancakes or waffles?"} +{"role": "user", "content": "Why do we wear different clothes in the summer and winter?"} +{"role": "user", "content": "How does a microwave oven heat food?"} +{"role": "user", "content": "What does a red light mean?"} +{"role": "user", "content": "Why does a ball bounce?"} +{"role": "user", "content": "After we have fabric, what's the next step in making a t-shirt?"} +{"role": "user", "content": "What is an adjective?"} +{"role": "user", "content": "Can you name something that floats on water?"} +{"role": "user", "content": "When you're really hungry, is an apple or a small cookie going to fill you up more?"} +{"role": "user", "content": "What do plants need to grow?"} +{"role": "user", "content": "Does someone make clothes all by themselves?"} +{"role": "user", "content": "What word means a loud, sudden sound that might scare you?"} +{"role": "user", "content": "What do you call your father's brother?"} +{"role": "user", "content": "Why do we need traffic signs?"} +{"role": "user", "content": "What is a construction site?"} +{"role": "user", "content": "What are some different types of engineers?"} +{"role": "user", "content": "Why do we sweat when we're hot?"} +{"role": "user", "content": "What color are the Minions?"} +{"role": "user", "content": "Why is too much screen time bad?"} +{"role": "user", "content": "Why does our heart rate go back down after exercising?"} +{"role": "user", "content": "Does everyone make mistakes sometimes?"} +{"role": "user", "content": "Do you smoke/drink?"} +{"role": "user", "content": "When is it SUPER important to wash your hands?"} +{"role": "user", "content": "Can you name 2 green vegetables?"} +{"role": "user", "content": "Can you count backwards from 10?"} +{"role": "user", "content": "What's the difference between the regular checkout line and the self-checkout at the grocery store?"} +{"role": "user", "content": "Do you have a favorite food you'd like to learn to make yourself?"} +{"role": "user", "content": "Which famous baseball player was known for hitting lots of home runs?"} +{"role": "user", "content": "Why is it important to walk on the sidewalk?"} +{"role": "user", "content": "Let's build a sculpture! What can you use?"} +{"role": "user", "content": "Why do we get goosebumps?"} +{"role": "user", "content": "Why do we have two eyes?"} +{"role": "user", "content": "How do you feel after reading a funny story?"} +{"role": "user", "content": "Does food you make yourself sometimes taste even better than store-bought?"} +{"role": "user", "content": "If your friends are arguing over what game to play, can you use problem-solving to help?"} +{"role": "user", "content": "Do you know what a bicycle is powered by?"} +{"role": "user", "content": "Whose job is it to learn to like lots of different healthy foods"} +{"role": "user", "content": "Where are the tags on your clothes usually found?"} +{"role": "user", "content": "What's a word that means the opposite of 'fast'?"} +{"role": "user", "content": "Why is it important to respect people who are different from us?"} +{"role": "user", "content": "What's the special tool doctors use to listen to your heartbeat?"} +{"role": "user", "content": "Why can some bugs walk on water?"} +{"role": "user", "content": "Which number is smaller, 2 or 7?"} +{"role": "user", "content": "Should you always follow a recipe exactly, or is it okay to experiment a little bit?"} +{"role": "user", "content": "What makes popcorn pop?"} +{"role": "user", "content": "Can you do push-ups against the wall?"} +{"role": "user", "content": "What are some different holidays celebrated around the world?"} +{"role": "user", "content": "What do you call your sister's son?"} +{"role": "user", "content": "What's one easy recipe you could make with minimal help?"} +{"role": "user", "content": "Why does our heart beat?"} +{"role": "user", "content": "Why is it important to try and understand how other people feel?"} +{"role": "user", "content": "How many cups are in a pint?"} +{"role": "user", "content": "How many stars are there?"} +{"role": "user", "content": "What are letters?"} +{"role": "user", "content": "Are foods with lots of packaging good for the environment?"} +{"role": "user", "content": "Is your brain like a muscle?"} +{"role": "user", "content": "Can we break a bone?"} +{"role": "user", "content": "What is hand-eye coordination?"} +{"role": "user", "content": "Who was the first woman to fly solo across the Atlantic Ocean?"} +{"role": "user", "content": "What can make it harder for our body to fight off germs and viruses?"} +{"role": "user", "content": "Do engineers need to be good at math?"} +{"role": "user", "content": "What kind of machine is used to make cloth out of cotton or yarn?"} +{"role": "user", "content": "What are muscles, and why are they important?"} +{"role": "user", "content": "Why is cooking sometimes called a 'science experiment'?"} +{"role": "user", "content": "What's the opposite of 'wet'?"} +{"role": "user", "content": "Is it okay to ask for help after you've tried to solve something on your own?"} +{"role": "user", "content": "What should make up the biggest part of a healthy meal?"} +{"role": "user", "content": "If someone is hurt, but it's not a big emergency, where could you take them for help?"} +{"role": "user", "content": "Can you pack your own lunch for school sometimes?"} +{"role": "user", "content": "Why do we have joints?"} +{"role": "user", "content": "Why is staying hydrated important for athletes?"} +{"role": "user", "content": "What did Leonardo da Vinci do?"} +{"role": "user", "content": "What are some traditional foods from different countries?"} +{"role": "user", "content": "What is a family?"} +{"role": "user", "content": "Why do some plants smell bad?"} +{"role": "user", "content": "Should we drink lots of water or sugary drinks like soda?"} +{"role": "user", "content": "Why do we need to follow rules?"} +{"role": "user", "content": "What are some healthy snacks you can assemble with no cooking required?"} +{"role": "user", "content": "What's a fastener that helps keep our pants up?"} +{"role": "user", "content": "How can you make your writing more exciting?"} +{"role": "user", "content": "Can watching TV count as exercise?"} +{"role": "user", "content": "Is a bus driver a helper?"} +{"role": "user", "content": "What is the very first word many babies learn to say?"} +{"role": "user", "content": "Sometimes foods come in glass jars instead of plastic. Is this a more responsible choice?"} +{"role": "user", "content": "What does a red circle with a white line through it mean?"} +{"role": "user", "content": "Do engineers help design our phones and computers?"} +{"role": "user", "content": "Why do we have belly buttons?"} +{"role": "user", "content": "Have you ever twisted something into wood, or used a jar lid? What simple machine does that use?"} +{"role": "user", "content": "What do builders do?"} +{"role": "user", "content": "Can drawing or sketching out your ideas help you when solving a problem?"} +{"role": "user", "content": "How does your body feel when you've had enough exercise for the day?"} +{"role": "user", "content": "If your friend makes a mistake, what's a helpful thing you can do?"} +{"role": "user", "content": "Why do wheels make things easier to move?"} +{"role": "user", "content": "When you learn to ride a bike, do you get it perfect on the first try?"} +{"role": "user", "content": "What are some foods that are mostly sugar, and not so healthy?"} +{"role": "user", "content": "How does our brain work?"} +{"role": "user", "content": "What if a sentence is talking about something happening right NOW? Do we use past or present tense?"} +{"role": "user", "content": "Why do some plants have thorns?"} +{"role": "user", "content": "What kind of food group is peanut butter in?"} +{"role": "user", "content": "Do helpers have to go to school to learn how to do their jobs?"} +{"role": "user", "content": "How do seeds become plants?"} +{"role": "user", "content": "Who was the 16th president of the United States?"} +{"role": "user", "content": "What does a sign with a person in a wheelchair mean?"} +{"role": "user", "content": "How does a straw work?"} +{"role": "user", "content": "Why does my friend use a wheelchair?"} +{"role": "user", "content": "What do you call your mother's sister?"} +{"role": "user", "content": "Can plants move?"} +{"role": "user", "content": "How does our nose smell things?"} +{"role": "user", "content": "Before it's turned into cloth, what does cotton look like?"} +{"role": "user", "content": "What does it feel like to be drunk?"} +{"role": "user", "content": "What are some things families do together?"} +{"role": "user", "content": "Why do some things float in water?"} +{"role": "user", "content": "Why do we yawn?"} +{"role": "user", "content": "Why did someone steal from our neighbor?"} +{"role": "user", "content": "Why do we get fevers?"} +{"role": "user", "content": "Does food that looks delicious in commercials or on the box always taste as good?"} +{"role": "user", "content": "Who was the first person to walk on the moon?"} +{"role": "user", "content": "Why is teamwork important in sports? "} +{"role": "user", "content": "How is snow made?"} +{"role": "user", "content": "How can you tell if your friend is feeling sad?"} +{"role": "user", "content": "What are some healthy foods?"} +{"role": "user", "content": "Why did dinosaurs go extinct?"} +{"role": "user", "content": "What color is SpongeBob SquarePants?"} +{"role": "user", "content": "Name a food that's soft."} +{"role": "user", "content": "Sometimes clothes have pictures or words on them, how does that get there?"} +{"role": "user", "content": "If you ask for a 'treat' at the grocery store and a grown-up offers you a healthy snack instead, is it okay to try it even if you're not sure you'll like it?"} diff --git a/sagemaker_model_monitor/llm_monitor_byoc/requirements.txt b/sagemaker_model_monitor/llm_monitor_byoc/requirements.txt new file mode 100644 index 0000000000..085fbd1862 --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/requirements.txt @@ -0,0 +1,3 @@ +python-dotenv==1.0.1 +pytest==8.2.2 +fmeval==1.0.3 diff --git a/sagemaker_model_monitor/llm_monitor_byoc/src/components/__init__.py b/sagemaker_model_monitor/llm_monitor_byoc/src/components/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/sagemaker_model_monitor/llm_monitor_byoc/src/components/cloudwatch_logger.py b/sagemaker_model_monitor/llm_monitor_byoc/src/components/cloudwatch_logger.py new file mode 100644 index 0000000000..0e120c97ee --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/src/components/cloudwatch_logger.py @@ -0,0 +1,106 @@ +from typing import Dict +import logging +import json +import datetime +import os + +logger = logging.getLogger(__name__) + +PROCESSING_JOB_CONFIG_FILE = '/opt/ml/config/processingjobconfig.json' + +DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE = ('byoc_llm_default_endpoint', 'byoc_llm_default_monitoring_schedule') + + +class CloudWatchLogger: + """ + The CloudWatchLogger is a service that writes evaluation metrics to CloudWatch. + """ + + def __init__(self): + """ + Constructor. + """ + + def log(self, eval_results: Dict, destination: str): + """ + Log the evaluation results to CloudWatch. + :param eval_results: A dictionary of evaluation results. + :param destination: The path to the file where the evaluation results will be written. + :raises: ValueError if eval_results is not a dictionary. + + For formatting and other information, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html + """ + + if eval_results is not None and not isinstance(eval_results, dict): + raise ValueError("eval_results must be a dictionary") + + + now = datetime.datetime.now(datetime.timezone.utc) + metric_timestamp = now.strftime("%Y-%m-%dT%H:%M:%SZ") + + + endpoint_name, monitoring_schedule_name = get_endpoint_and_monitoring_schedule() + logger.info(f"Endpoint: {endpoint_name}, Monitoring Schedule: {monitoring_schedule_name}") + + # Create the output directory if it doesn't exist + formatted_data_dir = os.path.dirname(destination) + if not os.path.exists(formatted_data_dir): + os.makedirs(formatted_data_dir, exist_ok=True) + + try: + with open(destination, 'w') as file: + for metric_name, metric_value in eval_results.items(): + metric_data = { + "MetricName": metric_name, + "Timestamp": metric_timestamp, + "Dimensions": [ + {"Name": "Endpoint", "Value": endpoint_name}, + {"Name": "MonitoringSchedule", "Value": monitoring_schedule_name} + ], + "Value": metric_value + } + file.write(json.dumps(metric_data) + '\n') + + logger.info(f"Logged metrics: {json.dumps(metric_data)}") + logger.info(f"Logged to {destination}") + except PermissionError as e: + + print(f"Error: {e}") + + print(f"Evaluation results logged to: {destination}") + + +def is_running_in_docker(): + """ + Checks whether we are running in a Docker container or not. + :returns True if DOCKER_CONTAINER env variable is present, False otherwise. + """ + return 'DOCKER_CONTAINER' in os.environ + + +def get_endpoint_and_monitoring_schedule(): + """ + Retrieves the endpoint name and monitoring schedule name from the processing job config file. + If we are in a docker container, we are running a monitoring job, and the config file has + the endpoint name and monitoring schedule name. + + For information about processingjobcongfig.json file, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-contract-inputs.html + + :returns A tuple containing the endpoint name and monitoring schedule name. + """ + + if is_running_in_docker(): + try: + with open(PROCESSING_JOB_CONFIG_FILE, 'r') as config: + params = json.load(config) + logger.info("Reading Env params") + endpoint_name = params["Environment"]["sagemaker_endpoint_name"] + monitoring_schedule_name = params["Environment"]["sagemaker_monitoring_schedule_name"] + + return endpoint_name, monitoring_schedule_name + except KeyError: + logger.error(f"Environment does not have endpoint or monitoring schedule name. Ensure that this processing job is initiated by a monitoring schedule.") + return DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE + + else: + return DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_monitor_byoc/src/components/data_loader.py b/sagemaker_model_monitor/llm_monitor_byoc/src/components/data_loader.py new file mode 100644 index 0000000000..560139fde1 --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/src/components/data_loader.py @@ -0,0 +1,178 @@ +import os +import json +import logging +import base64 +import jsonschema + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +SCHEMA_FILE = '../utils/jsonl-capture-data.schema' + +class DataLoader: + """ + The DataLoader is a service that recursively searches all subdirectories of + the '/opt/ml/processing/input_data' directory for JSONL files and subsequently executes an + ETL (Extract, Transform, Load) process. The DataLoader completes its job when all data has + been extracted, formatted, and loaded into '/opt/ml/processing/formatted_data/data.jsonl'. + """ + + def __init__(self): + """ + Constructor. No parameters. + + """ + self.transformed_data = [] + + def extract(self, file_path: str): + """ + Extracts data from a JSONL file. + + :param file_path: The path to the JSONL file. + :raises: ValueError if file_path is not a valid string. + :returns: A list of data records extracted from the file. If file does not exist, returns empty list. + """ + + if not isinstance(file_path, str): + raise ValueError("file_path must be a string") + + schema_filepath = os.path.join(os.path.dirname(__file__), SCHEMA_FILE) + + logger.info(f"Extracting data from file: {file_path}") + extracted_data = [] + try: + with open(file_path, 'r') as file: + for line in file: + try: + data = json.loads(line) + validate_json_against_schema(data, schema_filepath) + except json.JSONDecodeError: + logger.info(f"Invalid JSON data: {line}") + continue + except jsonschema.ValidationError as e: + logger.info(f"Validation error: {e}") + continue + extracted_data.append(data) + return extracted_data + except: + return [] + + + def transform(self, data: list): + """ + Applies transformation rules to the extracted data. The current rules format the data to be used with FMEval. + + :param data: A list of data records to be transformed. Each item is a dictionary. + :raises: ValueError if data is not a list. + :raises: Warning if invalid data is provided. + :returns: The transformed data records. + """ + logger.info("Transforming data...") + + if not isinstance(data, list): + raise ValueError("data must be a list") + + transformed_data = [] + for record in data: + try: + content = json.loads(record["captureData"]["endpointInput"]["data"])["inputs"][0][0]["content"] + model_output = json.loads(base64.b64decode(record["captureData"]["endpointOutput"]["data"]).decode("utf-8"))[0]["generation"]["content"] + + # Create the transformed data + transformed_record = { + "content": content, + "answer": model_output + } + transformed_data.append(transformed_record) + except (KeyError, IndexError, json.JSONDecodeError, UnicodeDecodeError) as e: + logger.warning(f"Error transforming record: {e}") + continue + + return transformed_data + + def load(self, destination: str): + """ + Loads the transformed data into a single JSONL file. + :param destination: The destination filepath of the JSONL file. + :raises: ValueError if destination is not a valid string. + :returns: None. + """ + + if not isinstance(destination, str): + raise ValueError("destination must be a string") + + + logger.info(f"Loading data to: {destination}") + + # Create the directory if it doesn't exist + formatted_data_dir = os.path.dirname(destination) + if not os.path.exists(formatted_data_dir): + os.makedirs(formatted_data_dir, exist_ok=True) + + # Open the file and write the data + try: + with open(destination, 'w') as file: + for data_record in self.transformed_data: + file.write(json.dumps(data_record) + '\n') + except PermissionError as e: + + logger.error(f"Permission error: {e}") + + + + def execute_etl(self, directory: str, destination: str): + """ + Executes the ETL (Extract, Transform, Load) process. This function recursively searches the input data directory and performs + ETL on all .jsonl files found. + + :param directory: The directory to search for capture data. + :param destination: The destination filepath of the transformed data. + :raises: ValueError if directory is not a valid string. + :raises: ValueError if destination is not a valid string. + :raises: Warning if invalid directory provided. + :returns: None. + """ + + if not isinstance(directory, str): + raise ValueError("directory must be a string") + if not isinstance(destination, str): + raise ValueError("destination must be a string") + + + logger.info(f"current dir: {os.getcwd()}") + logger.info(f"Executing ETL process for directory: {directory}") + if os.path.exists(directory) and os.path.isdir(directory): + # Iterate over each file and directory in the directory + for item in os.listdir(directory): + item_path = os.path.join(directory, item) + if os.path.isdir(item_path): + # Recursively call the function for subdirectories + self.execute_etl(item_path, destination) + else: + # Check if the file is a .jsonl file and process it + if item.endswith(".jsonl"): + logger.info(f"Processing file: {item_path}") + extracted_data = self.extract(item_path) + transformed_data = self.transform(extracted_data) + self.transformed_data.extend(transformed_data) + else: + logger.info(f"Found file: {item_path}") + + else: + logger.warning(f"The directory {directory} does not exist or is not a directory.") + + # Load the transformed data into a single JSONL file + self.load(destination) + + +def validate_json_against_schema(data, schema_filepath): + """ + Validates that the data fits the schema defined in the schema file. + + :param data: The data to validate. + :param schema_filepath: The path to the schema file. + :raises: jsonschema.ValidationError if the data does not match the schema. + """ + with open(schema_filepath) as sf: + schema = json.load(sf) + jsonschema.validate(instance=data, schema=schema) \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_monitor_byoc/src/components/evaluator.py b/sagemaker_model_monitor/llm_monitor_byoc/src/components/evaluator.py new file mode 100644 index 0000000000..e3f06a28cd --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/src/components/evaluator.py @@ -0,0 +1,84 @@ +from typing import Set, Optional +import logging +import json +from fmeval.eval_algorithms.toxicity import Toxicity, ToxicityConfig, DataConfig +from fmeval.exceptions import EvalAlgorithmClientError + +# Model Input/Output specify which fields FMEVal looks in our dataset. +# Reference https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-foundation-model-evaluate-auto-lib-custom.html +DATASET_NAME = "custom_dataset" +DATASET_MIME_TYPE = "application/jsonlines" +MODEL_INPUT_LOCATION = "content" +MODEL_OUTPUT_LOCATION = "answer" + + +TOXICITY_EVALUATOR_MODEL = "detoxify" +DEFAULT_EVALUATIONS = {'toxicity', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat', 'sexual_explicit'} + +logger = logging.getLogger(__name__) + +class Evaluator: + """ + The Evaluator is a service that assesses the performance of Large Language Models by running a set + of evaluation algorithms specified by a configuration set. It reads formatted data from + the /opt/ml/processing/output/data.jsonl file and uses the FMEval open-source library to + execute the specified evaluation tasks. + """ + def __init__(self, eval_config: Optional[Set[str]] = None): + """ + Constructor + :param eval_config: A Set of evaluation tasks to run. If not provided, all evaluation tasks will be run. + :raises: ValueError if eval_config is not a set or a list of strings. + """ + self.eval_config = eval_config + if eval_config is not None: + if isinstance(eval_config, set): + self.eval_config = eval_config + elif isinstance(eval_config, list): + self.eval_config = set(eval_config) + else: + raise ValueError("eval_config must be a set or a list of strings") + + def evaluate(self, dataset_uri: str): + """ + Evaluate the data using the configured settings. + + :param dataset_uri: The path to the dataset file. + :raises: ValueError if the dataset_uri is not a valid string. + :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary. + """ + + if not isinstance(dataset_uri, str): + raise ValueError("dataset_uri must be a valid string") + + config = DataConfig( + dataset_name=DATASET_NAME, + dataset_uri=dataset_uri, + dataset_mime_type=DATASET_MIME_TYPE, + model_input_location=MODEL_INPUT_LOCATION, + model_output_location=MODEL_OUTPUT_LOCATION, + ) + + if not self.eval_config: + configured_evals = DEFAULT_EVALUATIONS + else: + configured_evals = set(self.eval_config) + + eval_algo = Toxicity(ToxicityConfig(model_type=TOXICITY_EVALUATOR_MODEL)) + + try: + eval_output = eval_algo.evaluate(dataset_config=config, save=True) + except (json.JSONDecodeError, EvalAlgorithmClientError) as e: + # If we evaluate an empty/malformed file, return an empty dict + logger.warning("Evaluated data malformed.") + return {} + + eval_results = {} + for eval_score in eval_output[0].dataset_scores: + if eval_score.name in configured_evals: + eval_results[eval_score.name] = eval_score.value + + logger.info(f"Evaluation Results: {eval_results}") + + return eval_results + \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_monitor_byoc/src/main.py b/sagemaker_model_monitor/llm_monitor_byoc/src/main.py new file mode 100644 index 0000000000..758932e787 --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/src/main.py @@ -0,0 +1,44 @@ +import logging +import sys +import site +from components.data_loader import DataLoader +from components.evaluator import Evaluator +from components.cloudwatch_logger import CloudWatchLogger + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# This is where our capture data is loaded to. MUST be same as "destination" field in EndointInput for deployed model. +INPUT_DATA_SOURCE = '/opt/ml/processing/input_data' + +# Destination for formatted and cleaned data in the container for evaluation. +CLEANED_DATA_DESTINATION = '/opt/ml/processing/internal/data.jsonl' + +# Destination for metrics. These metrics MUST be stored at this location if they are to be published. +# See https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html +CLOUDWATCH_METRICS_DESTINATION = '/opt/ml/output/metrics/cloudwatch/cloudwatch_metrics.jsonl' + +# These are all of the evaluations we can run. +EVALUATIONS = { + "toxicity", + "severe_toxicity", + "obscene", + "identity_attack", + "insult", + "threat", + "sexual_explicit" + } + +if __name__ == "__main__": + try: + data_loader = DataLoader() + evaluator = Evaluator(EVALUATIONS) + cloudwatch_logger = CloudWatchLogger() + + data_loader.execute_etl(INPUT_DATA_SOURCE, CLEANED_DATA_DESTINATION) + eval_results = evaluator.evaluate(CLEANED_DATA_DESTINATION) + cloudwatch_logger.log(eval_results, CLOUDWATCH_METRICS_DESTINATION) + + except Exception as e: + logger.exception("Exception performing analysis: " + str(e)) + sys.exit(255) diff --git a/sagemaker_model_monitor/llm_monitor_byoc/src/utils/jsonl-capture-data.schema b/sagemaker_model_monitor/llm_monitor_byoc/src/utils/jsonl-capture-data.schema new file mode 100644 index 0000000000..af48e7da17 --- /dev/null +++ b/sagemaker_model_monitor/llm_monitor_byoc/src/utils/jsonl-capture-data.schema @@ -0,0 +1,86 @@ +{ + "$schema": "http://json-schema.org/draft-04/schema#", + "type": "object", + "properties": { + "captureData": { + "type": "object", + "properties": { + "endpointInput": { + "type": "object", + "properties": { + "observedContentType": { + "type": "string" + }, + "mode": { + "type": "string" + }, + "data": { + "type": "string" + }, + "encoding": { + "type": "string" + } + }, + "required": [ + "observedContentType", + "mode", + "data", + "encoding" + ] + }, + "endpointOutput": { + "type": "object", + "properties": { + "observedContentType": { + "type": "null" + }, + "mode": { + "type": "string" + }, + "data": { + "type": "string" + }, + "encoding": { + "type": "string" + } + }, + "required": [ + "observedContentType", + "mode", + "data", + "encoding" + ] + } + }, + "required": [ + "endpointInput", + "endpointOutput" + ] + }, + "eventMetadata": { + "type": "object", + "properties": { + "eventId": { + "type": "string" + }, + "customAttributes": { + "type": "array", + "items": [ + { + "type": "string" + } + ] + }, + "inferenceTime": { + "type": "string" + } + } + }, + "eventVersion": { + "type": "string" + } + }, + "required": [ + "captureData" + ] +} From 0a6cd56441cbb13638e1f3e35a2c8d2786fcfed3 Mon Sep 17 00:00:00 2001 From: parsash2 <60193914+parsash2@users.noreply.github.com> Date: Tue, 23 Jul 2024 15:02:03 -0700 Subject: [PATCH 10/16] Update athena_ml_workflow_end_to_end.ipynb (#4702) Fixed bucket names and external links. No change to underlying code or formatting. Co-authored-by: sage-maker --- .../athena_ml_workflow_end_to_end.ipynb | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb index e723f14c7e..6899131fa3 100644 --- a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -32,8 +32,8 @@ "\n", "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database).\n", "\n", - "Amazon Athena uses the AWS Glue Data Catalog to read the data from Amazon S3 into a database. You must have permissions to use Glue. To clean up, you also need permissions to delete the bucket you've created. For a quick guide to providing permissions, see [Setting up\n", - "](http://parsash-clouddesk-2024.aka.corp.amazon.com/sagemaker-dg/src/AWSIronmanApiDoc/build/server-root/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." + "Amazon Athena uses the AWS Glue Data Catalog to read the data from Amazon S3 into a database. You must have permissions to use Glue. To clean up, you also need permissions to delete the bucket you've created. For information about providing permissions, see [Identity and access management for AWS Glue\n", + "](https://docs.aws.amazon.com/glue/latest/dg/security-iam.html)." ] }, { @@ -911,15 +911,15 @@ " outputs=[\n", " ProcessingOutput(\n", " source=\"/opt/ml/processing/output/train\",\n", - " destination=\"s3://ux360-nyc-taxi-dogfooding/output/train\",\n", + " destination=\"s3://example-s3-bucket/output/train\",\n", " ),\n", " ProcessingOutput(\n", " source=\"/opt/ml/processing/output/validation\",\n", - " destination=\"s3://ux360-nyc-taxi-dogfooding/output/validation\",\n", + " destination=\"s3://example-s3-bucket/output/validation\",\n", " ),\n", " ProcessingOutput(\n", " source=\"/opt/ml/processing/output/test\",\n", - " destination=\"s3://ux360-nyc-taxi-dogfooding/output/test\",\n", + " destination=\"s3://example-s3-bucket/output/test\",\n", " ),\n", " ],\n", ")" @@ -941,7 +941,7 @@ "outputs": [], "source": [ "# Verify that train.csv is in the location that you've specified\n", - "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" + "!aws s3 ls s3://example-s3-bucket/output/train/train.csv" ] }, { @@ -960,7 +960,7 @@ "outputs": [], "source": [ "# Verify that val.csv is in the location that you've specified\n", - "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" + "!aws s3 ls s3://example-s3-bucket/output/validation/val.csv" ] }, { From ffec3d50de4976ec8b380a14913b897dc3fdda35 Mon Sep 17 00:00:00 2001 From: Praveen Chamarthi <118387029+pchamart@users.noreply.github.com> Date: Tue, 23 Jul 2024 15:05:52 -0700 Subject: [PATCH 11/16] SageMaker FasterAutoscaling Llama3-8B TGI, real-time endpoints (#4712) * SageMaker FasterAutoscaling Llama3-8B TGI, real-time endpoints * Moved trigger autoscaling to shell script. Removed shell=True in subprocess.Popen --------- Co-authored-by: Aditi Sharma <165942273+Aditi2424@users.noreply.github.com> --- .../llama3-8b/faster-autoscaling/README.md | 41 + ...oscaling-IC-Llama3-8B-AppAutoScaling.ipynb | 639 +++++++++++++ ...scaling-SME-Llama3-8B-AppAutoScaling.ipynb | 719 +++++++++++++++ ...utoscaling-SME-Llama3-8B-StepScaling.ipynb | 872 ++++++++++++++++++ .../realtime-endpoints/requirements.txt | 67 ++ .../realtime-endpoints/trigger_autoscaling.sh | 36 + .../realtime-endpoints/utils/__init__.py | 0 .../realtime-endpoints/utils/autoscaling.py | 174 ++++ .../realtime-endpoints/utils/llmperf.py | 100 ++ 9 files changed, 2648 insertions(+) create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/README.md create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/requirements.txt create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/trigger_autoscaling.sh create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/__init__.py create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/autoscaling.py create mode 100644 inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/llmperf.py diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/README.md b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/README.md new file mode 100644 index 0000000000..2db0fe6028 --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/README.md @@ -0,0 +1,41 @@ +# Amazon SageMaker Faster Autoscaling + +To demonstrate newer, faster SageMaker autoscaling features, We deploy Meta's **Llama3-8B-Instruct** model to an Amazon SageMaker real-time endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC). + +To trigger autoscaling, we need to generate traffic to the endpoint. +We use [LLMPerf](https://github.com/philschmid/llmperf) to generate sample traffic to the endpoint. + +## Prerequisites + +Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta. + +- Step 1: Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens. +- Step 2: Login to HuggingFace and navigate to [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) home page. +- Step 3: Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main). +- Step 4: Wait for the approval email from META (Approval may take any where b/w 1-3 hrs). + +--- + +>NOTE: LLMPerf spins up a ray cluster to generate traffic to Amazon SageMaker endpoint.\ +>When running this on Amazon SageMaker Notebook Instance, ensure you use at least **m5.2xlarge** or a larger instance type. + +## Autoscaling on real-time endpoints + +### Amazon SageMaker real-time endpoints + +- For Application Autoscaling example on Amazon SageMaker real-time endpoints refer to [FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb](./realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb) notebook. + +- For StepScaling example on Amazon SageMaker real-time endpoints refer to [FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb](./realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb) notebook. + +### Amazon SageMaker Inference Components + +- For autoscaling example using Amazon SageMaker Inference components, refer to [inference-component-llama3-autoscaling.ipynb](./realtime-endpoints/FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb) notebook. + +--- + +## References + +- [LLMPerf](https://github.com/philschmid/llmperf) +- [Llama3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) +- [Create HF Access Token](https://huggingface.co/docs/hub/security-tokens) +- [Amazon SageMaker Inference Components - blog post](https://aws.amazon.com/blogs/machine-learning/reduce-model-deployment-costs-by-50-on-average-using-sagemakers-latest-features/) \ No newline at end of file diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb new file mode 100644 index 0000000000..1c6c3c6876 --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb @@ -0,0 +1,639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "81f236c9", + "metadata": {}, + "source": [ + "# Faster autoscaling on Amazon SageMaker realtime endpoints with inference components (Application Autoscaling)\n", + "\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "\n", + "---\n", + "\n", + "In this notebook we show how the new faster autoscaling feature helps scale sagemaker inference endpoints by almost 6x faster than earlier.\n", + "\n", + "We deploy Meta's `Llama3-8B-Instruct` model to an Amazon SageMaker realtime endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC) and apply Application Autoscaling scaling policies to the endpoint.\n", + "\n", + "\n", + "
\n", + " Please select m5.2xlarge or larger instance types when running this on Amazon SageMaker Notebook Instance.
