\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",
+ "
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",
+ "
INFO:ℹ️ Refer to
utils.autoscaling for `monitor_scaling_events` function implementation\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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\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/archived/notebooks/inference-benchmarking/benchmarking/__init__.py b/inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/__init__.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/__init__.py
rename to inference/generativeai/huggingfacetgi/meta-llama/llama3-8b/faster-autoscaling/realtime-endpoints/utils/__init__.py
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
diff --git a/archived/notebooks/huggingface-large-model-inference-santacoder/app.py b/inference/generativeai/huggingfacetgi/santacoder/app.py
similarity index 100%
rename from archived/notebooks/huggingface-large-model-inference-santacoder/app.py
rename to inference/generativeai/huggingfacetgi/santacoder/app.py
diff --git a/archived/notebooks/huggingface-large-model-inference-santacoder/gradioUI.png b/inference/generativeai/huggingfacetgi/santacoder/gradioUI.png
similarity index 100%
rename from archived/notebooks/huggingface-large-model-inference-santacoder/gradioUI.png
rename to inference/generativeai/huggingfacetgi/santacoder/gradioUI.png
diff --git a/archived/notebooks/huggingface-large-model-inference-santacoder/huggingface-large-model-inference-santacoder.ipynb b/inference/generativeai/huggingfacetgi/santacoder/huggingface-large-model-inference-santacoder.ipynb
similarity index 100%
rename from archived/notebooks/huggingface-large-model-inference-santacoder/huggingface-large-model-inference-santacoder.ipynb
rename to inference/generativeai/huggingfacetgi/santacoder/huggingface-large-model-inference-santacoder.ipynb
diff --git a/archived/notebooks/workshops/dolly-12b-deepspeed-sagemaker.ipynb b/inference/generativeai/llm-workshop/deploy-dolly-12b/dolly-12b-deepspeed-sagemaker.ipynb
similarity index 100%
rename from archived/notebooks/workshops/dolly-12b-deepspeed-sagemaker.ipynb
rename to inference/generativeai/llm-workshop/deploy-dolly-12b/dolly-12b-deepspeed-sagemaker.ipynb
diff --git a/archived/notebooks/workshops/deploy-falcon-40b-and-7b/falcon-40b-accelerate.ipynb b/inference/generativeai/llm-workshop/deploy-falcon-40b-and-7b/falcon-40b-accelerate.ipynb
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rename from archived/notebooks/workshops/deploy-falcon-40b-and-7b/falcon-40b-accelerate.ipynb
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diff --git a/archived/notebooks/workshops/deploy-falcon-40b-and-7b/falcon-40b-deepspeed.ipynb b/inference/generativeai/llm-workshop/deploy-falcon-40b-and-7b/falcon-40b-deepspeed.ipynb
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diff --git a/archived/notebooks/workshops/deploy-falcon-40b-and-7b/falcon-40b-mpi.ipynb b/inference/generativeai/llm-workshop/deploy-falcon-40b-and-7b/falcon-40b-mpi.ipynb
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diff --git a/archived/notebooks/workshops/lab-inference-components-with-scaling/1_create_endpoint.ipynb b/inference/generativeai/llm-workshop/lab-inference-components-with-scaling/1_create_endpoint.ipynb
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diff --git a/archived/notebooks/workshops/lab-inference-components-with-scaling/2a_codegen25_FT_7b.ipynb b/inference/generativeai/llm-workshop/lab-inference-components-with-scaling/2a_codegen25_FT_7b.ipynb
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diff --git a/archived/notebooks/workshops/lab-inference-components-with-scaling/2b_flant5_xxl-tgi.ipynb b/inference/generativeai/llm-workshop/lab-inference-components-with-scaling/2b_flant5_xxl-tgi.ipynb
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diff --git a/archived/notebooks/workshops/lab-inference-components-with-scaling/2c_meta-llama2-7b-lmi-autoscaling.ipynb b/inference/generativeai/llm-workshop/lab-inference-components-with-scaling/2c_meta-llama2-7b-lmi-autoscaling.ipynb
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diff --git a/archived/notebooks/workshops/lab-inference-components-with-scaling/3_misc_cleanup.ipynb b/inference/generativeai/llm-workshop/lab-inference-components-with-scaling/3_misc_cleanup.ipynb
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diff --git a/archived/notebooks/workshops/lab-inference-components-with-scaling/README.md b/inference/generativeai/llm-workshop/lab-inference-components-with-scaling/README.md
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diff --git a/archived/notebooks/workshops/lab10-open-llama/open_llama_7b.ipynb b/inference/generativeai/llm-workshop/lab10-open-llama/open-llama-7b/open_llama_7b.ipynb
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diff --git a/archived/notebooks/workshops/lab11-llama2/meta-llama-2-13b-lmi.ipynb b/inference/generativeai/llm-workshop/lab11-llama2/meta-llama-2-13b-lmi.ipynb
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diff --git a/archived/notebooks/workshops/lab11-llama2/meta-llama-2-70b-lmi.ipynb b/inference/generativeai/llm-workshop/lab11-llama2/meta-llama-2-70b-lmi.ipynb
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diff --git a/archived/notebooks/workshops/lab11-llama2/meta-llama-2-7b-lmi.ipynb b/inference/generativeai/llm-workshop/lab11-llama2/meta-llama-2-7b-lmi.ipynb
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rename from archived/notebooks/workshops/lab11-llama2/meta-llama-2-7b-lmi.ipynb
rename to inference/generativeai/llm-workshop/lab11-llama2/meta-llama-2-7b-lmi.ipynb
diff --git a/archived/notebooks/workshops/lab2-stable-diffusion/Amazon_JumpStart_Text_To_Image.ipynb b/inference/generativeai/llm-workshop/lab2-stable-diffusion/option1-jumpstart/Amazon_JumpStart_Text_To_Image.ipynb
similarity index 100%
rename from archived/notebooks/workshops/lab2-stable-diffusion/Amazon_JumpStart_Text_To_Image.ipynb
rename to inference/generativeai/llm-workshop/lab2-stable-diffusion/option1-jumpstart/Amazon_JumpStart_Text_To_Image.ipynb
diff --git a/archived/notebooks/workshops/lab2-stable-diffusion/BONUS_Amazon_JumpStart_Upscaling.ipynb b/inference/generativeai/llm-workshop/lab2-stable-diffusion/option1-jumpstart/BONUS_Amazon_JumpStart_Upscaling.ipynb
similarity index 100%
rename from archived/notebooks/workshops/lab2-stable-diffusion/BONUS_Amazon_JumpStart_Upscaling.ipynb
rename to inference/generativeai/llm-workshop/lab2-stable-diffusion/option1-jumpstart/BONUS_Amazon_JumpStart_Upscaling.ipynb
