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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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* ...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).
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* ...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).
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* ...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).
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* ...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).
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* ...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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* [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.
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* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
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* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
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* [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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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
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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:
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`ImportError: cannot import name 'AutoMLConfig'`
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This can be resolved by running:
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`pip uninstall azureml-train-automl` and then
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`pip install azureml-train-automl`
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The automl_setup.cmd script does this automatically.
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## workspace.from_config fails
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If the call `ws = Workspace.from_config()` fails:
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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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"from azureml.explain.model._internal.explanation_client import ExplanationClient"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Accessing the Azure ML workspace requires authentication with Azure.\n",
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"\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",
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"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",
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"```\n",
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"from azureml.core.authentication import InteractiveLoginAuthentication\n",
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"auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
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"ws = Workspace.from_config(auth = auth)\n",
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"```\n",
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"\n",
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"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",
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"```\n",
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"from azureml.core.authentication import ServicePrincipalAuthentication\n",
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"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)"
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]
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},
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{
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"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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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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- 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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- azureml-train-automl
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- azureml-widgets
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- matplotlib
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- 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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- azureml-train-automl
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- azureml-widgets
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- matplotlib
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- azurmel-train

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