\n", + " Select conda_pytorch_p310 kernel when running this notebook on Amazon SageMaker Notebook Instance.

\n", + " Ensure python version for the kernel is 3.10.x (3.11 is not supported).
\n", + "
\n", + "\n", + "---\n", + "\n", + "## Prerequisites\n", + "\n", + "\n", + "\n", + "
\n", + "Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta.\n", + "\n", + "- **Step 1:** Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens.\n", + "- **Step 2:** Login to [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) and navigate to *Meta-Llama-3-8B-Instruct** home page.\n", + "- **Step 3:** Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main)\n", + "- **Step 4:** Wait for the approval email from META (Approval may take any where b/w 1-3 hrs)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8a241652-091a-4769-9480-ba64b9e30c9d", + "metadata": { + "tags": [] + }, + "source": [ + "Install packages using uv, an extremely fast python package installer\\\n", + "Read more about uv here " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7767c519-29c9-4794-8a4e-67cb43779697", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# ensure python version of the selected kernel is not greater than 3.10\n", + "!python --version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d260cb1-1355-448e-8fd9-3eebb1584ba0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install uv && uv pip install -U ipywidgets\n", + "!uv pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2abe082-5ce0-4a26-bae8-68f9bff4104c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# restart kernel\n", + "from IPython.core.display import HTML\n", + "\n", + "HTML(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "590ec770-d05e-474d-80da-d2f2bab63db2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load rich extension\n", + "%load_ext rich" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a97edd4-8bba-4806-bce5-c559e23da05d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "import time\n", + "from getpass import getpass\n", + "import boto3\n", + "import sagemaker\n", + "from rich import print\n", + "from sagemaker.deserializers import JSONDeserializer\n", + "from sagemaker.huggingface import get_huggingface_llm_image_uri\n", + "from sagemaker.predictor import Predictor\n", + "from sagemaker.serializers import JSONSerializer\n", + "from sagemaker.deserializers import JSONDeserializer" + ] + }, + { + "cell_type": "markdown", + "id": "9c02d12b-2109-4f01-8da8-8972ba493398", + "metadata": {}, + "source": [ + "## Initiate sagemaker session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "349a795d-df01-494e-b5fa-5f14971a1431", + "metadata": {}, + "outputs": [], + "source": [ + "sess = sagemaker.Session()\n", + "role = sagemaker.get_execution_role()\n", + "region = sess.boto_region_name\n", + "\n", + "boto_session = boto3.Session(region_name=region)\n", + "\n", + "sagemaker_client = sess.sagemaker_client\n", + "sagemaker_runtime_client = sess.sagemaker_runtime_client\n", + "cloudwatch_client = boto3.client(\"cloudwatch\", region_name=region)\n", + "\n", + "hf_model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n", + "\n", + "# retrieve the llm image uri\n", + "# tgi_dlc = f\"763104351884.dkr.ecr.{region}.amazonaws.com/huggingface-pytorch-tgi-inference:2.1-tgi2.0-gpu-py310-cu121-ubuntu22.04\"\n", + "tgi_dlc = get_huggingface_llm_image_uri(\"huggingface\", version=\"2.0.0\")\n", + "\n", + "print(f\"TGI DLC: \\n[b i green]{tgi_dlc}[/b i green]\")\n", + "print(f\"Region: [b blue]{region}[/b blue]\")\n", + "print(f\"Role: [b red]{role}[/b red]\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3d06b2c-dec7-4b42-af1a-423d39f211d6", + "metadata": {}, + "source": [ + "## Create Endpoint\n", + "\n", + "1. Create `EndpointConfiguration`\n", + "2. Create Endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cb8a39a-0f45-4e0e-a678-25c158a268c2", + "metadata": {}, + "outputs": [], + "source": [ + "# Set an unique endpoint config name\n", + "prefix = sagemaker.utils.unique_name_from_base(\"llama3\")\n", + "print(f\"prefix: {prefix}\")\n", + "\n", + "endpoint_config_name = f\"{prefix}-endpoint-config\"\n", + "print(f\"Endpoint config name: {endpoint_config_name}\")\n", + "\n", + "# Set varient name and instance type for hosting\n", + "variant_name = \"AllTraffic\"\n", + "instance_type = \"ml.g5.2xlarge\"\n", + "model_data_download_timeout_in_seconds = 3600\n", + "container_startup_health_check_timeout_in_seconds = 3600\n", + "\n", + "initial_instance_count = 1\n", + "max_instance_count = 2\n", + "print(f\"Initial instance count: {initial_instance_count}\")\n", + "print(f\"Max instance count: {max_instance_count}\")\n", + "\n", + "epc_response = sagemaker_client.create_endpoint_config(\n", + " EndpointConfigName=endpoint_config_name,\n", + " ExecutionRoleArn=role,\n", + " ProductionVariants=[\n", + " {\n", + " \"VariantName\": variant_name,\n", + " \"InstanceType\": instance_type,\n", + " \"InitialInstanceCount\": 1,\n", + " \"ModelDataDownloadTimeoutInSeconds\": model_data_download_timeout_in_seconds,\n", + " \"ContainerStartupHealthCheckTimeoutInSeconds\": container_startup_health_check_timeout_in_seconds,\n", + " \"ManagedInstanceScaling\": {\n", + " \"Status\": \"ENABLED\",\n", + " \"MinInstanceCount\": initial_instance_count,\n", + " \"MaxInstanceCount\": max_instance_count,\n", + " },\n", + " \"RoutingConfig\": {\"RoutingStrategy\": \"LEAST_OUTSTANDING_REQUESTS\"},\n", + " }\n", + " ],\n", + ")\n", + "print(epc_response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17259935-6a85-46af-89dd-b10e064a1c3b", + "metadata": {}, + "outputs": [], + "source": [ + "# Set a unique endpoint name\n", + "endpoint_name = f\"{prefix}-endpoint\"\n", + "\n", + "ep_response = sagemaker_client.create_endpoint(\n", + " EndpointName=endpoint_name,\n", + " EndpointConfigName=endpoint_config_name,\n", + ")\n", + "# print(ep_response)\n", + "print(f\"Creating endpoint: [b blue]{endpoint_name}...\")\n", + "sess.wait_for_endpoint(endpoint_name)" + ] + }, + { + "cell_type": "markdown", + "id": "043f7d75-de75-4687-a2e2-ab4aa7168ef6", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "Create and deploy model using Amazon SageMaker HuggingFace TGI DLC\n", + "\n", + "\n", + "\n", + "
\n", + "NOTE: Remember to copy your Hugging Face Access Token from https://hf.co/ before running the below cell.

\n", + "Refer here to learn about creating HF tokens.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "74991ddd-3b2c-4f07-b35b-55f5d8c19ada", + "metadata": {}, + "source": [ + "## Configure container and environment " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b89da4d-9ce7-4e5b-a02a-3f2c690cd26d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# print ecr image uri\n", + "print(f\"llm image uri: [b green]{tgi_dlc}\")\n", + "\n", + "HF_TOKEN = os.getenv(\"HUGGING_FACE_HUB_TOKEN\") or getpass(\"Enter HUGGINGFACE Access Token: \")\n", + "\n", + "llama3model = {\n", + " \"Image\": tgi_dlc,\n", + " \"Environment\": {\n", + " \"HF_MODEL_ID\": \"meta-llama/Meta-Llama-3-8B-Instruct\", # model_id from hf.co/models\n", + " \"SM_NUM_GPUS\": \"1\", # Number of GPU used per replica\n", + " \"MAX_INPUT_LENGTH\": \"2048\", # Max length of input text\n", + " \"MAX_TOTAL_TOKENS\": \"4096\", # Max length of the generation (including input text)\n", + " \"MAX_BATCH_TOTAL_TOKENS\": \"8192\", # Limits the number of tokens that can be processed in parallel during the generation\n", + " \"MESSAGES_API_ENABLED\": \"true\", # Enable the messages API\n", + " \"HUGGING_FACE_HUB_TOKEN\": HF_TOKEN,\n", + " },\n", + "}\n", + "\n", + "# create Model\n", + "deployment_name = \"sm\"\n", + "model_name = f\"{deployment_name}-model-llama3\"\n", + "\n", + "print(f\"Creating model: [b green]{model_name}...\")\n", + "model_response = sagemaker_client.create_model(\n", + " ModelName=model_name,\n", + " ExecutionRoleArn=role,\n", + " Containers=[llama3model],\n", + ")\n", + "\n", + "print(model_response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a93cea6-703e-4eb9-8b4d-d92b60597a7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Deploy model to Amazon SageMaker Inference Component\n", + "inference_component_name_llama3b = f\"{prefix}-IC-llama3b\"\n", + "variant_name = \"AllTraffic\"\n", + "\n", + "ic_response = sagemaker_client.create_inference_component(\n", + " InferenceComponentName=inference_component_name_llama3b,\n", + " EndpointName=endpoint_name,\n", + " VariantName=variant_name,\n", + " Specification={\n", + " \"ModelName\": f\"{deployment_name}-model-llama3\",\n", + " \"ComputeResourceRequirements\": {\n", + " \"NumberOfAcceleratorDevicesRequired\": 1,\n", + " \"NumberOfCpuCoresRequired\": 1,\n", + " \"MinMemoryRequiredInMb\": 1024,\n", + " },\n", + " },\n", + " RuntimeConfig={\"CopyCount\": 1},\n", + ")\n", + "\n", + "# print(ic_response)\n", + "\n", + "# Wait for IC to come InService\n", + "print(f\"InferenceComponent [b magenta]{inference_component_name_llama3b}...\")\n", + "while True:\n", + " desc = sagemaker_client.describe_inference_component(\n", + " InferenceComponentName=inference_component_name_llama3b\n", + " )\n", + " status = desc[\"InferenceComponentStatus\"]\n", + " print(status)\n", + " sys.stdout.flush()\n", + " if status in [\"InService\", \"Failed\"]:\n", + " break\n", + " time.sleep(30)" + ] + }, + { + "cell_type": "markdown", + "id": "83e1af5c-e713-4cf8-bc23-1c96f1e61327", + "metadata": {}, + "source": [ + "## Inference\n", + "\n", + "Invoke and test endpoint using messages API. Refer to HF [Messages API](https://huggingface.co/docs/text-generation-inference/messages_api) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59fa5f76-5498-47a1-a443-5483c3077172", + "metadata": {}, + "outputs": [], + "source": [ + "# create predictor object\n", + "predictor = Predictor(\n", + " endpoint_name=endpoint_name,\n", + " sagemaker_session=sess,\n", + " component_name=inference_component_name_llama3b,\n", + " serializer=JSONSerializer(),\n", + " deserializer=JSONDeserializer(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6d9ecc2-fffe-4ff1-b78b-1222fe6d32de", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Prompt to generate\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"What is deep learning?\"},\n", + "]\n", + "\n", + "# Generation arguments\n", + "parameters = {\n", + " \"model\": hf_model_id, # model id is required\n", + " \"top_p\": 0.6,\n", + " \"temperature\": 0.9,\n", + " \"max_tokens\": 512,\n", + " \"stop\": [\"<|eot_id|>\"],\n", + "}\n", + "\n", + "chat = predictor.predict({\"messages\": messages, **parameters})\n", + "\n", + "# Unpack and print response\n", + "print(chat[\"choices\"][0][\"message\"][\"content\"].strip())" + ] + }, + { + "cell_type": "markdown", + "id": "29f7a5ab-0264-4b12-8243-b4aa649335b7", + "metadata": {}, + "source": [ + "## Apply Autoscaling policies to the endpoint\n", + "\n", + "Apply Application Autoscaling Policy to endpoint\n", + "\n", + "1. Register Scalable Target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bbf762f-beec-42ed-9ff8-5b06f76269ab", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "as_min_capacity = 1\n", + "as_max_capacity = 2\n", + "\n", + "resource_id = f\"inference-component/{inference_component_name_llama3b}\"\n", + "\n", + "autoscaling_client = boto3.client(\"application-autoscaling\", region_name=region)\n", + "\n", + "# Register scalable target\n", + "scalable_target = autoscaling_client.register_scalable_target(\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:inference-component:DesiredCopyCount\",\n", + " MinCapacity=as_min_capacity,\n", + " MaxCapacity=as_max_capacity, # Replace with your desired maximum instances\n", + ")\n", + "\n", + "scalable_target_arn = scalable_target[\"ScalableTargetARN\"]\n", + "print(f\"Resource ID: [b blue]{resource_id}\")\n", + "print(f\"Scalable_target_arn:\\n[b green]{scalable_target_arn}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0af2e234-d1c7-4575-b943-5291c70c326d", + "metadata": {}, + "source": [ + "## Use the latest high-resolution Metrics to trigger auto-scaling\n", + "\n", + "- New feature introduces a new PredefinedMetricType for scaling policy configuration i.e. SageMakerVariantConcurrentRequestsPerModelHighResolution to trigger scaling actions.\n", + "- Creating a scaling policy with this metric type will create cloudwatch alarms that track a new metric called ConcurrentRequestsPerModel.\n", + "- These high-resolution metrics are published at sub-minute intervals (10s intervals to CW + any additional jitter + delays)\n", + "- We should observe significant improvement in scale out times with this new metric\n", + "\n", + "\n", + "### Steps to create Application autoscaling policy\n", + "\n", + "- Create scaling policy\n", + " - Set `PolicyType` to `TargetTrackingScaling`\n", + " - Set `TargetValue` to `5.0`. i.e., Scaling triggers when endpoint receives 5 `ConcurrentRequestsPerModel`\n", + " - Set `PredefinedMetricType` to `SageMakerVariantConcurrentRequestsPerModelHighResolution`\n", + " - Set `ScaleInCoolDown` and `ScaleOutCoolDown` values to `300` seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44cc8c60-37cd-4852-a03d-e08149ccad17", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Create Target Tracking Scaling Policy\n", + "target_tracking_policy_response = autoscaling_client.put_scaling_policy(\n", + " PolicyName=\"SageMakerICScalingPolicy\",\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:inference-component:DesiredCopyCount\",\n", + " PolicyType=\"TargetTrackingScaling\",\n", + " TargetTrackingScalingPolicyConfiguration={\n", + " \"TargetValue\": 5.0, # Scaling triggers when endpoint receives 5 ConcurrentRequestsPerModel\n", + " \"PredefinedMetricSpecification\": {\n", + " \"PredefinedMetricType\": \"SageMakerInferenceComponentConcurrentRequestsPerCopyHighResolution\"\n", + " },\n", + " \"ScaleInCooldown\": 300, # Cooldown period after scale-in activity\n", + " \"ScaleOutCooldown\": 300, # Cooldown period after scale-out activity\n", + " },\n", + ")\n", + "\n", + "# print(target_tracking_policy_response)\n", + "print(f\"Policy ARN: [i blue]{target_tracking_policy_response['PolicyARN']}\")\n", + "\n", + "# print Cloudwatch Alarms\n", + "alarms = target_tracking_policy_response[\"Alarms\"]\n", + "\n", + "for alarm in alarms:\n", + " print(f\"[b]Alarm Name:[/b] [b magenta]{alarm['AlarmName']}\")\n", + " # print(f\"[b]Alarm ARN:[/b] [i green]{alarm['AlarmARN']}[/i green]\")\n", + " print(\"===\" * 15)" + ] + }, + { + "cell_type": "markdown", + "id": "02a2d5b0-dc4b-40e3-8ada-ceddecfdac1a", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "- Deregister scalable target. This automatically deletes associated cloudwatch alarms.\n", + "- Delete model\n", + "- Delete endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7aacabc3-2b60-41c6-b903-9de5e31fc8e7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "try:\n", + " # Deregister the scalable target for AAS\n", + " autoscaling_client.deregister_scalable_target(\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " )\n", + " print(f\"Scalable target for [b]{resource_id}[/b] deregistered. ✅\")\n", + "except autoscaling_client.exceptions.ObjectNotFoundException:\n", + " print(f\"Scalable target for [b]{resource_id}[/b] not found!.\")\n", + "\n", + "print(\"---\" * 10)\n", + "\n", + "try:\n", + " print(f\"Deleting inference components: [b magenta]{inference_component_name_llama3b} ✅\")\n", + " # Delete inference component\n", + " sagemaker_client.delete_inference_component(\n", + " InferenceComponentName=inference_component_name_llama3b\n", + " )\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "\n", + "try:\n", + " print(f\"Deleting model: [b magenta]{deployment_name}-model-llama3 ✅\")\n", + " predictor.delete_model()\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "\n", + "try:\n", + " print(f\"Deleting endpoint: [b magenta]{predictor.endpoint_name} ✅\")\n", + " predictor.delete_endpoint()\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "print(\"---\" * 10)\n", + "print(\"Done\")" + ] + }, + { + "cell_type": "markdown", + "id": "9fdee8f2", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-IC-Llama3-8B-AppAutoScaling.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb new file mode 100644 index 0000000000..a43a566c7c --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb @@ -0,0 +1,719 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "81f236c9", + "metadata": {}, + "source": [ + "# Faster autoscaling on Amazon SageMaker realtime endpoints (Application Autoscaling)\n", + "\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "\n", + "---\n", + "\n", + "In this notebook we show how the new faster autoscaling feature helps scale sagemaker inference endpoints by almost 6x faster than earlier.\n", + "\n", + "We deploy Meta's `Llama3-8B-Instruct` model to an Amazon SageMaker realtime endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC) and apply Application Autoscaling scaling policies to the endpoint.\n", + "\n", + "\n", + "
\n", + " Please select m5.2xlarge or larger instance types when running this on Amazon SageMaker Notebook Instance.
\n", + " Select conda_pytorch_p310 kernel when running this notebook on Amazon SageMaker Notebook Instance.

\n", + " Ensure python version for the kernel is 3.10.x (3.11 is not supported).
\n", + "
\n", + "\n", + "---\n", + "\n", + "## Prerequisites\n", + "\n", + "\n", + "\n", + "
\n", + "Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta.\n", + "\n", + "- **Step 1:** Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens.\n", + "- **Step 2:** Login to [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) and navigate to *Meta-Llama-3-8B-Instruct** home page.\n", + "- **Step 3:** Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main)\n", + "- **Step 4:** Wait for the approval email from META (Approval may take any where b/w 1-3 hrs)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8a241652-091a-4769-9480-ba64b9e30c9d", + "metadata": { + "tags": [] + }, + "source": [ + "Install packages using uv, an extremely fast python package installer\\\n", + "Read more about uv here " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7767c519-29c9-4794-8a4e-67cb43779697", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# ensure python version of the selected kernel is not greater than 3.10\n", + "!python --version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d260cb1-1355-448e-8fd9-3eebb1584ba0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install uv && uv pip install -U ipywidgets\n", + "!uv pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2abe082-5ce0-4a26-bae8-68f9bff4104c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# restart kernel\n", + "from IPython.core.display import HTML\n", + "\n", + "HTML(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "590ec770-d05e-474d-80da-d2f2bab63db2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load rich extension\n", + "%load_ext rich" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a97edd4-8bba-4806-bce5-c559e23da05d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import glob\n", + "import json\n", + "import os\n", + "import subprocess\n", + "import time\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from datetime import datetime\n", + "from getpass import getpass\n", + "from pathlib import Path\n", + "from statistics import mean\n", + "from uuid import uuid4\n", + "\n", + "import boto3\n", + "import botocore\n", + "import sagemaker\n", + "from rich import box, print\n", + "from rich.console import Console\n", + "from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn\n", + "from rich.table import Table\n", + "from sagemaker.deserializers import JSONDeserializer\n", + "from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri\n", + "from sagemaker.predictor import Predictor\n", + "from sagemaker.serializers import JSONSerializer\n", + "\n", + "from utils.autoscaling import (\n", + " monitor_scaling_events,\n", + " print_scaling_times,\n", + " test_concurrency_level,\n", + ")\n", + "\n", + "from utils.llmperf import (\n", + " print_llmperf_results,\n", + " trigger_auto_scaling,\n", + " monitor_process,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9c02d12b-2109-4f01-8da8-8972ba493398", + "metadata": {}, + "source": [ + "## Initiate sagemaker session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11d1c2d-787e-4792-a276-897a9cd183cf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sess = sagemaker.Session()\n", + "role = sagemaker.get_execution_role()\n", + "region = sess.boto_region_name\n", + "\n", + "boto_session = boto3.Session(region_name=region)\n", + "\n", + "sagemaker_client = sess.sagemaker_client\n", + "sagemaker_runtime_client = sess.sagemaker_runtime_client\n", + "cloudwatch_client = boto3.client(\"cloudwatch\", region_name=region)\n", + "\n", + "hf_model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n", + "\n", + "# retrieve the llm image uri\n", + "# tgi_dlc = f\"763104351884.dkr.ecr.{region}.amazonaws.com/huggingface-pytorch-tgi-inference:2.1-tgi2.0-gpu-py310-cu121-ubuntu22.04\"\n", + "tgi_dlc = get_huggingface_llm_image_uri(\"huggingface\", version=\"2.0.0\")\n", + "\n", + "print(f\"TGI DLC: \\n[b i green]{tgi_dlc}\")\n", + "print(f\"Region: [b blue]{region}\")\n", + "print(f\"Role: [b red]{role}\")" + ] + }, + { + "cell_type": "markdown", + "id": "043f7d75-de75-4687-a2e2-ab4aa7168ef6", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "Create and deploy model using Amazon SageMaker HuggingFace TGI DLC\n", + "\n", + "\n", + "\n", + "
\n", + "NOTE: Remember to copy your Hugging Face Access Token from https://hf.co/ before running the below cell.

\n", + "Refer here to learn about creating HF tokens.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b89da4d-9ce7-4e5b-a02a-3f2c690cd26d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# sagemaker config\n", + "instance_type = \"ml.g5.2xlarge\"\n", + "suffix = f\"{str(uuid4())[:5]}-{datetime.now().strftime('%d%b%Y')}\"\n", + "model_name = f\"Llama3-8B-fas-{suffix}\"\n", + "endpoint_name = model_name\n", + "health_check_timeout = 900\n", + "\n", + "HF_TOKEN = os.getenv(\"HUGGING_FACE_HUB_TOKEN\") or getpass(\"Enter HUGGINGFACE Access Token: \")\n", + "# Define Model and Endpoint configuration parameter\n", + "config = {\n", + " \"HF_MODEL_ID\": \"meta-llama/Meta-Llama-3-8B-Instruct\", # model_id from hf.co/models\n", + " \"SM_NUM_GPUS\": \"1\", # Number of GPU used per replica\n", + " \"MAX_INPUT_LENGTH\": \"2048\", # Max length of input text\n", + " \"MAX_TOTAL_TOKENS\": \"4096\", # Max length of the generation (including input text)\n", + " \"MAX_BATCH_TOTAL_TOKENS\": \"8192\", # Limits the number of tokens that can be processed in parallel during the generation\n", + " \"MESSAGES_API_ENABLED\": \"true\", # Enable the messages API\n", + " \"HUGGING_FACE_HUB_TOKEN\": HF_TOKEN,\n", + "}\n", + "\n", + "# create HuggingFaceModel with the image uri\n", + "print(f\"Creating model: [b green]{model_name}...\")\n", + "llm_model = HuggingFaceModel(name=model_name, role=role, image_uri=tgi_dlc, env=config)\n", + "\n", + "# Deploy model to Amazon SageMaker endpoint\n", + "print(f\"Deploying model to endpoint: [b magenta]{endpoint_name}...\")\n", + "predictor = llm_model.deploy(\n", + " endpoint_name=endpoint_name,\n", + " initial_instance_count=1,\n", + " instance_type=instance_type,\n", + " container_startup_health_check_timeout=health_check_timeout, # 15 minutes to be able to load the model\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "83e1af5c-e713-4cf8-bc23-1c96f1e61327", + "metadata": {}, + "source": [ + "## Inference\n", + "\n", + "Invoke and test endpoint using messages API. Refer to HF [Messages API](https://huggingface.co/docs/text-generation-inference/messages_api) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6d9ecc2-fffe-4ff1-b78b-1222fe6d32de", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Prepare prompt in messages API format\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"What is deep learning?\"},\n", + "]\n", + "\n", + "# Generation arguments\n", + "parameters = {\n", + " \"model\": hf_model_id, # model id is required\n", + " \"top_p\": 0.6,\n", + " \"temperature\": 0.9,\n", + " \"max_tokens\": 512,\n", + " \"stop\": [\"<|eot_id|>\"],\n", + "}\n", + "\n", + "chat = predictor.predict({\"messages\": messages, **parameters})\n", + "\n", + "# Unpack and print response\n", + "print(chat[\"choices\"][0][\"message\"][\"content\"].strip())" + ] + }, + { + "cell_type": "markdown", + "id": "3cdb619d-b402-46bf-9451-62f50f70e878", + "metadata": {}, + "source": [ + "## Baseline average latency at various concurrency levels (Optional)\n", + "\n", + "By capturing average latency across various concurrency levels, we can get a fair idea on after how many concurrent request does endpoint performance would degrade significantly.\n", + "\n", + "Having this information can help define values for scaling policy accordingly.\n", + "\n", + "
\n", + "Running below cell is optional

\n", + "INFO: ℹ️ Signal here is, at a given concurrency level you start to see average latency increase significantly.
\n", + "At this concurrency level the endpoint gets overloaded and cannot serve requests in a timely fashion.