diff --git a/archived/notebooks/workshops/lab6-token-streaming-eleutherai-gpt-j-6b-lmi.ipynb b/inference/generativeai/llm-workshop/lab6-stream-with-pagination/lab6-token-streaming-eleutherai-gpt-j-6b-lmi.ipynb
similarity index 100%
rename from archived/notebooks/workshops/lab6-token-streaming-eleutherai-gpt-j-6b-lmi.ipynb
rename to inference/generativeai/llm-workshop/lab6-stream-with-pagination/lab6-token-streaming-eleutherai-gpt-j-6b-lmi.ipynb
diff --git a/archived/notebooks/workshops/lab9-inf2-stable-diffusion/NoCode-SD21-INF2.ipynb b/inference/generativeai/llm-workshop/lab9-inf2-stable-diffusion/NoCode-SD21-INF2.ipynb
similarity index 100%
rename from archived/notebooks/workshops/lab9-inf2-stable-diffusion/NoCode-SD21-INF2.ipynb
rename to inference/generativeai/llm-workshop/lab9-inf2-stable-diffusion/NoCode-SD21-INF2.ipynb
diff --git a/archived/notebooks/workshops/lab9-inf2-stable-diffusion/SageMaker-SD21-INF2.ipynb b/inference/generativeai/llm-workshop/lab9-inf2-stable-diffusion/SageMaker-SD21-INF2.ipynb
similarity index 100%
rename from archived/notebooks/workshops/lab9-inf2-stable-diffusion/SageMaker-SD21-INF2.ipynb
rename to inference/generativeai/llm-workshop/lab9-inf2-stable-diffusion/SageMaker-SD21-INF2.ipynb
diff --git a/archived/notebooks/workshops/lab9-openassistant-sft-12b/oasst-sft-1-pythia-12b-sagemaker.ipynb b/inference/generativeai/llm-workshop/lab9-openassistant-sft-12b/oasst-sft-1-pythia-12b-sagemaker.ipynb
similarity index 100%
rename from archived/notebooks/workshops/lab9-openassistant-sft-12b/oasst-sft-1-pythia-12b-sagemaker.ipynb
rename to inference/generativeai/llm-workshop/lab9-openassistant-sft-12b/oasst-sft-1-pythia-12b-sagemaker.ipynb
diff --git a/archived/notebooks/bloom-z-176b-few-shot-and-zero-shot-learning.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/bloom-z-176b-few-shot-and-zero-shot-learning.ipynb
similarity index 100%
rename from archived/notebooks/bloom-z-176b-few-shot-and-zero-shot-learning.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/bloom-z-176b-few-shot-and-zero-shot-learning.ipynb
diff --git a/archived/notebooks/falcon-7b-instruction-domain-adaptation-finetuning.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/falcon-7b-instruction-domain-adaptation-finetuning.ipynb
similarity index 100%
rename from archived/notebooks/falcon-7b-instruction-domain-adaptation-finetuning.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/falcon-7b-instruction-domain-adaptation-finetuning.ipynb
diff --git a/archived/notebooks/instruction-fine-tuning-flan-t5.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/instruction-fine-tuning-flan-t5.ipynb
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rename from archived/notebooks/instruction-fine-tuning-flan-t5.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/instruction-fine-tuning-flan-t5.ipynb
diff --git a/archived/notebooks/question_answering_jumpstart_knn.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_jumpstart_knn.ipynb
similarity index 100%
rename from archived/notebooks/question_answering_jumpstart_knn.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_jumpstart_knn.ipynb
diff --git a/archived/notebooks/question_answering_pinecone_llama-2_jumpstart.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_pinecone_llama-2_jumpstart.ipynb
similarity index 100%
rename from archived/notebooks/question_answering_pinecone_llama-2_jumpstart.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_pinecone_llama-2_jumpstart.ipynb
diff --git a/archived/notebooks/inference-benchmarking/.gitignore b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/.gitignore
similarity index 100%
rename from archived/notebooks/inference-benchmarking/.gitignore
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/.gitignore
diff --git a/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/__init__.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/clients.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/clients.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/clients.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/clients.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/concurrency_probe.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/concurrency_probe.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/concurrency_probe.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/concurrency_probe.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/constants.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/constants.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/constants.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/constants.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/custom_predictor.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/custom_predictor.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/custom_predictor.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/custom_predictor.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/load_test.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/load_test.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/load_test.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/load_test.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/logging.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/logging.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/logging.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/logging.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/payload.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/payload.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/payload.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/payload.py
diff --git a/archived/notebooks/inference-benchmarking/benchmarking/runner.py b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/runner.py
similarity index 100%
rename from archived/notebooks/inference-benchmarking/benchmarking/runner.py
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/benchmarking/runner.py
diff --git a/archived/notebooks/inference-benchmarking/inference-benchmarking-customization-options-example.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/inference-benchmarking-customization-options-example.ipynb
similarity index 100%
rename from archived/notebooks/inference-benchmarking/inference-benchmarking-customization-options-example.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/inference-benchmarking-customization-options-example.ipynb
diff --git a/archived/notebooks/inference-benchmarking/inference-benchmarking-example.ipynb b/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/inference-benchmarking-example.ipynb
similarity index 100%
rename from archived/notebooks/inference-benchmarking/inference-benchmarking-example.ipynb
