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update samples from Release-169 as a part of 1.0.85 SDK release (Azure#742)
Co-authored-by: vizhur <[email protected]>
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‎README.md

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Original file line numberDiff line numberDiff line change
@@ -20,8 +20,8 @@ If you want to...
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* ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
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* ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
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* ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
23-
* ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
24-
* ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
23+
* ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
24+
* ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
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* ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
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## Tutorials

‎configuration.ipynb

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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.0.83 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.0.85 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/README.md

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@@ -9,7 +9,6 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
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* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
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* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
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* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
12-
* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
1312
* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
1413
* [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
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‎how-to-use-azureml/automated-machine-learning/README.md

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@@ -197,6 +197,17 @@ If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execut
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4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
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5) Check that you have access to the region using the Azure Portal.
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## import AutoMLConfig fails after upgrade from before 1.0.76 to 1.0.76 or later
201+
There were package changes in automated machine learning version 1.0.76, which require the previous version to be uninstalled before upgrading to the new version.
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If you have manually upgraded from a version of automated machine learning before 1.0.76 to 1.0.76 or later, you may get the error:
203+
`ImportError: cannot import name 'AutoMLConfig'`
204+
205+
This can be resolved by running:
206+
`pip uninstall azureml-train-automl` and then
207+
`pip install azureml-train-automl`
208+
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The automl_setup.cmd script does this automatically.
210+
200211
## workspace.from_config fails
201212
If the call `ws = Workspace.from_config()` fails:
202213
1) Make sure that you have run the `configuration.ipynb` notebook successfully.

‎how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

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@@ -92,6 +92,32 @@
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"from azureml.explain.model._internal.explanation_client import ExplanationClient"
9393
]
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},
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{
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"cell_type": "markdown",
97+
"metadata": {},
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"source": [
99+
"Accessing the Azure ML workspace requires authentication with Azure.\n",
100+
"\n",
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"The default authentication is interactive authentication using the default tenant. Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n",
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"\n",
103+
"If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
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"\n",
105+
"```\n",
106+
"from azureml.core.authentication import InteractiveLoginAuthentication\n",
107+
"auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
108+
"ws = Workspace.from_config(auth = auth)\n",
109+
"```\n",
110+
"\n",
111+
"If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
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"\n",
113+
"```\n",
114+
"from azureml.core.authentication import ServicePrincipalAuthentication\n",
115+
"auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
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"ws = Workspace.from_config(auth = auth)\n",
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"```\n",
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"For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)"
119+
]
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},
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{
96122
"cell_type": "code",
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"execution_count": null,

‎how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml

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@@ -2,3 +2,10 @@ name: auto-ml-classification-bank-marketing-all-features
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dependencies:
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- pip:
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- azureml-sdk
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- azureml-train-automl
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- azureml-widgets
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- matplotlib
8+
- interpret
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- onnxruntime==1.0.0
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- azureml-explain-model
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- azureml-contrib-interpret

‎how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

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"automl_settings = {\n",
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'average_precision_score_weighted',\n",
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" \"preprocess\": True,\n",
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" \"enable_early_stopping\": True,\n",
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" \"max_concurrent_iterations\": 2, # This is a limit for testing purpose, please increase it as per cluster size\n",
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" \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",

‎how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml

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dependencies:
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- pip:
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- azureml-sdk
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- azureml-train-automl
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- azureml-widgets
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- matplotlib
8+
- interpret
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- azureml-explain-model

‎how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

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"automl_settings = {\n",
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" \"experiment_timeout_minutes\": 20,\n",
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" \"primary_metric\": 'accuracy',\n",
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" \"preprocess\": True,\n",
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" \"max_concurrent_iterations\": 4, \n",
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" \"max_cores_per_iteration\": -1,\n",
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" \"enable_dnn\": True,\n",

‎how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml

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dependencies:
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- pip:
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- azureml-sdk
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- azureml-train-automl
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- azureml-widgets
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- matplotlib
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- azurmel-train

‎how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

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" \"experiment_timeout_hours\": 0.2,\n",
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'r2_score',\n",
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" \"preprocess\": True,\n",
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" \"max_concurrent_iterations\": 3,\n",
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" \"max_cores_per_iteration\": -1,\n",
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" \"verbosity\": logging.INFO,\n",

‎how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml

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@@ -2,3 +2,7 @@ name: auto-ml-continuous-retraining
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dependencies:
33
- pip:
44
- azureml-sdk
5+
- azureml-train-automl
6+
- azureml-widgets
7+
- matplotlib
8+
- azureml-pipeline

‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml

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name: auto-ml-forecasting-beer-remote
22
dependencies:
3+
- fbprophet==0.5
4+
- py-xgboost<=0.80
35
- pip:
46
- azureml-sdk
7+
- azureml-train-automl
8+
- azureml-widgets
9+
- matplotlib
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- azureml-train

‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py

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@@ -76,9 +76,12 @@ def get_result_df(remote_run):
7676
def run_inference(test_experiment, compute_target, script_folder, train_run,
7777
test_dataset, lookback_dataset, max_horizon,
7878
target_column_name, time_column_name, freq):
79-
train_run.download_file('outputs/model.pkl', 'inference/model.pkl')
80-
train_run.download_file('outputs/conda_env_v_1_0_0.yml',
81-
'inference/condafile.yml')
79+
model_base_name = 'model.pkl'
80+
if 'model_data_location' in train_run.properties:
81+
model_location = train_run.properties['model_data_location']
82+
_, model_base_name = model_location.rsplit('/', 1)
83+
train_run.download_file('outputs/{}'.format(model_base_name), 'inference/{}'.format(model_base_name))
84+
train_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/condafile.yml')
8285

8386
inference_env = Environment("myenv")
8487
inference_env.docker.enabled = True
@@ -91,7 +94,8 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
9194
'--max_horizon': max_horizon,
9295
'--target_column_name': target_column_name,
9396
'--time_column_name': time_column_name,
94-
'--frequency': freq
97+
'--frequency': freq,
98+
'--model_path': model_base_name
9599
},
96100
inputs=[test_dataset.as_named_input('test_data'),
97101
lookback_dataset.as_named_input('lookback_data')],

‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py

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@@ -232,20 +232,25 @@ def MAPE(actual, pred):
232232
parser.add_argument(
233233
'--frequency', type=str, dest='freq',
234234
help='Frequency of prediction')
235+
parser.add_argument(
236+
'--model_path', type=str, dest='model_path',
237+
default='model.pkl', help='Filename of model to be loaded')
235238

236239

237240
args = parser.parse_args()
238241
max_horizon = args.max_horizon
239242
target_column_name = args.target_column_name
240243
time_column_name = args.time_column_name
241244
freq = args.freq
245+
model_path = args.model_path
242246

243247

244248
print('args passed are: ')
245249
print(max_horizon)
246250
print(target_column_name)
247251
print(time_column_name)
248252
print(freq)
253+
print(model_path)
249254

250255
run = Run.get_context()
251256
# get input dataset by name
@@ -267,7 +272,8 @@ def MAPE(actual, pred):
267272
y_lookback_df = lookback_dataset.with_timestamp_columns(
268273
None).keep_columns(columns=[target_column_name])
269274

270-
fitted_model = joblib.load('model.pkl')
275+
fitted_model = joblib.load(model_path)
276+
271277

272278
if hasattr(fitted_model, 'get_lookback'):
273279
lookback = fitted_model.get_lookback()

‎how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml

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11
name: auto-ml-forecasting-bike-share
22
dependencies:
3+
- fbprophet==0.5
4+
- py-xgboost<=0.80
35
- pip:
46
- azureml-sdk
7+
- azureml-train-automl
8+
- azureml-widgets
9+
- matplotlib

‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

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253253
"source": [
254254
"# split into train based on time\n",
255255
"train = dataset.time_before(datetime(2017, 8, 8, 5), include_boundary=True)\n",
256-
"train.to_pandas_dataframe().sort_values(time_column_name).tail(5).reset_index(drop=True)"
256+
"train.to_pandas_dataframe().reset_index(drop=True).sort_values(time_column_name).tail(5)"
257257
]
258258
},
259259
{
@@ -264,7 +264,7 @@
264264
"source": [
265265
"# split into test based on time\n",
266266
"test = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n",
267-
"test.to_pandas_dataframe().head(5).reset_index(drop=True)"
267+
"test.to_pandas_dataframe().reset_index(drop=True).head(5)"
268268
]
269269
},
270270
{

‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml

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dependencies:
33
- pip:
44
- azureml-sdk
5+
- azureml-train-automl
6+
- azureml-widgets
7+
- matplotlib
8+
- interpret
9+
- azureml-explain-model
10+
- azureml-contrib-interpret