\n", + "We use these values to set as threshold values for autoscaling.\n", + "

\n", + "NOTE: ⚠️ As concurrent requests to the endpoint increase you might observe ThrottlingException errors as we haven't incorporated exponential backoff and retry mechanisms.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7232ecd-bc78-4d0d-bf44-17c3e060cd99", + "metadata": {}, + "outputs": [], + "source": [ + "# Define list of prompts\n", + "prompts = [\n", + " \"what is deep learning?\",\n", + " \"what are various inference modes in Amazon SageMaker?\",\n", + " \"Can I host Large language models on Amazon SageMaker?\",\n", + " \"Does Amazon SageMaker support TensorRT-LLM?\",\n", + " \"what is step scaling policy in the context of autoscaling ec2 instances on AWS?\",\n", + " \"Why is the sky blue?\",\n", + " \"List 5 benefits of incorporating limes into the diet.\",\n", + "]\n", + "\n", + "# Test different concurrency levels and measure average latency\n", + "concurrency_levels = [10, 50, 75, 100] # Adjust these values as needed\n", + "\n", + "for concurrency_level in concurrency_levels:\n", + " try:\n", + " avg_latency = test_concurrency_level(\n", + " concurrency_level,\n", + " prompts,\n", + " messages,\n", + " parameters,\n", + " endpoint_name,\n", + " sagemaker_runtime_client,\n", + " )\n", + " print(\n", + " f\"[b]Concurrency:[/b] {concurrency_level} requests,\"\n", + " f\" [b]Average latency:[/b] {avg_latency:.2f} seconds\"\n", + " )\n", + " except Exception as e:\n", + " print(f\"[b]At Concurrency[/b] {concurrency_level} requests,\" f\"[b]Exception:[/b] \\n{e}\")\n", + " continue" + ] + }, + { + "cell_type": "markdown", + "id": "29f7a5ab-0264-4b12-8243-b4aa649335b7", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Apply Autoscaling policies to the endpoint\n", + "\n", + "Apply Application Autoscaling Policy to endpoint\n", + "\n", + "1. Register Scalable Target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bbf762f-beec-42ed-9ff8-5b06f76269ab", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "variant_name = \"AllTraffic\"\n", + "as_min_capacity = 1\n", + "as_max_capacity = 2\n", + "\n", + "resource_id = f\"endpoint/{endpoint_name}/variant/{variant_name}\"\n", + "\n", + "autoscaling_client = boto3.client(\"application-autoscaling\", region_name=region)\n", + "\n", + "# Register scalable target\n", + "scalable_target = autoscaling_client.register_scalable_target(\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " MinCapacity=as_min_capacity,\n", + " MaxCapacity=as_max_capacity, # Replace with your desired maximum instances\n", + ")\n", + "\n", + "scalable_target_arn = scalable_target[\"ScalableTargetARN\"]\n", + "print(f\"Resource ID: [b blue]{resource_id}\")\n", + "print(f\"Scalable_target_arn:\\n[b green]{scalable_target_arn}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0af2e234-d1c7-4575-b943-5291c70c326d", + "metadata": {}, + "source": [ + "## Use the latest high-resolution Metrics to trigger auto-scaling\n", + "\n", + "- New feature introduces a new PredefinedMetricType for scaling policy configuration i.e. SageMakerVariantConcurrentRequestsPerModelHighResolution to trigger scaling actions.\n", + "- Creating a scaling policy with this metric type will create cloudwatch alarms that track a new metric called ConcurrentRequestsPerModel.\n", + "- These high-resolution metrics are published at sub-minute intervals (10s intervals to CW + any additional jitter + delays)\n", + "- We should observe significant improvement in scale out times with this new metric\n", + "\n", + "\n", + "### Steps to create Application autoscaling policy\n", + "\n", + "- Create scaling policy\n", + " - Set `PolicyType` to `TargetTrackingScaling`\n", + " - Set `TargetValue` to `5.0`. i.e., Scaling triggers when endpoint receives 5 `ConcurrentRequestsPerModel`\n", + " - Set `PredefinedMetricType` to `SageMakerVariantConcurrentRequestsPerModelHighResolution`\n", + " - Set `ScaleInCoolDown` and `ScaleOutCoolDown` values to `300` seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44cc8c60-37cd-4852-a03d-e08149ccad17", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Create Target Tracking Scaling Policy\n", + "target_tracking_policy_response = autoscaling_client.put_scaling_policy(\n", + " PolicyName=\"SageMakerEndpointScalingPolicy\",\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " PolicyType=\"TargetTrackingScaling\",\n", + " TargetTrackingScalingPolicyConfiguration={\n", + " \"TargetValue\": 5.0, # Scaling triggers when endpoint receives 5 ConcurrentRequestsPerModel\n", + " \"PredefinedMetricSpecification\": {\n", + " \"PredefinedMetricType\": \"SageMakerVariantConcurrentRequestsPerModelHighResolution\"\n", + " },\n", + " \"ScaleInCooldown\": 300, # Cooldown period after scale-in activity\n", + " \"ScaleOutCooldown\": 300, # Cooldown period after scale-out activity\n", + " },\n", + ")\n", + "\n", + "# print(target_tracking_policy_response)\n", + "print(f\"[b]Policy ARN:[/b] [i blue]{target_tracking_policy_response['PolicyARN']}\")\n", + "\n", + "# print Cloudwatch Alarms\n", + "alarms = target_tracking_policy_response[\"Alarms\"]\n", + "\n", + "for alarm in alarms:\n", + " print(f\"[b]Alarm Name:[/b] [b magenta]{alarm['AlarmName']}\")\n", + " # print(f\"[b]Alarm ARN:[/b] [i green]{alarm['AlarmARN']}[/i green]\")\n", + " print(\"===\" * 15)" + ] + }, + { + "cell_type": "markdown", + "id": "d27a4cba-8aec-4b5c-b9ea-97d4ea82d9f0", + "metadata": {}, + "source": [ + "## Trigger autoscaling action\n", + "\n", + "### LLMPerf to generate traffic to the endpoint\n", + "\n", + "Refer to for more details on LLMPerf.\n", + "\n", + "Run the LLMPerf traffic generation script in the background using `subprocess.Popen`\n", + "\n", + "
\n", + "INFO:ℹ️ Refer to utils/llmperf.py for trigger_autoscaling function implementation\n", + "
\n", + "\n", + "### Monitor Scale-Out Alarm Trigger times and scaling event times\n", + "\n", + "As llmperf generates traffic to the endpoint continuously this trigger auto-scaling.\n", + "\n", + "The `monitor_scaling_events` function does the following:\n", + "- Calculates time taken for alarm to go into InAlarm state.\n", + "- checks if alarm is InAlarm state. If yes, then starts the scaling timer\n", + "- continuously monitors the `DesiredInstanceCount` property of the endpoint\n", + " - waits till `CurrentInstanceCount == DesiredInstanceCount` and `EndpointStatus` is `InService`\n", + "- Calculates time taken to scale out instances prints the times in a table\n", + "\n", + "The below cell triggers auto scaling action and calls the monitor_scaling_events immediately on the AlarmHigh\n", + "\n", + "
\n", + "INFO: ℹ️ Refer to utils/autoscaling.py for monitor_scaling_events function implementation\n", + "
\n", + "\n", + "
\n", + "NOTE: ⚠️The AlarmHigh Alarm triggers scale out actions only after the threshold of ConcurrentRequestsPerModel >5 for 3 datapoints within 30 seconds is breached.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d00ca1-f058-4dfb-9993-e231b58e413c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Trigger LLMPerf script to generate traffic to endpoint\n", + "num_concurrent_requests = 100\n", + "# LLMperf requires session credentials be passed in via environment variables.\n", + "# We'll use the current session to get these credentials.\n", + "creds = boto_session.get_credentials()\n", + "process = trigger_auto_scaling(creds, region, endpoint_name, num_concurrent_requests)\n", + "print(f\"[b green]Process ID for LLMPerf: {process.pid}\")\n", + "\n", + "# get AlarmHigh alarm name\n", + "scaleout_alarm_name = [alarm[\"AlarmName\"] for alarm in alarms if \"AlarmHigh\" in alarm[\"AlarmName\"]][\n", + " 0\n", + "]\n", + "\n", + "# Start monitoring scaling events\n", + "SLEEP_TIME = 5 # time to sleep\n", + "scaling_times = monitor_scaling_events(\n", + " endpoint_name, scaleout_alarm_name, SLEEP_TIME, cloudwatch_client, sagemaker_client\n", + ")\n", + "\n", + "# Print scaling times\n", + "console = Console()\n", + "table = print_scaling_times(scaling_times)\n", + "console.print(table)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b43ce1-dde3-42aa-9cbe-0716b5f85496", + "metadata": {}, + "source": [ + "### Monitor if the background process (llmperf) is completed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93abbbca-a3b4-49ee-9994-8ccfe7a13874", + "metadata": {}, + "outputs": [], + "source": [ + "monitor_process(process)" + ] + }, + { + "cell_type": "markdown", + "id": "2a3f1cd2-84ba-41c3-833a-d2acfe2f2af8", + "metadata": {}, + "source": [ + "## Print LLMPerf results\n", + "\n", + "LLMPerf writes the results to **\"results/\"** directory. `summary.json` file has the endpoint benchmarking data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "281d502c-e8d6-4023-a9bc-9e011b63c2d1", + "metadata": {}, + "outputs": [], + "source": [ + "print_llmperf_results(num_concurrent_requests)" + ] + }, + { + "cell_type": "markdown", + "id": "f8bd184b-fcfe-4260-95ce-5bdd557ad6e2", + "metadata": {}, + "source": [ + "### Monitor Scale-in Alarm Trigger times and scaling event times\n", + "\n", + "
\n", + "NOTE: ⚠️The AlarmLow Alarm triggers scale-in actions only after the threshold of ConcurrentRequestsPerModel < 4.5 for 90 datapoints within 15 minutes is breached.\n", + "
Running the below cell with take approximately 15 minutes to complete.
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "883924cc-9f29-48cf-85ac-1d96c0a3dd16", + "metadata": {}, + "outputs": [], + "source": [ + "# get AlarmHigh alarm name\n", + "scalein_alarm_name = [alarm[\"AlarmName\"] for alarm in alarms if \"AlarmLow\" in alarm[\"AlarmName\"]][0]\n", + "\n", + "# Start monitoring scaling events\n", + "SLEEP_TIME = 5 # time to sleep\n", + "scaling_times = monitor_scaling_events(\n", + " endpoint_name, scalein_alarm_name, SLEEP_TIME, cloudwatch_client, sagemaker_client\n", + ")\n", + "\n", + "# Print scaling times\n", + "console = Console()\n", + "table = print_scaling_times(scaling_times)\n", + "console.print(table)" + ] + }, + { + "cell_type": "markdown", + "id": "02a2d5b0-dc4b-40e3-8ada-ceddecfdac1a", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "- Deregister scalable target. This automatically deletes associated cloudwatch alarms.\n", + "- Delete model\n", + "- Delete endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7aacabc3-2b60-41c6-b903-9de5e31fc8e7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Deregister the scalable target for AAS\n", + "try:\n", + " autoscaling_client.deregister_scalable_target(\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " )\n", + " print(f\"Scalable target for [b]{resource_id}[/b] deregistered. ✅\")\n", + "except autoscaling_client.exceptions.ObjectNotFoundException:\n", + " print(f\"Scalable target for [b]{resource_id}[/b] not found!.\")\n", + "\n", + "print(\"---\" * 10)\n", + "# Delete model and endpoint\n", + "try:\n", + " print(f\"Deleting model: [b green]{model_name} ✅\")\n", + " predictor.delete_model()\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "try:\n", + " print(f\"Deleting endpoint: [b magenta]{predictor.endpoint_name} ✅\")\n", + " predictor.delete_endpoint()\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "print(\"---\" * 10)\n", + "print(f\"Done\")" + ] + }, + { + "cell_type": "markdown", + "id": "9fdee8f2", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)\n", + "\n", + "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-AppAutoScaling.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb new file mode 100644 index 0000000000..b340a20bdc --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb @@ -0,0 +1,872 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "81f236c9", + "metadata": {}, + "source": [ + "# Faster autoscaling on Amazon SageMaker realtime endpoints (Step Scaling)\n", + "\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb)\n", + "\n", + "\n", + "---\n", + "\n", + "In this notebook we show how the new faster autoscaling feature helps scale sagemaker inference endpoints by almost 6x faster than earlier.\n", + "\n", + "We deploy Meta's `Llama3-8B-Instruct` model to an Amazon SageMaker realtime endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC) and apply Step Scaling autoscaling policies to the endpoint.\n", + "\n", + "\n", + "Please use at least `m5.2xlarge` or larger instance types if running this on Amazon SageMaker Notebook Instance.\n", + "\n", + "\n", + "## Prerequisites\n", + "\n", + "
\n", + "Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta.\n", + "\n", + "- **Step 1:** Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens.\n", + "- **Step 2:** Login to [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) and navigate to *Meta-Llama-3-8B-Instruct** home page.\n", + "- **Step 3:** Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main)\n", + "- **Step 4:** Wait for the approval email from META (Approval may take any where b/w 1-3 hrs)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "650293f5-969e-4019-ba54-be21a363915d", + "metadata": {}, + "source": [ + "Ensure python version of kernel is 3.10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "660d71a7-50d2-45b2-8b9f-b02ed52218b3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!python --version" + ] + }, + { + "cell_type": "markdown", + "id": "932827cb-caaa-4baa-9c95-9cf057468cf0", + "metadata": { + "tags": [] + }, + "source": [ + "Install packages using uv, an extremely fast python package installer. Read more about uv here " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d260cb1-1355-448e-8fd9-3eebb1584ba0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install uv && uv pip install -U ipywidgets\n", + "!uv pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "cfe9e981-5df5-4b01-9b64-945e01c06423", + "metadata": { + "tags": [] + }, + "source": [ + "Restart kernel after installing packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2abe082-5ce0-4a26-bae8-68f9bff4104c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# restart kernel\n", + "from IPython.core.display import HTML\n", + "\n", + "HTML(\"\")\n", + "print(\"Kernel restarted successfully!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "590ec770-d05e-474d-80da-d2f2bab63db2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load rich extension\n", + "%load_ext rich" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a97edd4-8bba-4806-bce5-c559e23da05d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import glob\n", + "import json\n", + "import os\n", + "import subprocess\n", + "import time\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from datetime import datetime\n", + "from getpass import getpass\n", + "from pathlib import Path\n", + "from statistics import mean\n", + "from uuid import uuid4\n", + "\n", + "import boto3\n", + "import botocore\n", + "import sagemaker\n", + "from botocore.config import Config\n", + "from rich import box, print\n", + "from rich.console import Console\n", + "from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn\n", + "from rich.table import Table\n", + "from sagemaker.deserializers import JSONDeserializer\n", + "from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri\n", + "from sagemaker.predictor import Predictor\n", + "from sagemaker.serializers import JSONSerializer\n", + "\n", + "from utils.autoscaling import (\n", + " monitor_scaling_events,\n", + " print_scaling_times,\n", + " test_concurrency_level,\n", + ")\n", + "\n", + "from utils.llmperf import (\n", + " print_llmperf_results,\n", + " trigger_auto_scaling,\n", + " monitor_process,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9c02d12b-2109-4f01-8da8-8972ba493398", + "metadata": {}, + "source": [ + "## Initiate sagemaker session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11d1c2d-787e-4792-a276-897a9cd183cf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sess = sagemaker.Session()\n", + "role = sagemaker.get_execution_role()\n", + "region = sess.boto_region_name\n", + "config = Config(retries=dict(max_attempts=10))\n", + "\n", + "boto_session = boto3.Session(region_name=region)\n", + "\n", + "sagemaker_client = sess.sagemaker_client\n", + "sagemaker_runtime_client = sess.sagemaker_runtime_client\n", + "cloudwatch_client = boto3.client(\"cloudwatch\", region_name=region, config=config)\n", + "\n", + "hf_model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n", + "\n", + "print(f\"HF Model ID: [b green]{hf_model_id}\")\n", + "print(f\"Region: [b blue]{region}\")\n", + "print(f\"Role: [b red]{role}\")" + ] + }, + { + "cell_type": "markdown", + "id": "043f7d75-de75-4687-a2e2-ab4aa7168ef6", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "Create and deploy model using Amazon SageMaker HuggingFace TGI DLC\n", + "\n", + "\n", + "\n", + "
\n", + "NOTE: Remember to copy your Hugging Face Access Token from https://hf.co/ before running the below cell.

\n", + "Refer here to learn about creating HF tokens.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b89da4d-9ce7-4e5b-a02a-3f2c690cd26d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instance_type = \"ml.g5.2xlarge\"\n", + "suffix = f\"{str(uuid4())[:5]}-{datetime.now().strftime('%d%b%Y')}\"\n", + "model_name = f\"Llama3-8B-fas-{suffix}\"\n", + "endpoint_name = model_name\n", + "health_check_timeout = 900\n", + "\n", + "HF_TOKEN = os.getenv(\"HUGGING_FACE_HUB_TOKEN\") or getpass(\"Enter HUGGINGFACE Access Token: \")\n", + "\n", + "# retrieve the llm image uri\n", + "# tgi_dlc = f\"763104351884.dkr.ecr.{region}.amazonaws.com/huggingface-pytorch-tgi-inference:2.1-tgi2.0-gpu-py310-cu121-ubuntu22.04\"\n", + "tgi_dlc = get_huggingface_llm_image_uri(\"huggingface\", version=\"2.0.0\")\n", + "\n", + "# Define Model and Endpoint configuration parameter\n", + "config = {\n", + " \"HF_MODEL_ID\": \"meta-llama/Meta-Llama-3-8B-Instruct\", # model_id from hf.co/models\n", + " \"SM_NUM_GPUS\": \"1\", # Number of GPU used per replica\n", + " \"MAX_INPUT_LENGTH\": \"2048\", # Max length of input text\n", + " \"MAX_TOTAL_TOKENS\": \"4096\", # Max length of the generation (including input text)\n", + " \"MAX_BATCH_TOTAL_TOKENS\": \"8192\", # Limits the number of tokens that can be processed in parallel during the generation\n", + " \"MESSAGES_API_ENABLED\": \"true\", # Enable the messages API\n", + " \"HUGGING_FACE_HUB_TOKEN\": HF_TOKEN,\n", + "}\n", + "\n", + "# create HuggingFaceModel with the image uri\n", + "print(f\"Creating model: [b green]{model_name}...\")\n", + "llm_model = HuggingFaceModel(name=model_name, role=role, image_uri=tgi_dlc, env=config)\n", + "\n", + "# Deploy model to Amazon SageMaker endpoint\n", + "print(f\"Deploying model to endpoint: [b magenta]{endpoint_name}...\")\n", + "predictor = llm_model.deploy(\n", + " endpoint_name=endpoint_name,\n", + " initial_instance_count=1,\n", + " instance_type=instance_type,\n", + " container_startup_health_check_timeout=health_check_timeout, # 15 minutes to be able to load the model\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "83e1af5c-e713-4cf8-bc23-1c96f1e61327", + "metadata": {}, + "source": [ + "## Inference\n", + "\n", + "Invoke and test endpoint using messages API. Refer to HF [Messages API](https://huggingface.co/docs/text-generation-inference/messages_api) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6d9ecc2-fffe-4ff1-b78b-1222fe6d32de", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Prompt to generate\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"What is deep learning?\"},\n", + "]\n", + "\n", + "# Generation arguments\n", + "parameters = {\n", + " \"model\": hf_model_id, # model id is required\n", + " \"top_p\": 0.6,\n", + " \"temperature\": 0.9,\n", + " \"max_tokens\": 512,\n", + " \"stop\": [\"<|eot_id|>\"],\n", + "}\n", + "\n", + "chat = predictor.predict({\"messages\": messages, **parameters})\n", + "\n", + "# Unpack and print response\n", + "print(chat[\"choices\"][0][\"message\"][\"content\"].strip())" + ] + }, + { + "cell_type": "markdown", + "id": "3cdb619d-b402-46bf-9451-62f50f70e878", + "metadata": {}, + "source": [ + "## Baseline average latency at various concurrency levels (Optional)\n", + "\n", + "
NOTE: Running the following cell is optional

\n", + "By capturing average latency across various concurrency levels, we can get a fair idea on after how many concurrent request does endpoint performance would degrade significantly.

\n", + "Having this information can help define values for scaling policy accordingly. \n", + "
\n", + "\n", + "
\n", + "INFO: ℹ️ Signal here is, at a given concurrency level you start to see average latency increase significantly.
\n", + "At this concurrency level the endpoint gets overloaded and cannot serve requests in a timely fashion.
\n", + "We use these values to set as threshold values for autoscaling.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7232ecd-bc78-4d0d-bf44-17c3e060cd99", + "metadata": {}, + "outputs": [], + "source": [ + "# Define list of prompts\n", + "prompts = [\n", + " \"what is deep learning?\",\n", + " \"what are various inference modes in Amazon SageMaker?\",\n", + " \"Can I host Large language models on Amazon SageMaker?\",\n", + " \"Does Amazon SageMaker support TensorRT-LLM?\",\n", + " \"what is step scaling policy in the context of autoscaling ec2 instances on AWS?\",\n", + " \"Why is the sky blue?\",\n", + " \"List 5 benefits of incorporating limes into the diet.\",\n", + "]\n", + "\n", + "# Test different concurrency levels and measure average latency\n", + "concurrency_levels = [10, 50, 75, 100] # Adjust these values as needed\n", + "\n", + "for concurrency_level in concurrency_levels:\n", + " try:\n", + " avg_latency = test_concurrency_level(\n", + " concurrency_level,\n", + " prompts,\n", + " messages,\n", + " parameters,\n", + " endpoint_name,\n", + " sagemaker_runtime_client,\n", + " )\n", + " print(\n", + " f\"[b]Concurrency:[/b] {concurrency_level} requests,\"\n", + " f\" [b]Average latency:[/b] {avg_latency:.2f} seconds\"\n", + " )\n", + " except Exception as e:\n", + " print(f\"[b]At Concurrency[/b] {concurrency_level} requests,\" f\"[b]Exception:[/b] \\n{e}\")\n", + " continue" + ] + }, + { + "cell_type": "markdown", + "id": "29f7a5ab-0264-4b12-8243-b4aa649335b7", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Apply Step-Scaling autoscaling policies to endpoint\n", + "\n", + "- **Step 1:** Register Scalable Target\n", + "- **Step 2:** Create Scale-Out Policy\n", + "- **Step 3:** Create Scale-In Policy\n", + "- **Step 4:** Create CloudWatch Alarms\n", + "\n", + "Define and apply the step-scaling policy for scaling out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bbf762f-beec-42ed-9ff8-5b06f76269ab", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "variant_name = \"AllTraffic\"\n", + "as_min_capacity = 1\n", + "as_max_capacity = 2\n", + "\n", + "resource_id = f\"endpoint/{endpoint_name}/variant/{variant_name}\"\n", + "\n", + "autoscaling_client = boto3.client(\"application-autoscaling\", region_name=region)\n", + "\n", + "# Register scalable target\n", + "scalable_target = autoscaling_client.register_scalable_target(\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " MinCapacity=as_min_capacity,\n", + " MaxCapacity=as_max_capacity, # Replace with your desired maximum instances\n", + ")\n", + "\n", + "scalable_target_arn = scalable_target[\"ScalableTargetARN\"]\n", + "print(f\"Resource ID: [b blue]{resource_id}\")\n", + "print(f\"Scalable_target_arn:\\n[b green]{scalable_target_arn}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0af2e234-d1c7-4575-b943-5291c70c326d", + "metadata": {}, + "source": [ + "### Create StepScaling Scale-out Policy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b35f32bf-126c-41ab-8213-10052f5351e4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Configure step scaling scale-out policy\n", + "scale_out_policy_response = autoscaling_client.put_scaling_policy(\n", + " PolicyName=f\"{endpoint_name}-ScaleOutPolicy\",\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " PolicyType=\"StepScaling\",\n", + " StepScalingPolicyConfiguration={\n", + " \"AdjustmentType\": \"ChangeInCapacity\",\n", + " \"Cooldown\": 300, # 5 minutes cooldown\n", + " \"MetricAggregationType\": \"Maximum\",\n", + " \"StepAdjustments\": [\n", + " {\n", + " \"MetricIntervalLowerBound\": 0,\n", + " \"MetricIntervalUpperBound\": 20,\n", + " \"ScalingAdjustment\": 1, # Increase by one instance\n", + " },\n", + " {\n", + " \"MetricIntervalLowerBound\": 20,\n", + " \"ScalingAdjustment\": 2, # Increase by 2 instances\n", + " },\n", + " ],\n", + " },\n", + ")\n", + "\n", + "# print(scale_out_policy_response)\n", + "scale_out_policy_arn = scale_out_policy_response[\"PolicyARN\"]\n", + "print(f\"Step scaling policy ARN: [i green]{scale_out_policy_arn}[/i green]\")" + ] + }, + { + "cell_type": "markdown", + "id": "8cc40cae-fe85-4e3b-8bfe-c1ef238ea76f", + "metadata": {}, + "source": [ + "### Create StepScaling Scale-In Policy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43b06c1e-c126-4203-b149-473e033ae879", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "scale_in_policy_response = autoscaling_client.put_scaling_policy(\n", + " PolicyName=f\"{endpoint_name}-ScaleInPolicy\",\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " PolicyType=\"StepScaling\",\n", + " StepScalingPolicyConfiguration={\n", + " \"AdjustmentType\": \"ChangeInCapacity\",\n", + " \"Cooldown\": 300, # Cooldown period after scale-in activity\n", + " \"MetricAggregationType\": \"Maximum\",\n", + " \"StepAdjustments\": [\n", + " {\n", + " \"MetricIntervalUpperBound\": 0,\n", + " \"MetricIntervalLowerBound\": -20,\n", + " \"ScalingAdjustment\": -1, # Decrease by 1 instance\n", + " },\n", + " {\"MetricIntervalUpperBound\": -20, \"ScalingAdjustment\": -2}, # Decrease by 2 instances\n", + " ],\n", + " },\n", + ")\n", + "\n", + "# print(scale_in_policy_response)\n", + "scale_in_policy_arn = scale_in_policy_response[\"PolicyARN\"]\n", + "print(f\"Step scaling policy ARN: [i green]{scale_in_policy_arn}[/i green]\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3c3f1ec-f4cb-4a1f-ad4d-5e6a1d4f7aee", + "metadata": {}, + "source": [ + "### Create CloudWatch alarms (Step-Scaling)\n", + "\n", + "Create CloudWatch Alarms using new ConcurrentRequestsPerModel high-resolution Metric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "830fdea0-6d59-4369-8dc3-db301daacf5c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define the alarm parameters for scale-out\n", + "alarm_name_scale_out = f\"Step-Scaling-AlarmHigh-SageMaker:{resource_id}\"\n", + "metric_name = \"ConcurrentRequestsPerModel\"\n", + "namespace = \"AWS/SageMaker\" # CloudWatch Namespace to write metric data\n", + "statistic = \"Maximum\"\n", + "period = 60 # 10 seconds\n", + "evaluation_periods = 3\n", + "threshold = 20.0 # Threshold for scale-out\n", + "comparison_operator = \"GreaterThanOrEqualToThreshold\"\n", + "dimensions = [\n", + " {\"Name\": \"EndpointName\", \"Value\": endpoint_name},\n", + " {\"Name\": \"VariantName\", \"Value\": \"AllTraffic\"},\n", + "]\n", + "alarm_actions = [scale_out_policy_response[\"PolicyARN\"]]\n", + "treat_missing_data = \"ignore\"\n", + "\n", + "# create CloudWatch alarm for scale-out\n", + "response = cloudwatch_client.put_metric_alarm(\n", + " AlarmName=alarm_name_scale_out,\n", + " MetricName=metric_name,\n", + " Namespace=namespace,\n", + " Statistic=statistic,\n", + " Period=period,\n", + " EvaluationPeriods=evaluation_periods,\n", + " Threshold=threshold,\n", + " ComparisonOperator=comparison_operator,\n", + " Dimensions=dimensions,\n", + " AlarmActions=alarm_actions,\n", + " TreatMissingData=treat_missing_data,\n", + ")\n", + "\n", + "print(f\"CloudWatch alarm created for scale-out:\\n[b blue]{alarm_name_scale_out}\")\n", + "\n", + "# Define the alarm parameters for scale-in\n", + "alarm_name_scale_in = f\"Step-Scaling-AlarmLow-SageMaker:{resource_id}\"\n", + "comparison_operator = \"LessThanOrEqualToThreshold\"\n", + "threshold = 10.0 # Adjust based on your requirements\n", + "alarm_actions = [scale_in_policy_response[\"PolicyARN\"]]\n", + "\n", + "# Create CloudWatch alarm for scale-in\n", + "response = cloudwatch_client.put_metric_alarm(\n", + " AlarmName=alarm_name_scale_in,\n", + " MetricName=metric_name,\n", + " Namespace=namespace,\n", + " Statistic=statistic,\n", + " Period=period,\n", + " EvaluationPeriods=evaluation_periods,\n", + " Threshold=threshold,\n", + " ComparisonOperator=comparison_operator,\n", + " Dimensions=dimensions,\n", + " AlarmActions=alarm_actions,\n", + " TreatMissingData=treat_missing_data,\n", + ")\n", + "\n", + "print(f\"CloudWatch alarm created for scale-in:\\n[b blue]{alarm_name_scale_in}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d27a4cba-8aec-4b5c-b9ea-97d4ea82d9f0", + "metadata": { + "tags": [] + }, + "source": [ + "## Trigger autoscaling action\n", + "\n", + "### Use LLMPerf to generate traffic to the endpoint\n", + "\n", + "Refer to for more details on LLMPerf.\n", + "\n", + "Run the LLMPerf traffic generation script in the background using `subprocess.Popen`\n", + "\n", + "
\n", + "INFO:ℹ️ Refer to utils.llmperf for `trigger_autoscaling` function implementation\n", + "
\n", + "\n", + "\n", + "### Monitor Alarm Trigger times and Scaling event times\n", + "As llmperf generates traffic to the endpoint continuously this trigger auto-scaling.\n", + "\n", + "The `monitor_scaling_events` function does the following:\n", + "- Calculates time taken for alarm to go into InAlarm state.\n", + "- checks if alarm is InAlarm state. If yes, then starts the scaling timer\n", + "- continuously monitors the `DesiredInstanceCount` property of the endpoint\n", + " - waits till `CurrentInstanceCount == DesiredInstanceCount` and `EndpointStatus` is `InService`\n", + "- Calculates time taken to scale out instances prints the times in a table\n", + "\n", + "The below cell triggers auto scaling action and calls the monitor_scaling_events immediately on the AlarmHigh\n", + "\n", + "
\n", + "INFO:ℹ️ Refer to utils.autoscaling for `monitor_scaling_events` function implementation\n", + "
\n", + "\n", + "
\n", + "NOTE: ⚠️Per the ScaleOut Alarm, scale-out actions only start after the threshold of ConcurrentRequestsPerModel >= 20 for 3 datapoints within 3 minutes is breached.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d00ca1-f058-4dfb-9993-e231b58e413c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Trigger LLMPerf script to generate traffic to endpoint\n", + "num_concurrent_requests = 100\n", + "# LLMperf requires session credentials be passed in via environment variables.\n", + "# We'll use the current session to get these credentials.\n", + "creds = boto_session.get_credentials()\n", + "process = trigger_auto_scaling(creds, region, endpoint_name, num_concurrent_requests)\n", + "print(f\"[b green]Process ID for LLMPerf: {process.pid}\")\n", + "\n", + "# Start monitoring scaling events\n", + "SLEEP_TIME = 5 # time to sleep\n", + "scaling_times = monitor_scaling_events(\n", + " endpoint_name, alarm_name_scale_out, SLEEP_TIME, cloudwatch_client, sagemaker_client\n", + ")\n", + "\n", + "# Print scaling times\n", + "console = Console()\n", + "table = print_scaling_times(scaling_times)\n", + "console.print(table)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b43ce1-dde3-42aa-9cbe-0716b5f85496", + "metadata": {}, + "source": [ + "### Monitor if the background process (llmperf) is completed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93abbbca-a3b4-49ee-9994-8ccfe7a13874", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "# Monitor the background traffic generation process for completion\n", + "monitor_process(process)" + ] + }, + { + "cell_type": "markdown", + "id": "b6829fa5-4a91-472e-8c3b-905612e778a0", + "metadata": {}, + "source": [ + "## Print LLMPerf results\n", + "\n", + "LLMPerf writes the results to **\"results/\"** directory. `summary.json` file has the endpoint benchmarking data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "281d502c-e8d6-4023-a9bc-9e011b63c2d1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print_llmperf_results(num_concurrent_requests)" + ] + }, + { + "cell_type": "markdown", + "id": "f8bd184b-fcfe-4260-95ce-5bdd557ad6e2", + "metadata": {}, + "source": [ + "### Monitor Scale-in action scaling times (Optional)\n", + "\n", + "\n", + "