rename to introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-benchmarking/inference-benchmarking-example.ipynb
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
+}
diff --git a/archived/notebooks/geospatial/geospatial_pipeline_processing/data/object_boundaries.json b/sagemaker-geospatial/geospatial-processing-pipeline/data/object_boundaries.json
similarity index 100%
rename from archived/notebooks/geospatial/geospatial_pipeline_processing/data/object_boundaries.json
rename to sagemaker-geospatial/geospatial-processing-pipeline/data/object_boundaries.json
diff --git a/archived/notebooks/geospatial/geospatial_pipeline_processing/geospatial_pipeline_processing.ipynb b/sagemaker-geospatial/geospatial-processing-pipeline/geospatial_pipeline_processing.ipynb
similarity index 100%
rename from archived/notebooks/geospatial/geospatial_pipeline_processing/geospatial_pipeline_processing.ipynb
rename to sagemaker-geospatial/geospatial-processing-pipeline/geospatial_pipeline_processing.ipynb
diff --git a/archived/notebooks/geospatial/geospatial_pipeline_processing/images/pipeline-execution.png b/sagemaker-geospatial/geospatial-processing-pipeline/images/pipeline-execution.png
similarity index 100%
rename from archived/notebooks/geospatial/geospatial_pipeline_processing/images/pipeline-execution.png
rename to sagemaker-geospatial/geospatial-processing-pipeline/images/pipeline-execution.png
diff --git a/archived/notebooks/geospatial/geospatial_pipeline_processing/images/processing-geospatial-pipeline.png b/sagemaker-geospatial/geospatial-processing-pipeline/images/processing-geospatial-pipeline.png
similarity index 100%
rename from archived/notebooks/geospatial/geospatial_pipeline_processing/images/processing-geospatial-pipeline.png
rename to sagemaker-geospatial/geospatial-processing-pipeline/images/processing-geospatial-pipeline.png
diff --git a/archived/notebooks/geospatial/segment_naip_geospatial_notebook-cpu_only.ipynb b/sagemaker-geospatial/segment-aerial-naip/segment_naip_geospatial_notebook-cpu_only.ipynb
similarity index 100%
rename from archived/notebooks/geospatial/segment_naip_geospatial_notebook-cpu_only.ipynb
rename to sagemaker-geospatial/segment-aerial-naip/segment_naip_geospatial_notebook-cpu_only.ipynb
diff --git a/archived/notebooks/geospatial/segment_naip_geospatial_notebook.ipynb b/sagemaker-geospatial/segment-aerial-naip/segment_naip_geospatial_notebook.ipynb
similarity index 100%
rename from archived/notebooks/geospatial/segment_naip_geospatial_notebook.ipynb
rename to sagemaker-geospatial/segment-aerial-naip/segment_naip_geospatial_notebook.ipynb
diff --git a/archived/notebooks/geospatial/sentinel1_insar_kumamoto.ipynb b/sagemaker-geospatial/sentinel1-insar-snap/sentinel1_insar_kumamoto.ipynb
similarity index 100%
rename from archived/notebooks/geospatial/sentinel1_insar_kumamoto.ipynb
rename to sagemaker-geospatial/sentinel1-insar-snap/sentinel1_insar_kumamoto.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",
+ ""
+ ]
+ },
+ {
+ "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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\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/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",
")"
]
},
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..5ae034da17
--- /dev/null
+++ b/sagemaker-pipelines/step-decorator/bedrock-examples/fine_tune_bedrock_step_decorator.ipynb
@@ -0,0 +1,1514 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Automate LLM fine-tuning workflows in Amazon Bedrock and Amazon SageMaker using Python decorators.\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",
+ "\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",
+ "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",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# cell 1\n",
+ "!pip install -r requirements.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "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",
+ "HTML(\"\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cell 3\n",
+ "\n",
+ "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": 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()"
+ ]
+ },
+ {
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cell 5\n",
+ "\n",
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ "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": "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": 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",
+ " 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",
+ "\"\"\"\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",
+ ")\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": "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ " 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": "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ ")\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": "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ ")\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": "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cell 12\n",
+ "\n",
+ "# 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 testing 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": "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": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# 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",
+ "\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": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# cell 14\n",
+ "\n",
+ "pipeline.upsert(role_arn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# cell 15\n",
+ "\n",
+ "execution = pipeline.start()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "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": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "# cell 17\n",
+ "execution.wait(delay=60, max_attempts=250)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# cell 18\n",
+ "\n",
+ "execution.list_steps()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cell 19\n",
+ "\n",
+ "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": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# cell 20\n",
+ "\n",
+ "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": null,
+ "metadata": {},
+ "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\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cell 22\n",
+ "\n",
+ "# Delete the SageMaker pipeline\n",
+ "response = pipeline.delete()\n",
+ "print(f'Deleted pipeline {response[\"PipelineArn\"]}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "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",