‎how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.yml

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dependencies:
33
- pip:
44
- azureml-sdk
5+
- azureml-train-automl
6+
- azureml-widgets
7+
- matplotlib
8+
- azureml-pipeline

‎how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/deploy.py

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11
import argparse
22
import json
33

4-
from azureml.core import Run, Model, Workspace
5-
from azureml.core.conda_dependencies import CondaDependencies
4+
from azureml.core import Run, Model
65
from azureml.core.model import InferenceConfig
6+
from azureml.core.environment import Environment
77
from azureml.core.webservice import AciWebservice
88

99

@@ -39,18 +39,16 @@
3939
run = Run.get_context()
4040
ws = run.experiment.workspace
4141

42+
myenv = Environment.from_conda_specification(name="env", file_path=conda_env_file_name)
43+
4244
deployment_config = AciWebservice.deploy_configuration(
4345
cpu_cores=1,
4446
memory_gb=2,
4547
tags={"method": "grouping"},
4648
description='grouping demo aci deployment'
4749
)
4850

49-
inference_config = InferenceConfig(
50-
entry_script=script_file_name,
51-
runtime='python',
52-
conda_file=conda_env_file_name
53-
)
51+
inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)
5452

5553
models = []
5654
for model_name in model_list:

‎how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml

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name: automl-forecasting-function
22
dependencies:
3+
- fbprophet==0.5
4+
- py-xgboost<=0.80
35
- pip:
46
- azureml-sdk
7+
- azureml-train-automl
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- azureml-widgets
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- matplotlib

‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

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@@ -631,9 +631,7 @@
631631
"outputs": [],
632632
"source": [
633633
"import json\n",
634-
"# The request data frame needs to have y_query column which corresponds to query.\n",
635634
"X_query = X_test.copy()\n",
636-
"X_query['y_query'] = np.NaN\n",
637635
"# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
638636
"X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
639637
"# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",

‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml

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11
name: auto-ml-forecasting-orange-juice-sales
22
dependencies:
3+
- fbprophet==0.5
4+
- py-xgboost<=0.80
35
- pip:
46
- azureml-sdk
7+
- azureml-train-automl
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- azureml-widgets
9+
- matplotlib

‎how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

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"automl_settings = {\n",
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'average_precision_score_weighted',\n",
158-
" \"preprocess\": True,\n",
159158
" \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n",
160159
" \"verbosity\": logging.INFO,\n",
161160
" \"enable_stack_ensemble\": False\n",

‎how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml

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dependencies:
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- pip:
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- azureml-sdk
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- azureml-train-automl
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- azureml-widgets
7+
- matplotlib
8+
- interpret
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- azureml-explain-model

‎how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb

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@@ -208,7 +208,7 @@
208208
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
209209
"|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
210210
"|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
211-
"|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Note: If the input data is sparse, featurization cannot be turned on.|\n",
211+
"|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n",
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"|**n_cross_validations**|Number of cross validation splits.|\n",
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"|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
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"|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|"
@@ -244,7 +244,7 @@
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"source": [
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"featurization_config = FeaturizationConfig()\n",
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"featurization_config.blocked_transformers = ['LabelEncoder']\n",
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"#featurization_config.drop_columns = ['ERP', 'MMIN']\n",
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"#featurization_config.drop_columns = ['MMIN']\n",
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"featurization_config.add_column_purpose('MYCT', 'Numeric')\n",
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"featurization_config.add_column_purpose('VendorName', 'CategoricalHash')\n",
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"#default strategy mean, add transformer param for for 3 columns\n",

‎how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.yml

+7
Original file line numberDiff line numberDiff line change
@@ -2,3 +2,10 @@ name: auto-ml-regression-hardware-performance-explanation-and-featurization
22
dependencies:
33
- pip:
44
- azureml-sdk
5+
- azureml-train-automl
6+
- azureml-widgets
7+
- matplotlib
8+
- interpret
9+
- azureml-explain-model
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- azureml-explain-model
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- azureml-contrib-interpret

‎how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

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@@ -198,7 +198,6 @@
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"automl_settings = {\n",
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'r2_score',\n",
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" \"preprocess\": True,\n",
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" \"enable_early_stopping\": True, \n",
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" \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
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" \"max_concurrent_iterations\": 4,\n",

‎how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml

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Original file line numberDiff line numberDiff line change
@@ -2,3 +2,6 @@ name: auto-ml-regression
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dependencies:
33
- pip:
44
- azureml-sdk
5+
- azureml-train-automl
6+
- azureml-widgets
7+
- matplotlib

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