\n", + "NOTE: ⚠️Per the ScaleIn Alarm, scale-in actions only start after the threshold of ConcurrentRequestsPerModel <= 10 for 3 datapoints within 3 minutes is breached.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "883924cc-9f29-48cf-85ac-1d96c0a3dd16", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Start monitoring scaling events\n", + "SLEEP_TIME = 5 # time to sleep\n", + "scaling_times = monitor_scaling_events(\n", + " endpoint_name,\n", + " alarm_name_scale_in, # scale_in cloudwatch metric alarm name\n", + " SLEEP_TIME,\n", + " cloudwatch_client,\n", + " sagemaker_client,\n", + ")\n", + "\n", + "# Print scaling times\n", + "console = Console()\n", + "table = print_scaling_times(scaling_times)\n", + "console.print(table)" + ] + }, + { + "cell_type": "markdown", + "id": "02a2d5b0-dc4b-40e3-8ada-ceddecfdac1a", + "metadata": {}, + "source": [ + "## Cleanup\n", + "\n", + "- Delete cloudwatch alarms\n", + "- Delete scaling policies\n", + "- Deregister scalable target\n", + "- Delete model\n", + "- Delete endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f44ef56-dbcc-4e23-97c2-af6cb062b498", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Delete CloudWatch alarms created for Step scaling policy\n", + "alarm_names = [alarm_name_scale_out, alarm_name_scale_in]\n", + "\n", + "for alarm in alarm_names:\n", + " try:\n", + " cloudwatch_client.delete_alarms(AlarmNames=[alarm])\n", + " print(f\"Deleted CloudWatch scale-out alarm [b]{alarm} ✅\")\n", + " except cloudwatch_client.exceptions.ResourceNotFoundException:\n", + " print(f\"CloudWatch scale-out alarm [b]{alarm}[/b] not found.\")\n", + "\n", + "\n", + "# Delete scaling policies\n", + "print(\"---\" * 10)\n", + "step_policies = [f\"{endpoint_name}-ScaleInPolicy\", f\"{endpoint_name}-ScaleOutPolicy\"]\n", + "for policy_name in step_policies:\n", + " try:\n", + " autoscaling_client.delete_scaling_policy(\n", + " PolicyName=policy_name,\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " )\n", + " print(f\"Deleted scaling policy [i green]{policy_name} ✅\")\n", + " except autoscaling_client.exceptions.ObjectNotFoundException:\n", + " print(f\"Scaling policy [i]{policy_name}[/i] not found.\")\n", + "\n", + "# Deregister scalable target\n", + "try:\n", + " autoscaling_client.deregister_scalable_target(\n", + " ServiceNamespace=\"sagemaker\",\n", + " ResourceId=resource_id,\n", + " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n", + " )\n", + " print(f\"Scalable target for [b]{resource_id}[/b] deregistered. ✅\")\n", + "except autoscaling_client.exceptions.ObjectNotFoundException:\n", + " print(f\"Scalable target for [b]{resource_id}[/b] not found!.\")\n", + "\n", + "print(\"---\" * 10)\n", + "# Delete model and endpoint\n", + "try:\n", + " print(f\"Deleting model: [b green]{model_name} ✅\")\n", + " predictor.delete_model()\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "try:\n", + " print(f\"Deleting endpoint: [b magenta]{predictor.endpoint_name} ✅\")\n", + " predictor.delete_endpoint()\n", + "except Exception as e:\n", + " print(f\"{e}\")\n", + "\n", + "print(\"---\" * 10)\n", + "print(\"Done\")" + ] + }, + { + "cell_type": "markdown", + "id": "f43d8011", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/inference|generativeai|huggingfacetgi|meta-llama|llama3-8b|faster-autoscaling|realtime-endpoints|FasterAutoscaling-SME-Llama3-8B-StepScaling.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/requirements.txt b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/requirements.txt new file mode 100644 index 0000000000..ee4b2a1529 --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/requirements.txt @@ -0,0 +1,67 @@ +asttokens==2.4.1 ; python_version >= "3.10" and python_version < "3.11" +attrs==23.2.0 ; python_version >= "3.10" and python_version < "3.11" +boto3==1.34.142 ; python_version >= "3.10" and python_version < "3.11" +botocore==1.34.142 ; python_version >= "3.10" and python_version < "3.11" +certifi==2024.2.2 ; python_version >= "3.10" and python_version < "3.11" +charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.11" +cloudpickle==2.2.1 ; python_version >= "3.10" and python_version < "3.11" +colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Windows" or sys_platform == "win32") +comm==0.2.2 ; python_version >= "3.10" and python_version < "3.11" +decorator==5.1.1 ; python_version >= "3.10" and python_version < "3.11" +dill==0.3.8 ; python_version >= "3.10" and python_version < "3.11" +docker==7.1.0 ; python_version >= "3.10" and python_version < "3.11" +exceptiongroup==1.2.1 ; python_version >= "3.10" and python_version < "3.11" +executing==2.0.1 ; python_version >= "3.10" and python_version < "3.11" +google-pasta==0.2.0 ; python_version >= "3.10" and python_version < "3.11" +idna==3.7 ; python_version >= "3.10" and python_version < "3.11" +importlib-metadata==6.11.0 ; python_version >= "3.10" and python_version < "3.11" +ipython==8.24.0 ; python_version >= "3.10" and python_version < "3.11" +ipywidgets==8.1.3 ; python_version >= "3.10" and python_version < "3.11" +jedi==0.19.1 ; python_version >= "3.10" and python_version < "3.11" +jmespath==1.0.1 ; python_version >= "3.10" and python_version < "3.11" +jsonschema-specifications==2023.12.1 ; python_version >= "3.10" and python_version < "3.11" +jsonschema==4.22.0 ; python_version >= "3.10" and python_version < "3.11" +jupyterlab-widgets==3.0.11 ; python_version >= "3.10" and python_version < "3.11" +markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "3.11" +matplotlib-inline==0.1.7 ; python_version >= "3.10" and python_version < "3.11" +mdurl==0.1.2 ; python_version >= "3.10" and python_version < "3.11" +multiprocess==0.70.16 ; python_version >= "3.10" and python_version < "3.11" +numpy==1.26.4 ; python_version >= "3.10" and python_version < "3.11" +packaging==24.0 ; python_version >= "3.10" and python_version < "3.11" +pandas==2.2.2 ; python_version >= "3.10" and python_version < "3.11" +parso==0.8.4 ; python_version >= "3.10" and python_version < "3.11" +pathos==0.3.2 ; python_version >= "3.10" and python_version < "3.11" +pexpect==4.9.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten") +platformdirs==4.2.2 ; python_version >= "3.10" and python_version < "3.11" +pox==0.3.4 ; python_version >= "3.10" and python_version < "3.11" +ppft==1.7.6.8 ; python_version >= "3.10" and python_version < "3.11" +prompt-toolkit==3.0.45 ; python_version >= "3.10" and python_version < "3.11" +protobuf==4.25.3 ; python_version >= "3.10" and python_version < "3.11" +psutil==5.9.8 ; python_version >= "3.10" and python_version < "3.11" +ptyprocess==0.7.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten") +pure-eval==0.2.2 ; python_version >= "3.10" and python_version < "3.11" +pygments==2.18.0 ; python_version >= "3.10" and python_version < "3.11" +python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "3.11" +pytz==2024.1 ; python_version >= "3.10" and python_version < "3.11" +pywin32==306 ; python_version >= "3.10" and python_version < "3.11" and sys_platform == "win32" +pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.11" +referencing==0.35.1 ; python_version >= "3.10" and python_version < "3.11" +requests==2.32.2 ; python_version >= "3.10" and python_version < "3.11" +rich==13.7.1 ; python_version >= "3.10" and python_version < "3.11" +rpds-py==0.18.1 ; python_version >= "3.10" and python_version < "3.11" +s3transfer==0.10.1 ; python_version >= "3.10" and python_version < "3.11" +sagemaker==2.225.0 ; python_version >= "3.10" and python_version < "3.11" +schema==0.7.7 ; python_version >= "3.10" and python_version < "3.11" +six==1.16.0 ; python_version >= "3.10" and python_version < "3.11" +smdebug-rulesconfig==1.0.1 ; python_version >= "3.10" and python_version < "3.11" +stack-data==0.6.3 ; python_version >= "3.10" and python_version < "3.11" +tblib==3.0.0 ; python_version >= "3.10" and python_version < "3.11" +tqdm==4.66.4 ; python_version >= "3.10" and python_version < "3.11" +traitlets==5.14.3 ; python_version >= "3.10" and python_version < "3.11" +typing-extensions==4.12.0 ; python_version >= "3.10" and python_version < "3.11" +tzdata==2024.1 ; python_version >= "3.10" and python_version < "3.11" +urllib3==2.2.1 ; python_version >= "3.10" and python_version < "3.11" +uv==0.2.5 ; python_version >= "3.10" and python_version < "3.11" +wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "3.11" +widgetsnbextension==4.0.11 ; python_version >= "3.10" and python_version < "3.11" +zipp==3.19.0 ; python_version >= "3.10" and python_version < "3.11" diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/trigger_autoscaling.sh b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/trigger_autoscaling.sh new file mode 100644 index 0000000000..653a0a9889 --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/trigger_autoscaling.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +# Check if required environment variables are set +if [ -z "$AWS_ACCESS_KEY_ID" ] || [ -z "$AWS_SECRET_ACCESS_KEY" ] || [ -z "$AWS_SESSION_TOKEN" ] || [ -z "$AWS_REGION" ] || [ -z "$EP_NAME" ] || [ -z "$NUM_CONCURRENT_REQUESTS" ]; then + echo "Error: Required environment variables are not set." + exit 1 +fi + +echo "Installing llmperf..." +rm -rf llmperf && \ +git clone https://github.com/philschmid/llmperf.git && \ +uv pip install -e llmperf/ + +DIR="results" + +if [ ! -d "$DIR" ]; then + mkdir -p "$DIR" + echo "Created $DIR directory." +else + echo "$DIR directory already exists." +fi + +echo "Starting benchmarking scripts on endpoint $EP_NAME ..." + +start_time=$(date +%s) + +MESSAGES_API=true python llmperf/token_benchmark_ray.py \ +--model $EP_NAME \ +--llm-api "sagemaker" \ +--max-num-completed-requests 1000 \ +--timeout 600 \ +--num-concurrent-requests $NUM_CONCURRENT_REQUESTS \ +--results-dir "results" + +end_time=$(date +%s) +echo "Execution time was $((end_time - start_time)) secs." diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/__init__.py b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/autoscaling.py b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/autoscaling.py new file mode 100644 index 0000000000..1792a0887f --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/autoscaling.py @@ -0,0 +1,174 @@ +import json +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from statistics import mean + +from rich import print +from rich.table import Table +from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn + + +# Function to update the user prompt in the messages list +def update_user_prompt(messages, prompt): + for message in messages: + if message["role"] == "user": + message["content"] = prompt + return messages + + +# helper function to record latency +def get_request_latency(payload, endpoint_name, sagemaker_runtime_client): + start_time = time.time() + _ = sagemaker_runtime_client.invoke_endpoint( + EndpointName=endpoint_name, + ContentType="application/json", + Body=json.dumps(payload), + ) + # _ = predictor.predict(payload) + end_time = time.time() + latency = end_time - start_time + # print(chat["choices"][0]["message"]["content"].strip()) + return latency + + +# Function to test concurrent requests with a given concurrency level +def test_concurrency_level( + concurrency_level, + prompts, + messages, + parameters, + endpoint_name, + sagemaker_runtime_client, +): + payloads = [ + {"messages": update_user_prompt(messages, prompt), **parameters} + for prompt in prompts * (concurrency_level // len(prompts)) + ] + latencies = [] + with ThreadPoolExecutor(max_workers=concurrency_level) as executor: + futures = [ + executor.submit( + get_request_latency, payload, endpoint_name, sagemaker_runtime_client + ) + for payload in payloads + ] + for future in as_completed(futures): + try: + latency = future.result() + latencies.append(latency) + except Exception as e: + print(f"Request failed: {e}") + + avg_latency = mean(latencies) + return avg_latency + + +# helper function to get the current instance count of the endpoint +def get_scaling_instance_counts(endpoint_name, sagemaker_client): + endpoint_description = sagemaker_client.describe_endpoint( + EndpointName=endpoint_name + ) + current = endpoint_description["ProductionVariants"][0]["CurrentInstanceCount"] + desired = endpoint_description["ProductionVariants"][0]["DesiredInstanceCount"] + current_status = endpoint_description["EndpointStatus"] + return current, desired, current_status + + +# Helper function to check if any alarm is in "InAlarm" state +def is_alarm_in_alarm_state(alarm_name, cloudwatch_client): + alarm_state = cloudwatch_client.describe_alarms(AlarmNames=[alarm_name])[ + "MetricAlarms" + ][0]["StateValue"] + if alarm_state == "ALARM": + return True + return False + + +# Helper function to monitor the endpoint for scaling events +def monitor_scaling_events( + endpoint_name, alarm_name, time_to_sleep, cloudwatch_client, sagemaker_client +): + scaling_times = {} + ( + current_instance_count, + desired_instance_count, + status, + ) = get_scaling_instance_counts(endpoint_name, sagemaker_client) + print(f"Initial instance count: {current_instance_count}", flush=True) + print(f"Tracking Alarm: [i green]{alarm_name}[/i green]", flush=True) + + with Progress( + SpinnerColumn(), *Progress.get_default_columns(), TimeElapsedColumn() + ) as progress: + alarm_task = progress.add_task( + "[green]Waiting for alarm to trigger...", total=None + ) + + alarm_timer_start = time.time() + + while True: + if is_alarm_in_alarm_state(alarm_name, cloudwatch_client): + start_time = time.time() + alarm_timer_end = time.time() + time_to_alarm = alarm_timer_end - alarm_timer_start + progress.update( + alarm_task, + description=f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.", + total=1, + completed=1, + ) + # print(f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.") + break + else: + progress.update(alarm_task, advance=1) + # Wait for time_to_sleep seconds before checking again + time.sleep(time_to_sleep) + + scaling_task = progress.add_task( + "[green]Waiting for scaling to complete...", total=None + ) + + while True: + ( + current_instance_count, + desired_instance_count, + status, + ) = get_scaling_instance_counts(endpoint_name, sagemaker_client) + + if current_instance_count == desired_instance_count: + # Add sleep here as endpoint status doesn't change to `Updating` instantaneously + time.sleep(time_to_sleep) + if status == "InService": + end_time = time.time() + scaling_time = end_time - start_time + scaling_times[desired_instance_count] = scaling_time + progress.update( + scaling_task, + description=f"[bold green]Scaling to {desired_instance_count} instances completed in {scaling_time:.2f} seconds.", + total=1, + completed=1, + ) + break + progress.update(scaling_task, advance=1) + # Wait for time_to_sleep seconds before checking again + time.sleep(time_to_sleep) + + return scaling_times + + +# function to print scaling times in a table +def print_scaling_times(scaling_times): + # Create a table + table = Table(title="Scaling Times") + + # Add columns + table.add_column( + "Target Instance Count", justify="right", style="cyan", no_wrap=True + ) + table.add_column("Scaling Time (seconds)", justify="right", style="magenta") + + # Add rows + for target_instance_count, scaling_time in scaling_times.items(): + table.add_row(str(target_instance_count), f"{scaling_time:.2f}") + + return table diff --git a/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/llmperf.py b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/llmperf.py new file mode 100644 index 0000000000..f9966be496 --- /dev/null +++ b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/llmperf.py @@ -0,0 +1,100 @@ +import glob +import json +import os +import subprocess +import time + +from rich import box, print +from rich.table import Table + + +# LLMPerf requires AWS Creds as ENV variables along with endpoint name +def trigger_auto_scaling(creds, region, endpoint_name, num_concurrent_requests): + # Set environment variables + os.environ["AWS_ACCESS_KEY_ID"] = creds.access_key + os.environ["AWS_SECRET_ACCESS_KEY"] = creds.secret_key + os.environ["AWS_SESSION_TOKEN"] = creds.token + os.environ["AWS_REGION"] = region + os.environ["EP_NAME"] = endpoint_name + os.environ["NUM_CONCURRENT_REQUESTS"] = str(num_concurrent_requests) + + # Path to the shell script + # script_path = "./trigger_autoscaling.sh" + # current_dir = os.getcwd() + script_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), "..", "trigger_autoscaling.sh") + ) + + # print(f"Current working directory: {current_dir}") + # print(f"Full path to script: {script_path}") + + # Check if the file exists + if os.path.exists(script_path): + print(f"Calling LLMPerf shell script: {script_path}") + else: + print(f"LLMPerf shell script file not found at {script_path}") + + # Make sure the script is executable + # os.chmod(script_path, 0o755) + + # Run the shell script + print(f"Launching LLMPerf with {num_concurrent_requests} concurrent requests") + process = subprocess.Popen([script_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE) + + return process + + +# helper function to monitor the process +def monitor_process(proc): + while True: + retcode = proc.poll() # Check if the process has terminated + if retcode is not None: + # Process has terminated + print(f"Process {proc.pid} finished with return code {retcode}") + + # Capture and print any output from the process + stdout, stderr = proc.communicate() + if stdout: + print(f"Process output:\n{stdout.decode('utf-8')}") + if stderr: + print(f"Process errors:\n{stderr.decode('utf-8')}") + + break + else: + # Process is still running + print(f"Process {proc.pid} is still running...") + time.sleep(15) # Check every 15 seconds + + +# helper function to print llmperf results +def print_llmperf_results(num_concurrent_requests): + # Reads the summary.json file and prints the results + with open(glob.glob("results/*summary.json")[0], "r") as file: + data = json.load(file) + + # Create a table + perf_table = Table( + title="LLMPerf Endpoint Metrics", + row_styles=["bold", "bold"], + box=box.MINIMAL_DOUBLE_HEAD, + ) + # Add columns + perf_table.add_column("Metric", justify="right", style="green", no_wrap=True) + perf_table.add_column("Units", justify="left", style="magenta") + + # Add rows + perf_table.add_row("Concurrent requests", f"{num_concurrent_requests}") + perf_table.add_row("Avg. Input token length", f"{data['mean_input_tokens']}") + perf_table.add_row("Avg. Output token length", f"{data['mean_output_tokens']}") + perf_table.add_row("Avg. First-Time-To-Token", f"{data['results_ttft_s_mean']*1000:.2f}ms") + perf_table.add_row( + "Avg. Thorughput", + f"{data['results_mean_output_throughput_token_per_s']:.2f} tokens/sec", + ) + perf_table.add_row( + "Avg. Latency", f"{data['results_inter_token_latency_s_mean']*1000:.2f}ms/token" + ) + + # Print the table + # console.print(perf_table) + return perf_table From 404b792477e79a22ce9aa97ac0c4ff749a608f27 Mon Sep 17 00:00:00 2001 From: brentfriedman725 <97409987+brentfriedman725@users.noreply.github.com> Date: Thu, 25 Jul 2024 21:56:17 +0000 Subject: [PATCH 12/16] Removed bug where automatic permission attachment errors (#4714) --- .../llm_monitor_byoc/byoc_llm_monitor.ipynb | 22 ++++--------------- 1 file changed, 4 insertions(+), 18 deletions(-) diff --git a/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb b/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb index bb31e58189..c70d15c448 100644 --- a/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb +++ b/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb @@ -54,7 +54,7 @@ "# Prerequisites\n", "- **IF RUNNING LOCALLY (not SageMaker Studio/Classic)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. For information on how to complete this, see this [documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html)\n", "\n", - "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: Please ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup." + "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. For information on how to complete this, see this [documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html). Please also ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup." ] }, { @@ -107,33 +107,19 @@ "\n", "sess = sagemaker.Session()\n", "# sagemaker session bucket -> used for uploading data, models and logs\n", - "# sagemaker will automatically create this bucket if it does not exist\n", + "# sagemaker will automatically create this bucket if it not exists\n", "sagemaker_session_bucket = None\n", "if sagemaker_session_bucket is None and sess is not None:\n", " sagemaker_session_bucket = sess.default_bucket()\n", "\n", - "iam = boto3.client(\"iam\")\n", "# Here, we create a role for SageMaker. The role ARN must be specified when calling the predict() method. If this fails, you can manually specify the role ARN in the except block.\n", "try:\n", " role = sagemaker.get_execution_role()\n", - " role_name = role.split(\"/\")[-1]\n", - "\n", - " # Attach the required policies to the role\n", - " iam.attach_role_policy(\n", - " RoleName=role_name,\n", - " PolicyArn=\"arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess\",\n", - " )\n", - " iam.attach_role_policy(\n", - " RoleName=role_name,\n", - " PolicyArn=\"arn:aws:iam::aws:policy/CloudWatchFullAccess\",\n", - " )\n", - "\n", - " role = sagemaker.get_execution_role()\n", "except ValueError:\n", - " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess','AmazonEC2ContainerRegistryFullAccess', and 'CloudWatchFullAccess' roles. See the linked documentation for help.\n", + " iam = boto3.client(\"iam\")\n", + " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess' role. See the linked documentation for help.\n", " role = iam.get_role(RoleName=\"\")[\"Role\"][\"Arn\"]\n", "\n", - "\n", "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n", "\n", "print(f\"sagemaker role arn: {role}\")\n", From 0abd974c89f7601e66cf2665b0d26975648f8b5d Mon Sep 17 00:00:00 2001 From: ashrawat Date: Wed, 31 Jul 2024 10:54:49 -0400 Subject: [PATCH 13/16] SageMaker pipeline step decorator blog (#4715) * initial commit of using step decorator for bedrock fine tuning * ran black command on the notebook * Added CI badges * Added CI badges * fixed typo in notebook title * added comments and reviewer feedback --------- Co-authored-by: Ashish Rawat Co-authored-by: Zhaoqi --- .../fine_tune_bedrock_step_decorator.ipynb | 740 +++++------------- 1 file changed, 181 insertions(+), 559 deletions(-) diff --git a/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb b/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb index 6e225e9cd5..5ae034da17 100644 --- a/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb +++ b/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb @@ -6,7 +6,7 @@ "tags": [] }, "source": [ - "# Using SageMaker @step decorator feature convert python functions for creating a custom Bedrock model into a SageMaker pipeline.\n", + "# Automate LLM fine-tuning workflows in Amazon Bedrock and Amazon SageMaker using Python decorators.\n", "\n", "---\n", "\n", @@ -25,10 +25,15 @@ "source": [ "> *This notebook has been tested with the **`Python 3`** kernel in SageMaker Studio (JupyterLab version).*\n", "\n", - "We will fine tune the [Amazon Titan Text Lite](#https://docs.aws.amazon.com/bedrock/latest/userguide/titan-text-models.html) model provided by Amazon Bedrock for a summarization use case. It uses a dataset from CNN that includes news articles and their summaries. The dataset called [cnn_dailymail v3.0](https://huggingface.co/datasets/cnn_dailymail) is available from Hugging Face. \n", + "This notebook addresses the scenario where a developer may have written code using Python functions for creating a custom Bedrock model and the code was tested locally. But before it can deployed, we need to convert the Python program into a SageMaker Pipeline. The @step decorator is a feature of Amamzon SageMaker pipelines that converts your local machine learning (ML) code into one or more pipeline steps. \n", + "\n", + "The @step decorator feature uses a yaml configuration file that includes properties that are passed to the decorator function. This file includes properties that are passed to the @step decorator. This keeps default settings seprate from the code. You will find a *config.yaml* file in the same folder as this notebook. \n", "\n", "A *config.yaml* file can be found in the same folder as this notebook. This file includes properties that are passed to the @step decorator.\n", "\n", + "We will fine tune the [Amazon Titan Text Lite](#https://docs.aws.amazon.com/bedrock/latest/userguide/titan-text-models.html) model provided by Amazon Bedrock for a summarization use case. It uses a dataset from CNN that includes news articles and their summaries. The dataset called [cnn_dailymail v3.0](https://huggingface.co/datasets/cnn_dailymail) is available from Hugging Face. \n", + "\n", + "\n", "
\n", "Warning: The last section in this notebook does the clean up by removing the resources created during fine tuning and testing. That includes the Bedrock provisioned throughput which is needed to access the fine tuned custom model. Note that you will continue to incur AWS charges, unless you run the cleanup step.\n", "
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"Downloading sagemaker-2.215.0-py3-none-any.whl (1.5 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m68.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hUsing cached docker-7.0.0-py3-none-any.whl (147 kB)\n", - "Installing collected packages: docker, sagemaker\n", - " Attempting uninstall: sagemaker\n", - " Found existing installation: sagemaker 2.198.1\n", - " Uninstalling sagemaker-2.198.1:\n", - " Successfully uninstalled sagemaker-2.198.1\n", - "Successfully installed docker-7.0.0 sagemaker-2.215.0\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 1\n", "!pip install -r requirements.txt" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# cell 2\n", + "\n", "# restart kernel for the packages installed above to take effect\n", "from IPython.core.display import HTML\n", "\n", @@ -228,19 +70,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 3\n", + "\n", "from datasets import load_dataset\n", "from itertools import islice\n", "import pandas as pd\n", @@ -266,10 +101,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# cell 4\n", + "\n", "# Set path to config file \"config.yaml\"\n", "# The config.yaml file contains the arguments that are passed to the step decorator functions.\n", "os.environ[\"SAGEMAKER_USER_CONFIG_OVERRIDE\"] = os.getcwd()" @@ -292,20 +129,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Fetched defaults config from location: /home/sagemaker-user/blog\n", - "IAM role: arn:aws:iam::095351214964:role/service-role/AmazonSageMaker-ExecutionRole-20200130T133110\n", - "S3 bucket: sagemaker-us-east-1-095351214964\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 5\n", + "\n", "sagemaker_session = sagemaker.session.Session()\n", "region = sagemaker_session.boto_region_name\n", "\n", @@ -319,45 +148,37 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SageMaker:\n", - " PythonSDK:\n", - " Modules:\n", - " RemoteFunction:\n", - " CustomFileFilter:\n", - " IgnoreNamePatterns:\n", - " - '*.ipynb'\n", - " Dependencies: ./requirements.txt\n", - " IncludeLocalWorkDir: true\n", - " InstanceType: ml.c5.2xlarge\n", - "SchemaVersion: '1.0'\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "# let's look at the contemts of config.yaml\n", - "# The properties in congig.ymk are passed into the @step function.\n", - "# print the contents of config.yaml\n", + "# cell 6\n", + "\n", + "# let's look at the contents of config.yaml\n", + "# The properties in congig.yml are passed into the @step function.\n", "# Notice that pipeline step runs on ml.c5.2xlarge as specified in the InstanceType property\n", "with open(\"./config.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", " print(yaml.dump(config, default_flow_style=False))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training dataset\n", + "In the next cell we define the functions to load the CNN/DailyMail dataset. The CNN/DailyMail dataset is an English-language dataset containing just over 300 thousand unique news articles as written by journalists at CNN and the Daily Mail. The raw dataset includes the articles and their summaries for training, validation, and test. Before we can use the dataset, it must be formatted to include the prompt.\n", + "\n", + "Each entry from the dataset is included in a prompt which will be the instruction to the model.\n" + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from datasets import load_dataset\n", + "# cell 7\n", "\n", "instruction = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", @@ -385,7 +206,7 @@ " return datapoints\n", "\n", "\n", - "#### Define step for downloading the dataset\n", + "# Define step for downloading the dataset\n", "@step(\n", " name=\"data-load-step\",\n", " keep_alive_period_in_seconds=300,\n", @@ -415,12 +236,23 @@ " return datapoints_train, datapoints_valid, datapoints_test" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Split the CNN dataset into training, validation, and testing. Since this example is focused on SageMaker pipeline step decorators, we will using a very small number of rows for training and validation to reduce the training time. " + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# cell 8\n", + "\n", + "\n", "# Restrict the number of rows and row length\n", "def reduce_dataset_size(data, max_row_length, max_rows):\n", " datapoints = []\n", @@ -434,10 +266,14 @@ " return datapoints\n", "\n", "\n", - "#### Define step for splitting the dataset into training, validation, and testing\n", - "# restrict the size of each row to 3000 words\n", - "# We also select 100 rows for training, 10 for validation, and 5 for testing\n", - "# to keep computation costs low for this example\n", + "\"\"\"\n", + "Define step for splitting the dataset into training, validation, and testing.\n", + "We will restrict the size of each row to 3000 letters.\n", + "We will select 100 rows for training, 10 for validation, and 5 for testing to \n", + "keep computation costs low for this example\n", + "\"\"\"\n", + "\n", + "\n", "@step(\n", " name=\"data-split-step\",\n", " keep_alive_period_in_seconds=300,\n", @@ -454,13 +290,24 @@ " return train_lines, validation_lines, test_lines" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Upload the data to S3. We will need the S3 URI of the test data in the testing step later. To do that we save the string value of the S3 URI as a parameter in the [Amazon Simple Systems Manager (SSM)](https://docs.aws.amazon.com/systems-manager/latest/userguide/what-is-systems-manager.html).\n" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Upload the training, validation, and test files to S3\n", + "# cell 9\n", + "\n", + "\n", + "# Upload the training, validation, and test files to S3.\n", "def upload_file_to_s3(bucket_name: str, file_names: tuple, s3_key_names: tuple):\n", " import boto3\n", "\n", @@ -470,7 +317,7 @@ "\n", "\n", "# Save the training, validation, and test files in jsonl format\n", - "# to the local file system\n", + "# to the local file system.\n", "def write_jsonl_file(abs_path: str, file_name: str, data) -> str:\n", " saved_file_path = f\"{abs_path}/{file_name}\"\n", "\n", @@ -489,7 +336,7 @@ " )\n", "\n", "\n", - "#### Define step for uploading the training, validation, and test data to S3\n", + "# Define step for uploading the training, validation, and test data to S3\n", "@step(\n", " name=\"data-upload-to-s3-step\",\n", " keep_alive_period_in_seconds=300,\n", @@ -535,13 +382,25 @@ " )" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Next we define the function to train and fine-tune the model. We will use the Amazon Titan Text Lite model provided by Amazon Bedrock for the CNN dataset summarization use case. The train function needs the S3 URIs of the training and validation.\n", + "We will also configure the [hyperparameters for fine tuning](https://docs.aws.amazon.com/bedrock/latest/userguide/cm-hp-titan-text.html) the Titan Text Lite model. " + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#### Define step for custom training the model\n", + "# cell 10\n", + "\n", + "\n", + "# Define step for custom training the model\n", "@step(\n", " name=\"model-training-step\",\n", " keep_alive_period_in_seconds=300,\n", @@ -611,13 +470,24 @@ " return model_id" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Define step for creating [provisioned throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html) for the Bedrock custom model. A custom model requires provisioned throughput.\n" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#### Define step for creating Provisioned throughput for the custom model\n", + "# cell 11\n", + "\n", + "\n", + "# Define step for creating Provisioned throughput for the custom model\n", "@step(\n", " name=\"create-provisioned-throughput-step\",\n", " keep_alive_period_in_seconds=300,\n", @@ -646,12 +516,22 @@ " return provisioned_model_id" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Test the custom model. Note we get the S3 URI of the test dataset from Amazon SSM where we had stored it as a parameter in an earlier step." + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# cell 12\n", + "\n", "# Test the custom model\n", "\n", "\n", @@ -662,7 +542,7 @@ " return response[\"Parameter\"][\"Value\"]\n", "\n", "\n", - "#### Define step for tesiing the custom model\n", + "# Define step for testing the custom model\n", "@step(\n", " name=\"model-testing-step\",\n", " keep_alive_period_in_seconds=300,\n", @@ -721,15 +601,24 @@ " return prompt, summary" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create and run the SageMaker pipeline. You can view the execution of the pipeline in SageMaker Studio. It will appear as a [multi-step directed acyclic graph (DAG)](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-studio-list-pipelines.html) in the studio UI." + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "#### Create the SageMaker pipeline\n", + "# cell 13\n", + "\n", + "# Create the SageMaker pipeline\n", "# You can see the multi-step directed acyclic graph (DAG) in the Studio UI as a pipeline\n", "\n", "pipeline_name = \"bedrock-fine-tune-pipeline\"\n", @@ -759,312 +648,87 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-12 21:36:37,429 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-testing-step/2024-04-12-21-36-35-895/function\n", - "2024-04-12 21:36:37,547 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-testing-step/2024-04-12-21-36-35-895/arguments\n", - "2024-04-12 21:36:37,851 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpv41q6gtg/requirements.txt'\n", - "2024-04-12 21:36:37,912 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-testing-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", - "2024-04-12 21:36:38,000 sagemaker.remote_function INFO Copied user workspace to '/tmp/tmpse97qmlu/temp_workspace/sagemaker_remote_function_workspace'\n", - "2024-04-12 21:36:38,946 sagemaker.remote_function INFO Successfully created workdir archive at '/tmp/tmpse97qmlu/workspace.zip'\n", - "2024-04-12 21:36:39,124 sagemaker.remote_function INFO Successfully uploaded workdir to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/sm_rf_user_ws/2024-04-12-21-36-35-895/workspace.zip'\n", - "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-12 21:36:40,298 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/create-provisioned-throughput-step/2024-04-12-21-36-35-895/function\n", - "2024-04-12 21:36:40,411 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/create-provisioned-throughput-step/2024-04-12-21-36-35-895/arguments\n", - "2024-04-12 21:36:40,487 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpnj3veih_/requirements.txt'\n", - "2024-04-12 21:36:40,519 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/create-provisioned-throughput-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", - "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-12 21:36:41,695 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-training-step/2024-04-12-21-36-35-895/function\n", - "2024-04-12 21:36:41,792 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-training-step/2024-04-12-21-36-35-895/arguments\n", - "2024-04-12 21:36:41,912 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpzxpgiqlm/requirements.txt'\n", - "2024-04-12 21:36:41,983 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/model-training-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", - "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-12 21:36:43,162 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-upload-to-s3-step/2024-04-12-21-36-35-895/function\n", - "2024-04-12 21:36:43,346 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-upload-to-s3-step/2024-04-12-21-36-35-895/arguments\n", - "2024-04-12 21:36:43,465 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmp7ujlj15s/requirements.txt'\n", - "2024-04-12 21:36:43,528 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-upload-to-s3-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", - "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-12 21:36:44,700 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-split-step/2024-04-12-21-36-35-895/function\n", - "2024-04-12 21:36:44,781 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-split-step/2024-04-12-21-36-35-895/arguments\n", - "2024-04-12 21:36:44,891 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmp9p4gw5b6/requirements.txt'\n", - "2024-04-12 21:36:44,919 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-split-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", - "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.IncludeLocalWorkDir\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.CustomFileFilter.IgnoreNamePatterns\n", - "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-12 21:36:46,092 sagemaker.remote_function INFO Uploading serialized function code to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-load-step/2024-04-12-21-36-35-895/function\n", - "2024-04-12 21:36:46,213 sagemaker.remote_function INFO Uploading serialized function arguments to s3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-load-step/2024-04-12-21-36-35-895/arguments\n", - "2024-04-12 21:36:46,292 sagemaker.remote_function INFO Copied dependencies file at './requirements.txt' to '/tmp/tmpx3pmqpqv/requirements.txt'\n", - "2024-04-12 21:36:46,319 sagemaker.remote_function INFO Successfully uploaded dependencies and pre execution scripts to 's3://sagemaker-us-east-1-095351214964/bedrock-fine-tune-pipeline/data-load-step/2024-04-12-21-36-35-895/pre_exec_script_and_dependencies'\n", - "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline',\n", - " 'ResponseMetadata': {'RequestId': '8de6e516-fdbf-4d34-bc19-4b61a6cb6474',\n", - " 'HTTPStatusCode': 200,\n", - " 'HTTPHeaders': {'x-amzn-requestid': '8de6e516-fdbf-4d34-bc19-4b61a6cb6474',\n", - " 'content-type': 'application/x-amz-json-1.1',\n", - " 'content-length': '94',\n", - " 'date': 'Fri, 12 Apr 2024 21:36:46 GMT'},\n", - " 'RetryAttempts': 0}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# cell 14\n", + "\n", "pipeline.upsert(role_arn)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ + "# cell 15\n", + "\n", "execution = pipeline.start()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline',\n", - " 'PipelineExecutionArn': 'arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline/execution/l040kjgtiq4n',\n", - " 'PipelineExecutionDisplayName': 'execution-1712957806959',\n", - " 'PipelineExecutionStatus': 'Executing',\n", - " 'CreationTime': datetime.datetime(2024, 4, 12, 21, 36, 46, 908000, tzinfo=tzlocal()),\n", - " 'LastModifiedTime': datetime.datetime(2024, 4, 12, 21, 36, 46, 908000, tzinfo=tzlocal()),\n", - " 'CreatedBy': {'UserProfileArn': 'arn:aws:sagemaker:us-east-1:095351214964:user-profile/d-ndkfwlyrojeq/blog',\n", - " 'UserProfileName': 'blog',\n", - " 'DomainId': 'd-ndkfwlyrojeq',\n", - " 'IamIdentity': {'Arn': 'arn:aws:sts::095351214964:assumed-role/AmazonSageMaker-ExecutionRole-20200130T133110/SageMaker',\n", - " 'PrincipalId': 'AROARMM3ACN2NE2XC3HPY:SageMaker'}},\n", - " 'LastModifiedBy': {'UserProfileArn': 'arn:aws:sagemaker:us-east-1:095351214964:user-profile/d-ndkfwlyrojeq/blog',\n", - " 'UserProfileName': 'blog',\n", - " 'DomainId': 'd-ndkfwlyrojeq',\n", - " 'IamIdentity': {'Arn': 'arn:aws:sts::095351214964:assumed-role/AmazonSageMaker-ExecutionRole-20200130T133110/SageMaker',\n", - " 'PrincipalId': 'AROARMM3ACN2NE2XC3HPY:SageMaker'}},\n", - " 'ResponseMetadata': {'RequestId': '36b7812f-9de8-4686-9066-107fcda06bee',\n", - " 'HTTPStatusCode': 200,\n", - " 'HTTPHeaders': {'x-amzn-requestid': '36b7812f-9de8-4686-9066-107fcda06bee',\n", - " 'content-type': 'application/x-amz-json-1.1',\n", - " 'content-length': '1041',\n", - " 'date': 'Fri, 12 Apr 2024 21:36:46 GMT'},\n", - " 'RetryAttempts': 0}}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# cell 16\n", + "\n", "execution.describe()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Wait for the pipeline to finish execution.