+ "\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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "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",
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+ {
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+ "memoryGiB": 128,
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+ },
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+ },
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+ },
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+ },
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+ "_isFastLaunch": false,
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 512,
+ "name": "ml.trn1n.32xlarge",
+ "vcpuNum": 128
+ }
+ ],
+ "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": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/sagemaker-pipelines/step-decorator/bedrock-examples/requirements.txt b/sagemaker-pipelines/step-decorator/bedrock-examples/requirements.txt
new file mode 100644
index 0000000000..09c8abbc3b
--- /dev/null
+++ b/sagemaker-pipelines/step-decorator/bedrock-examples/requirements.txt
@@ -0,0 +1,10 @@
+botocore>=1.31.57
+boto3>=1.28.57
+sagemaker>=2.198.1,<3
+typing_extensions
+pypdf
+# urllib3==2.1.0
+ipywidgets==7.7.2
+jsonlines
+datasets==2.15.0
+pandas==2.1.3
\ No newline at end of file
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",
+ "\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: numpy<2.0,>=1.9.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.24.3)\n",
+ "Requirement already satisfied: requests in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.28.2)\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: cloudpickle==2.2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.2.1)\n",
+ "Requirement already satisfied: psutil in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (5.9.4)\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: docker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (6.1.3)\n",
+ "Requirement already satisfied: boto3<2.0,>=1.33.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.34.22)\n",
+ "Requirement already satisfied: PyYAML~=6.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (6.0)\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: google-pasta in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.2.0)\n",
+ "Requirement already satisfied: pathos in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.3.1)\n",
+ "Requirement already satisfied: tqdm in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.66.1)\n",
+ "Requirement already satisfied: platformdirs in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (3.10.0)\n",
+ "Requirement already satisfied: pandas in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.1.0)\n",
+ "Requirement already satisfied: uvicorn==0.22.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.22.0)\n",
+ "Requirement already satisfied: packaging>=20.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (23.1)\n",
+ "Requirement already satisfied: protobuf<5.0,>=3.12 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (3.20.3)\n",
+ "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.0.1)\n",
+ "Requirement already satisfied: urllib3<1.27 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.26.16)\n",
+ "Requirement already satisfied: importlib-metadata<7.0,>=1.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.13.0)\n",
+ "Requirement already satisfied: schema in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.7.5)\n",
+ "Requirement already satisfied: pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (1.10.13)\n",
+ "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (0.27.0)\n",
+ "Requirement already satisfied: click>=7.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (8.1.3)\n",
+ "Requirement already satisfied: h11>=0.8 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (0.14.0)\n",
+ "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (0.10.0)\n",
+ "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (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<2.0,>=1.33.3->sagemaker) (1.34.22)\n",
+ "Requirement already satisfied: zipp>=0.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.17.0)\n",
+ "Requirement already satisfied: websocket-client>=0.32.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from docker->sagemaker) (1.5.1)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (3.0.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (3.4)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (2022.12.7)\n",
+ "Requirement already satisfied: six in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from google-pasta->sagemaker) (1.16.0)\n",
+ "Requirement already satisfied: referencing>=0.28.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.30.2)\n",
+ "Requirement already satisfied: rpds-py>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.10.3)\n",
+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (2023.7.1)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2023.3.post1)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2.8.2)\n",
+ "Requirement already satisfied: tzdata>=2022.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2023.3)\n",
+ "Requirement already satisfied: multiprocess>=0.70.15 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.70.15)\n",
+ "Requirement already satisfied: dill>=0.3.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.7)\n",
+ "Requirement already satisfied: ppft>=1.7.6.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (1.7.6.7)\n",
+ "Requirement already satisfied: pox>=0.3.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.3)\n",
+ "Requirement already satisfied: contextlib2>=0.5.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from schema->sagemaker) (21.6.0)\n",
+ "Requirement already satisfied: typing-extensions>=4.2.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2->fastapi==0.95.2->sagemaker) (4.8.0)\n",
+ "Requirement already satisfied: anyio<5,>=3.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (3.7.1)\n",
+ "Requirement already satisfied: sniffio>=1.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.3.0)\n",
+ "Requirement already satisfied: exceptiongroup in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.1.0)\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\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",
+ "\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) (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) (0.10.0)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (2.8.2)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (1.26.16)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.35.0,>=1.34.22->boto3) (1.16.0)\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[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",