\n", + "\n", + "**Note:** *If you get an error \"Waiter PipelineExecutionComplete failed\" in the following cell, check CloudWatch logs for error details. Most likely, you will see a ServiceQuotaExceededException for provisioned throughput units for the model. You will have to request Amazon support for quota increase. The model quota has to be reqiested for each model type, e.g. amazon.titan-text-lite-v1.*\n", + "\n", + "You can also see the execution status of each step in the pipeline in the output of cell 18." + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.44 s, sys: 87.4 ms, total: 1.53 s\n", - "Wall time: 1h 31min 17s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", + "# cell 17\n", "execution.wait(delay=60, max_attempts=250)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'StepName': 'model-testing-step',\n", - " 'StepDisplayName': '__main__.test_model',\n", - " 'StartTime': datetime.datetime(2024, 4, 12, 23, 4, 43, 688000, tzinfo=tzlocal()),\n", - " 'EndTime': datetime.datetime(2024, 4, 12, 23, 7, 33, 776000, tzinfo=tzlocal()),\n", - " 'StepStatus': 'Succeeded',\n", - " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-model-testing-step-pr4gGsj2Rt'}},\n", - " 'AttemptCount': 1},\n", - " {'StepName': 'create-provisioned-throughput-step',\n", - " 'StepDisplayName': '__main__.create_prov_thruput',\n", - " 'StartTime': datetime.datetime(2024, 4, 12, 22, 49, 35, 654000, tzinfo=tzlocal()),\n", - " 'EndTime': datetime.datetime(2024, 4, 12, 23, 4, 42, 774000, tzinfo=tzlocal()),\n", - " 'StepStatus': 'Succeeded',\n", - " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-create-provisioned-t-xDN4wVqlsC'}},\n", - " 'AttemptCount': 1},\n", - " {'StepName': 'model-training-step',\n", - " 'StepDisplayName': '__main__.train',\n", - " 'StartTime': datetime.datetime(2024, 4, 12, 21, 46, 28, 754000, tzinfo=tzlocal()),\n", - " 'EndTime': datetime.datetime(2024, 4, 12, 22, 49, 34, 878000, tzinfo=tzlocal()),\n", - " 'StepStatus': 'Succeeded',\n", - " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-model-training-step-Kc1rJEbgzv'}},\n", - " 'AttemptCount': 1},\n", - " {'StepName': 'data-upload-to-s3-step',\n", - " 'StepDisplayName': '__main__.data_upload_to_s3',\n", - " 'StartTime': datetime.datetime(2024, 4, 12, 21, 43, 39, 142000, tzinfo=tzlocal()),\n", - " 'EndTime': datetime.datetime(2024, 4, 12, 21, 46, 27, 822000, tzinfo=tzlocal()),\n", - " 'StepStatus': 'Succeeded',\n", - " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-data-upload-to-s3-st-eQlNAJKWnc'}},\n", - " 'AttemptCount': 1},\n", - " {'StepName': 'data-split-step',\n", - " 'StepDisplayName': '__main__.data_split',\n", - " 'StartTime': datetime.datetime(2024, 4, 12, 21, 40, 37, 342000, tzinfo=tzlocal()),\n", - " 'EndTime': datetime.datetime(2024, 4, 12, 21, 43, 38, 277000, tzinfo=tzlocal()),\n", - " 'StepStatus': 'Succeeded',\n", - " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-data-split-step-68JtnNtXxn'}},\n", - " 'AttemptCount': 1},\n", - " {'StepName': 'data-load-step',\n", - " 'StartTime': datetime.datetime(2024, 4, 12, 21, 36, 48, 342000, tzinfo=tzlocal()),\n", - " 'EndTime': datetime.datetime(2024, 4, 12, 21, 40, 34, 16000, tzinfo=tzlocal()),\n", - " 'StepStatus': 'Succeeded',\n", - " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:095351214964:training-job/pipelines-l040kjgtiq4n-data-load-step-cYwRdw1Qg1'}},\n", - " 'AttemptCount': 1}]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# cell 18\n", + "\n", "execution.list_steps()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('\\n Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\ninstruction:\\n\\nSummarize the news article provided below.\\n\\ninput:\\n\\n(CNN)Remains of up to nearly 400 unaccounted for service members tied to the USS Oklahoma at Pearl Harbor will be exhumed this year, the Defense Department announced Tuesday. The hope is that most of the battleship\\'s sailors and Marines can be identified. \"The secretary of defense and I will work tirelessly to ensure your loved one\\'s remains will be recovered, identified, and returned to you as expeditiously as possible, and we will do so with dignity, respect and care,\" Deputy Secretary of Defense Bob Work said in a statement. \"While not all families will receive an individual identification, we will strive to provide resolution to as many families as possible.\" The USS Oklahoma sank when it was hit by torpedoes on December 7, 1941, during the Japanese attack on Pearl Harbor. A total of 429 sailors and Marines on the ship were killed. Thirty-five crew members were positively identified and buried in the years immediately after the attack, according to the Defense Department. By 1950, all unidentified remains were laid to rest as unknowns at the National Memorial Cemetery of the Pacific. In 2003, five more service members were identified, with the help of historical evidence from Pearl Harbor survivor Ray Emory, 93. Emory, a native of Peoria, Illinois, was serving as a seaman first class on the light cruiser USS Honolulu that fateful day. After the war, Emory worked in Washington state before moving to Hawaii about 30 years ago. The retiree made it his mission to ensure graves are properly identified. \"It\\'s something I looked forward to for a long time,\" he told CNN about Tuesday\\'s announcement. Speaking by phone from Honolulu, Emory said that proper identification means a lot to the families of those who lost loved ones -- and to him. Next of kin were being notified starting Tuesday. Service members who are identified will be returned to their families for burial, with full military honors. WWII pilot, 99, reunited with historic C-47 plane . CNN\\'s Phil Gast contributed to this report.\\n ', '\\nThe USS Oklahoma sank during the Japanese attack on Pearl Harbor on December 7, 1941.\\nThe battleship was hit by torpedoes, killing 429 sailors and Marines.\\nThe Defense Department says it will work to identify as many of the remains as possible.')\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 19\n", + "\n", "print(execution.result(step_name=\"model-testing-step\"))" ] }, @@ -1078,21 +742,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Provisoned throughput deleted for model: arn:aws:bedrock:us-east-1:095351214964:provisioned-model/fj8dou88yq5q\n", - "Custom model arn:aws:bedrock:us-east-1:095351214964:custom-model/amazon.titan-text-lite-v1:0:4k/2zefi5rp4ez1 deleted.\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 20\n", + "\n", "bedrock = boto3.client(service_name=\"bedrock\")\n", "\n", "# delete Bedrock provisioned throughput\n", @@ -1116,29 +773,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ResponseMetadata': {'RequestId': '4a830460-d5d5-48bd-94fa-729f0b5dbfcd',\n", - " 'HTTPStatusCode': 200,\n", - " 'HTTPHeaders': {'server': 'Server',\n", - " 'date': 'Fri, 12 Apr 2024 23:08:07 GMT',\n", - " 'content-type': 'application/x-amz-json-1.1',\n", - " 'content-length': '2',\n", - " 'connection': 'keep-alive',\n", - " 'x-amzn-requestid': '4a830460-d5d5-48bd-94fa-729f0b5dbfcd'},\n", - " 'RetryAttempts': 0}}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# cell 21\n", + "\n", "# delete the SSM parameter\n", "ssm_client = boto3.client(\"ssm\")\n", "ssm_client.delete_parameter(Name=\"s3_test_uri\")" @@ -1146,25 +786,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.workflow.pipeline:If triggers have been setup for this target, they will become orphaned.You will need to clean them up manually via the CLI or EventBridge console.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleted pipeline arn:aws:sagemaker:us-east-1:095351214964:pipeline/bedrock-fine-tune-pipeline\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 22\n", + "\n", "# Delete the SageMaker pipeline\n", "response = pipeline.delete()\n", "print(f'Deleted pipeline {response[\"PipelineArn\"]}')" @@ -1172,18 +799,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objects in Bucket sagemaker-us-east-1-095351214964 have been deleted.\n" - ] - } - ], + "outputs": [], "source": [ + "# cell 23\n", + "\n", + "\n", "# delete objects in S3\n", "def delete_objects_with_prefix(bucket_name, prefix):\n", " s3 = boto3.client(\"s3\")\n", @@ -1884,7 +1506,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, From e642782acb9ae6c8d9939da3047d34b103a068db Mon Sep 17 00:00:00 2001 From: brentfriedman725 <97409987+brentfriedman725@users.noreply.github.com> Date: Fri, 2 Aug 2024 18:27:06 +0000 Subject: [PATCH 14/16] LLM Monitoring Multiple Evals (#4718) * Removed bug where automatic permission attachment errors * Adds notebook for monitoring llm with multiple eval libraries --------- Co-authored-by: Brent Friedman Co-authored-by: nileshvd <113946607+nileshvd@users.noreply.github.com> --- sagemaker_model_monitor/index.rst | 8 + .../Dockerfile | 32 + .../byoc_llm_multiple_evals_monitor.ipynb | 1391 +++++++++++++++++ .../data/questions.jsonl | 729 +++++++++ .../requirements.txt | 8 + .../src/components/__init__.py | 0 .../src/components/cloudwatch_logger.py | 106 ++ .../src/components/data_loader.py | 178 +++ .../src/components/evaluator.py | 326 ++++ .../src/main.py | 75 + .../src/utils/__init__.py | 0 .../src/utils/jsonl-capture-data.schema | 86 + 12 files changed, 2939 insertions(+) create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/Dockerfile create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/byoc_llm_multiple_evals_monitor.ipynb create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/data/questions.jsonl create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/requirements.txt create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/__init__.py create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/cloudwatch_logger.py create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/data_loader.py create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/evaluator.py create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/main.py create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/__init__.py create mode 100644 sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/jsonl-capture-data.schema diff --git a/sagemaker_model_monitor/index.rst b/sagemaker_model_monitor/index.rst index 6e92940ce8..015493af5b 100644 --- a/sagemaker_model_monitor/index.rst +++ b/sagemaker_model_monitor/index.rst @@ -58,4 +58,12 @@ LLM Monitoring :maxdepth: 1 llm_monitor_byoc/byoc_llm_monitor + +LLM Mutliple Evauation Monitoring +============================== + +.. toctree:: + :maxdepth: 1 + + llm_multiple_evals_monitor_byoc/byoc_llm_multiple_evals_monitor diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/Dockerfile b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/Dockerfile new file mode 100644 index 0000000000..b6a100119e --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/Dockerfile @@ -0,0 +1,32 @@ +FROM --platform=linux/amd64 ubuntu:22.04 as build + +# Install required packages +RUN apt-get update && apt-get install -y \ + python3.10 \ + python3.10-dev \ + python3-pip \ + build-essential \ + libssl-dev \ + libffi-dev \ + git \ + && rm -rf /var/lib/apt/lists/* + +# Set the default Python version to 3.10 +RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1 +RUN update-alternatives --config python3 + +# Copy requirements.txt and install dependencies +COPY requirements.txt /opt/program/requirements.txt +RUN pip3 install -r /opt/program/requirements.txt + +# Set working directory and copy application files +WORKDIR /opt/program +COPY src /opt/program + +ENV DOCKER_CONTAINER=1 EVAL_RESULTS_PATH=/opt/ml/processing/output/ + +# Set execute permission for main.py +RUN chmod +x /opt/program/main.py + +# Set entrypoint to main.py +ENTRYPOINT ["python3", "/opt/program/main.py"] \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/byoc_llm_multiple_evals_monitor.ipynb b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/byoc_llm_multiple_evals_monitor.ipynb new file mode 100644 index 0000000000..84a6c5c2a8 --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/byoc_llm_multiple_evals_monitor.ipynb @@ -0,0 +1,1391 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8af3794b", + "metadata": {}, + "source": [ + "# BYOC LLM Monitoring: Bring Your Own Container Llama2 Multiple Evaluations Monitoring with SageMaker Model Monitor" + ] + }, + { + "cell_type": "markdown", + "id": "16dc5ce1", + "metadata": {}, + "source": [ + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_monitor|llm_multiple_evals_monitor_byoc|byoc_llm_multiple_evals_monitor.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "446b1b24", + "metadata": {}, + "source": [ + "---\n", + "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy and monitor a JumpStart Llama 2 fine-tuned model for Toxicity, Answer Relevance and Accuracy, and Readability. The container associated with this notebook employs [FMEval](https://github.com/aws/fmeval) for LLM Toxicity evaluation, [LangChain](https://python.langchain.com/v0.1/docs/guides/productionization/evaluation/) for Answer Relevance and Accuracy, and [WhyLabs LangKit](https://whylabs.ai/langkit) for Readability.\n", + "\n", + "To perform inference on these models, you need to pass custom_attributes='accept_eula=true' as part of header. This means you have read and accept the end-user-license-agreement (EULA) of the model. EULA can be found in model card description or from https://ai.meta.com/resources/models-and-libraries/llama-downloads/. By default, this notebook sets custom_attributes='accept_eula=false', so all inference requests will fail until you explicitly change this custom attribute.\n", + "\n", + "Note: Custom_attributes used to pass EULA are key/value pairs. The key and value are separated by '=' and pairs are separated by ';'. If the user passes the same key more than once, the last value is kept and passed to the script handler (i.e., in this case, used for conditional logic). For example, if 'accept_eula=false; accept_eula=true' is passed to the server, then 'accept_eula=true' is kept and passed to the script handler.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "471e31d9", + "metadata": {}, + "source": [ + "# Background\n", + "\n", + "SageMaker Model Monitor allows users to provide images of their own custom-built containers to be run at each monitoring job. This notebook leverages the [BYOC](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-containers.html) feature to monitor the Llama2-7b model for 7 different Toxicity levels." + ] + }, + { + "cell_type": "markdown", + "id": "2b79c05c", + "metadata": {}, + "source": [ + "# Prerequisites\n", + "- **IF RUNNING LOCALLY (not SageMaker Studio/Classic)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. For information on how to complete this, see this [documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html)\n", + "\n", + "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. Please also ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup." + ] + }, + { + "cell_type": "markdown", + "id": "35642ab2", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "f39994bc", + "metadata": {}, + "source": [ + "**This notebook is best suited for a kernel of python verion >= 3.11**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b55e677-3429-4668-b100-bd63d2a4c401", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "9eeebb0b", + "metadata": {}, + "source": [ + "## Retreive your SageMaker Session and Configure Execution Role" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6854ff02", + "metadata": {}, + "outputs": [], + "source": [ + "import sagemaker\n", + "import boto3\n", + "\n", + "sess = sagemaker.Session()\n", + "# sagemaker session bucket -> used for uploading data, models and logs\n", + "# sagemaker will automatically create this bucket if it not exists\n", + "sagemaker_session_bucket = None\n", + "if sagemaker_session_bucket is None and sess is not None:\n", + " sagemaker_session_bucket = sess.default_bucket()\n", + "\n", + "# Here, we create a role for SageMaker. The role ARN must be specified when calling the predict() method. If this fails, you can manually specify the role ARN in the except block.\n", + "try:\n", + " role = sagemaker.get_execution_role()\n", + "except ValueError:\n", + " iam = boto3.client(\"iam\")\n", + " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess' role. See the linked documentation for help.\n", + " role = iam.get_role(RoleName=\"\")[\"Role\"][\"Arn\"]\n", + "\n", + "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n", + "\n", + "print(f\"sagemaker role arn: {role}\")\n", + "print(f\"sagemaker session region: {sess.boto_region_name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7d458cf0-02e2-4066-927b-25fa5ef2a07e", + "metadata": {}, + "source": [ + "***\n", + "You can continue with the default model or choose a different model: this notebook will run with the following model IDs :\n", + "- `meta-textgeneration-llama-2-7b-f`\n", + "- `meta-textgeneration-llama-2-13b-f`\n", + "- `meta-textgeneration-llama-2-70b-f`\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a882ae62", + "metadata": { + "jumpStartAlterations": [ + "modelIdVersion" + ], + "tags": [] + }, + "outputs": [], + "source": [ + "model_id, model_version = \"meta-textgeneration-llama-2-7b-f\", \"2.*\"" + ] + }, + { + "cell_type": "markdown", + "id": "11eef0dd", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "***\n", + "You can now deploy the model using SageMaker JumpStart.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "fd598868", + "metadata": {}, + "source": [ + "### Set up DataCapture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83b865cd", + "metadata": {}, + "outputs": [], + "source": [ + "bucket = sess.default_bucket()\n", + "print(\"Demo Bucket:\", bucket)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f445381", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import DataCaptureConfig\n", + "\n", + "s3_root_dir = \"byoc-multiple-eval-monitor-llm\"\n", + "\n", + "s3_capture_upload_path = f\"s3://{bucket}/{s3_root_dir}/datacapture\"\n", + "\n", + "data_capture_config = DataCaptureConfig(\n", + " enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_upload_path\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b2bc731", + "metadata": {}, + "outputs": [], + "source": [ + "print(s3_capture_upload_path)" + ] + }, + { + "cell_type": "markdown", + "id": "d033889e", + "metadata": {}, + "source": [ + "### Note: This next cell will take ~10 minutes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e52afae-868d-4736-881f-7180f393003a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.jumpstart.model import JumpStartModel\n", + "\n", + "model = JumpStartModel(model_id=model_id, model_version=model_version, role=role)\n", + "predictor = model.deploy(data_capture_config=data_capture_config)\n", + "print(model.endpoint_name)" + ] + }, + { + "cell_type": "markdown", + "id": "5ef7207e-01ba-4ac2-b4a9-c8f6f0e1c498", + "metadata": { + "tags": [] + }, + "source": [ + "## Invoke the endpoint\n", + "\n", + "***\n", + "### Supported Parameters\n", + "This model supports the following inference payload parameters:\n", + "\n", + "* **max_new_tokens:** Model generates text until the output length (excluding the input context length) reaches max_new_tokens. If specified, it must be a positive integer.\n", + "* **temperature:** Controls the randomness in the output. Higher temperature results in output sequence with low-probability words and lower temperature results in output sequence with high-probability words. If `temperature` -> 0, it results in greedy decoding. If specified, it must be a positive float.\n", + "* **top_p:** In each step of text generation, sample from the smallest possible set of words with cumulative probability `top_p`. If specified, it must be a float between 0 and 1.\n", + "\n", + "You may specify any subset of the parameters mentioned above while invoking an endpoint. \n", + "\n", + "***\n", + "### Notes\n", + "- If `max_new_tokens` is not defined, the model may generate up to the maximum total tokens allowed, which is 4K for these models. This may result in endpoint query timeout errors, so it is recommended to set `max_new_tokens` when possible. For 7B, 13B, and 70B models, we recommend to set `max_new_tokens` no greater than 1500, 1000, and 500 respectively, while keeping the total number of tokens less than 4K.\n", + "- In order to support a 4k context length, this model has restricted query payloads to only utilize a batch size of 1. Payloads with larger batch sizes will receive an endpoint error prior to inference.\n", + "- This model only supports 'system', 'user' and 'assistant' roles, starting with 'system', then 'user' and alternating (u/a/u/a/u...).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5adf9b4-c7e1-4090-aefe-9cae0d096968", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def print_dialog(payload, response):\n", + " dialog = payload[\"inputs\"][0]\n", + " for msg in dialog:\n", + " print(f\"{msg['role'].capitalize()}: {msg['content']}\\n\")\n", + " print(\n", + " f\">>>> {response[0]['generation']['role'].capitalize()}: {response[0]['generation']['content']}\"\n", + " )\n", + " print(\"\\n==================================\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2fbb9af", + "metadata": {}, + "source": [ + "### Example of a single invocation\n", + "\n", + "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbde5e7-1068-41f9-999a-70ef04e1cbbb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "payload = {\n", + " \"inputs\": [\n", + " [\n", + " {\"role\": \"user\", \"content\": \"what is the recipe of mayonnaise?\"},\n", + " ]\n", + " ],\n", + " \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6},\n", + "}\n", + "try:\n", + " response = predictor.predict(payload, custom_attributes=\"accept_eula=false\")\n", + " print_dialog(payload, response)\n", + "except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "id": "92c7ac9d", + "metadata": {}, + "source": [ + "### Send artificial traffic to the endpoint." + ] + }, + { + "cell_type": "markdown", + "id": "04c200cf", + "metadata": {}, + "source": [ + "The following cell will send questions to the endpoint until stopped. Feel free to stop the cell whenever you feel you have captured enough data.\n", + "\n", + "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d894f9eb", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "line_count = 0\n", + "with open(\"./data/questions.jsonl\", \"r\") as datafile:\n", + " for line in datafile:\n", + " if line_count == 10:\n", + " break\n", + " line_count += 1\n", + " data = json.loads(line)\n", + " payload = {\n", + " \"inputs\": [\n", + " [\n", + " data,\n", + " ]\n", + " ],\n", + " \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6},\n", + " }\n", + " try:\n", + " response = predictor.predict(payload, custom_attributes=\"accept_eula=false\")\n", + " print_dialog(payload, response)\n", + " except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "id": "862ab1d3", + "metadata": {}, + "source": [ + "# Build and Push the Container to ECR" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ea8d8ed", + "metadata": {}, + "outputs": [], + "source": [ + "ecr_repo_name = \"byoc-llm-multiple-eval\"\n", + "aws_region = sess.boto_region_name\n", + "aws_account_id = sess.account_id()" + ] + }, + { + "cell_type": "markdown", + "id": "42ebf7fe", + "metadata": {}, + "source": [ + "#### **IMPORTANT:** If running locally (not on SageMaker Studio), delete ' --network sagemaker'\n", + "Build the image. This will take ~5 mins." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84b2f742", + "metadata": {}, + "outputs": [], + "source": [ + "!set -Eeuxo pipefail\n", + "!docker build -t \"{ecr_repo_name}\" . --network sagemaker" + ] + }, + { + "cell_type": "markdown", + "id": "a9cbcb3d", + "metadata": {}, + "source": [ + "Create the repository. Ensure the role you have assumed has the AmazonEC2ContainerRegistryFullAccess permission attached." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "992e26ae", + "metadata": {}, + "outputs": [], + "source": [ + "ecr = boto3.client(\"ecr\")\n", + "\n", + "try:\n", + " response = ecr.create_repository(\n", + " repositoryName=ecr_repo_name,\n", + " imageTagMutability=\"MUTABLE\",\n", + " imageScanningConfiguration={\"scanOnPush\": False},\n", + " )\n", + "except ecr.exceptions.RepositoryAlreadyExistsException:\n", + " print(f\"Repository {ecr_repo_name} already exists. Skipping creation.\")" + ] + }, + { + "cell_type": "markdown", + "id": "50cc4260", + "metadata": {}, + "source": [ + "Push the image to ECR. This will take some time, as the image is ~9GB. Ensure that your AWS credentials are fresh." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0043e9d4", + "metadata": {}, + "outputs": [], + "source": [ + "!LATEST_IMAGE_ID=$(docker images --filter=reference='{ecr_repo_name}:latest' --format \"{{.ID}}\" | head -n 1)\n", + "!echo $LATEST_IMAGE_ID\n", + "\n", + "!aws ecr get-login-password --region '{aws_region}' | docker login --username AWS --password-stdin '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com\n", + "\n", + "!docker tag '{ecr_repo_name}':latest '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest\n", + "\n", + "!echo 'Pushing to ECR Repo: ''{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest\n", + "!docker push '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest" + ] + }, + { + "cell_type": "markdown", + "id": "b1a9722f", + "metadata": {}, + "source": [ + "# Set a Monitoring Schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7aa6e4c", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import ModelMonitor\n", + "\n", + "image_uri = f\"{aws_account_id}.dkr.ecr.{aws_region}.amazonaws.com/{ecr_repo_name}:latest\"\n", + "bucket = sess.default_bucket()\n", + "\n", + "monitor = ModelMonitor(\n", + " base_job_name=\"byoc-llm-multiple-eval-monitor\",\n", + " role=role,\n", + " image_uri=image_uri,\n", + " instance_count=1,\n", + " instance_type=\"ml.c5.9xlarge\",\n", + " env={\n", + " \"bucket\": bucket,\n", + " \"TOXICITY\": \"Enabled\",\n", + " \"READABILITY\": \"Enabled\",\n", + " \"RELEVANCE_AND_ACCURACY\": \"Enabled\",\n", + " }, # Change one to DISABLED if metrics not desired.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fb40b933", + "metadata": {}, + "source": [ + "**Note**: The following cell sets a **one-time** monitoring schedule for demonstration purposes. A one-time monioring schedule will execute immediately. If you would like to set an hourly schedule, swap out the commented line. It is important to know that hourly schedules will only begin at the start of the next full hour, so you will not see immediate results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b05c5b5", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.model_monitor import CronExpressionGenerator, MonitoringOutput, EndpointInput\n", + "\n", + "# Do not change\n", + "container_data_destination = \"/opt/ml/processing/input_data\"\n", + "container_evaluation_source = \"/opt/ml/processing/output\"\n", + "s3_report_upload_path = f\"s3://{bucket}/{s3_root_dir}/results\"\n", + "\n", + "\n", + "endpoint_input = EndpointInput(\n", + " endpoint_name=predictor.endpoint_name,\n", + " destination=container_data_destination,\n", + ")\n", + "\n", + "monitor.create_monitoring_schedule(\n", + " endpoint_input=endpoint_input,\n", + " output=MonitoringOutput(source=container_evaluation_source, destination=s3_report_upload_path),\n", + " schedule_cron_expression=CronExpressionGenerator.now(), # CronExpressionGenerator.hourly()\n", + " # data sampling is from 3hrs prior to execution to time of execution\n", + " data_analysis_start_time=\"-PT3H\",\n", + " data_analysis_end_time=\"-PT0H\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e9a3b7d9", + "metadata": {}, + "source": [ + "# View Results\n", + "\n", + "The following cell prints the output report stored in Amazon S3. It includes evaluations for at most 100 samples of the captured data.\n", + "\n", + "**NOTE:** The report will show up once the job is finished. Please try again in a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6777ba57", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker import s3\n", + "\n", + "try:\n", + " execution_output = monitor.list_executions()[-1].output\n", + " s3_path_to_toxicity_report = f\"{execution_output.destination}/toxicity_custom_dataset.jsonl\"\n", + " s3_path_to_readability_report = f\"{execution_output.destination}/readability_eval_results.jsonl\"\n", + " s3_path_to_relevance_and_accuracy_report = (\n", + " f\"{execution_output.destination}/relevance_and_accuracy_eval_results.jsonl\"\n", + " )\n", + " print(\"Toxicity report: \\n\")\n", + " print(s3.S3Downloader.read_file(s3_path_to_toxicity_report), \"\\n\")\n", + " print(\"Readability report: \\n\")\n", + " print(s3.S3Downloader.read_file(s3_path_to_readability_report), \"\\n\")\n", + " print(\"Relevance and Accuracy report: \\n\")\n", + " print(s3.S3Downloader.read_file(s3_path_to_relevance_and_accuracy_report))\n", + "except:\n", + " print(\"Report not found. Please wait and try again.\")" + ] + }, + { + "cell_type": "markdown", + "id": "ff6f2ca9", + "metadata": {}, + "source": [ + "### View Cloudwatch Dashboard Graph\n", + "The following cell will generate a CloudWatch Dashboard for the monitoring schedule you created. For more information on dashboard formatting, see [here](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/CloudWatch-Dashboard-Body-Structure.html#Dashboard-Body-Overall-Structure)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b55ea736", + "metadata": {}, + "outputs": [], + "source": [ + "cwClient = boto3.client(\"cloudwatch\")\n", + "monitoring_schedule_name = monitor.describe_schedule()[\"MonitoringScheduleName\"]\n", + "endpoint_name = monitor.describe_schedule()[\"EndpointName\"]\n", + "\n", + "# Get the metrics for this monitoring schedule\n", + "metric_list = cwClient.list_metrics(\n", + " Dimensions=[\n", + " {\"Name\": \"Endpoint\", \"Value\": endpoint_name},\n", + " {\"Name\": \"MonitoringSchedule\", \"Value\": monitoring_schedule_name},\n", + " ],\n", + ")\n", + "metric_names = [metric[\"MetricName\"] for metric in metric_list[\"Metrics\"]]\n", + "print(metric_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23a5f4d1", + "metadata": {}, + "outputs": [], + "source": [ + "linear_interpolate_metric = [\n", + " {\n", + " \"expression\": \"FILL(METRICS(), LINEAR)\",\n", + " \"label\": \"Linear Interpolated\",\n", + " \"id\": \"e1\",\n", + " \"region\": sess.boto_region_name,\n", + " }\n", + "]\n", + "metrics = [linear_interpolate_metric]\n", + "for i, metric_name in enumerate(metric_names):\n", + " metrics.append(\n", + " [\n", + " \"aws/sagemaker/Endpoints/data-metrics\",\n", + " metric_name,\n", + " \"Endpoint\",\n", + " endpoint_name,\n", + " \"MonitoringSchedule\",\n", + " monitoring_schedule_name,\n", + " {\"id\": f\"m{i+1}\", \"region\": sess.boto_region_name, \"visible\": False},\n", + " ]\n", + " )\n", + "\n", + "widget_title = \"LLM Multiple Evaluations Graph\"\n", + "\n", + "dash_data = json.dumps(\n", + " {\n", + " \"start\": \"-PT6H\",\n", + " \"periodOverride\": \"inherit\",\n", + " \"widgets\": [\n", + " {\n", + " \"type\": \"metric\",\n", + " \"x\": 0,\n", + " \"y\": 0,\n", + " \"width\": 13,\n", + " \"height\": 10,\n", + " \"properties\": {\n", + " \"metrics\": metrics,\n", + " \"view\": \"timeSeries\",\n", + " \"stacked\": False,\n", + " \"region\": sess.boto_region_name,\n", + " \"stat\": \"Average\",\n", + " \"period\": 300,\n", + " \"title\": widget_title,\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"text\",\n", + " \"x\": 13,\n", + " \"y\": 0,\n", + " \"width\": 11,\n", + " \"height\": 11,\n", + " \"properties\": {\n", + " \"markdown\": \"# LLM Evaluation Descriptions\\n## Toxicity\\nToxicity is measured in 7 different categories:\\n- `toxicity`\\n- `severe_toxicity`\\n- `obscene`\\n- `threat`\\n- `insult`\\n- `identity_attack`\\n- `sexual_explicit`\\n\\nEach score is a number between 0 and 1, with 1 denoting extreme toxicity. To obtain the toxicity scores, the FMEval library uses the open-source [Detoxify](https://github.com/unitaryai/detoxify) model to grade each LLM output.\\n \\n\\n\\n## Readability\\nReadability is measured in 11 different categories. These measurements are created and aggregating by the WhyLabs LangKit `textstat` module. For information on scoring for each metric, read their documentation [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).\\n\\n## Relevance and Accuracy\\nRelevance and accuracy is graded on a single score from 1-10. The prompt and response from the monitored LLM are provided to an evaluator LLM with intructions as follows:\\n\\n> Please act as an impartial judge and evaluate the quality of the response provided by an AI assistant to the user question displayed below. For this evaluation, you should primarily consider the following criteria:\\n> - helpfulness: Is the submission helpful, insightful, and appropriate?\\n> - relevance: Is the submission referring to a real quote from the text?\\n> - correctness: Is the submission correct, accurate, and factual?\\n> - depth: Does the submission demonstrate depth of thought?\\n\\n> Begin your evaluation by providing a short explanation. Be as objective as possible. After providing your explanation, you must rate the response on a scale of 1 to 10 by strictly following this format: '[[rating]]', for example: 'Rating: [[5]]'.\",\n", + " },\n", + " },\n", + " ],\n", + " }\n", + ")\n", + "\n", + "dashboard_name = \"byoc-llm-multiple-monitoring\"\n", + "cwClient.put_dashboard(DashboardName=dashboard_name, DashboardBody=dash_data)" + ] + }, + { + "cell_type": "markdown", + "id": "8af7479b", + "metadata": {}, + "source": [ + "Click the link from the following cell output to view the created CloudWatch Dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd247c95", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display, Markdown\n", + "\n", + "display(\n", + " Markdown(\n", + " f\"[CloudWatch Dashboard](https://{aws_region}.console.aws.amazon.com/cloudwatch/home?region={aws_region}#dashboards/dashboard/{dashboard_name})\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c2189335-4d40-44bb-bef1-4bd3597801b2", + "metadata": {}, + "source": [ + "### Clean up resources" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec2391e3-bde2-4a7f-bb5c-7af8d1d1c7ad", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "# Delete monitoring job\n", + "\n", + "name = monitor.monitoring_schedule_name\n", + "monitor.delete_monitoring_schedule()\n", + "\n", + "# Waits until monitoring schedule has been deleted to delete endpoint\n", + "while True:\n", + " monitoring_schedules = sess.list_monitoring_schedules()\n", + " if any(\n", + " schedule[\"MonitoringScheduleName\"] == name\n", + " for schedule in monitoring_schedules[\"MonitoringScheduleSummaries\"]\n", + " ):\n", + " time.sleep(5)\n", + " else:\n", + " print(\"Monitoring schedule deleted\")\n", + " break\n", + "\n", + "sess.delete_endpoint(endpoint_name=predictor.endpoint_name) # delete model endpoint" + ] + }, + { + "cell_type": "markdown", + "id": "1d444fa3", + "metadata": {}, + "source": [ + "# SageMaker Studio Docker Guide\n", + "\n", + "To set up docker in your SageMaker studio environment, follow these steps:\n", + "1. Run the following command in the AWS CLI, inputting your region and SageMaker domain ID:\n", + "```bash\n", + "aws --region \\\n", + " sagemaker update-domain --domain-id \\\n", + " --domain-settings-for-update '{\"DockerSettings\": {\"EnableDockerAccess\": \"ENABLED\"}}'\n", + "```\n", + "2. Open a new notebook instance. Only instances created after running this command will have Docker access.\n", + "3. Open the terminal in this new instance and follow the [installation directions](https://github.com/aws-samples/amazon-sagemaker-local-mode/blob/main/sagemaker_studio_docker_cli_install/README.md)" + ] + }, + { + "cell_type": "markdown", + "id": "ee93fb1a", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "\n", + "![This us-east-1 badge failed to load. 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has won lots of Olympic medals?"} +{"role": "user", "content": "Can you see germs with your eyes?"} +{"role": "user", "content": "Do all sports use a ball?"} +{"role": "user", "content": "What does a yellow light mean?"} +{"role": "user", "content": "Did you know there's a lady with a mysterious smile in a super famous painting? Who painted it?"} +{"role": "user", "content": "Should you try a food more than once to decide if you really don't like it?"} +{"role": "user", "content": "What word means to feel like you need to sleep?"} +{"role": "user", "content": "What makes thunder?"} +{"role": "user", "content": "What tool can you use to measure how tall you are?"} +{"role": "user", "content": "Is pizza a healthy food to eat every single day?"} +{"role": "user", "content": "Do you have a favorite way to exercise?"} +{"role": "user", "content": "What are some kitchen tools kids can use?"} +{"role": "user", "content": "Are there healthy snacks you can keep in your backpack or lunchbox?"} +{"role": "user", "content": "Why do we have different colored skin?"} +{"role": "user", "content": "Do engineers design the cars we drive?"} +{"role": "user", "content": "Which country is famous for men wearing skirts called kilts?"} +{"role": "user", "content": "If you're hungry and there's no food in the house, what are some solutions?"} +{"role": "user", "content": "Have you ever seen someone making clothes by hand?"} +{"role": "user", "content": "If you have six cookies and eat three, how many would be left?"} +{"role": "user", "content": "What are clothes made of?"} +{"role": "user", "content": "How do you know how much something costs at the grocery store?"} +{"role": "user", "content": "Can you think of another word for 'run'?"} +{"role": "user", "content": "Why do we wear seatbelts in cars?"} +{"role": "user", "content": "Can food be healthy AND delicious?"} +{"role": "user", "content": "Is there a place called 9-1-1 that you should call if you need help in an emergency?"} +{"role": "user", "content": "Why do we measure things?"} +{"role": "user", "content": "Setting the table is part of cooking too! Do you like to help with that?"} +{"role": "user", "content": "Why do some things in the grocery store have barcodes on them?"} +{"role": "user", "content": "Are all germs bad?"} +{"role": "user", "content": "Why do we sometimes 'pull a muscle'?"} +{"role": "user", "content": "Where can we find different types of rocks?"} +{"role": "user", "content": "Why do we need to wash our hands?"} +{"role": "user", "content": "What were the pyramids in Egypt built for?"} +{"role": "user", "content": "Where do babies come from?"} +{"role": "user", "content": "What are some kind things you could say to your friend if they're feeling sad?"} +{"role": "user", "content": "What are the main food groups?"} +{"role": "user", "content": "Who is a famous athlete who became a boxer and activist?"} +{"role": "user", "content": "How can you add more vegetables to a pizza you make at home?"} +{"role": "user", "content": "Is it important to warm up before playing hard?"} +{"role": "user", "content": "What kind of big machines do you sometimes see on construction sites? "} +{"role": "user", "content": "What are some foods that have a very long shelf life, meaning they last a long time?"} +{"role": "user", "content": "Should you cough or sneeze into your hand?"} +{"role": "user", "content": "Why do we get tired after exercising?"} +{"role": "user", "content": "What causes a storm?"} +{"role": "user", "content": "How do we taste things?"} +{"role": "user", "content": "Think of a water well with a bucket on a rope. What simple machines are being used to draw water up?"} +{"role": "user", "content": "What rhymes with 'blue'?"} +{"role": "user", "content": "Besides sandwiches, what else can you spread peanut butter on?"} +{"role": "user", "content": "Why do we need money?"} +{"role": "user", "content": "If your friend is good at drawing and you're not, does that mean you never will be?"} +{"role": "user", "content": "Why do sneezes come out so fast?"} +{"role": "user", "content": "Why do doctors sometimes give you a shot (vaccine)?"} +{"role": "user", "content": "Why do we blink?"} +{"role": "user", "content": "Whose job is it to try the healthy foods grown-ups make, even just a bite?"} +{"role": "user", "content": "Is the number four odd or even?"} +{"role": "user", "content": "Where can you donate food if you buy too much, or have cans in your pantry you won't eat?"} +{"role": "user", "content": "What if your friend is happy about something, how can you share their excitement?"} +{"role": "user", "content": "Why do sunflowers follow the sun?"} +{"role": "user", "content": "Did people always have supermarkets to get their food?"} +{"role": "user", "content": "What's one food that comes from a chicken?"} +{"role": "user", "content": "Why do we need to go to the doctor for check-ups?"} +{"role": "user", "content": "What's a better snack choice, an apple or cookies?"} +{"role": "user", "content": "Why do some animals migrate?"} +{"role": "user", "content": "What kind of story usually starts with 'Once upon a time'?"} +{"role": "user", "content": "What happened during World War II?"} +{"role": "user", "content": "Why do some people snore?"} +{"role": "user", "content": "If you drop food on the floor, is it safe to eat if you pick it up really fast?"} +{"role": "user", "content": "Who were the ancient Greeks famous for?"} +{"role": "user", "content": "What does a crossing guard do?"} +{"role": "user", "content": "Why do we need to eat foods from all the food groups?"} +{"role": "user", "content": "Why do bubbles float in the air?"} +{"role": "user", "content": "What is the Milky Way?"} +{"role": "user", "content": "Do helpers sometimes wear special uniforms or clothes so we know what their job is?"} +{"role": "user", "content": "What do doctors and nurses wear sometimes to protect themselves from germs?"} +{"role": "user", "content": "Who is a famous athlete who became a boxer and activist?"} +{"role": "user", "content": "What solid shape is round like a ball?"} +{"role": "user", "content": "Can you name a famous tennis player known for her powerful serve?"} +{"role": "user", "content": "Can you think of a long, flowing dress worn by women in India?"} +{"role": "user", "content": "What does hand sanitizer do?"} +{"role": "user", "content": "Why do we put bandages on cuts?"} +{"role": "user", "content": "What is blood made of?"} +{"role": "user", "content": "Why does oil splatter when you add water?"} +{"role": "user", "content": "What's death?"} +{"role": "user", "content": "What word describes a color that has no color at all, like snow?"} +{"role": "user", "content": "Is it okay to ask politely to be excused if you really don't like the food that's served?"} +{"role": "user", "content": "Are aliens real?"} +{"role": "user", "content": "What kind of animal gives us soft, fluffy wool for sweaters?"} +{"role": "user", "content": "If something is 'delicious', how does it taste?"} +{"role": "user", "content": "When eating at a restaurant, is it important to use good manners?"} +{"role": "user", "content": "Why do old people sometimes get wrinkles?"} +{"role": "user", "content": "Why do we have to wash our hands?"} +{"role": "user", "content": "What does an illustrator do?"} +{"role": "user", "content": "What does Dora the Explorer always carry?"} +{"role": "user", "content": "Why do you think learning about clothes from other places is interesting?"} +{"role": "user", "content": "Can you solve problems without using any words, just by doing or trying things?"} +{"role": "user", "content": "What is a healthy protein food that swims in the ocean?"} +{"role": "user", "content": "What are some different kinds of hats?"} +{"role": "user", "content": "Why is space dark?"} +{"role": "user", "content": "What do we use to carry our groceries around the store?"} +{"role": "user", "content": "Why is it important to be kind?"} +{"role": "user", "content": "Can you think of a small problem you might have?"} +{"role": "user", "content": "Someone showed me their private parts. Is that okay?"} +{"role": "user", "content": "How does recycling help the environment?"} +{"role": "user", "content": "What are fossils?"} +{"role": "user", "content": "Do people in different parts of the world speak the same language?"} +{"role": "user", "content": "Is Santa Claus real?"} +{"role": "user", "content": "How does our heart know to beat faster during exercise?"} +{"role": "user", "content": "Is there a difference between rushing to try and solve a problem, and taking some time to think about it first?"} +{"role": "user", "content": "Why are our legs stronger than our arms?"} +{"role": "user", "content": "Why do we sometimes get hiccups?"} +{"role": "user", "content": "If there's leftover birthday cake, when is it okay to have some?"} +{"role": "user", "content": "What are black holes?"} +{"role": "user", "content": "What animal gives us soft, warm wool?"} +{"role": "user", "content": "Where can you find lots of words to learn?"} +{"role": "user", "content": "What's a carpenter?"} +{"role": "user", "content": "When you bake cookies, do you measure the ingredients?"} +{"role": "user", "content": "After clothes are made, how do they get to a store where you can buy them?"} +{"role": "user", "content": "If a fruit or vegetable has a small bruise or funny shape, is it still okay to eat?"} +{"role": "user", "content": "Why do camels have humps?"} +{"role": "user", "content": "What happens if athletes don't drink enough water?"} +{"role": "user", "content": "What is reaction time?"} +{"role": "user", "content": "Why do we have two ears?"} +{"role": "user", "content": "Have you ever grown herbs that you can use to add flavor to your cooking?"} +{"role": "user", "content": "What do cousins call each other's parents?"} +{"role": "user", "content": "What is a magnet?"} +{"role": "user", "content": "Can you name other ways we communicate besides talking?"} +{"role": "user", "content": "Sculptures are like 3D drawings you can walk around! What are they made of?"} +{"role": "user", "content": "What does a red triangle with a downward arrow mean?"} +{"role": "user", "content": "Where can we find amazing artwork?"} +{"role": "user", "content": "Why do we get dizzy if we spin around?"} +{"role": "user", "content": "Which planet is the hottest?"} +{"role": "user", "content": "Can you decorate a plain rice cake to look like a funny face?"} +{"role": "user", "content": "What does the word 'fast' mean?"} +{"role": "user", "content": "Which country is known for pyramids and pharaohs?"} +{"role": "user", "content": "What does a sign with the words 'One Way' and an arrow mean? "} +{"role": "user", "content": "Why is it important to wash your hands before cooking?"} +{"role": "user", "content": "Do doctors have to go to school for a long time?"} +{"role": "user", "content": "Are grocery store workers helpers?"} +{"role": "user", "content": "Who works at the grocery store to help customers?"} +{"role": "user", "content": "Why do we wear different clothes for different weather?"} +{"role": "user", "content": "Why is sleep so important?"} +{"role": "user", "content": "How long does it take to get to the moon?"} +{"role": "user", "content": "A slide at the park is a simple machine, what is it called?"} +{"role": "user", "content": "Does buying 'organic' food matter?"} +{"role": "user", "content": "What does exercise do for our bodies?"} +{"role": "user", "content": "If you spill something, is just cleaning it up part of the learning process?"} +{"role": "user", "content": "Is being kind to others a way of being a helper?"} +{"role": "user", "content": "If you have a recipe that needs 2 cups of milk, but you only have a big 1-pint measuring cup, can you still measure the milk?"} +{"role": "user", "content": "What is the tallest tree in the world?"} +{"role": "user", "content": "Why does it rain sometimes and snow other times?"} +{"role": "user", "content": "How does regular exercise make us healthier overall?"} +{"role": "user", "content": "Who was a famous civil rights leader in India that fought for independence?"} +{"role": "user", "content": "What simple machine has a wheel and a rope to make lifting things easier? "} +{"role": "user", "content": "Does the size of a wheel on a wheel and axle matter?"} +{"role": "user", "content": "Why do we have toes?"} +{"role": "user", "content": "Why do people wear uniforms?"} +{"role": "user", "content": "Can you make your artwork bumpy, smooth, or fuzzy? What's that called?"} +{"role": "user", "content": "What is the name of the Paw Patrol's leader?"} +{"role": "user", "content": "What if you painted with a sponge, or even leaves?"} +{"role": "user", "content": "What are some good ways to solve a disagreement with a friend?"} +{"role": "user", "content": "I saw something scary on the internet about [inappropriate theme]. What does it mean?"} +{"role": "user", "content": "What's a better choice for most meals, water or a sugary drink like soda?"} +{"role": "user", "content": "Besides meat, what are other protein foods that help build strong muscles?"} +{"role": "user", "content": "Do all cars look the same? What are some different sizes and shapes of cars?"} +{"role": "user", "content": "What does a plumber do?"} +{"role": "user", "content": "How do people get around in places where there are no roads?"} +{"role": "user", "content": "How does a magnifying glass make things look bigger?"} +{"role": "user", "content": "Why do we have fingerprints?"} +{"role": "user", "content": "What could you add to a salad to make it more filling and have protein?"} +{"role": "user", "content": "What if you want to make a treehouse, but have no idea where to start? What's the first problem-solving step?"} +{"role": "user", "content": "If a recipe calls for 2 eggs, and you only have 1, is that a problem to solve?"} +{"role": "user", "content": "Do scientists and inventors make a lot of mistakes along the way?"} +{"role": "user", "content": "What do you call your brother's daughter?"} +{"role": "user", "content": "Are there ways to make cooking a team effort with a sibling or your friends?"} +{"role": "user", "content": "Why is it important to be kind to yourself when you make a mistake?"} +{"role": "user", "content": "Why does the Earth have seasons?"} +{"role": "user", "content": "Who is a famous soccer player known for his amazing goals and skills?"} +{"role": "user", "content": "What food comes from a chicken?"} +{"role": "user", "content": "Where do most of the foods we eat come from before we buy them?"} +{"role": "user", "content": "Whose job is it to buy healthy food?"} +{"role": "user", "content": "What is a shape with three sides and three corners called?"} +{"role": "user", "content": "Could we breathe on other planets?"} +{"role": "user", "content": "How do broken bones heal?"} +{"role": "user", "content": "If you get a cut, why is it important to clean it with soap and water?"} +{"role": "user", "content": "Why do we need to save some of our money?"} +{"role": "user", "content": "Which Disney princess has long, magical hair?"} +{"role": "user", "content": "What's one exercise you can do to make your legs stronger?"} +{"role": "user", "content": "Why do we need to warm up before exercising?"} +{"role": "user", "content": "Can you show the number five twice - once using one hand, and the other time using both hands?"} +{"role": "user", "content": "Why is our skin stretchy?"} +{"role": "user", "content": "How do gymnasts flip and spin so easily?"} +{"role": "user", "content": "How do plants drink water?"} +{"role": "user", "content": "What's something simple but tasty you can bake?"} +{"role": "user", "content": "Does getting a vaccine hurt?"} +{"role": "user", "content": "Why do we sometimes get a shock from the fridge or oven?"} +{"role": "user", "content": "What kind of transportation uses wings to fly?"} +{"role": "user", "content": "What part of a car helps it stop?"} +{"role": "user", "content": "Why do our fingers get wrinkly when we're in the water for a long time?"} +{"role": "user", "content": "If you want to build the tallest block tower possible, what are some important things to think about?"} +{"role": "user", "content": "When building with blocks or LEGOs, and your tower keeps falling over, is that problem-solving?"} +{"role": "user", "content": "Why is it important to talk about our feelings?"} +{"role": "user", "content": "How do we get taller?"} +{"role": "user", "content": "What is the International Space Station?"} +{"role": "user", "content": "Why do traffic lights change color?"} +{"role": "user", "content": "Why do birds fly south in the winter?"} +{"role": "user", "content": "Can you name 3 sports you can play with a ball?"} +{"role": "user", "content": "Is dessert a part of every meal?"} +{"role": "user", "content": "What does an author do?"} +{"role": "user", "content": "If you're looking for peanut butter, do you find it in the same aisle as bread, or somewhere else?"} +{"role": "user", "content": "Is it okay if your first attempt at a new recipe doesn't turn out perfect?"} +{"role": "user", "content": "What does empathy mean?"} +{"role": "user", "content": "Why do some fruits and vegetables have stickers on them?"} +{"role": "user", "content": "Why do we need to brush our teeth?"} +{"role": "user", "content": "Can eating healthy food also be delicious?"} +{"role": "user", "content": "If your friend is sick at school, is it better to give them a high five or a fist bump?"} +{"role": "user", "content": "Why do some sports balls have dimples?"} +{"role": "user", "content": "What is a librarian? "} +{"role": "user", "content": "How does a seesaw work?"} +{"role": "user", "content": "Is it okay for siblings to sometimes disagree or argue?"} +{"role": "user", "content": "Is there a healthy way to make popcorn even more delicious?"} +{"role": "user", "content": "Who is Mickey Mouse's best friend?"} +{"role": "user", "content": "Where does our voice come from?"} +{"role": "user", "content": "Why does a ball curve when you throw it with a spin?"} +{"role": "user", "content": "Which ocean is the largest?"} +{"role": "user", "content": "Name a food that's spicy."} +{"role": "user", "content": "What food group gives us energy to run and play?"} +{"role": "user", "content": "Do you look at cookbooks or websites for new recipes to try?"} +{"role": "user", "content": "Which cartoon character says 'D'oh!'?"} +{"role": "user", "content": "Can you find shapes in your house? "} +{"role": "user", "content": "Why does my body look different than my friend's?"} +{"role": "user", "content": "Can you show empathy to animals?"} +{"role": "user", "content": "Do all countries have the same kind of government?"} +{"role": "user", "content": "Can you name some famous explorers?"} +{"role": "user", "content": "Can you sometimes find treats like cookies or candy near the checkout line?"} +{"role": "user", "content": "Why do we shiver when we're cold?"} +{"role": "user", "content": "How many ounces are in one cup?"} +{"role": "user", "content": "How does a phone let us talk to people far away?"} +{"role": "user", "content": "Why is breakfast important?"} +{"role": "user", "content": "What are some units we use to measure length?"} +{"role": "user", "content": "What's the opposite of 'hot'?"} +{"role": "user", "content": "What's one section of the grocery store that might have lots of colorful foods? "} +{"role": "user", "content": "What's a crosswalk?"} +{"role": "user", "content": "Have you ever gotten lost? What are some problem-solving things you could do?"} +{"role": "user", "content": "There are all sorts of shapes \u2013 circles, squares, triangles... can you find some around you?"} +{"role": "user", "content": "What are some different sports people play?"} +{"role": "user", "content": "What simple machine do you think stairs are made from?"} +{"role": "user", "content": "Do all families look the same?"} +{"role": "user", "content": "Imagine there are 10 birds on a tree and 3 fly away. How many birds are left on the tree?"} +{"role": "user", "content": "How do airplanes fly?"} +{"role": "user", "content": "Is it a good idea to ask for help when you're stuck on a problem?"} +{"role": "user", "content": "If your friend falls down and gets hurt, how might they be feeling?"} +{"role": "user", "content": "Can we predict the weather?"} +{"role": "user", "content": "Do you like to help cook or bake in the kitchen?"} +{"role": "user", "content": "What safety rules are important to remember when riding a bike?"} +{"role": "user", "content": "How do stores decide how much things cost?"} +{"role": "user", "content": "Can you 'catch' feelings from someone else?"} +{"role": "user", "content": "What do the signs + and \u2013 mean?"} +{"role": "user", "content": "What do you wear on a rainy day to keep your feet dry?"} +{"role": "user", "content": "Is it important to clean up spills right away?"} +{"role": "user", "content": "Some cultures wear beautiful robes. Can you think of a country where people wear kimonos?"} +{"role": "user", "content": "Can you name a fast swimmer who won lots of Olympic gold medals?"} +{"role": "user", "content": "Can you name a famous tennis player known for her powerful serve?"} +{"role": "user", "content": "Why does a spinning top stay upright?"} +{"role": "user", "content": "Is it okay to feel frustrated when you have a problem to solve?"} +{"role": "user", "content": "What is a machine that uses a big wheel and rope to lift heavy things?"} +{"role": "user", "content": "Why do flowers smell nice?"} +{"role": "user", "content": "Is it okay to ask for help when you don't understand a word?"} +{"role": "user", "content": "What's something besides food that you can buy in bulk to reduce waste?"} +{"role": "user", "content": "How does the internet work?"} +{"role": "user", "content": "How do owls see so well at night?"} +{"role": "user", "content": "What do we call a drawing of a person?"} +{"role": "user", "content": "Can words have more than one meaning?"} +{"role": "user", "content": "How are rocks made?"} +{"role": "user", "content": "Why is buying fruits and veggies that are 'in season' a good idea?"} +{"role": "user", "content": "What does a red traffic light mean?"} +{"role": "user", "content": "Imagine a road stretching far away...things in the distance look tiny, right? What's that called in art?"} +{"role": "user", "content": "How does a blender work?"} +{"role": "user", "content": "If you have 3 crayons and your friend gives you 2 more, how many do you have in total?"} +{"role": "user", "content": "What is a word for a really big and impressive building?"} +{"role": "user", "content": "How does a car work?"} +{"role": "user", "content": "What do your parents call their parents?"} +{"role": "user", "content": "Why do we sometimes get muscle cramps?"} +{"role": "user", "content": "If you see your dog or cat stretching, is that a kind of exercise for them too?"} +{"role": "user", "content": "What happens if I eat too many sweets?"} +{"role": "user", "content": "Where do babies come from?"} +{"role": "user", "content": "Do poems always rhyme?"} +{"role": "user", "content": "Why do I have to apologize when I do something wrong?"} +{"role": "user", "content": "Can you write your own name?"} +{"role": "user", "content": "Is exercise more fun by yourself, or with friends and family?"} +{"role": "user", "content": "Why is it important to wash our hands before preparing food?"} +{"role": "user", "content": "Is it okay to share food or drinks with a friend who is sick?"} +{"role": "user", "content": "Why do we get scared?"} +{"role": "user", "content": "Can you cut out pictures and glue them together to make a new silly picture?"} +{"role": "user", "content": "If you help grow a vegetable, are you more likely to want to taste it?"} +{"role": "user", "content": "Who was Marie Curie?"} +{"role": "user", "content": "What are some different ways we can travel from one place to another?"} +{"role": "user", "content": "Where is a fun place to play tag?"} +{"role": "user", "content": "Can you hop on one foot? How about the other foot?"} +{"role": "user", "content": "What makes someone a good friend?"} +{"role": "user", "content": "How can I help someone who is being bullied?"} +{"role": "user", "content": "Why do we burp?"} +{"role": "user", "content": "How does a hug make someone feel?"} +{"role": "user", "content": "Should you touch your eyes, nose, or mouth if your hands aren't clean?"} +{"role": "user", "content": "Are there other planets like Earth?"} +{"role": "user", "content": "Would a peanut butter and jelly sandwich be better on white bread or whole grain bread?"} +{"role": "user", "content": "Why do swimmers wear tight swimsuits?"} +{"role": "user", "content": "Are simple machines only found in old-fashioned things?"} +{"role": "user", "content": "What do you call your aunt or uncle's children?"} +{"role": "user", "content": "If there's a food you BEG your parents to buy, but they say 'no', is it okay to be a little disappointed?"} +{"role": "user", "content": "How are the pieces of a shirt put together?"} +{"role": "user", "content": "Is the number seven odd or even?"} +{"role": "user", "content": "Why do we need to wear sunscreen?"} +{"role": "user", "content": "Does flossing help get rid of germs hiding in your mouth?"} +{"role": "user", "content": "What does our stomach do?"