+ "\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: botocore 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 botocore) (1.0.1)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (2.8.2)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (1.26.16)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore) (1.16.0)\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[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": "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}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 1}, {\\\"label\\\": 0}, {\\\"label\\\": 1}, 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+ " \"encoding\": \"JSON\"\n",
+ " },\n",
+ " \"captureData\": {\n",
+ " \"batchTransformOutput\": {\n",
+ " \"data\": 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+ " \"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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\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": "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
+ }
+ ],
+ "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",
+ "\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 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.28.2)\n",
+ "Requirement already satisfied: psutil in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (5.9.4)\n",
+ "Requirement already satisfied: importlib-metadata<7.0,>=1.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.13.0)\n",
+ "Requirement already satisfied: uvicorn==0.22.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.22.0)\n",
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+ "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (0.27.0)\n",
+ "Requirement already satisfied: click>=7.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (8.1.3)\n",
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+ "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (0.10.0)\n",
+ "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (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<2.0,>=1.33.3->sagemaker) (1.34.22)\n",
+ "Requirement already satisfied: zipp>=0.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.17.0)\n",
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+ "Requirement already satisfied: referencing>=0.28.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.30.2)\n",
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+ "Requirement already satisfied: dill>=0.3.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.7)\n",
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+ "Requirement already satisfied: pox>=0.3.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.3)\n",
+ "Requirement already satisfied: contextlib2>=0.5.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from schema->sagemaker) (21.6.0)\n",
+ "Requirement already satisfied: typing-extensions>=4.2.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2->fastapi==0.95.2->sagemaker) (4.8.0)\n",
+ "Requirement already satisfied: anyio<5,>=3.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (3.7.1)\n",
+ "Requirement already satisfied: sniffio>=1.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.3.0)\n",
+ "Requirement already satisfied: exceptiongroup in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.1.0)\n"
+ ]
+ },
+ {
+ "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[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",
+ "\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: 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: s3transfer<0.11.0,>=0.10.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3) (0.10.0)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (1.26.16)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (2.8.2)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.35.0,>=1.34.22->boto3) (1.16.0)\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[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",
+ "\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: botocore in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (1.34.22)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (1.26.16)\n",
+ "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (1.0.1)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (2.8.2)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore) (1.16.0)\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[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",
+ "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": 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",
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+ " \"encoding\": \"JSON\"\n",
+ " },\n",
+ " \"captureData\": {\n",
+ " \"endpointInput\": {\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]}, {\\\"features\\\": [34, 2, 162604, 11, 9, 4, 2, 2, 2, 1, 0, 0, 40, 37]}, {\\\"features\\\": [20, 2, 258509, 11, 9, 4, 6, 3, 2, 1, 0, 0, 40, 37]}, {\\\"features\\\": [27, 2, 446947, 9, 13, 4, 0, 4, 2, 0, 0, 0, 55, 37]}, {\\\"features\\\": [20, 2, 95552, 11, 9, 4, 11, 3, 4, 1, 0, 0, 40, 37]}, {\\\"features\\\": [46, 2, 145636, 11, 9, 2, 3, 0, 4, 1, 3103, 0, 50, 37]}, {\\\"features\\\": [18, 2, 150675, 0, 6, 4, 11, 3, 4, 1, 0, 0, 40, 37]}, {\\\"features\\\": [22, 2, 197050, 11, 9, 4, 7, 3, 4, 0, 0, 0, 20, 37]}, {\\\"features\\\": [20, 2, 246635, 15, 10, 4, 11, 3, 4, 0, 2597, 0, 20, 37]}, {\\\"features\\\": [65, 0, 200764, 11, 9, 6, 0, 1, 4, 0, 0, 0, 40, 37]}, {\\\"features\\\": [38, 2, 175665, 15, 10, 2, 9, 5, 4, 0, 0, 0, 40, 37]}, {\\\"features\\\": [34, 3, 337995, 9, 13, 0, 3, 4, 2, 1, 15020, 0, 50, 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\\\"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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "availableInstances": [
+ {
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+ },
+ {
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+ "name": "python3"
+ },
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+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
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+}
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",
+ "\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: platformdirs in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (3.10.0)\n",
+ "Requirement already satisfied: protobuf<5.0,>=3.12 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (3.20.3)\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: psutil in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (5.9.4)\n",
+ "Requirement already satisfied: docker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (6.1.3)\n",
+ "Requirement already satisfied: PyYAML~=6.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (6.0)\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: requests in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.28.2)\n",
+ "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.0.1)\n",
+ "Requirement already satisfied: tqdm in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.66.1)\n",
+ "Requirement already satisfied: uvicorn==0.22.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.22.0)\n",
+ "Requirement already satisfied: numpy<2.0,>=1.9.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.24.3)\n",
+ "Requirement already satisfied: packaging>=20.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (23.1)\n",
+ "Requirement already satisfied: google-pasta in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.2.0)\n",
+ "Requirement already satisfied: pathos in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.3.1)\n",
+ "Requirement already satisfied: boto3<2.0,>=1.33.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.34.22)\n",
+ "Requirement already satisfied: urllib3<1.27 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.26.16)\n",
+ "Requirement already satisfied: cloudpickle==2.2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.2.1)\n",
+ "Requirement already satisfied: pandas in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.1.0)\n",
+ "Requirement already satisfied: schema in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.7.5)\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: importlib-metadata<7.0,>=1.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.13.0)\n",
+ "Requirement already satisfied: pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (1.10.13)\n",
+ "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (0.27.0)\n",
+ "Requirement already satisfied: h11>=0.8 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (0.14.0)\n",
+ "Requirement already satisfied: click>=7.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (8.1.3)\n",
+ "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (1.0.1)\n",
+ "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (0.10.0)\n",
+ "Requirement already satisfied: botocore<1.35.0,>=1.34.22 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (1.34.22)\n",
+ "Requirement already satisfied: zipp>=0.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.17.0)\n",
+ "Requirement already satisfied: websocket-client>=0.32.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from docker->sagemaker) (1.5.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (3.4)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (2022.12.7)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (3.0.1)\n",
+ "Requirement already satisfied: six in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from google-pasta->sagemaker) (1.16.0)\n",
+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (2023.7.1)\n",
+ "Requirement already satisfied: referencing>=0.28.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.30.2)\n",
+ "Requirement already satisfied: rpds-py>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.10.3)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2023.3.post1)\n",
+ "Requirement already satisfied: tzdata>=2022.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2023.3)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2.8.2)\n",
+ "Requirement already satisfied: multiprocess>=0.70.15 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.70.15)\n",
+ "Requirement already satisfied: ppft>=1.7.6.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (1.7.6.7)\n",
+ "Requirement already satisfied: pox>=0.3.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.3)\n",
+ "Requirement already satisfied: dill>=0.3.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.7)\n",
+ "Requirement already satisfied: contextlib2>=0.5.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from schema->sagemaker) (21.6.0)\n",
+ "Requirement already satisfied: typing-extensions>=4.2.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2->fastapi==0.95.2->sagemaker) (4.8.0)\n",
+ "Requirement already satisfied: anyio<5,>=3.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (3.7.1)\n",
+ "Requirement already satisfied: exceptiongroup in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.1.0)\n",
+ "Requirement already satisfied: sniffio>=1.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.3.0)\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\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",
+ "\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) (0.10.0)\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: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (1.26.16)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (2.8.2)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.35.0,>=1.34.22->boto3) (1.16.0)\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[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",
+ "\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: botocore 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 botocore) (1.0.1)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (1.26.16)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (2.8.2)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore) (1.16.0)\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[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": "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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
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+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
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+}
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",
+ "\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",
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+ "Requirement already satisfied: numpy<2.0,>=1.9.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.24.3)\n",
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+ "Requirement already satisfied: platformdirs in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (3.10.0)\n",
+ "Requirement already satisfied: pandas in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.1.0)\n",
+ "Requirement already satisfied: docker in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (6.1.3)\n",
+ "Requirement already satisfied: packaging>=20.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (23.1)\n",
+ "Requirement already satisfied: urllib3<1.27 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.26.16)\n",
+ "Requirement already satisfied: PyYAML~=6.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (6.0)\n",
+ "Requirement already satisfied: importlib-metadata<7.0,>=1.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (4.13.0)\n",
+ "Requirement already satisfied: schema in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.7.5)\n",
+ "Requirement already satisfied: psutil in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (5.9.4)\n",
+ "Requirement already satisfied: boto3<2.0,>=1.33.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.34.22)\n",
+ "Requirement already satisfied: pathos in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (0.3.1)\n",
+ "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (1.0.1)\n",
+ "Requirement already satisfied: requests in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from sagemaker) (2.28.2)\n",
+ "Requirement already satisfied: pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (1.10.13)\n",
+ "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from fastapi==0.95.2->sagemaker) (0.27.0)\n",
+ "Requirement already satisfied: click>=7.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (8.1.3)\n",
+ "Requirement already satisfied: h11>=0.8 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from uvicorn==0.22.0->sagemaker) (0.14.0)\n",
+ "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3<2.0,>=1.33.3->sagemaker) (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<2.0,>=1.33.3->sagemaker) (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<2.0,>=1.33.3->sagemaker) (0.10.0)\n",
+ "Requirement already satisfied: zipp>=0.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.17.0)\n",
+ "Requirement already satisfied: websocket-client>=0.32.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from docker->sagemaker) (1.5.1)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (3.0.1)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from requests->sagemaker) (2022.12.7)\n",
+ "Requirement already satisfied: six in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from google-pasta->sagemaker) (1.16.0)\n",
+ "Requirement already satisfied: rpds-py>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.10.3)\n",
+ "Requirement already satisfied: referencing>=0.28.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.30.2)\n",
+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from jsonschema->sagemaker) (2023.7.1)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2023.3.post1)\n",
+ "Requirement already satisfied: tzdata>=2022.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2023.3)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pandas->sagemaker) (2.8.2)\n",
+ "Requirement already satisfied: ppft>=1.7.6.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (1.7.6.7)\n",
+ "Requirement already satisfied: pox>=0.3.3 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.3)\n",
+ "Requirement already satisfied: dill>=0.3.7 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.7)\n",
+ "Requirement already satisfied: multiprocess>=0.70.15 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pathos->sagemaker) (0.70.15)\n",
+ "Requirement already satisfied: contextlib2>=0.5.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from schema->sagemaker) (21.6.0)\n",
+ "Requirement already satisfied: typing-extensions>=4.2.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2->fastapi==0.95.2->sagemaker) (4.8.0)\n",
+ "Requirement already satisfied: anyio<5,>=3.4.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (3.7.1)\n",
+ "Requirement already satisfied: exceptiongroup in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.1.0)\n",
+ "Requirement already satisfied: sniffio>=1.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi==0.95.2->sagemaker) (1.3.0)\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -otocore (/local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\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",
+ "\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: s3transfer<0.11.0,>=0.10.0 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from boto3) (0.10.0)\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: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (2.8.2)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore<1.35.0,>=1.34.22->boto3) (1.26.16)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.35.0,>=1.34.22->boto3) (1.16.0)\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[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",
+ "\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: botocore in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (1.34.22)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (2.8.2)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (1.26.16)\n",
+ "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from botocore) (1.0.1)\n",
+ "Requirement already satisfied: six>=1.5 in /local/home/zicanl/.virtualenvs/venv/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore) (1.16.0)\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[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 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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "availableInstances": [
+ {
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diff --git a/sagemaker_model_monitor/fairness_and_explainability_json/model/ll-adult-prediction-model.tar.gz b/sagemaker_model_monitor/fairness_and_explainability_json/model/ll-adult-prediction-model.tar.gz
new file mode 100644
index 0000000000..a066dbdfa3
Binary files /dev/null and b/sagemaker_model_monitor/fairness_and_explainability_json/model/ll-adult-prediction-model.tar.gz differ
diff --git a/sagemaker_model_monitor/fairness_and_explainability_json/test_data/test-dataset.json b/sagemaker_model_monitor/fairness_and_explainability_json/test_data/test-dataset.json
new file mode 100644
index 0000000000..0da5ba0c3f
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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..015493af5b 100644
--- a/sagemaker_model_monitor/index.rst
+++ b/sagemaker_model_monitor/index.rst
@@ -46,3 +46,24 @@ 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
+
+LLM Monitoring
+==============================
+
+.. toctree::
+ :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_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..c70d15c448
--- /dev/null
+++ b/sagemaker_model_monitor/llm_monitor_byoc/byoc_llm_monitor.ipynb
@@ -0,0 +1,1377 @@
+{
+ "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",
+ "\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)**: 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."
+ ]
+ },
+ {
+ "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 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-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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\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": "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
+ }
+ ],
+ "instance_type": "ml.g5.12xlarge",
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.11.7"
+ }
+ },
+ "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"
+ ]
+}
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",
+ "\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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\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",
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+ },
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+ "_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",
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+ },
+ {
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+ "_isFastLaunch": false,
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 256,
+ "name": "ml.m5.16xlarge",
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+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "General purpose",
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 384,
+ "name": "ml.m5.24xlarge",
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+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "General purpose",
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 8,
+ "name": "ml.m5d.large",
+ "vcpuNum": 2
+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "General purpose",
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+ "name": "ml.m5d.xlarge",
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+ },
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+ },
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+ "_isFastLaunch": false,
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+ "gpuNum": 0,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 64,
+ "name": "ml.m5d.4xlarge",
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+ },
+ {
+ "_defaultOrder": 16,
+ "_isFastLaunch": false,
+ "category": "General purpose",
+ "gpuNum": 0,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 128,
+ "name": "ml.m5d.8xlarge",
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+ },
+ {
+ "_defaultOrder": 17,
+ "_isFastLaunch": false,
+ "category": "General purpose",
+ "gpuNum": 0,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 192,
+ "name": "ml.m5d.12xlarge",
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+ },
+ {
+ "_defaultOrder": 18,
+ "_isFastLaunch": false,
+ "category": "General purpose",
+ "gpuNum": 0,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 256,
+ "name": "ml.m5d.16xlarge",
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+ },
+ {
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+ "_isFastLaunch": false,
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 384,
+ "name": "ml.m5d.24xlarge",
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+ },
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+ "_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
+ },
+ {
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+ "_isFastLaunch": false,
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 8,
+ "name": "ml.c5.xlarge",
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+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "Compute optimized",
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+ "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",
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+ },
+ {
+ "_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",
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+ },
+ {
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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,
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+ "name": "ml.g4dn.2xlarge",
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+ },
+ {
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+ "category": "Accelerated computing",
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+ "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",
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 192,
+ "name": "ml.g4dn.12xlarge",
+ "vcpuNum": 48
+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "Accelerated computing",
+ "gpuNum": 1,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 256,
+ "name": "ml.g4dn.16xlarge",
+ "vcpuNum": 64
+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "Accelerated computing",
+ "gpuNum": 1,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 61,
+ "name": "ml.p3.2xlarge",
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+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "Accelerated computing",
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 244,
+ "name": "ml.p3.8xlarge",
+ "vcpuNum": 32
+ },
+ {
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+ "_isFastLaunch": false,
+ "category": "Accelerated computing",
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+ "hideHardwareSpecs": false,
+ "memoryGiB": 488,
+ "name": "ml.p3.16xlarge",
+ "vcpuNum": 64
+ },
+ {
+ "_defaultOrder": 38,
+ "_isFastLaunch": false,
+ "category": "Accelerated computing",
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+ "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
+ }
+ ],
+ "instance_type": "ml.g5.12xlarge",
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/data/questions.jsonl b/sagemaker_model_monitor/llm_multiple_evals_monitor_byoc/data/questions.jsonl
new file mode 100644
index 0000000000..198686d11b
--- /dev/null
+++ b/sagemaker_model_monitor/llm_multiple_evals_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_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"
+ ]
+}
diff --git a/archived/notebooks/smp-gpt-sharded-data-parallel/data_pipeline.py b/training/distributed_training/pytorch/model_parallel/flan-t5/data_pipeline.py
similarity index 100%
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similarity index 100%
rename from archived/notebooks/smp-train-t5-sharded-data-parallel/memory_tracker.py
rename to training/distributed_training/pytorch/model_parallel/gpt2/memory_tracker.py
diff --git a/archived/notebooks/smp-train-t5-sharded-data-parallel/model_config.py b/training/distributed_training/pytorch/model_parallel/gpt2/model_config.py
similarity index 100%
rename from archived/notebooks/smp-train-t5-sharded-data-parallel/model_config.py
rename to training/distributed_training/pytorch/model_parallel/gpt2/model_config.py
diff --git a/archived/notebooks/smp-gpt-sharded-data-parallel/requirements.txt b/training/distributed_training/pytorch/model_parallel/gpt2/requirements.txt
similarity index 100%
rename from archived/notebooks/smp-gpt-sharded-data-parallel/requirements.txt
rename to training/distributed_training/pytorch/model_parallel/gpt2/requirements.txt
diff --git a/archived/notebooks/smp-train-t5-sharded-data-parallel/sdp_utils.py b/training/distributed_training/pytorch/model_parallel/gpt2/sdp_utils.py
similarity index 100%
rename from archived/notebooks/smp-train-t5-sharded-data-parallel/sdp_utils.py
rename to training/distributed_training/pytorch/model_parallel/gpt2/sdp_utils.py
diff --git a/archived/notebooks/smp-gpt-sharded-data-parallel/smp-fine-tune-gpt-sharded-data-parallel.ipynb b/training/distributed_training/pytorch/model_parallel/gpt2/smp-fine-tune-gpt-sharded-data-parallel.ipynb
similarity index 100%
rename from archived/notebooks/smp-gpt-sharded-data-parallel/smp-fine-tune-gpt-sharded-data-parallel.ipynb
rename to 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
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
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..6899131fa3
--- /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",
+ "\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 information about providing permissions, see [Identity and access management for AWS Glue\n",
+ "](https://docs.aws.amazon.com/glue/latest/dg/security-iam.html)."
+ ]
+ },
+ {
+ "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://example-s3-bucket/output/train\",\n",
+ " ),\n",
+ " ProcessingOutput(\n",
+ " source=\"/opt/ml/processing/output/validation\",\n",
+ " destination=\"s3://example-s3-bucket/output/validation\",\n",
+ " ),\n",
+ " ProcessingOutput(\n",
+ " source=\"/opt/ml/processing/output/test\",\n",
+ " destination=\"s3://example-s3-bucket/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://example-s3-bucket/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://example-s3-bucket/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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\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/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",
+ "\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",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ }
+ ],
+ "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
+}