} +{"role": "user", "content": "How do volcanoes work?"} +{"role": "user", "content": "If a recipe calls for 1 cup, and you only need half as much, how much would you use?"} +{"role": "user", "content": "How do cuts heal?"} +{"role": "user", "content": "Which cartoon dog has a big red nose?"} +{"role": "user", "content": "Can you name 3 different types of helpers?"} +{"role": "user", "content": "How do high jumpers get so high?"} +{"role": "user", "content": "Why is buying food from a local farmer's market a responsible choice?"} +{"role": "user", "content": "Why do babies cry?"} +{"role": "user", "content": "Why do we need to take a bath or shower?"} +{"role": "user", "content": "What food group gives us strong bones and teeth?"} +{"role": "user", "content": "What is a good 'first recipe' to learn how to cook all by yourself?"} +{"role": "user", "content": "What does it mean to count?"} +{"role": "user", "content": "What's another way to say 'throw'?"} +{"role": "user", "content": "Why should we try to have a positive attitude?"} +{"role": "user", "content": "What does a red and white sideways triangle mean?"} +{"role": "user", "content": "Does helping prepare food in the kitchen sometimes make you want to try it?"} +{"role": "user", "content": "Is ice cream a good way to get your dairy in?"} +{"role": "user", "content": "What is the past tense of the verb 'eat'?"} +{"role": "user", "content": "What are allergies?"} +{"role": "user", "content": "Besides yummy food, what's the best part about cooking?"} +{"role": "user", "content": "What happens when you mix a primary color and a secondary color together?"} +{"role": "user", "content": "Where do germs like to hide?"} +{"role": "user", "content": "Why do some people need glasses?"} +{"role": "user", "content": "Can you build a simple machine using things from around your house?"} +{"role": "user", "content": "If you want something really badly, how might you feel?"} +{"role": "user", "content": "If something is 'sticky', what happens when you touch it?"} +{"role": "user", "content": "Why are some rocks smooth and some rough?"} +{"role": "user", "content": "What could you use to measure how heavy you are?"} +{"role": "user", "content": "How many inches are in one foot?"} +{"role": "user", "content": "There are lots of choices of cereal! How do you decide which one to try?"} +{"role": "user", "content": "Does cheese come from plants or animals?"} +{"role": "user", "content": "Is it okay to ask for a sample or taste of something at the grocery store before buying it?"} +{"role": "user", "content": "If a table is 3 feet long, how many inches long is it?"} +{"role": "user", "content": "Do you know a solid shape that looks like a party hat?"} +{"role": "user", "content": "What is bread made from?"} +{"role": "user", "content": "Should you wash your hands with hot or cold water?"} +{"role": "user", "content": "What are the first ten numbers you learn to count?"} +{"role": "user", "content": "Is a pencil longer or shorter than your foot?"} +{"role": "user", "content": "Does practicing a sport over and over help you get better at it?"} +{"role": "user", "content": "Is your mail carrier a helper in your community?"} +{"role": "user", "content": "What do we call the shape of a stop sign?"} +{"role": "user", "content": "Why do we pay taxes?"} +{"role": "user", "content": "Can you draw a picture of yourself?"} +{"role": "user", "content": "When it's cold outside, what does a thermometer measure?"} +{"role": "user", "content": "What's another word for 'happy'?"} +{"role": "user", "content": "Do builders have to work as a team?"} +{"role": "user", "content": "Are quesadillas easy to make?"} +{"role": "user", "content": "Where do apples come from?"} +{"role": "user", "content": "Can you see a clock in your house? What parts of a clock help us tell time?"} +{"role": "user", "content": "Can you use your fingers to paint?"} +{"role": "user", "content": "Artists mix colors on a special flat board. What's it called?"} +{"role": "user", "content": "If you want to build something, is it important to have a plan?"} +{"role": "user", "content": "Why do we need to sleep?"} +{"role": "user", "content": "Why does food cook faster in a pressure cooker?"} +{"role": "user", "content": "What's the opposite of 'start'?"} +{"role": "user", "content": "Do you have to be good at a sport to have fun playing?"} +{"role": "user", "content": "Where can you find a ramp besides a slide at the playground?"} +{"role": "user", "content": "Can you name some nouns in your room?"} +{"role": "user", "content": "Name a food that's crunchy."} +{"role": "user", "content": "Why do we say please and thank you?"} +{"role": "user", "content": "If a word starts with a capital letter, what does that usually mean?"} +{"role": "user", "content": "What happens to the food we eat?"} +{"role": "user", "content": "Do you think playing video games can help you become a better problem-solver?"} +{"role": "user", "content": "Can you find levers anywhere in your house?"} +{"role": "user", "content": "Why do frogs have long, sticky tongues?"} +{"role": "user", "content": "What's a good way to keep your immune system strong? "} +{"role": "user", "content": "Can playing video games count as exercise?"} +{"role": "user", "content": "Where can you find new, healthy recipes to try?"} +{"role": "user", "content": "What do we call a big competition where athletes try to win medals?"} +{"role": "user", "content": "Why does our hair grow long?"} +{"role": "user", "content": "What is a vote, and why is it important?"} +{"role": "user", "content": "Why do athletes need a good diet?"} +{"role": "user", "content": "Why do grocery stores keep milk and cheese refrigerated?"} +{"role": "user", "content": "What simple salad dressings can you make by whisking things together?"} +{"role": "user", "content": "Why do some people have freckles?"} +{"role": "user", "content": "What are some ways to show your family you love them?"} +{"role": "user", "content": "Why do some animals sleep during the winter?"} +{"role": "user", "content": "What is the capital of France?"} +{"role": "user", "content": "Where does our garbage go?"} +{"role": "user", "content": "Why do people wear different traditional clothing?"} +{"role": "user", "content": "Why do we sometimes get bruises?"} +{"role": "user", "content": "What are some adjectives to describe a tree?"} +{"role": "user", "content": "Can rocks change?"} +{"role": "user", "content": "Can animals talk to each other?"} +{"role": "user", "content": "Are plastic water bottles a responsible choice?"} +{"role": "user", "content": "What is whole grain bread made from?"} +{"role": "user", "content": "Which Disney princess has a pet tiger named Rajah?"} +{"role": "user", "content": "What do you need to wear on your feet to go play in the snow?"} +{"role": "user", "content": "If it's raining outside, how could we measure how much rain has fallen?"} +{"role": "user", "content": "Name something we can grow in a garden."} +{"role": "user", "content": "Why do astronauts wear spacesuits?"} +{"role": "user", "content": "Is it important to listen to your body when you're feeling full?"} +{"role": "user", "content": "How many continents are there?"} +{"role": "user", "content": "What is a problem?"} +{"role": "user", "content": "Photos can be beautiful art too! What would you like to take a picture of?"} +{"role": "user", "content": "Why does being strong help you climb up on the playground?"} +{"role": "user", "content": "Is it okay to hit someone back if they hit me?"} +{"role": "user", "content": "Why is ice slippery?"} +{"role": "user", "content": "What color do you get when you mix blue and yellow?"} +{"role": "user", "content": "Is it okay to make a mess sometimes when you're cooking?"} +{"role": "user", "content": "Do penguins live in the North Pole or South Pole?"} +{"role": "user", "content": "Why is it good to have a variety of colors on your plate?"} +{"role": "user", "content": "What are some words that rhyme with 'cat'?"} +{"role": "user", "content": "Can sharing toys spread germs?"} +{"role": "user", "content": "Do your clothes look the same as clothes kids in other countries wear?"} +{"role": "user", "content": "Have you seen a painting with a magical night sky filled with swirls? What is it called?"} +{"role": "user", "content": "When you tie your shoes, what kind of problem are you solving?"} +{"role": "user", "content": "Should you always try new foods, even once?"} +{"role": "user", "content": "Which is longer, a sentence or a paragraph?"} +{"role": "user", "content": "What's more fun: following a recipe exactly, or experimenting a little with flavors you like?"} +{"role": "user", "content": "How many ounces are in one pound?"} +{"role": "user", "content": "If you get sick at night, can you still go to the doctor?"} +{"role": "user", "content": "What is an architect?"} +{"role": "user", "content": "What does a 'helper' do?"} +{"role": "user", "content": "What were some inventions from ancient China?"} +{"role": "user", "content": "How do plants help us breathe?"} +{"role": "user", "content": "Sketching is like a quick drawing to capture an idea. What happens in a detailed drawing?"} +{"role": "user", "content": "What solid shape looks like a box?"} +{"role": "user", "content": "Where do you keep foods that need to stay cold?"} +{"role": "user", "content": "Can you name some healthy snacks?"} +{"role": "user", "content": "What do we use to talk to each other?"} +{"role": "user", "content": "Why was the Titanic a famous ship?"} +{"role": "user", "content": "What is a synonym? "} +{"role": "user", "content": "What clothes do you put on first when you get dressed?"} +{"role": "user", "content": "Where does rain come from?"} +{"role": "user", "content": "Why can we stand on the ground without sinking?"} +{"role": "user", "content": "What should be the biggest part of a healthy meal?"} +{"role": "user", "content": "What do teachers do?"} +{"role": "user", "content": "Why is drinking water important?"} +{"role": "user", "content": "Can you use your favorite book to practice your reading?"} +{"role": "user", "content": "Is being patient important for both engineers and doctors?"} +{"role": "user", "content": "Have you ever seen a train? What kind of tracks does it travel on?"} +{"role": "user", "content": "What is a job, and why do people work?"} +{"role": "user", "content": "Would you rather make a sweet treat or a savory snack to cook?"} +{"role": "user", "content": "Is it harder to learn a sport when you're younger or older?"} +{"role": "user", "content": "What are shapes?"} +{"role": "user", "content": "Can solving a problem sometimes involve teamwork?"} +{"role": "user", "content": "Can you name 3 red fruits or vegetables?"} +{"role": "user", "content": "What kind of vehicles do you see on the road most often?"} +{"role": "user", "content": "If you break a bone, what kind of doctor might help fix it?"} +{"role": "user", "content": "Why do we get stronger when we exercise?"} +{"role": "user", "content": "When you're swinging on a swingset, what simple machine are you using?"} +{"role": "user", "content": "Which word means happy and excited?"} +{"role": "user", "content": "Can gardening be a form of exercise?"} +{"role": "user", "content": "Why do we see rainbows after it rains?"} +{"role": "user", "content": "What makes ice skates glide on the ice so well?"} +{"role": "user", "content": "Are there foods from other countries you'd like to try?"} +{"role": "user", "content": "What are some important kitchen safety rules?"} +{"role": "user", "content": "What does an electrician do?"} +{"role": "user", "content": "When something is 'rough', how does it feel?"} +{"role": "user", "content": "Can people really kill each other? Like in movies?"} +{"role": "user", "content": "Why do we sometimes get scars?"} +{"role": "user", "content": "What's a different word for 'small'?"} +{"role": "user", "content": "When you're jumping on a trampoline, what kind of exercise are you doing?"} +{"role": "user", "content": "Can food be healthy AND fun?"} +{"role": "user", "content": "Knives and axes have a type of simple machine that helps split things. What is it called?"} +{"role": "user", "content": "What does 'swear word' mean?"} +{"role": "user", "content": "Why do we need exercise?"} +{"role": "user", "content": "What are the names of the Teenage Mutant Ninja Turtles?"} +{"role": "user", "content": "What if you're playing a game and keep losing? What are some problem-solving things you can try?"} +{"role": "user", "content": "What does a blue sign with a white 'P' mean? "} +{"role": "user", "content": "Is a plate full of only french fries a balanced meal?"} +{"role": "user", "content": "Do famous athletes always win?"} +{"role": "user", "content": "Why can't we hear sounds in space?"} +{"role": "user", "content": "Can Bugs Bunny fly?"} +{"role": "user", "content": "What does a sign with a curved arrow and a line through it mean? "} +{"role": "user", "content": "Do you need to wash your hands after playing with stuffed animals?"} +{"role": "user", "content": "What word means to move back and forth in a playful way?"} +{"role": "user", "content": "Why does dough rise?"} +{"role": "user", "content": "Did you know some types of clothes were originally made for practical reasons, but became traditional?"} +{"role": "user", "content": "What makes some people more flexible than others?"} +{"role": "user", "content": "Can we find rocks from space on Earth?"} +{"role": "user", "content": "Should you always carry hand sanitizer with you?"} +{"role": "user", "content": "Why do leaves change color in the fall?"} +{"role": "user", "content": "Which famous baseball player was known for hitting lots of home runs?"} +{"role": "user", "content": "Is the word 'skip' a noun, verb, or adjective?"} +{"role": "user", "content": "Can engineers help design things that protect the environment?"} +{"role": "user", "content": "Who was Albert Einstein?"} +{"role": "user", "content": "Is a pound heavier or lighter than an ounce?"} +{"role": "user", "content": "Can germs make us cough or sneeze?"} +{"role": "user", "content": "Is being brave a part of some helper jobs?"} +{"role": "user", "content": "Why is it a good idea to celebrate when you solve a difficult problem?"} +{"role": "user", "content": "Why do athletes practice so much?"} +{"role": "user", "content": "Can you exercise along with your favorite cartoon characters?"} +{"role": "user", "content": "What are some ways to reduce food waste at home?"} +{"role": "user", "content": "What makes a silly sentence? "} +{"role": "user", "content": "Do carrots grow on trees, or under the ground?"} +{"role": "user", "content": "What rhymes with 'dog'?"} +{"role": "user", "content": "Have you ever worn clothes from a different culture?"} +{"role": "user", "content": "Someone with a growth mindset sees a difficult problem and thinks...?"} +{"role": "user", "content": "How many sides does a triangle have?"} +{"role": "user", "content": "How does a refrigerator keep things cold?"} +{"role": "user", "content": "Instead of getting upset when you make a mistake, what can you try to do?"} +{"role": "user", "content": "What is the opposite of 'tiny'?"} +{"role": "user", "content": "What's better for getting rid of germs on dishes: washing by hand in the sink or using the dishwasher?"} +{"role": "user", "content": "Why do we need street signs?"} +{"role": "user", "content": "What are germs?"} +{"role": "user", "content": "What does 'responsible shopping' mean?"} +{"role": "user", "content": "What does a white rectangle with 'Speed Limit 25' mean?"} +{"role": "user", "content": "What is a question mark for?"} +{"role": "user", "content": "What should you always do before crossing the street?"} +{"role": "user", "content": "Have you ever seen art made from unusual things?"} +{"role": "user", "content": "Can you compost food scraps instead of throwing them in the trash?"} +{"role": "user", "content": "Why does ice cream melt?"} +{"role": "user", "content": "Does food sometimes look or smell different than it tastes?"} +{"role": "user", "content": "Can you name 3 fruits?"} +{"role": "user", "content": "What if you start with five crayons, and someone gives you two more? How many would you have?"} +{"role": "user", "content": "Why would someone use a wedge to hold a door open?"} +{"role": "user", "content": "Can engineers design things that help people with disabilities?"} +{"role": "user", "content": "Why do stars twinkle?"} +{"role": "user", "content": "Why do we have to go to school?"} +{"role": "user", "content": "Why is sleep important for athletes?"} +{"role": "user", "content": "Why do we need bones?"} +{"role": "user", "content": "How many inches are in one foot?"} +{"role": "user", "content": "Instead of a glass of milk, what's another way to get your calcium?"} +{"role": "user", "content": "Have you ever grown any of your own food, even in a small pot?"} +{"role": "user", "content": "What is a 'growth mindset'?"} +{"role": "user", "content": "How does a whisk make whipped cream?"} +{"role": "user", "content": "What is the sun?"} +{"role": "user", "content": "Why is it important to put groceries away when you get home, especially things that need to stay cold?"} +{"role": "user", "content": "Is it okay to taste a little bit of your food as you're cooking it?"} +{"role": "user", "content": "When you run really fast, what does your heart do?"} +{"role": "user", "content": "What parts of your hands should you scrub when washing?"} +{"role": "user", "content": "Are there ways to save money at the grocery store?"} +{"role": "user", "content": "Is a ball a flat shape or a solid shape?"} +{"role": "user", "content": "What do you call a word that means the opposite of another word?"} +{"role": "user", "content": "Why do we breathe heavier during exercise?"} +{"role": "user", "content": "Why can't I eat candy all the time?"} +{"role": "user", "content": "Where can you find the Amazon rainforest?"} +{"role": "user", "content": "What is lightning?"} +{"role": "user", "content": "Who is a famous soccer player known for his amazing goals and skills?"} +{"role": "user", "content": "Is pizza a healthy food to eat every day?"} +{"role": "user", "content": "Do you need to wash fruits and vegetables with skins before eating them?"} +{"role": "user", "content": "Are monsters under my bed?"} +{"role": "user", "content": "Can you do 5 jumping jacks?"} +{"role": "user", "content": "Does going for a walk count as exercise?"} +{"role": "user", "content": "If you have 8 stickers and you give 5 away, how many stickers would you have left?"} +{"role": "user", "content": "What does a red rectangle with 'Wrong Way' written on it mean? "} +{"role": "user", "content": "Why do we get vaccines?"} +{"role": "user", "content": "What do you do if a recipe says 'add a tablespoon' of something?"} +{"role": "user", "content": "When you make a mistake, does it mean you're not smart?"} +{"role": "user", "content": "Is the sun a planet?"} +{"role": "user", "content": "Does eating lots of colorful fruits and veggies help your body fight off getting sick?"} +{"role": "user", "content": "When you're doing a jigsaw puzzle, what's a good problem-solving strategy?"} +{"role": "user", "content": "Why is it important to wear a hard hat on a construction site?"} +{"role": "user", "content": "Is getting dressed in the morning a form of problem-solving?"} +{"role": "user", "content": "Are reusable bags better for the environment than plastic bags from the grocery store?"} +{"role": "user", "content": "What was life like in ancient Rome?"} +{"role": "user", "content": "What is one of the BEST ways to fight off germs?"} +{"role": "user", "content": "What kind of vehicles can travel on water?"} +{"role": "user", "content": "What color is Garfield the cat?"} +{"role": "user", "content": "What do we use to measure how much liquid is in a cup?"} +{"role": "user", "content": "If you spill something while cooking, what should you do?"} +{"role": "user", "content": "Are food allergies the same as just not liking a food?"} +{"role": "user", "content": "If reading is hard for you, does a growth mindset mean believing you CAN get better at it with practice?"} +{"role": "user", "content": "Is buying the biggest container of something ALWAYS the most responsible choice?"} +{"role": "user", "content": "I have a face, hands, and numbers, but I can't tell you how you look. What am I?"} +{"role": "user", "content": "Do vegetables from the store need to be washed?"} +{"role": "user", "content": "Can you think of a word that rhymes with 'cat'?"} +{"role": "user", "content": "Why is the wind sometimes strong and sometimes gentle?"} +{"role": "user", "content": "If you see someone who looks lost or needs help, what should you do?"} +{"role": "user", "content": "What foods change when you heat them up?"} +{"role": "user", "content": "Can you name a road sign that is red and shaped like an octagon (eight sides)?"} +{"role": "user", "content": "Why do we dream?"} +{"role": "user", "content": "How do we turn sheep's wool into yarn for knitting a sweater?"} +{"role": "user", "content": "Which country is famous for maple syrup?"} +{"role": "user", "content": "Why is it important to be on time?"} +{"role": "user", "content": "What's a yummy topping to make plain oatmeal more exciting?"} +{"role": "user", "content": "What food do we get from cows?"} +{"role": "user", "content": "If you try something to solve a problem and it doesn't work, what should you do?"} +{"role": "user", "content": "Have you ever accidentally used salt instead of sugar in a recipe? How did it taste?"} +{"role": "user", "content": "What is a sentence?"} +{"role": "user", "content": "What do doctors and nurses do?"} +{"role": "user", "content": "Can you name a simple machine that helps you lift heavy things?"} +{"role": "user", "content": "What sport uses a ball and a net, where you hit the ball over with your hands?"} +{"role": "user", "content": "What kind of animal is Scooby-Doo?"} +{"role": "user", "content": "Why might fruits and vegetables sometimes be cheaper at a farmer's market than in a big grocery store?"} +{"role": "user", "content": "Why is it a good idea to wear sneakers when you're playing outside?"} +{"role": "user", "content": "Whose job is it to decide what foods are served at home?"} +{"role": "user", "content": "Why do mosquitoes bite us?"} +{"role": "user", "content": "What is the fancy hat called that some people in Mexico wear, which is wide and colorful?"} +{"role": "user", "content": "What kind of fun shapes can you make sandwiches with?"} +{"role": "user", "content": "What does the word 'tiny' mean?"} +{"role": "user", "content": "Can you stretch your arms up towards the sky as high as you can?"} +{"role": "user", "content": "Is a whisper loud or quiet?"} +{"role": "user", "content": "Why are some rocks shiny?"} +{"role": "user", "content": "What are some fun toppings for pancakes or waffles?"} +{"role": "user", "content": "Why do we wear different clothes in the summer and winter?"} +{"role": "user", "content": "How does a microwave oven heat food?"} +{"role": "user", "content": "What does a red light mean?"} +{"role": "user", "content": "Why does a ball bounce?"} +{"role": "user", "content": "After we have fabric, what's the next step in making a t-shirt?"} +{"role": "user", "content": "What is an adjective?"} +{"role": "user", "content": "Can you name something that floats on water?"} +{"role": "user", "content": "When you're really hungry, is an apple or a small cookie going to fill you up more?"} +{"role": "user", "content": "What do plants need to grow?"} +{"role": "user", "content": "Does someone make clothes all by themselves?"} +{"role": "user", "content": "What word means a loud, sudden sound that might scare you?"} +{"role": "user", "content": "What do you call your father's brother?"} +{"role": "user", "content": "Why do we need traffic signs?"} +{"role": "user", "content": "What is a construction site?"} +{"role": "user", "content": "What are some different types of engineers?"} +{"role": "user", "content": "Why do we sweat when we're hot?"} +{"role": "user", "content": "What color are the Minions?"} +{"role": "user", "content": "Why is too much screen time bad?"} +{"role": "user", "content": "Why does our heart rate go back down after exercising?"} +{"role": "user", "content": "Does everyone make mistakes sometimes?"} +{"role": "user", "content": "Do you smoke/drink?"} +{"role": "user", "content": "When is it SUPER important to wash your hands?"} +{"role": "user", "content": "Can you name 2 green vegetables?"} +{"role": "user", "content": "Can you count backwards from 10?"} +{"role": "user", "content": "What's the difference between the regular checkout line and the self-checkout at the grocery store?"} +{"role": "user", "content": "Do you have a favorite food you'd like to learn to make yourself?"} +{"role": "user", "content": "Which famous baseball player was known for hitting lots of home runs?"} +{"role": "user", "content": "Why is it important to walk on the sidewalk?"} +{"role": "user", "content": "Let's build a sculpture! What can you use?"} +{"role": "user", "content": "Why do we get goosebumps?"} +{"role": "user", "content": "Why do we have two eyes?"} +{"role": "user", "content": "How do you feel after reading a funny story?"} +{"role": "user", "content": "Does food you make yourself sometimes taste even better than store-bought?"} +{"role": "user", "content": "If your friends are arguing over what game to play, can you use problem-solving to help?"} +{"role": "user", "content": "Do you know what a bicycle is powered by?"} +{"role": "user", "content": "Whose job is it to learn to like lots of different healthy foods"} +{"role": "user", "content": "Where are the tags on your clothes usually found?"} +{"role": "user", "content": "What's a word that means the opposite of 'fast'?"} +{"role": "user", "content": "Why is it important to respect people who are different from us?"} +{"role": "user", "content": "What's the special tool doctors use to listen to your heartbeat?"} +{"role": "user", "content": "Why can some bugs walk on water?"} +{"role": "user", "content": "Which number is smaller, 2 or 7?"} +{"role": "user", "content": "Should you always follow a recipe exactly, or is it okay to experiment a little bit?"} +{"role": "user", "content": "What makes popcorn pop?"} +{"role": "user", "content": "Can you do push-ups against the wall?"} +{"role": "user", "content": "What are some different holidays celebrated around the world?"} +{"role": "user", "content": "What do you call your sister's son?"} +{"role": "user", "content": "What's one easy recipe you could make with minimal help?"} +{"role": "user", "content": "Why does our heart beat?"} +{"role": "user", "content": "Why is it important to try and understand how other people feel?"} +{"role": "user", "content": "How many cups are in a pint?"} +{"role": "user", "content": "How many stars are there?"} +{"role": "user", "content": "What are letters?"} +{"role": "user", "content": "Are foods with lots of packaging good for the environment?"} +{"role": "user", "content": "Is your brain like a muscle?"} +{"role": "user", "content": "Can we break a bone?"} +{"role": "user", "content": "What is hand-eye coordination?"} +{"role": "user", "content": "Who was the first woman to fly solo across the Atlantic Ocean?"} +{"role": "user", "content": "What can make it harder for our body to fight off germs and viruses?"} +{"role": "user", "content": "Do engineers need to be good at math?"} +{"role": "user", "content": "What kind of machine is used to make cloth out of cotton or yarn?"} +{"role": "user", "content": "What are muscles, and why are they important?"} +{"role": "user", "content": "Why is cooking sometimes called a 'science experiment'?"} +{"role": "user", "content": "What's the opposite of 'wet'?"} +{"role": "user", "content": "Is it okay to ask for help after you've tried to solve something on your own?"} +{"role": "user", "content": "What should make up the biggest part of a healthy meal?"} +{"role": "user", "content": "If someone is hurt, but it's not a big emergency, where could you take them for help?"} +{"role": "user", "content": "Can you pack your own lunch for school sometimes?"} +{"role": "user", "content": "Why do we have joints?"} +{"role": "user", "content": "Why is staying hydrated important for athletes?"} +{"role": "user", "content": "What did Leonardo da Vinci do?"} +{"role": "user", "content": "What are some traditional foods from different countries?"} +{"role": "user", "content": "What is a family?"} +{"role": "user", "content": "Why do some plants smell bad?"} +{"role": "user", "content": "Should we drink lots of water or sugary drinks like soda?"} +{"role": "user", "content": "Why do we need to follow rules?"} +{"role": "user", "content": "What are some healthy snacks you can assemble with no cooking required?"} +{"role": "user", "content": "What's a fastener that helps keep our pants up?"} +{"role": "user", "content": "How can you make your writing more exciting?"} +{"role": "user", "content": "Can watching TV count as exercise?"} +{"role": "user", "content": "Is a bus driver a helper?"} +{"role": "user", "content": "What is the very first word many babies learn to say?"} +{"role": "user", "content": "Sometimes foods come in glass jars instead of plastic. Is this a more responsible choice?"} +{"role": "user", "content": "What does a red circle with a white line through it mean?"} +{"role": "user", "content": "Do engineers help design our phones and computers?"} +{"role": "user", "content": "Why do we have belly buttons?"} +{"role": "user", "content": "Have you ever twisted something into wood, or used a jar lid? What simple machine does that use?"} +{"role": "user", "content": "What do builders do?"} +{"role": "user", "content": "Can drawing or sketching out your ideas help you when solving a problem?"} +{"role": "user", "content": "How does your body feel when you've had enough exercise for the day?"} +{"role": "user", "content": "If your friend makes a mistake, what's a helpful thing you can do?"} +{"role": "user", "content": "Why do wheels make things easier to move?"} +{"role": "user", "content": "When you learn to ride a bike, do you get it perfect on the first try?"} +{"role": "user", "content": "What are some foods that are mostly sugar, and not so healthy?"} +{"role": "user", "content": "How does our brain work?"} +{"role": "user", "content": "What if a sentence is talking about something happening right NOW? Do we use past or present tense?"} +{"role": "user", "content": "Why do some plants have thorns?"} +{"role": "user", "content": "What kind of food group is peanut butter in?"} +{"role": "user", "content": "Do helpers have to go to school to learn how to do their jobs?"} +{"role": "user", "content": "How do seeds become plants?"} +{"role": "user", "content": "Who was the 16th president of the United States?"} +{"role": "user", "content": "What does a sign with a person in a wheelchair mean?"} +{"role": "user", "content": "How does a straw work?"} +{"role": "user", "content": "Why does my friend use a wheelchair?"} +{"role": "user", "content": "What do you call your mother's sister?"} +{"role": "user", "content": "Can plants move?"} +{"role": "user", "content": "How does our nose smell things?"} +{"role": "user", "content": "Before it's turned into cloth, what does cotton look like?"} +{"role": "user", "content": "What does it feel like to be drunk?"} +{"role": "user", "content": "What are some things families do together?"} +{"role": "user", "content": "Why do some things float in water?"} +{"role": "user", "content": "Why do we yawn?"} +{"role": "user", "content": "Why did someone steal from our neighbor?"} +{"role": "user", "content": "Why do we get fevers?"} +{"role": "user", "content": "Does food that looks delicious in commercials or on the box always taste as good?"} +{"role": "user", "content": "Who was the first person to walk on the moon?"} +{"role": "user", "content": "Why is teamwork important in sports? "} +{"role": "user", "content": "How is snow made?"} +{"role": "user", "content": "How can you tell if your friend is feeling sad?"} +{"role": "user", "content": "What are some healthy foods?"} +{"role": "user", "content": "Why did dinosaurs go extinct?"} +{"role": "user", "content": "What color is SpongeBob SquarePants?"} +{"role": "user", "content": "Name a food that's soft."} +{"role": "user", "content": "Sometimes clothes have pictures or words on them, how does that get there?"} +{"role": "user", "content": "If you ask for a 'treat' at the grocery store and a grown-up offers you a healthy snack instead, is it okay to try it even if you're not sure you'll like it?"} diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/requirements.txt b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/requirements.txt new file mode 100644 index 0000000000..f167474dcc --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/requirements.txt @@ -0,0 +1,8 @@ +python-dotenv==1.0.1 +pytest==8.2.2 +pytest-cov==5.0.0 +fmeval==1.0.3 +langkit==0.0.32 +langchain==0.2.6 +langchain-community==0.2.6 +gpt4all==2.7.0 \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/__init__.py b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/cloudwatch_logger.py b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/cloudwatch_logger.py new file mode 100644 index 0000000000..a38ba7b020 --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/cloudwatch_logger.py @@ -0,0 +1,106 @@ +from typing import Dict +import logging +import json +import datetime +import os + +logger = logging.getLogger(__name__) + +PROCESSING_JOB_CONFIG_FILE = '/opt/ml/config/processingjobconfig.json' + +DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE = ('byoc_llm_default_endpoint', 'byoc_llm_default_monitoring_schedule') + + +class CloudWatchLogger: + """ + The CloudWatchLogger is a service that writes evaluation metrics to CloudWatch. + """ + + def __init__(self): + """ + Constructor. + """ + + def log(self, eval_results: Dict, destination: str): + """ + Log the evaluation results to CloudWatch. + :param eval_results: A dictionary of evaluation results. + :param destination: The path to the file where the evaluation results will be written. + :raises: ValueError if eval_results is not a dictionary. + + For formatting and other information, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html + """ + + if eval_results is not None and not isinstance(eval_results, dict): + raise ValueError("eval_results must be a dictionary") + + + now = datetime.datetime.now(datetime.timezone.utc) + metric_timestamp = now.strftime("%Y-%m-%dT%H:%M:%SZ") + + + endpoint_name, monitoring_schedule_name = get_endpoint_and_monitoring_schedule() + logger.info(f"Endpoint: {endpoint_name}, Monitoring Schedule: {monitoring_schedule_name}") + + # Create the output directory if it doesn't exist + formatted_data_dir = os.path.dirname(destination) + if not os.path.exists(formatted_data_dir): + os.makedirs(formatted_data_dir, exist_ok=True) + + try: + with open(destination, 'w') as file: + for metric_name, metric_value in eval_results.items(): + metric_data = { + "MetricName": metric_name, + "Timestamp": metric_timestamp, + "Dimensions": [ + {"Name": "Endpoint", "Value": endpoint_name}, + {"Name": "MonitoringSchedule", "Value": monitoring_schedule_name} + ], + "Value": metric_value + } + file.write(json.dumps(metric_data) + '\n') + + logger.info(f"Logged metrics: {json.dumps(metric_data)}") + logger.info(f"Logged to {destination}") + except PermissionError as e: + logger.warning(f"Unable to write to {destination}") + print(f"Error: {e}") + + print(f"Evaluation results logged to: {destination}") + + +def is_running_in_docker(): + """ + Checks whether we are running in a Docker container or not. + :returns True if DOCKER_CONTAINER env variable is present, False otherwise. + """ + return 'DOCKER_CONTAINER' in os.environ + + +def get_endpoint_and_monitoring_schedule(): + """ + Retrieves the endpoint name and monitoring schedule name from the processing job config file. + If we are in a docker container, we are running a monitoring job, and the config file has + the endpoint name and monitoring schedule name. + + For information about processingjobcongfig.json file, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-contract-inputs.html + + :returns A tuple containing the endpoint name and monitoring schedule name. + """ + + if is_running_in_docker(): + try: + with open(PROCESSING_JOB_CONFIG_FILE, 'r') as config: + params = json.load(config) + logger.info("Reading Env params") + endpoint_name = params["Environment"]["sagemaker_endpoint_name"] + monitoring_schedule_name = params["Environment"]["sagemaker_monitoring_schedule_name"] + + return endpoint_name, monitoring_schedule_name + except KeyError: + logger.error(f"Environment does not have endpoint or monitoring schedule name. Ensure that this processing job is initiated by a monitoring schedule.") + return DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE + + else: + return DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/data_loader.py b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/data_loader.py new file mode 100644 index 0000000000..560139fde1 --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/data_loader.py @@ -0,0 +1,178 @@ +import os +import json +import logging +import base64 +import jsonschema + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +SCHEMA_FILE = '../utils/jsonl-capture-data.schema' + +class DataLoader: + """ + The DataLoader is a service that recursively searches all subdirectories of + the '/opt/ml/processing/input_data' directory for JSONL files and subsequently executes an + ETL (Extract, Transform, Load) process. The DataLoader completes its job when all data has + been extracted, formatted, and loaded into '/opt/ml/processing/formatted_data/data.jsonl'. + """ + + def __init__(self): + """ + Constructor. No parameters. + + """ + self.transformed_data = [] + + def extract(self, file_path: str): + """ + Extracts data from a JSONL file. + + :param file_path: The path to the JSONL file. + :raises: ValueError if file_path is not a valid string. + :returns: A list of data records extracted from the file. If file does not exist, returns empty list. + """ + + if not isinstance(file_path, str): + raise ValueError("file_path must be a string") + + schema_filepath = os.path.join(os.path.dirname(__file__), SCHEMA_FILE) + + logger.info(f"Extracting data from file: {file_path}") + extracted_data = [] + try: + with open(file_path, 'r') as file: + for line in file: + try: + data = json.loads(line) + validate_json_against_schema(data, schema_filepath) + except json.JSONDecodeError: + logger.info(f"Invalid JSON data: {line}") + continue + except jsonschema.ValidationError as e: + logger.info(f"Validation error: {e}") + continue + extracted_data.append(data) + return extracted_data + except: + return [] + + + def transform(self, data: list): + """ + Applies transformation rules to the extracted data. The current rules format the data to be used with FMEval. + + :param data: A list of data records to be transformed. Each item is a dictionary. + :raises: ValueError if data is not a list. + :raises: Warning if invalid data is provided. + :returns: The transformed data records. + """ + logger.info("Transforming data...") + + if not isinstance(data, list): + raise ValueError("data must be a list") + + transformed_data = [] + for record in data: + try: + content = json.loads(record["captureData"]["endpointInput"]["data"])["inputs"][0][0]["content"] + model_output = json.loads(base64.b64decode(record["captureData"]["endpointOutput"]["data"]).decode("utf-8"))[0]["generation"]["content"] + + # Create the transformed data + transformed_record = { + "content": content, + "answer": model_output + } + transformed_data.append(transformed_record) + except (KeyError, IndexError, json.JSONDecodeError, UnicodeDecodeError) as e: + logger.warning(f"Error transforming record: {e}") + continue + + return transformed_data + + def load(self, destination: str): + """ + Loads the transformed data into a single JSONL file. + :param destination: The destination filepath of the JSONL file. + :raises: ValueError if destination is not a valid string. + :returns: None. + """ + + if not isinstance(destination, str): + raise ValueError("destination must be a string") + + + logger.info(f"Loading data to: {destination}") + + # Create the directory if it doesn't exist + formatted_data_dir = os.path.dirname(destination) + if not os.path.exists(formatted_data_dir): + os.makedirs(formatted_data_dir, exist_ok=True) + + # Open the file and write the data + try: + with open(destination, 'w') as file: + for data_record in self.transformed_data: + file.write(json.dumps(data_record) + '\n') + except PermissionError as e: + + logger.error(f"Permission error: {e}") + + + + def execute_etl(self, directory: str, destination: str): + """ + Executes the ETL (Extract, Transform, Load) process. This function recursively searches the input data directory and performs + ETL on all .jsonl files found. + + :param directory: The directory to search for capture data. + :param destination: The destination filepath of the transformed data. + :raises: ValueError if directory is not a valid string. + :raises: ValueError if destination is not a valid string. + :raises: Warning if invalid directory provided. + :returns: None. + """ + + if not isinstance(directory, str): + raise ValueError("directory must be a string") + if not isinstance(destination, str): + raise ValueError("destination must be a string") + + + logger.info(f"current dir: {os.getcwd()}") + logger.info(f"Executing ETL process for directory: {directory}") + if os.path.exists(directory) and os.path.isdir(directory): + # Iterate over each file and directory in the directory + for item in os.listdir(directory): + item_path = os.path.join(directory, item) + if os.path.isdir(item_path): + # Recursively call the function for subdirectories + self.execute_etl(item_path, destination) + else: + # Check if the file is a .jsonl file and process it + if item.endswith(".jsonl"): + logger.info(f"Processing file: {item_path}") + extracted_data = self.extract(item_path) + transformed_data = self.transform(extracted_data) + self.transformed_data.extend(transformed_data) + else: + logger.info(f"Found file: {item_path}") + + else: + logger.warning(f"The directory {directory} does not exist or is not a directory.") + + # Load the transformed data into a single JSONL file + self.load(destination) + + +def validate_json_against_schema(data, schema_filepath): + """ + Validates that the data fits the schema defined in the schema file. + + :param data: The data to validate. + :param schema_filepath: The path to the schema file. + :raises: jsonschema.ValidationError if the data does not match the schema. + """ + with open(schema_filepath) as sf: + schema = json.load(sf) + jsonschema.validate(instance=data, schema=schema) \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/evaluator.py b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/evaluator.py new file mode 100644 index 0000000000..0ae7564325 --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/components/evaluator.py @@ -0,0 +1,326 @@ +from typing import Set, Optional +import logging +from langkit import light_metrics, extract +from fmeval.eval_algorithms.toxicity import Toxicity, ToxicityConfig, DataConfig +from fmeval.exceptions import EvalAlgorithmClientError +from langchain_community.llms.gpt4all import GPT4All +from gpt4all import GPT4All as fileDownloader +from langchain.evaluation.scoring import ScoreStringEvalChain +import json +from json import JSONDecodeError +from typing import Any, Callable, Optional, Sequence, Tuple +import re +import os +import random + +# Model Input/Output specify which fields FMEVal looks in our dataset. +# Reference https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-foundation-model-evaluate-auto-lib-custom.html +DATASET_NAME = "custom_dataset" +DATASET_MIME_TYPE = "application/jsonlines" +MODEL_INPUT_LOCATION = "content" +MODEL_OUTPUT_LOCATION = "answer" + + +TOXICITY_EVALUATOR_MODEL = "detoxify" +DEFAULT_EVALUATIONS = {'toxicity', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat', 'sexual_explicit'} + +DEFAULT_REPORT_PATH = './tests/output' +READABILITY_REPORT_FILENAME = 'readability_eval_results.jsonl' +RELEVANCE_AND_ACCURACY_REPORT_FILENAME = 'relevance_and_accuracy_eval_results.jsonl' +REPORT_PATH = os.getenv("EVAL_RESULTS_PATH") if "EVAL_RESULTS_PATH" in os.environ else DEFAULT_REPORT_PATH + +# These are all of the readability evaluations we can run. +READABILITY_EVALUATIONS = { + "flesch_reading_ease", + "automated_readability_index", + "aggregate_reading_level", + "syllable_count", + "lexicon_count", + "sentence_count", + "character_count", + "letter_count", + "polysyllable_count", + "monosyllable_count", + "difficult_words", + } + +# These are all of the toxicity evaluations we can run. +TOXICITY_EVALUATIONS = { + "toxicity", + "severe_toxicity", + "obscene", + "identity_attack", + "insult", + "threat", + "sexual_explicit" + } + +RELEVANCE_AND_ACCURACY_EVALUATIONS = { + "relevance_and_accuracy_score" +} + +ANSWER_RELEVANCY_MODEL = "Meta-Llama-3-8B-Instruct.Q4_0.gguf" + +DEFAULT_EVALUATIONS = {"TOXICITY", "READABILITY", "RELEVANCE_AND_ACCURACY"} + +logger = logging.getLogger(__name__) + +class Evaluator: + """ + The Evaluator is a service that assesses the performance of Large Language Models by running a set + of evaluation algorithms specified by a configuration set. It reads formatted data from + the /opt/ml/processing/output/data.jsonl file and uses the FMEval open-source library to + execute the specified evaluation tasks. + """ + def __init__(self, eval_config: Optional[Set[str]] = DEFAULT_EVALUATIONS): + """ + Constructor + :param eval_config: A Set of evaluation tasks to run. If not provided, all evaluation tasks will be run. + :raises: ValueError if eval_config is not a set or a list of strings. + """ + self.eval_config = eval_config + if eval_config is not None: + if isinstance(eval_config, set): + self.eval_config = eval_config + elif isinstance(eval_config, list): + self.eval_config = set(eval_config) + else: + raise ValueError("eval_config must be a set or a list of strings") + + def evaluate(self, dataset_uri: str): + """ + Evaluate the data using the configured settings. + + :param dataset_uri: The path to the dataset file. + :raises: ValueError if the dataset_uri is not a valid string. + :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary. + """ + + + if not isinstance(dataset_uri, str): + raise ValueError("dataset_uri must be a valid string") + + if not isinstance(REPORT_PATH, str): + raise ValueError("report_path must be a valid string") + + toxicity_results = {} + readability_results = {} + relevance_and_accuracy_results = {} + if "TOXICITY" in self.eval_config: + toxicity_results = self._evaluate_toxicity(dataset_uri) + + if "READABILITY" in self.eval_config: + readability_results = self._evaluate_readability(dataset_uri) + + if "RELEVANCE_AND_ACCURACY" in self.eval_config: + relevance_and_accuracy_results = self._evaluate_relevance_and_accuracy(dataset_uri) + + return {**toxicity_results, **readability_results, **relevance_and_accuracy_results} + + + def _evaluate_toxicity(self, dataset_uri: str): + """ + Evaluates the data for Toxicity using the FMEval library. + + :param dataset_uri: The path to the dataset file. + :raises: ValueError if the dataset_uri is not a valid string. + :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary. + """ + if not isinstance(dataset_uri, str): + raise ValueError("dataset_uri must be a valid string") + + config = DataConfig( + dataset_name=DATASET_NAME, + dataset_uri=dataset_uri, + dataset_mime_type=DATASET_MIME_TYPE, + model_input_location=MODEL_INPUT_LOCATION, + model_output_location=MODEL_OUTPUT_LOCATION, + ) + + eval_algo = Toxicity(ToxicityConfig(model_type=TOXICITY_EVALUATOR_MODEL)) + + try: + eval_output = eval_algo.evaluate(dataset_config=config, save=True) + except (json.JSONDecodeError, EvalAlgorithmClientError) as e: + # If we evaluate an empty/malformed file, return an empty dict + logger.warning("Evaluated data malformed.") + return {} + + eval_results = {} + for eval_score in eval_output[0].dataset_scores: + eval_results[eval_score.name] = eval_score.value + + logger.info(f"Evaluation Results: {eval_results}") + + return eval_results + + + def _evaluate_readability(self, dataset_uri: str): + """ + Evaluates the data for readability using the WhyLabs Langkit Library. + + :param dataset_uri: The path to the dataset file. + :raises: ValueError if the dataset_uri is not a valid string. + :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary. + """ + + text_schema = light_metrics.init() + + line_count = 0 + try: + with open(dataset_uri, 'r') as file: + lines = file.readlines() + except: + logger.error("Could not read file.") + return {} + + if len(lines) == 0: + logger.info("No data to evaluate") + return {} + + results = [] + totals = {field: 0 for field in READABILITY_EVALUATIONS} + + if len(lines) <= 100: + sample_lines = lines + else: + sample_lines = random.sample(lines, 100) + + for line in sample_lines: + try: + data = json.loads(line) + line_count += 1 + + readability_evals = clean_readability_dict(extract({"prompt": data['answer']}, schema=text_schema)) + result_dict = { + "prompt": data["content"], + "response": data["answer"], + **readability_evals, + } + results.append(result_dict) + for key, value in result_dict.items(): + if key in totals: + totals[key] += value + except (KeyError, JSONDecodeError) as e: + logger.error(f"Data malformed. {e}") + return {} + + report_filepath = os.path.join(REPORT_PATH, READABILITY_REPORT_FILENAME) + + logger.info(f"Writing readability evaluation results to {report_filepath}") + write_eval_result_file(report_filepath, results) + + return {key: value / (line_count if line_count > 0 else 1) for key, value in totals.items()} + + def _evaluate_relevance_and_accuracy(self, dataset_uri: str): + """ + Evaluates the data for relevance and accuracy using the FMEval library. + + :param dataset_uri: The path to the dataset file. + :raises: ValueError if the dataset_uri is not a valid string. + :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary. + """ + + if not isinstance(dataset_uri, str): + raise ValueError("dataset_uri must be a valid string") + + + fileDownloader.retrieve_model(ANSWER_RELEVANCY_MODEL) # downloads / loads a 4.66GB LLM + model = GPT4All(model=ANSWER_RELEVANCY_MODEL, verbose=False, n_batch=128, n_threads=36 if 'DOCKER_CONTAINER' in os.environ else None) + evaluator_model = ScoreStringEvalChain.from_llm( + llm=model, verbose=False + ) + + line_count = 0 + try: + with open(dataset_uri, 'r') as file: + lines = file.readlines() + except: + logger.error("Could not read file.") + return {} + + if not lines: + logger.info("No data to evaluate") + return {} + + # Initialize our list of individualy response scores and summed total scores (for later averaging) + results = [] + totals = {field: 0 for field in RELEVANCE_AND_ACCURACY_EVALUATIONS} + # Randomly sample 10 prompt and responses for evaluation + if len(lines) <= 10: + sample_lines = lines + else: + sample_lines = random.sample(lines, 10) + + logger.info("Starting evaluation") + for line in sample_lines: + try: + data = json.loads(line) + line_count += 1 + logger.info(f"Evaluating line: {line_count}") + + accuracy_relevance_eval_result = evaluator_model.evaluate_strings( + prediction=data["answer"], + input=data["content"], + ) + + result_dict = { + "prompt": data["content"], + "response": data["answer"], + "relevance_and_accuracy_analysis": accuracy_relevance_eval_result["reasoning"], + "relevance_and_accuracy_score": accuracy_relevance_eval_result["score"], + } + # Add all scores for this response to result list and sum total scores + results.append(result_dict) + for key, value in result_dict.items(): + if key in totals: + totals[key] += value + except ValueError as e: + logger.warning(f"Error evaluating line, continuing: {e}") + continue + except (KeyError, JSONDecodeError) as e: + logger.error(f"Data malformed {e}") + return {} + + report_filepath = os.path.join(REPORT_PATH, RELEVANCE_AND_ACCURACY_REPORT_FILENAME) + write_eval_result_file(report_filepath, results) + + # Returns average scores + return {key: value / (line_count if line_count > 0 else 1) for key, value in totals.items()} + + +def clean_readability_dict(evals): + """ + Cleans the readability dictionary by removing the 'prompt' and 'has_patterns' keys. Also, removes 'prompt.' prefix from fields which is + the default behavior of the LangKit extract function. + :param evals: The dictionary to clean. + :return: The cleaned dictionary. + """ + evals.pop('prompt') + + # Remove 'prompt.' from every key + new_evals = {} + for key, value in evals.items(): + new_key = key.replace('prompt.', '') + new_evals[new_key] = value + + try: + new_evals.pop('has_patterns') + except: + logger.info("No patterns found") + + return new_evals + +def write_eval_result_file(report_filepath, results): + """ + Writes the evaluation results to a file in the specified directory. + :param formatted_data_dir: The directory to write the file to. + :param report_path: The directory to write the file to + :param results: The evaluation results to write. + :return: None + """ + formatted_data_dir = os.path.dirname(report_filepath) + os.makedirs(formatted_data_dir, exist_ok=True) + with open(report_filepath, 'w') as output_file: + for result_dict in results: + output_file.write(json.dumps(result_dict) + '\n') \ No newline at end of file diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/main.py b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/main.py new file mode 100644 index 0000000000..24737bab9c --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/main.py @@ -0,0 +1,75 @@ +import logging +import sys +import site +import json +import os +from components.data_loader import DataLoader +from components.evaluator import Evaluator +from components.cloudwatch_logger import CloudWatchLogger +from langkit import textstat +from whylogs.experimental.core.udf_schema import udf_schema + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# This is where our capture data is loaded to. MUST be same as "destination" field in EndointInput for deployed model. +INPUT_DATA_SOURCE = '/opt/ml/processing/input_data' + +# Destination for formatted and cleaned data in the container for evaluation. +CLEANED_DATA_DESTINATION = '/opt/ml/processing/internal/data.jsonl' + +# Destination for metrics. These metrics MUST be stored at this location if they are to be published. +# See https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html +CLOUDWATCH_METRICS_DESTINATION = '/opt/ml/output/metrics/cloudwatch/cloudwatch_metrics.jsonl' + +PROCESSING_JOB_CONFIG_FILE = '/opt/ml/config/processingjobconfig.json' + +DEFAULT_EVAL_LIST = {"TOXICITY", "READABILITY", "RELEVANCE_AND_ACCURACY"} + +def get_evaluations(): + """ + Retrieves the specified evaluations from the processing job config file. + If we are in a docker container, we are running a monitoring job, and the config file has + the endpoint name and monitoring schedule name. + + For information about processingjobcongfig.json file, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-contract-inputs.html + + :returns A tuple containing the endpoint name and monitoring schedule name. + """ + + if 'DOCKER_CONTAINER' in os.environ: + try: + with open(PROCESSING_JOB_CONFIG_FILE, 'r') as config: + params = json.load(config) + logger.info("Reading Env params") + eval_list = set() + + if params["Environment"]["TOXICITY"] == "Enabled": + eval_list.add("TOXICITY") + if params["Environment"]["READABILITY"] == "Enabled": + eval_list.add("READABILITY") + if params["Environment"]["RELEVANCE_AND_ACCURACY"] == "Enabled": + eval_list.add("RELEVANCE_AND_ACCURACY") + + return eval_list + except KeyError as e: + logger.error(f"Environment does not have any evaluations enables.") + raise e + else: + return DEFAULT_EVAL_LIST + +if __name__ == "__main__": + + try: + evaluations = get_evaluations() + data_loader = DataLoader() + evaluator = Evaluator(eval_config=evaluations) + cloudwatch_logger = CloudWatchLogger() + + data_loader.execute_etl(INPUT_DATA_SOURCE, CLEANED_DATA_DESTINATION) + eval_results = evaluator.evaluate(CLEANED_DATA_DESTINATION) + cloudwatch_logger.log(eval_results, CLOUDWATCH_METRICS_DESTINATION) + + except Exception as e: + logger.exception("Exception performing analysis: " + str(e)) + sys.exit(255) diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/__init__.py b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/jsonl-capture-data.schema b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/jsonl-capture-data.schema new file mode 100644 index 0000000000..af48e7da17 --- /dev/null +++ b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/src/utils/jsonl-capture-data.schema @@ -0,0 +1,86 @@ +{ + "$schema": "http://json-schema.org/draft-04/schema#", + "type": "object", + "properties": { + "captureData": { + "type": "object", + "properties": { + "endpointInput": { + "type": "object", + "properties": { + "observedContentType": { + "type": "string" + }, + "mode": { + "type": "string" + }, + "data": { + "type": "string" + }, + "encoding": { + "type": "string" + } + }, + "required": [ + "observedContentType", + "mode", + "data", + "encoding" + ] + }, + "endpointOutput": { + "type": "object", + "properties": { + "observedContentType": { + "type": "null" + }, + "mode": { + "type": "string" + }, + "data": { + "type": "string" + }, + "encoding": { + "type": "string" + } + }, + "required": [ + "observedContentType", + "mode", + "data", + "encoding" + ] + } + }, + "required": [ + "endpointInput", + "endpointOutput" + ] + }, + "eventMetadata": { + "type": "object", + "properties": { + "eventId": { + "type": "string" + }, + "customAttributes": { + "type": "array", + "items": [ + { + "type": "string" + } + ] + }, + "inferenceTime": { + "type": "string" + } + } + }, + "eventVersion": { + "type": "string" + } + }, + "required": [ + "captureData" + ] +} From 653a1f4dab2d0347fdef6202ab755bef10ac2f30 Mon Sep 17 00:00:00 2001 From: Zhaoqi Date: Thu, 8 Aug 2024 18:50:16 -0400 Subject: [PATCH 15/16] Revert "New folder structure (#4680)" (#4728) This reverts commit 24855357f751d642e6d8e65f51db784fe735ddaf. --- .../ap-batch-transform.ipynb | 0 .../huggingfacetgi/bloom-560m}/tgi-bloom-560m.ipynb | 0 .../huggingfacetgi/bloom-7b1}/README.md | 0 .../huggingfacetgi/bloom-7b1}/hf-tgi-bloom7b1.ipynb | 0 .../flan-t5-xl}/hf-tgi-flan-t5-xl.ipynb | 0 .../gpt-neox-20b}/tgi-gpt-neox-20b.ipynb | 0 .../huggingfacetgi/gpt2}/gpt2-tgi.ipynb | 0 .../generativeai/huggingfacetgi/santacoder}/app.py | 0 .../huggingfacetgi/santacoder}/gradioUI.png | Bin ...ggingface-large-model-inference-santacoder.ipynb | 0 .../dolly-12b-deepspeed-sagemaker.ipynb | 0 .../falcon-40b-accelerate.ipynb | 0 .../falcon-40b-deepspeed.ipynb | 0 .../deploy-falcon-40b-and-7b/falcon-40b-mpi.ipynb | 0 .../1_create_endpoint.ipynb | 0 .../2a_codegen25_FT_7b.ipynb | 0 .../2b_flant5_xxl-tgi.ipynb | 0 .../2c_meta-llama2-7b-lmi-autoscaling.ipynb | 0 .../3_misc_cleanup.ipynb | 0 .../lab-inference-components-with-scaling/README.md | 0 .../open-llama-7b}/open_llama_7b.ipynb | 0 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training/distributed_training/pytorch/model_parallel/gpt2/smp-fine-tune-gpt-sharded-data-parallel.ipynb diff --git a/archived/notebooks/smp-gpt-sharded-data-parallel/smp-train-gpt-sharded-data-parallel.ipynb b/training/distributed_training/pytorch/model_parallel/gpt2/smp-train-gpt-sharded-data-parallel.ipynb similarity index 100% rename from archived/notebooks/smp-gpt-sharded-data-parallel/smp-train-gpt-sharded-data-parallel.ipynb rename to training/distributed_training/pytorch/model_parallel/gpt2/smp-train-gpt-sharded-data-parallel.ipynb diff --git a/archived/notebooks/smp-gpt-sharded-data-parallel/train.py b/training/distributed_training/pytorch/model_parallel/gpt2/train.py similarity index 100% rename from archived/notebooks/smp-gpt-sharded-data-parallel/train.py rename to training/distributed_training/pytorch/model_parallel/gpt2/train.py From faf8648f9002fc8a29eba53de22286c778b06cac Mon Sep 17 00:00:00 2001 From: Zhaoqi Date: Sun, 22 Sep 2024 11:37:35 -0400 Subject: [PATCH 16/16] Update package version (#4754) Update installed package version to fix broken notebook --- .../interactive_analysis_profiling_data.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sagemaker-debugger/debugger_interactive_analysis_profiling/interactive_analysis_profiling_data.ipynb b/sagemaker-debugger/debugger_interactive_analysis_profiling/interactive_analysis_profiling_data.ipynb index 7837bca6f7..4488418dda 100644 --- a/sagemaker-debugger/debugger_interactive_analysis_profiling/interactive_analysis_profiling_data.ipynb +++ b/sagemaker-debugger/debugger_interactive_analysis_profiling/interactive_analysis_profiling_data.ipynb @@ -194,7 +194,7 @@ "import sys\n", "\n", "!{sys.executable} -m pip install \"smdebug==1.0.3\"\n", - "!{sys.executable} -m pip install \"bokeh==2.3.0\"" + "!{sys.executable} -m pip install \"bokeh==2.4.0\"" ] }, { @@ -978,4 +978,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +}