update samples from Release-146 as a part of SDK release

Merge pull request #1763 from Azure/release_update/Release-144
update samples from Release-144 as a part of SDK release
2025-12-20 09:37:04 -05:00 · 2022-06-20 20:45:50 +00:00 · 2022-06-03 11:04:13 -07:00 · 2022-06-03 17:28:37 +00:00 · 2022-06-01 11:29:19 -07:00 · 2022-06-01 17:26:59 +00:00
117 changed files with 7593 additions and 3302 deletions
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -103,7 +103,7 @@
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
+++ b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
@@ -188,13 +188,6 @@
        "### Script to process data and train model"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS Mortgage E2E example](https://github.com/rapidsai/notebooks-contrib/blob/master/intermediate_notebooks/E2E/mortgage/mortgage_e2e.ipynb)."
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -373,7 +366,7 @@
        "run_config.target = gpu_cluster_name\n",
        "run_config.environment.docker.enabled = True\n",
        "run_config.environment.docker.gpu_support = True\n",
-        "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/base-gpu:intelmpi2018.3-cuda10.0-cudnn7-ubuntu16.04\"\n",
+        "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04\"\n",
        "run_config.environment.spark.precache_packages = False\n",
        "run_config.data_references={'data':data_ref.to_config()}"
      ]
--- a/contrib/fairness/fairlearn-azureml-mitigation.ipynb
+++ b/contrib/fairness/fairlearn-azureml-mitigation.ipynb
@@ -49,7 +49,7 @@
        "* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
        "* `joblib`\n",
        "* `liac-arff`\n",
-        "* `raiwidgets~=0.7.0`\n",
+        "* `raiwidgets`\n",
        "\n",
        "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
      ]
--- a/contrib/fairness/fairlearn-azureml-mitigation.yml
+++ b/contrib/fairness/fairlearn-azureml-mitigation.yml
@@ -6,4 +6,6 @@ dependencies:
  - fairlearn>=0.6.2
  - joblib
  - liac-arff
-  - raiwidgets~=0.15.0
+  - raiwidgets~=0.18.1
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
--- a/contrib/fairness/upload-fairness-dashboard.ipynb
+++ b/contrib/fairness/upload-fairness-dashboard.ipynb
@@ -51,7 +51,7 @@
        "* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
        "* `joblib`\n",
        "* `liac-arff`\n",
-        "* `raiwidgets~=0.7.0`\n",
+        "* `raiwidgets`\n",
        "\n",
        "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
      ]
--- a/contrib/fairness/upload-fairness-dashboard.yml
+++ b/contrib/fairness/upload-fairness-dashboard.yml
@@ -6,4 +6,6 @@ dependencies:
  - fairlearn>=0.6.2
  - joblib
  - liac-arff
-  - raiwidgets~=0.15.0
+  - raiwidgets~=0.18.1
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -1,29 +1,31 @@
 name: azure_automl
+channels:
+  - conda-forge
+  - pytorch
+  - main
 dependencies:
  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
- pip==21.1.2
- python>=3.5.2,<3.8
- boto3==1.15.18
- matplotlib==2.1.0
- numpy==1.18.5
- cython
- urllib3<1.24
- scipy>=1.4.1,<=1.5.2
- scikit-learn==0.22.1
- pandas==0.25.1
- py-xgboost<=0.90
- conda-forge::fbprophet==0.5
- holidays==0.9.11
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip==20.2.4
+- python>=3.6,<3.9
+- matplotlib==3.2.1
+- py-xgboost==1.3.3
 - pytorch::pytorch=1.4.0
+- conda-forge::fbprophet==0.7.1
 - cudatoolkit=10.1.243
- tornado==6.1.0
+- scipy==1.5.2
+- notebook
+- pywin32==227
+- PySocks==1.7.1
+- jsonschema==4.5.1
+- conda-forge::pyqt==5.12.3

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.37.0
+  - azureml-widgets~=1.42.0
  - pytorch-transformers==1.0.0
-  - spacy==2.1.8
+  - spacy==2.2.4
+  - pystan==2.19.1.1
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.37.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_win32_requirements.txt [--no-deps]
  - arch==4.14
--- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
@@ -1,30 +1,33 @@
 name: azure_automl
+channels:
+  - conda-forge
+  - pytorch
+  - main
 dependencies:
  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
- pip==21.1.2
- python>=3.5.2,<3.8
- nb_conda
- boto3==1.15.18
- matplotlib==2.1.0
- numpy==1.18.5
- cython
- urllib3<1.24
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip==20.2.4
+- python>=3.6,<3.9
+- boto3==1.20.19
+- botocore<=1.23.19
+- matplotlib==3.2.1
+- numpy==1.19.5
+- cython==0.29.14
+- urllib3==1.26.7
 - scipy>=1.4.1,<=1.5.2
 - scikit-learn==0.22.1
- pandas==0.25.1
- py-xgboost<=0.90
- conda-forge::fbprophet==0.5
- holidays==0.9.11
+- py-xgboost<=1.3.3
+- holidays==0.10.3
+- conda-forge::fbprophet==0.7.1
 - pytorch::pytorch=1.4.0
 - cudatoolkit=10.1.243
- tornado==6.1.0

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.37.0
+  - azureml-widgets~=1.42.0
  - pytorch-transformers==1.0.0
-  - spacy==2.1.8
+  - spacy==2.2.4
+  - pystan==2.19.1.1
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.37.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_linux_requirements.txt [--no-deps]
  - arch==4.14
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -1,31 +1,34 @@
 name: azure_automl
+channels:
+  - conda-forge
+  - pytorch
+  - main
 dependencies:
  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
- pip==21.1.2
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip==20.2.4
 - nomkl
- python>=3.5.2,<3.8
- nb_conda
- boto3==1.15.18
- matplotlib==2.1.0
- numpy==1.18.5
- cython
- urllib3<1.24
+- python>=3.6,<3.9
+- boto3==1.20.19
+- botocore<=1.23.19
+- matplotlib==3.2.1
+- numpy==1.19.5
+- cython==0.29.14
+- urllib3==1.26.7
 - scipy>=1.4.1,<=1.5.2
 - scikit-learn==0.22.1
- pandas==0.25.1
- py-xgboost<=0.90
- conda-forge::fbprophet==0.5
- holidays==0.9.11
+- py-xgboost<=1.3.3
+- holidays==0.10.3
+- conda-forge::fbprophet==0.7.1
 - pytorch::pytorch=1.4.0
 - cudatoolkit=9.0
- tornado==6.1.0

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.37.0
+  - azureml-widgets~=1.42.0
  - pytorch-transformers==1.0.0
-  - spacy==2.1.8
+  - spacy==2.2.4
+  - pystan==2.19.1.1
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.37.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_darwin_requirements.txt [--no-deps]
  - arch==4.14
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -30,6 +14,7 @@
        "1. [Results](#Results)\n",
        "1. [Deploy](#Deploy)\n",
        "1. [Test](#Test)\n",
+        "1. [Use auto-generated code for retraining](#Using-the-auto-generated-model-training-code-for-retraining-on-new-data)\n",
        "1. [Acknowledgements](#Acknowledgements)"
      ]
    },
@@ -55,6 +40,7 @@
        "7. Create a container image.\n",
        "8. Create an Azure Container Instance (ACI) service.\n",
        "9. Test the ACI service.\n",
+        "10. Leverage the auto generated training code and use it for retraining on an updated dataset\n",
        "\n",
        "In addition this notebook showcases the following features\n",
        "- **Blocking** certain pipelines\n",
@@ -74,7 +60,9 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "automl-import"
+      },
      "outputs": [],
      "source": [
        "import json\n",
@@ -99,16 +87,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -138,24 +116,27 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "ws-setup"
+      },
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-bmarketing-all'\n",
+        "experiment_name = \"automl-classification-bmarketing-all\"\n",
        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
+        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -176,7 +157,9 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
@@ -188,12 +171,12 @@
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "                                                           max_nodes=6)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "\n",
        "compute_target.wait_for_completion(show_output=True)"
      ]
    },
@@ -226,7 +209,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "data = pd.read_csv(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\")\n",
+        "data = pd.read_csv(\n",
+        "    \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
+        ")\n",
        "data.head()"
      ]
    },
@@ -241,7 +226,12 @@
        "\n",
        "missing_rate = 0.75\n",
        "n_missing_samples = int(np.floor(data.shape[0] * missing_rate))\n",
-        "missing_samples = np.hstack((np.zeros(data.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
+        "missing_samples = np.hstack(\n",
+        "    (\n",
+        "        np.zeros(data.shape[0] - n_missing_samples, dtype=np.bool),\n",
+        "        np.ones(n_missing_samples, dtype=np.bool),\n",
+        "    )\n",
+        ")\n",
        "rng = np.random.RandomState(0)\n",
        "rng.shuffle(missing_samples)\n",
        "missing_features = rng.randint(0, data.shape[1], n_missing_samples)\n",
@@ -254,19 +244,21 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
+        "if not os.path.isdir(\"data\"):\n",
+        "    os.mkdir(\"data\")\n",
        "# Save the train data to a csv to be uploaded to the datastore\n",
        "pd.DataFrame(data).to_csv(\"data/train_data.csv\", index=False)\n",
        "\n",
        "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='bankmarketing', overwrite=True, show_progress=True)\n",
+        "ds.upload(\n",
+        "    src_dir=\"./data\", target_path=\"bankmarketing\", overwrite=True, show_progress=True\n",
+        ")\n",
        "\n",
-        " \n",
        "\n",
        "# Upload the training data as a tabular dataset for access during training on remote compute\n",
-        "train_data = Dataset.Tabular.from_delimited_files(path=ds.path('bankmarketing/train_data.csv'))\n",
+        "train_data = Dataset.Tabular.from_delimited_files(\n",
+        "    path=ds.path(\"bankmarketing/train_data.csv\")\n",
+        ")\n",
        "label = \"y\""
      ]
    },
@@ -326,6 +318,7 @@
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**training_data**|Input dataset, containing both features and label column.|\n",
        "|**label_column_name**|The name of the label column.|\n",
+        "|**enable_code_generation**|Flag to enable generation of training code for each of the models that AutoML is creating.\n",
        "\n",
        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
      ]
@@ -337,33 +330,37 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"experiment_timeout_hours\" : 0.3,\n",
-        "    \"enable_early_stopping\" : True,\n",
+        "    \"experiment_timeout_hours\": 0.3,\n",
+        "    \"enable_early_stopping\": True,\n",
        "    \"iteration_timeout_minutes\": 5,\n",
        "    \"max_concurrent_iterations\": 4,\n",
        "    \"max_cores_per_iteration\": -1,\n",
-        "    #\"n_cross_validations\": 2,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"featurization\": 'auto',\n",
+        "    # \"n_cross_validations\": 2,\n",
+        "    \"primary_metric\": \"AUC_weighted\",\n",
+        "    \"featurization\": \"auto\",\n",
        "    \"verbosity\": logging.INFO,\n",
+        "    \"enable_code_generation\": True,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             compute_target=compute_target,\n",
-        "                             experiment_exit_score = 0.9984,\n",
-        "                             blocked_models = ['KNN','LinearSVM'],\n",
-        "                             enable_onnx_compatible_models=True,\n",
-        "                             training_data = train_data,\n",
-        "                             label_column_name = label,\n",
-        "                             validation_data = validation_dataset,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    compute_target=compute_target,\n",
+        "    experiment_exit_score=0.9984,\n",
+        "    blocked_models=[\"KNN\", \"LinearSVM\"],\n",
+        "    enable_onnx_compatible_models=True,\n",
+        "    training_data=train_data,\n",
+        "    label_column_name=label,\n",
+        "    validation_data=validation_dataset,\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
      "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
      ]
@@ -371,15 +368,19 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "experiment-submit"
+      },
      "outputs": [],
      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
      ]
    },
    {
      "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
      "source": [
        "Run the following cell to access previous runs. Uncomment the cell below and update the run_id."
      ]
@@ -390,9 +391,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
-        "#remote_run"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
+        "# remote_run"
      ]
    },
    {
@@ -430,8 +431,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Download the featuurization summary JSON file locally\n",
-        "best_run.download_file(\"outputs/featurization_summary.json\", \"featurization_summary.json\")\n",
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
        "\n",
        "# Render the JSON as a pandas DataFrame\n",
        "with open(\"featurization_summary.json\", \"r\") as f:\n",
@@ -450,11 +453,14 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "run-details"
+      },
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
+        "\n",
+        "RunDetails(remote_run).show()"
      ]
    },
    {
@@ -473,9 +479,12 @@
      "source": [
        "# Wait for the best model explanation run to complete\n",
        "from azureml.core.run import Run\n",
+        "\n",
        "model_explainability_run_id = remote_run.id + \"_\" + \"ModelExplain\"\n",
        "print(model_explainability_run_id)\n",
-        "model_explainability_run = Run(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "model_explainability_run = Run(\n",
+        "    experiment=experiment, run_id=model_explainability_run_id\n",
+        ")\n",
        "model_explainability_run.wait_for_completion()\n",
        "\n",
        "# Get the best run object\n",
@@ -556,6 +565,7 @@
      "outputs": [],
      "source": [
        "from azureml.automl.runtime.onnx_convert import OnnxConverter\n",
+        "\n",
        "onnx_fl_path = \"./best_model.onnx\"\n",
        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
      ]
@@ -580,13 +590,17 @@
        "\n",
        "from azureml.automl.runtime.onnx_convert import OnnxInferenceHelper\n",
        "\n",
+        "\n",
        "def get_onnx_res(run):\n",
-        "    res_path = 'onnx_resource.json'\n",
-        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
+        "    res_path = \"onnx_resource.json\"\n",
+        "    run.download_file(\n",
+        "        name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path\n",
+        "    )\n",
        "    with open(res_path) as f:\n",
        "        result = json.load(f)\n",
        "    return result\n",
        "\n",
+        "\n",
        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
        "    test_df = test_dataset.to_pandas_dataframe()\n",
        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
@@ -598,7 +612,7 @@
        "    print(pred_onnx)\n",
        "    print(pred_prob_onnx)\n",
        "else:\n",
-        "    print('Please use Python version 3.6 or 3.7 to run the inference helper.')"
+        "    print(\"Please use Python version 3.6 or 3.7 to run the inference helper.\")"
      ]
    },
    {
@@ -609,7 +623,7 @@
        "\n",
        "### Retrieve the Best Model\n",
        "\n",
-        "Below we select the best pipeline from our iterations.  The `get_best_child` method returns the Run object for the best model based on the default primary metric. There are additional flags that can be passed to the method if we want to retrieve the best Run based on any of the other supported metrics, or if we are just interested in the best run among the ONNX compatible runs. As always, you can execute `remote_run.get_best_child??` in a new cell to view the source or docs for the function."
+        "Below we select the best pipeline from our iterations.  The `get_best_child` method returns the Run object for the best model based on the default primary metric. There are additional flags that can be passed to the method if we want to retrieve the best Run based on any of the other supported metrics, or if we are just interested in the best run among the ONNX compatible runs. As always, you can execute `??remote_run.get_best_child` in a new cell to view the source or docs for the function."
      ]
    },
    {
@@ -618,7 +632,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "remote_run.get_best_child??"
+        "??remote_run.get_best_child"
      ]
    },
    {
@@ -647,11 +661,11 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "model_name = best_run.properties['model_name']\n",
+        "model_name = best_run.properties[\"model_name\"]\n",
        "\n",
-        "script_file_name = 'inference/score.py'\n",
+        "script_file_name = \"inference/score.py\"\n",
        "\n",
-        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')"
+        "best_run.download_file(\"outputs/scoring_file_v_1_0_0.py\", \"inference/score.py\")"
      ]
    },
    {
@@ -668,11 +682,15 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "description = 'AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
+        "description = \"AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit\"\n",
        "tags = None\n",
-        "model = remote_run.register_model(model_name = model_name, description = description, tags = tags)\n",
+        "model = remote_run.register_model(\n",
+        "    model_name=model_name, description=description, tags=tags\n",
+        ")\n",
        "\n",
-        "print(remote_run.model_id) # This will be written to the script file later in the notebook."
+        "print(\n",
+        "    remote_run.model_id\n",
+        ")  # This will be written to the script file later in the notebook."
      ]
    },
    {
@@ -690,16 +708,20 @@
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
        "\n",
-        "inference_config = InferenceConfig(environment = best_run.get_environment(), entry_script=script_file_name)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name)\n",
        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
-        "                                               memory_gb = 2, \n",
-        "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
-        "                                               description = 'sample service for Automl Classification')\n",
+        "aciconfig = AciWebservice.deploy_configuration(\n",
+        "    cpu_cores=2,\n",
+        "    memory_gb=2,\n",
+        "    tags={\"area\": \"bmData\", \"type\": \"automl_classification\"},\n",
+        "    description=\"sample service for Automl Classification\",\n",
+        ")\n",
        "\n",
-        "aci_service_name = 'automl-sample-bankmarketing-all'\n",
+        "aci_service_name = model_name.lower()\n",
        "print(aci_service_name)\n",
        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
        "aci_service.wait_for_deployment(True)\n",
@@ -721,7 +743,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#aci_service.get_logs()"
+        "# aci_service.get_logs()"
      ]
    },
    {
@@ -751,8 +773,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "X_test = test_dataset.drop_columns(columns=['y'])\n",
-        "y_test = test_dataset.keep_columns(columns=['y'], validate=True)\n",
+        "X_test = test_dataset.drop_columns(columns=[\"y\"])\n",
+        "y_test = test_dataset.keep_columns(columns=[\"y\"], validate=True)\n",
        "test_dataset.take(5).to_pandas_dataframe()"
      ]
    },
@@ -774,13 +796,13 @@
      "source": [
        "import requests\n",
        "\n",
-        "X_test_json = X_test.to_json(orient='records')\n",
-        "data = \"{\\\"data\\\": \" + X_test_json +\"}\"\n",
-        "headers = {'Content-Type': 'application/json'}\n",
+        "X_test_json = X_test.to_json(orient=\"records\")\n",
+        "data = '{\"data\": ' + X_test_json + \"}\"\n",
+        "headers = {\"Content-Type\": \"application/json\"}\n",
        "\n",
        "resp = requests.post(aci_service.scoring_uri, data, headers=headers)\n",
        "\n",
-        "y_pred = json.loads(json.loads(resp.text))['result']"
+        "y_pred = json.loads(json.loads(resp.text))[\"result\"]"
      ]
    },
    {
@@ -790,7 +812,7 @@
      "outputs": [],
      "source": [
        "actual = array(y_test)\n",
-        "actual = actual[:,0]\n",
+        "actual = actual[:, 0]\n",
        "print(len(y_pred), \" \", len(actual))"
      ]
    },
@@ -806,27 +828,35 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "scrolled": true
+      },
      "outputs": [],
      "source": [
        "%matplotlib notebook\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
-        "cf =confusion_matrix(actual,y_pred)\n",
-        "plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
+        "cf = confusion_matrix(actual, y_pred)\n",
+        "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n",
        "plt.colorbar()\n",
-        "plt.title('Confusion Matrix')\n",
-        "plt.xlabel('Predicted')\n",
-        "plt.ylabel('Actual')\n",
-        "class_labels = ['no','yes']\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Actual\")\n",
+        "class_labels = [\"no\", \"yes\"]\n",
        "tick_marks = np.arange(len(class_labels))\n",
-        "plt.xticks(tick_marks,class_labels)\n",
-        "plt.yticks([-0.5,0,1,1.5],['','no','yes',''])\n",
+        "plt.xticks(tick_marks, class_labels)\n",
+        "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"no\", \"yes\", \"\"])\n",
        "# plotting text value inside cells\n",
-        "thresh = cf.max() / 2.\n",
-        "for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
-        "    plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
+        "thresh = cf.max() / 2.0\n",
+        "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n",
+        "    plt.text(\n",
+        "        j,\n",
+        "        i,\n",
+        "        format(cf[i, j], \"d\"),\n",
+        "        horizontalalignment=\"center\",\n",
+        "        color=\"white\" if cf[i, j] > thresh else \"black\",\n",
+        "    )\n",
        "plt.show()"
      ]
    },
@@ -848,6 +878,142 @@
        "aci_service.delete()"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using the auto generated model training code for retraining on new data\n",
+        "\n",
+        "Because we enabled code generation when the original experiment was created, we now have access to the code that was used to generate any of the AutoML tried models. Below we'll be using the generated training script of the best model to retrain on a new dataset.\n",
+        "\n",
+        "For this demo, we'll begin by creating new retraining dataset by combining the Train & Validation datasets that were used in the original experiment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "original_train_data = pd.read_csv(\n",
+        "    \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
+        ")\n",
+        "\n",
+        "valid_data = pd.read_csv(\n",
+        "    \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
+        ")\n",
+        "\n",
+        "# we'll emulate an updated dataset for retraining by combining the Train & Validation datasets into a new one\n",
+        "retrain_pd = pd.concat([original_train_data, valid_data])\n",
+        "retrain_pd.to_csv(\"data/retrain_data.csv\", index=False)\n",
+        "ds.upload_files(\n",
+        "    files=[\"data/retrain_data.csv\"],\n",
+        "    target_path=\"bankmarketing/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\n",
+        "retrain_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=ds.path(\"bankmarketing/retrain_data.csv\")\n",
+        ")\n",
+        "\n",
+        "# after creating and uploading the retraining dataset, let's register it with the workspace for reuse\n",
+        "retrain_dataset = retrain_dataset.register(\n",
+        "    workspace=ws,\n",
+        "    name=\"Bankmarketing_retrain\",\n",
+        "    description=\"Updated training dataset, includes validation data\",\n",
+        "    create_new_version=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we'll download the generated script for the best run and use it for retraining. For more advanced scenarios, you can customize the training script as you need: change the featurization pipeline, change the learner algorithm or its hyperparameters, etc. \n",
+        "\n",
+        "For this exercise, we'll leave the script as it was generated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# download the autogenerated training script into the generated_code folder\n",
+        "best_run.download_file(\n",
+        "    \"outputs/generated_code/script.py\", \"generated_code/training_script.py\"\n",
+        ")\n",
+        "\n",
+        "# view the contents of the autogenerated training script\n",
+        "! cat generated_code/training_script.py"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import uuid\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "from azureml._restclient.models import RunTypeV2\n",
+        "from azureml._restclient.models.create_run_dto import CreateRunDto\n",
+        "from azureml._restclient.run_client import RunClient\n",
+        "\n",
+        "codegen_runid = str(uuid.uuid4())\n",
+        "client = RunClient(\n",
+        "    experiment.workspace.service_context,\n",
+        "    experiment.name,\n",
+        "    codegen_runid,\n",
+        "    experiment_id=experiment.id,\n",
+        ")\n",
+        "\n",
+        "# override the training_dataset_id to point to our new retraining dataset we just registered above\n",
+        "dataset_arguments = [\"--training_dataset_id\", retrain_dataset.id]\n",
+        "\n",
+        "# create the retraining run as a child of the AutoML generated training run\n",
+        "create_run_dto = CreateRunDto(\n",
+        "    run_id=codegen_runid,\n",
+        "    parent_run_id=best_run.id,\n",
+        "    description=\"AutoML Codegen Script Run using an updated training dataset\",\n",
+        "    target=cpu_cluster_name,\n",
+        "    run_type_v2=RunTypeV2(orchestrator=\"Execution\", traits=[\"automl-codegen\"]),\n",
+        ")\n",
+        "\n",
+        "# the script for retraining run is pointing to the AutoML generated script\n",
+        "src = ScriptRunConfig(\n",
+        "    source_directory=\"generated_code\",\n",
+        "    script=\"training_script.py\",\n",
+        "    arguments=dataset_arguments,\n",
+        "    compute_target=cpu_cluster_name,\n",
+        "    environment=best_run.get_environment(),\n",
+        ")\n",
+        "run_dto = client.create_run(run_id=codegen_runid, create_run_dto=create_run_dto)\n",
+        "\n",
+        "# submit the experiment\n",
+        "retraining_run = experiment.submit(config=src, run_id=codegen_runid)\n",
+        "retraining_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After the run completes, we can get download/test/deploy to the model it has built."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "retraining_run.wait_for_completion()\n",
+        "\n",
+        "retraining_run.download_file(\"outputs/model.pkl\", \"generated_code/model.pkl\")"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -890,6 +1056,9 @@
    ],
    "friendly_name": "Automated ML run with basic edition features.",
    "index_order": 5,
+    "kernel_info": {
+      "name": "python3-azureml"
+    },
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
@@ -905,7 +1074,10 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
    },
    "tags": [
      "featurization",
@@ -916,5 +1088,5 @@
    "task": "Classification"
  },
  "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 1
 }
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -87,16 +71,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -106,18 +80,19 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-ccard-remote'\n",
+        "experiment_name = \"automl-classification-ccard-remote\"\n",
        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
+        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -150,12 +125,12 @@
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "                                                           max_nodes=6)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "\n",
        "compute_target.wait_for_completion(show_output=True)"
      ]
    },
@@ -178,13 +153,15 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "load-data"
+      },
      "outputs": [],
      "source": [
        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
-        "label_column_name = 'Class'"
+        "label_column_name = \"Class\""
      ]
    },
    {
@@ -210,25 +187,28 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "automl-config"
+      },
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
+        "    \"primary_metric\": \"average_precision_score_weighted\",\n",
        "    \"enable_early_stopping\": True,\n",
-        "    \"max_concurrent_iterations\": 2, # This is a limit for testing purpose, please increase it as per cluster size\n",
-        "    \"experiment_timeout_hours\": 0.25, # 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",
+        "    \"max_concurrent_iterations\": 2,  # This is a limit for testing purpose, please increase it as per cluster size\n",
+        "    \"experiment_timeout_hours\": 0.25,  # 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",
        "    \"verbosity\": logging.INFO,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             compute_target = compute_target,\n",
-        "                             training_data = training_data,\n",
-        "                             label_column_name = label_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    compute_target=compute_target,\n",
+        "    training_data=training_data,\n",
+        "    label_column_name=label_column_name,\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
@@ -244,7 +224,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
      ]
    },
    {
@@ -254,8 +234,8 @@
      "outputs": [],
      "source": [
        "# If you need to retrieve a run that already started, use the following code\n",
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
      ]
    },
    {
@@ -287,6 +267,7 @@
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
+        "\n",
        "RunDetails(remote_run).show()"
      ]
    },
@@ -353,8 +334,12 @@
      "outputs": [],
      "source": [
        "# convert the test data to dataframe\n",
-        "X_test_df = validation_data.drop_columns(columns=[label_column_name]).to_pandas_dataframe()\n",
-        "y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True).to_pandas_dataframe()"
+        "X_test_df = validation_data.drop_columns(\n",
+        "    columns=[label_column_name]\n",
+        ").to_pandas_dataframe()\n",
+        "y_test_df = validation_data.keep_columns(\n",
+        "    columns=[label_column_name], validate=True\n",
+        ").to_pandas_dataframe()"
      ]
    },
    {
@@ -388,20 +373,26 @@
        "import numpy as np\n",
        "import itertools\n",
        "\n",
-        "cf =confusion_matrix(y_test_df.values,y_pred)\n",
-        "plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
+        "cf = confusion_matrix(y_test_df.values, y_pred)\n",
+        "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n",
        "plt.colorbar()\n",
-        "plt.title('Confusion Matrix')\n",
-        "plt.xlabel('Predicted')\n",
-        "plt.ylabel('Actual')\n",
-        "class_labels = ['False','True']\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Actual\")\n",
+        "class_labels = [\"False\", \"True\"]\n",
        "tick_marks = np.arange(len(class_labels))\n",
-        "plt.xticks(tick_marks,class_labels)\n",
-        "plt.yticks([-0.5,0,1,1.5],['','False','True',''])\n",
+        "plt.xticks(tick_marks, class_labels)\n",
+        "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"False\", \"True\", \"\"])\n",
        "# plotting text value inside cells\n",
-        "thresh = cf.max() / 2.\n",
-        "for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
-        "    plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
+        "thresh = cf.max() / 2.0\n",
+        "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n",
+        "    plt.text(\n",
+        "        j,\n",
+        "        i,\n",
+        "        format(cf[i, j], \"d\"),\n",
+        "        horizontalalignment=\"center\",\n",
+        "        color=\"white\" if cf[i, j] > thresh else \"black\",\n",
+        "    )\n",
        "plt.show()"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -78,7 +62,7 @@
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.core.run import Run\n",
        "from azureml.widgets import RunDetails\n",
-        "from azureml.core.model import Model \n",
+        "from azureml.core.model import Model\n",
        "from helper import run_inference, get_result_df\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from sklearn.datasets import fetch_20newsgroups"
@@ -91,16 +75,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -117,18 +91,19 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose an experiment name.\n",
-        "experiment_name = 'automl-classification-text-dnn'\n",
+        "experiment_name = \"automl-classification-text-dnn\"\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace Name\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -161,13 +136,16 @@
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_DS12_V2\" \n",
-        "                                                           # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\" \n",
-        "                                                           # or similar GPU option\n",
-        "                                                           # available in your workspace\n",
-        "                                                           max_nodes = num_nodes)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_NC6\",  # CPU for BiLSTM, such as \"STANDARD_D2_V2\"\n",
+        "        # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\"\n",
+        "        # or similar GPU option\n",
+        "        # available in your workspace\n",
+        "        idle_seconds_before_scaledown=60,\n",
+        "        max_nodes=num_nodes,\n",
+        "    )\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True)"
@@ -187,41 +165,55 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "data_dir = \"text-dnn-data\" # Local directory to store data\n",
-        "blobstore_datadir = data_dir # Blob store directory to store data in\n",
-        "target_column_name = 'y'\n",
-        "feature_column_name = 'X'\n",
+        "data_dir = \"text-dnn-data\"  # Local directory to store data\n",
+        "blobstore_datadir = data_dir  # Blob store directory to store data in\n",
+        "target_column_name = \"y\"\n",
+        "feature_column_name = \"X\"\n",
+        "\n",
        "\n",
        "def get_20newsgroups_data():\n",
-        "    '''Fetches 20 Newsgroups data from scikit-learn\n",
-        "       Returns them in form of pandas dataframes\n",
-        "    '''\n",
-        "    remove = ('headers', 'footers', 'quotes')\n",
+        "    \"\"\"Fetches 20 Newsgroups data from scikit-learn\n",
+        "    Returns them in form of pandas dataframes\n",
+        "    \"\"\"\n",
+        "    remove = (\"headers\", \"footers\", \"quotes\")\n",
        "    categories = [\n",
-        "        'rec.sport.baseball',\n",
-        "        'rec.sport.hockey',\n",
-        "        'comp.graphics',\n",
-        "        'sci.space',\n",
-        "        ]\n",
+        "        \"rec.sport.baseball\",\n",
+        "        \"rec.sport.hockey\",\n",
+        "        \"comp.graphics\",\n",
+        "        \"sci.space\",\n",
+        "    ]\n",
        "\n",
-        "    data = fetch_20newsgroups(subset = 'train', categories = categories,\n",
-        "                                    shuffle = True, random_state = 42,\n",
-        "                                    remove = remove)\n",
-        "    data = pd.DataFrame({feature_column_name: data.data, target_column_name: data.target})\n",
+        "    data = fetch_20newsgroups(\n",
+        "        subset=\"train\",\n",
+        "        categories=categories,\n",
+        "        shuffle=True,\n",
+        "        random_state=42,\n",
+        "        remove=remove,\n",
+        "    )\n",
+        "    data = pd.DataFrame(\n",
+        "        {feature_column_name: data.data, target_column_name: data.target}\n",
+        "    )\n",
        "\n",
        "    data_train = data[:200]\n",
-        "    data_test = data[200:300]    \n",
+        "    data_test = data[200:300]\n",
        "\n",
-        "    data_train = remove_blanks_20news(data_train, feature_column_name, target_column_name)\n",
+        "    data_train = remove_blanks_20news(\n",
+        "        data_train, feature_column_name, target_column_name\n",
+        "    )\n",
        "    data_test = remove_blanks_20news(data_test, feature_column_name, target_column_name)\n",
-        "    \n",
+        "\n",
        "    return data_train, data_test\n",
-        "    \n",
+        "\n",
+        "\n",
        "def remove_blanks_20news(data, feature_column_name, target_column_name):\n",
-        "    \n",
-        "    data[feature_column_name] = data[feature_column_name].replace(r'\\n', ' ', regex=True).apply(lambda x: x.strip())\n",
-        "    data = data[data[feature_column_name] != '']\n",
-        "    \n",
+        "\n",
+        "    data[feature_column_name] = (\n",
+        "        data[feature_column_name]\n",
+        "        .replace(r\"\\n\", \" \", regex=True)\n",
+        "        .apply(lambda x: x.strip())\n",
+        "    )\n",
+        "    data = data[data[feature_column_name] != \"\"]\n",
+        "\n",
        "    return data"
      ]
    },
@@ -242,16 +234,15 @@
        "\n",
        "if not os.path.isdir(data_dir):\n",
        "    os.mkdir(data_dir)\n",
-        "    \n",
-        "train_data_fname = data_dir + '/train_data.csv'\n",
-        "test_data_fname = data_dir + '/test_data.csv'\n",
+        "\n",
+        "train_data_fname = data_dir + \"/train_data.csv\"\n",
+        "test_data_fname = data_dir + \"/test_data.csv\"\n",
        "\n",
        "data_train.to_csv(train_data_fname, index=False)\n",
        "data_test.to_csv(test_data_fname, index=False)\n",
        "\n",
        "datastore = ws.get_default_datastore()\n",
-        "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir,\n",
-        "                    overwrite=True)"
+        "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir, overwrite=True)"
      ]
    },
    {
@@ -260,7 +251,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "train_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/train_data.csv')])"
+        "train_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, blobstore_datadir + \"/train_data.csv\")]\n",
+        ")"
      ]
    },
    {
@@ -285,8 +278,8 @@
      "source": [
        "automl_settings = {\n",
        "    \"experiment_timeout_minutes\": 30,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"max_concurrent_iterations\": num_nodes, \n",
+        "    \"primary_metric\": \"accuracy\",\n",
+        "    \"max_concurrent_iterations\": num_nodes,\n",
        "    \"max_cores_per_iteration\": -1,\n",
        "    \"enable_dnn\": True,\n",
        "    \"enable_early_stopping\": True,\n",
@@ -296,14 +289,15 @@
        "    \"enable_stack_ensemble\": False,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             compute_target=compute_target,\n",
-        "                             training_data=train_dataset,\n",
-        "                             label_column_name=target_column_name,\n",
-        "                             blocked_models = ['LightGBM', 'XGBoostClassifier'],\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    compute_target=compute_target,\n",
+        "    training_data=train_dataset,\n",
+        "    label_column_name=target_column_name,\n",
+        "    blocked_models=[\"LightGBM\", \"XGBoostClassifier\"],\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
@@ -342,8 +336,7 @@
      "metadata": {},
      "source": [
        "For local inferencing, you can load the model locally via. the method `remote_run.get_output()`. For more information on the arguments expected by this method, you can run `remote_run.get_output??`.\n",
-        "Note that when the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your MachineLearningNotebooks folder here:\n",
-        "MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl_env.yml\n"
+        "Note that when the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your azureml-examples folder here: \"azureml-examples/python-sdk/tutorials/automl-with-azureml\""
      ]
    },
    {
@@ -369,15 +362,17 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Download the featuurization summary JSON file locally\n",
-        "best_run.download_file(\"outputs/featurization_summary.json\", \"featurization_summary.json\")\n",
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
        "\n",
        "# Render the JSON as a pandas DataFrame\n",
        "with open(\"featurization_summary.json\", \"r\") as f:\n",
        "    records = json.load(f)\n",
        "\n",
        "featurization_summary = pd.DataFrame.from_records(records)\n",
-        "featurization_summary['Transformations'].tolist()"
+        "featurization_summary[\"Transformations\"].tolist()"
      ]
    },
    {
@@ -402,7 +397,7 @@
      "outputs": [],
      "source": [
        "summary_df = get_result_df(automl_run)\n",
-        "best_dnn_run_id = summary_df['run_id'].iloc[0]\n",
+        "best_dnn_run_id = summary_df[\"run_id\"].iloc[0]\n",
        "best_dnn_run = Run(experiment, best_dnn_run_id)"
      ]
    },
@@ -412,11 +407,11 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "model_dir = 'Model' # Local folder where the model will be stored temporarily\n",
+        "model_dir = \"Model\"  # Local folder where the model will be stored temporarily\n",
        "if not os.path.isdir(model_dir):\n",
        "    os.mkdir(model_dir)\n",
-        "    \n",
-        "best_dnn_run.download_file('outputs/model.pkl', model_dir + '/model.pkl')"
+        "\n",
+        "best_dnn_run.download_file(\"outputs/model.pkl\", model_dir + \"/model.pkl\")"
      ]
    },
    {
@@ -433,11 +428,10 @@
      "outputs": [],
      "source": [
        "# Register the model\n",
-        "model_name = 'textDNN-20News'\n",
-        "model = Model.register(model_path = model_dir + '/model.pkl',\n",
-        "                       model_name = model_name,\n",
-        "                       tags=None,\n",
-        "                       workspace=ws)"
+        "model_name = \"textDNN-20News\"\n",
+        "model = Model.register(\n",
+        "    model_path=model_dir + \"/model.pkl\", model_name=model_name, tags=None, workspace=ws\n",
+        ")"
      ]
    },
    {
@@ -462,7 +456,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/test_data.csv')])\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, blobstore_datadir + \"/test_data.csv\")]\n",
+        ")\n",
        "\n",
        "# preview the first 3 rows of the dataset\n",
        "test_dataset.take(3).to_pandas_dataframe()"
@@ -483,9 +479,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "script_folder = os.path.join(os.getcwd(), 'inference')\n",
+        "script_folder = os.path.join(os.getcwd(), \"inference\")\n",
        "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy('infer.py', script_folder)"
+        "shutil.copy(\"infer.py\", script_folder)"
      ]
    },
    {
@@ -494,8 +490,15 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
-        "                         test_dataset, target_column_name, model_name)"
+        "test_run = run_inference(\n",
+        "    test_experiment,\n",
+        "    compute_target,\n",
+        "    script_folder,\n",
+        "    best_dnn_run,\n",
+        "    test_dataset,\n",
+        "    target_column_name,\n",
+        "    model_name,\n",
+        ")"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py
+++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py
@@ -4,52 +4,65 @@ from azureml.train.estimator import Estimator
 from azureml.core.run import Run


-def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  test_dataset, target_column_name, model_name):
+def run_inference(
+    test_experiment,
+    compute_target,
+    script_folder,
+    train_run,
+    test_dataset,
+    target_column_name,
+    model_name,
+):

    inference_env = train_run.get_environment()

-    est = Estimator(source_directory=script_folder,
-                    entry_script='infer.py',
-                    script_params={
-                        '--target_column_name': target_column_name,
-                        '--model_name': model_name
-                    },
-                    inputs=[
-                        test_dataset.as_named_input('test_data')
-                    ],
-                    compute_target=compute_target,
-                    environment_definition=inference_env)
+    est = Estimator(
+        source_directory=script_folder,
+        entry_script="infer.py",
+        script_params={
+            "--target_column_name": target_column_name,
+            "--model_name": model_name,
+        },
+        inputs=[test_dataset.as_named_input("test_data")],
+        compute_target=compute_target,
+        environment_definition=inference_env,
+    )

    run = test_experiment.submit(
-        est, tags={
-            'training_run_id': train_run.id,
-            'run_algorithm': train_run.properties['run_algorithm'],
-            'valid_score': train_run.properties['score'],
-            'primary_metric': train_run.properties['primary_metric']
-        })
+        est,
+        tags={
+            "training_run_id": train_run.id,
+            "run_algorithm": train_run.properties["run_algorithm"],
+            "valid_score": train_run.properties["score"],
+            "primary_metric": train_run.properties["primary_metric"],
+        },
+    )

-    run.log("run_algorithm", run.tags['run_algorithm'])
+    run.log("run_algorithm", run.tags["run_algorithm"])
    return run


 def get_result_df(remote_run):

    children = list(remote_run.get_children(recursive=True))
-    summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
-                                     'primary_metric', 'Score'])
+    summary_df = pd.DataFrame(
+        index=["run_id", "run_algorithm", "primary_metric", "Score"]
+    )
    goal_minimize = False
    for run in children:
-        if('run_algorithm' in run.properties and 'score' in run.properties):
-            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
-                                  run.properties['primary_metric'],
-                                  float(run.properties['score'])]
-            if('goal' in run.properties):
-                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
+        if "run_algorithm" in run.properties and "score" in run.properties:
+            summary_df[run.id] = [
+                run.id,
+                run.properties["run_algorithm"],
+                run.properties["primary_metric"],
+                float(run.properties["score"]),
+            ]
+            if "goal" in run.properties:
+                goal_minimize = run.properties["goal"].split("_")[-1] == "min"

    summary_df = summary_df.T.sort_values(
-        'Score',
-        ascending=goal_minimize).drop_duplicates(['run_algorithm'])
-    summary_df = summary_df.set_index('run_algorithm')
+        "Score", ascending=goal_minimize
+    ).drop_duplicates(["run_algorithm"])
+    summary_df = summary_df.set_index("run_algorithm")

    return summary_df
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py
+++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py
@@ -12,19 +12,22 @@ from azureml.core.model import Model

 parser = argparse.ArgumentParser()
 parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
+    "--target_column_name",
+    type=str,
+    dest="target_column_name",
+    help="Target Column Name",
+)
 parser.add_argument(
-    '--model_name', type=str, dest='model_name',
-    help='Name of registered model')
+    "--model_name", type=str, dest="model_name", help="Name of registered model"
+)

 args = parser.parse_args()
 target_column_name = args.target_column_name
 model_name = args.model_name

-print('args passed are: ')
-print('Target column name: ', target_column_name)
-print('Name of registered model: ', model_name)
+print("args passed are: ")
+print("Target column name: ", target_column_name)
+print("Name of registered model: ", model_name)

 model_path = Model.get_model_path(model_name)
 # deserialize the model file back into a sklearn model
@@ -32,13 +35,16 @@ model = joblib.load(model_path)

 run = Run.get_context()
 # get input dataset by name
-test_dataset = run.input_datasets['test_data']
+test_dataset = run.input_datasets["test_data"]

-X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
-                        .to_pandas_dataframe()
-y_test_df = test_dataset.with_timestamp_columns(None) \
-                        .keep_columns(columns=[target_column_name]) \
-                        .to_pandas_dataframe()
+X_test_df = test_dataset.drop_columns(
+    columns=[target_column_name]
+).to_pandas_dataframe()
+y_test_df = (
+    test_dataset.with_timestamp_columns(None)
+    .keep_columns(columns=[target_column_name])
+    .to_pandas_dataframe()
+)

 predicted = model.predict_proba(X_test_df)

@@ -47,11 +53,13 @@ if isinstance(predicted, pd.DataFrame):

 # Use the AutoML scoring module
 train_labels = model.classes_
-class_labels = np.unique(np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1)))))
+class_labels = np.unique(
+    np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1))))
+)
 classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
-scores = scoring.score_classification(y_test_df.values, predicted,
-                                      classification_metrics,
-                                      class_labels, train_labels)
+scores = scoring.score_classification(
+    y_test_df.values, predicted, classification_metrics, class_labels, train_labels
+)

 print("scores:")
 print(scores)
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -1,20 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/continous-retraining/auto-ml-continuous-retraining.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -75,16 +60,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -118,17 +93,18 @@
        "dstor = ws.get_default_datastore()\n",
        "\n",
        "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'retrain-noaaweather'\n",
+        "experiment_name = \"retrain-noaaweather\"\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -164,12 +140,12 @@
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "                                                           max_nodes=4)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
-        "\n",
        "compute_target.wait_for_completion(show_output=True)"
      ]
    },
@@ -196,12 +172,19 @@
        "\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n",
-        "                              conda_packages=['numpy==1.16.2'], \n",
-        "                              pin_sdk_version=False)\n",
+        "cd = CondaDependencies.create(\n",
+        "    pip_packages=[\n",
+        "        \"azureml-sdk[automl]\",\n",
+        "        \"applicationinsights\",\n",
+        "        \"azureml-opendatasets\",\n",
+        "        \"azureml-defaults\",\n",
+        "    ],\n",
+        "    conda_packages=[\"numpy==1.19.5\"],\n",
+        "    pin_sdk_version=False,\n",
+        ")\n",
        "conda_run_config.environment.python.conda_dependencies = cd\n",
        "\n",
-        "print('run config is ready')"
+        "print(\"run config is ready\")"
      ]
    },
    {
@@ -218,7 +201,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# The name and target column of the Dataset to create \n",
+        "# The name and target column of the Dataset to create\n",
        "dataset = \"NOAA-Weather-DS4\"\n",
        "target_column_name = \"temperature\""
      ]
@@ -242,12 +225,14 @@
        "from azureml.pipeline.steps import PythonScriptStep\n",
        "\n",
        "ds_name = PipelineParameter(name=\"ds_name\", default_value=dataset)\n",
-        "upload_data_step = PythonScriptStep(script_name=\"upload_weather_data.py\", \n",
-        "                                         allow_reuse=False,\n",
-        "                                         name=\"upload_weather_data\",\n",
-        "                                         arguments=[\"--ds_name\", ds_name],\n",
-        "                                         compute_target=compute_target, \n",
-        "                                         runconfig=conda_run_config)"
+        "upload_data_step = PythonScriptStep(\n",
+        "    script_name=\"upload_weather_data.py\",\n",
+        "    allow_reuse=False,\n",
+        "    name=\"upload_weather_data\",\n",
+        "    arguments=[\"--ds_name\", ds_name],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=conda_run_config,\n",
+        ")"
      ]
    },
    {
@@ -264,10 +249,11 @@
      "outputs": [],
      "source": [
        "data_pipeline = Pipeline(\n",
-        "    description=\"pipeline_with_uploaddata\",\n",
-        "    workspace=ws,    \n",
-        "    steps=[upload_data_step])\n",
-        "data_pipeline_run = experiment.submit(data_pipeline, pipeline_parameters={\"ds_name\":dataset})"
+        "    description=\"pipeline_with_uploaddata\", workspace=ws, steps=[upload_data_step]\n",
+        ")\n",
+        "data_pipeline_run = experiment.submit(\n",
+        "    data_pipeline, pipeline_parameters={\"ds_name\": dataset}\n",
+        ")"
      ]
    },
    {
@@ -307,13 +293,14 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "data_prep_step = PythonScriptStep(script_name=\"check_data.py\", \n",
-        "                                         allow_reuse=False,\n",
-        "                                         name=\"check_data\",\n",
-        "                                         arguments=[\"--ds_name\", ds_name,\n",
-        "                                                    \"--model_name\", model_name],\n",
-        "                                         compute_target=compute_target, \n",
-        "                                         runconfig=conda_run_config)"
+        "data_prep_step = PythonScriptStep(\n",
+        "    script_name=\"check_data.py\",\n",
+        "    allow_reuse=False,\n",
+        "    name=\"check_data\",\n",
+        "    arguments=[\"--ds_name\", ds_name, \"--model_name\", model_name],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=conda_run_config,\n",
+        ")"
      ]
    },
    {
@@ -323,6 +310,7 @@
      "outputs": [],
      "source": [
        "from azureml.core import Dataset\n",
+        "\n",
        "train_ds = Dataset.get_by_name(ws, dataset)\n",
        "train_ds = train_ds.drop_columns([\"partition_date\"])"
      ]
@@ -348,21 +336,22 @@
        "    \"iteration_timeout_minutes\": 10,\n",
        "    \"experiment_timeout_hours\": 0.25,\n",
        "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'normalized_root_mean_squared_error',\n",
+        "    \"primary_metric\": \"r2_score\",\n",
        "    \"max_concurrent_iterations\": 3,\n",
        "    \"max_cores_per_iteration\": -1,\n",
        "    \"verbosity\": logging.INFO,\n",
-        "    \"enable_early_stopping\": True\n",
+        "    \"enable_early_stopping\": True,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             path = \".\",\n",
-        "                             compute_target=compute_target,\n",
-        "                             training_data = train_ds,\n",
-        "                             label_column_name = target_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"regression\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    path=\".\",\n",
+        "    compute_target=compute_target,\n",
+        "    training_data=train_ds,\n",
+        "    label_column_name=target_column_name,\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
@@ -373,17 +362,21 @@
      "source": [
        "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
        "\n",
-        "metrics_output_name = 'metrics_output'\n",
-        "best_model_output_name = 'best_model_output'\n",
+        "metrics_output_name = \"metrics_output\"\n",
+        "best_model_output_name = \"best_model_output\"\n",
        "\n",
-        "metrics_data = PipelineData(name='metrics_data',\n",
-        "                           datastore=dstor,\n",
-        "                           pipeline_output_name=metrics_output_name,\n",
-        "                           training_output=TrainingOutput(type='Metrics'))\n",
-        "model_data = PipelineData(name='model_data',\n",
-        "                           datastore=dstor,\n",
-        "                           pipeline_output_name=best_model_output_name,\n",
-        "                           training_output=TrainingOutput(type='Model'))"
+        "metrics_data = PipelineData(\n",
+        "    name=\"metrics_data\",\n",
+        "    datastore=dstor,\n",
+        "    pipeline_output_name=metrics_output_name,\n",
+        "    training_output=TrainingOutput(type=\"Metrics\"),\n",
+        ")\n",
+        "model_data = PipelineData(\n",
+        "    name=\"model_data\",\n",
+        "    datastore=dstor,\n",
+        "    pipeline_output_name=best_model_output_name,\n",
+        "    training_output=TrainingOutput(type=\"Model\"),\n",
+        ")"
      ]
    },
    {
@@ -393,10 +386,11 @@
      "outputs": [],
      "source": [
        "automl_step = AutoMLStep(\n",
-        "    name='automl_module',\n",
+        "    name=\"automl_module\",\n",
        "    automl_config=automl_config,\n",
        "    outputs=[metrics_data, model_data],\n",
-        "    allow_reuse=False)"
+        "    allow_reuse=False,\n",
+        ")"
      ]
    },
    {
@@ -413,13 +407,22 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "register_model_step = PythonScriptStep(script_name=\"register_model.py\",\n",
-        "                                       name=\"register_model\",\n",
-        "                                       allow_reuse=False,\n",
-        "                                       arguments=[\"--model_name\", model_name, \"--model_path\", model_data, \"--ds_name\", ds_name],\n",
-        "                                       inputs=[model_data],\n",
-        "                                       compute_target=compute_target,\n",
-        "                                       runconfig=conda_run_config)"
+        "register_model_step = PythonScriptStep(\n",
+        "    script_name=\"register_model.py\",\n",
+        "    name=\"register_model\",\n",
+        "    allow_reuse=False,\n",
+        "    arguments=[\n",
+        "        \"--model_name\",\n",
+        "        model_name,\n",
+        "        \"--model_path\",\n",
+        "        model_data,\n",
+        "        \"--ds_name\",\n",
+        "        ds_name,\n",
+        "    ],\n",
+        "    inputs=[model_data],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=conda_run_config,\n",
+        ")"
      ]
    },
    {
@@ -437,8 +440,9 @@
      "source": [
        "training_pipeline = Pipeline(\n",
        "    description=\"training_pipeline\",\n",
-        "    workspace=ws,    \n",
-        "    steps=[data_prep_step, automl_step, register_model_step])"
+        "    workspace=ws,\n",
+        "    steps=[data_prep_step, automl_step, register_model_step],\n",
+        ")"
      ]
    },
    {
@@ -447,8 +451,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "training_pipeline_run = experiment.submit(training_pipeline, pipeline_parameters={\n",
-        "        \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
+        "training_pipeline_run = experiment.submit(\n",
+        "    training_pipeline,\n",
+        "    pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
+        ")"
      ]
    },
    {
@@ -477,8 +483,8 @@
        "pipeline_name = \"Retraining-Pipeline-NOAAWeather\"\n",
        "\n",
        "published_pipeline = training_pipeline.publish(\n",
-        "    name=pipeline_name, \n",
-        "    description=\"Pipeline that retrains AutoML model\")\n",
+        "    name=pipeline_name, description=\"Pipeline that retrains AutoML model\"\n",
+        ")\n",
        "\n",
        "published_pipeline"
      ]
@@ -490,13 +496,17 @@
      "outputs": [],
      "source": [
        "from azureml.pipeline.core import Schedule\n",
-        "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule\",\n",
-        "                           pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
-        "                           pipeline_id=published_pipeline.id, \n",
-        "                           experiment_name=experiment_name, \n",
-        "                           datastore=dstor,\n",
-        "                           wait_for_provisioning=True,\n",
-        "                           polling_interval=1440)"
+        "\n",
+        "schedule = Schedule.create(\n",
+        "    workspace=ws,\n",
+        "    name=\"RetrainingSchedule\",\n",
+        "    pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
+        "    pipeline_id=published_pipeline.id,\n",
+        "    experiment_name=experiment_name,\n",
+        "    datastore=dstor,\n",
+        "    wait_for_provisioning=True,\n",
+        "    polling_interval=1440,\n",
+        ")"
      ]
    },
    {
@@ -520,8 +530,8 @@
        "pipeline_name = \"DataIngestion-Pipeline-NOAAWeather\"\n",
        "\n",
        "published_pipeline = training_pipeline.publish(\n",
-        "    name=pipeline_name, \n",
-        "    description=\"Pipeline that updates NOAAWeather Dataset\")\n",
+        "    name=pipeline_name, description=\"Pipeline that updates NOAAWeather Dataset\"\n",
+        ")\n",
        "\n",
        "published_pipeline"
      ]
@@ -533,13 +543,17 @@
      "outputs": [],
      "source": [
        "from azureml.pipeline.core import Schedule\n",
-        "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule-DataIngestion\",\n",
-        "                           pipeline_parameters={\"ds_name\":dataset},\n",
-        "                           pipeline_id=published_pipeline.id, \n",
-        "                           experiment_name=experiment_name, \n",
-        "                           datastore=dstor,\n",
-        "                           wait_for_provisioning=True,\n",
-        "                           polling_interval=1440)"
+        "\n",
+        "schedule = Schedule.create(\n",
+        "    workspace=ws,\n",
+        "    name=\"RetrainingSchedule-DataIngestion\",\n",
+        "    pipeline_parameters={\"ds_name\": dataset},\n",
+        "    pipeline_id=published_pipeline.id,\n",
+        "    experiment_name=experiment_name,\n",
+        "    datastore=dstor,\n",
+        "    wait_for_provisioning=True,\n",
+        "    polling_interval=1440,\n",
+        ")"
      ]
    }
  ],
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py
@@ -31,7 +31,7 @@ try:
    model = Model(ws, args.model_name)
    last_train_time = model.created_time
    print("Model was last trained on {0}.".format(last_train_time))
-except Exception:
+except Exception as e:
    print("Could not get last model train time.")
    last_train_time = datetime.min.replace(tzinfo=pytz.UTC)

--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py
@@ -25,9 +25,11 @@ datasets = [(Dataset.Scenario.TRAINING, train_ds)]

 # Register model with training dataset

-model = Model.register(workspace=ws,
-                       model_path=args.model_path,
-                       model_name=args.model_name,
-                       datasets=datasets)
+model = Model.register(
+    workspace=ws,
+    model_path=args.model_path,
+    model_name=args.model_name,
+    datasets=datasets,
+)

 print("Registered version {0} of model {1}".format(model.version, model.name))
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py
@@ -16,26 +16,82 @@ if type(run) == _OfflineRun:
 else:
    ws = run.experiment.workspace

-usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',
-             '725513', '725254', '726430', '720381', '723074', '726682',
-             '725486', '727883', '723177', '722075', '723086', '724053',
-             '725070', '722073', '726060', '725224', '725260', '724520',
-             '720305', '724020', '726510', '725126', '722523', '703333',
-             '722249', '722728', '725483', '722972', '724975', '742079',
-             '727468', '722193', '725624', '722030', '726380', '720309',
-             '722071', '720326', '725415', '724504', '725665', '725424',
-             '725066']
+usaf_list = [
+    "725724",
+    "722149",
+    "723090",
+    "722159",
+    "723910",
+    "720279",
+    "725513",
+    "725254",
+    "726430",
+    "720381",
+    "723074",
+    "726682",
+    "725486",
+    "727883",
+    "723177",
+    "722075",
+    "723086",
+    "724053",
+    "725070",
+    "722073",
+    "726060",
+    "725224",
+    "725260",
+    "724520",
+    "720305",
+    "724020",
+    "726510",
+    "725126",
+    "722523",
+    "703333",
+    "722249",
+    "722728",
+    "725483",
+    "722972",
+    "724975",
+    "742079",
+    "727468",
+    "722193",
+    "725624",
+    "722030",
+    "726380",
+    "720309",
+    "722071",
+    "720326",
+    "725415",
+    "724504",
+    "725665",
+    "725424",
+    "725066",
+]


 def get_noaa_data(start_time, end_time):
-    columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation',
-               'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']
+    columns = [
+        "usaf",
+        "wban",
+        "datetime",
+        "latitude",
+        "longitude",
+        "elevation",
+        "windAngle",
+        "windSpeed",
+        "temperature",
+        "stationName",
+        "p_k",
+    ]
    isd = NoaaIsdWeather(start_time, end_time, cols=columns)
    noaa_df = isd.to_pandas_dataframe()
    df_filtered = noaa_df[noaa_df["usaf"].isin(usaf_list)]
    df_filtered.reset_index(drop=True)
-    print("Received {0} rows of training data between {1} and {2}".format(
-        df_filtered.shape[0], start_time, end_time))
+    print(
+        "Received {0} rows of training data between {1} and {2}".format(
+            df_filtered.shape[0], start_time, end_time
+        )
+    )
    return df_filtered


@@ -54,11 +110,12 @@ end_time = datetime.utcnow()
 try:
    ds = Dataset.get_by_name(ws, args.ds_name)
    end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
-    print("Dataset {0} last updated on {1}".format(args.ds_name,
-                                                   end_time_last_slice))
+    print("Dataset {0} last updated on {1}".format(args.ds_name, end_time_last_slice))
 except Exception:
    print(traceback.format_exc())
-    print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
+    print(
+        "Dataset with name {0} not found, registering new dataset.".format(args.ds_name)
+    )
    register_dataset = True
    end_time = datetime(2021, 5, 1, 0, 0)
    end_time_last_slice = end_time - relativedelta(weeks=2)
@@ -66,26 +123,35 @@ except Exception:
 train_df = get_noaa_data(end_time_last_slice, end_time)

 if train_df.size > 0:
-    print("Received {0} rows of new data after {1}.".format(
-        train_df.shape[0], end_time_last_slice))
-    folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(args.ds_name, end_time.year,
-                                                                        end_time.month, end_time.day,
-                                                                        end_time.hour, end_time.minute,
-                                                                        end_time.second)
+    print(
+        "Received {0} rows of new data after {1}.".format(
+            train_df.shape[0], end_time_last_slice
+        )
+    )
+    folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(
+        args.ds_name,
+        end_time.year,
+        end_time.month,
+        end_time.day,
+        end_time.hour,
+        end_time.minute,
+        end_time.second,
+    )
    file_path = "{0}/data.csv".format(folder_name)

    # Add a new partition to the registered dataset
    os.makedirs(folder_name, exist_ok=True)
    train_df.to_csv(file_path, index=False)

-    dstor.upload_files(files=[file_path],
-                       target_path=folder_name,
-                       overwrite=True,
-                       show_progress=True)
+    dstor.upload_files(
+        files=[file_path], target_path=folder_name, overwrite=True, show_progress=True
+    )
 else:
    print("No new data since {0}.".format(end_time_last_slice))

 if register_dataset:
-    ds = Dataset.Tabular.from_delimited_files(dstor.path("{}/**/*.csv".format(
-        args.ds_name)), partition_format='/{partition_date:yyyy/MM/dd/HH/mm/ss}/data.csv')
+    ds = Dataset.Tabular.from_delimited_files(
+        dstor.path("{}/**/*.csv".format(args.ds_name)),
+        partition_format="/{partition_date:yyyy/MM/dd/HH/mm/ss}/data.csv",
+    )
    ds.register(ws, name=args.ds_name)
--- a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml
@@ -1,17 +1,21 @@
 name: azure_automl_experimental
 dependencies:
  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
- pip<=19.3.1
- python>=3.5.2,<3.8
- nb_conda
- cython
- urllib3<1.24
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip<=20.2.4
+- python>=3.6.0,<3.9
+- cython==0.29.14
+- urllib3==1.26.7
 - PyJWT < 2.0.0
 - numpy==1.18.5
+- pywin32==227
+- cryptography<37.0.0

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
+  - azure-mgmt-core==1.3.0
+  - azure-core==1.21.1
+  - azure-identity==1.7.0
  - azureml-defaults
  - azureml-sdk
  - azureml-widgets
--- a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml
@@ -1,18 +1,23 @@
 name: azure_automl_experimental
+channels:
+  - conda-forge
+  - main
 dependencies:
  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
- pip<=19.3.1
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip<=20.2.4
 - nomkl
- python>=3.5.2,<3.8
- nb_conda
- cython
- urllib3<1.24
+- python>=3.6.0,<3.9
+- urllib3==1.26.7
 - PyJWT < 2.0.0
- numpy==1.18.5
+- numpy==1.19.5
+- cryptography<37.0.0

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
+  - azure-mgmt-core==1.3.0
+  - azure-core==1.21.1
+  - azure-identity==1.7.0
  - azureml-defaults
  - azureml-sdk
  - azureml-widgets
--- a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb
@@ -92,7 +92,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -115,7 +115,7 @@
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
+        "pd.set_option('display.max_colwidth', None)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
--- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
@@ -91,7 +91,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -180,6 +180,29 @@
        "label = \"ERP\"\n"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The split data will be used in the remote compute by ModelProxy and locally to compare results.\n",
+        "So, we need to persist the split data to avoid descrepencies from different package versions in the local and remote."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "\n",
+        "train_data = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    train_data.to_pandas_dataframe(), target=(ds, \"machineTrainData\"), name=\"train_data\")\n",
+        "\n",
+        "test_data = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    test_data.to_pandas_dataframe(), target=(ds, \"machineTestData\"), name=\"test_data\")"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -304,7 +327,8 @@
      "metadata": {},
      "source": [
        "#### Show hyperparameters\n",
-        "Show the model pipeline used for the best run with its hyperparameters."
+        "Show the model pipeline used for the best run with its hyperparameters.\n",
+        "For ensemble pipelines it shows the iterations and algorithms that are ensembled."
      ]
    },
    {
@@ -313,8 +337,19 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "run_properties = json.loads(best_run.get_details()['properties']['pipeline_script'])\n",
-        "print(json.dumps(run_properties, indent = 1)) "
+        "run_properties = best_run.get_details()['properties']\n",
+        "pipeline_script = json.loads(run_properties['pipeline_script'])\n",
+        "print(json.dumps(pipeline_script, indent = 1)) \n",
+        "\n",
+        "if 'ensembled_iterations' in run_properties:\n",
+        "    print(\"\")\n",
+        "    print(\"Ensembled Iterations\")\n",
+        "    print(run_properties['ensembled_iterations'])\n",
+        "    \n",
+        "if 'ensembled_algorithms' in run_properties:\n",
+        "    print(\"\")\n",
+        "    print(\"Ensembled Algorithms\")\n",
+        "    print(run_properties['ensembled_algorithms'])"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py
@@ -5,6 +5,7 @@ import json
 import os
 import re

+import numpy as np
 import pandas as pd

 from matplotlib import pyplot as plt
@@ -121,7 +122,7 @@ def calculate_scores_and_build_plots(
    input_dir: str, output_dir: str, automl_settings: Dict[str, Any]
 ):
    os.makedirs(output_dir, exist_ok=True)
-    grains = automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES)
+    grains = automl_settings.get(constants.TimeSeries.TIME_SERIES_ID_COLUMN_NAMES)
    time_column_name = automl_settings.get(constants.TimeSeries.TIME_COLUMN_NAME)
    if grains is None:
        grains = []
@@ -146,6 +147,9 @@ def calculate_scores_and_build_plots(
        _draw_one_plot(one_forecast, time_column_name, grains, pdf)
    pdf.close()
    forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False)
+    # Remove np.NaN and np.inf from the prediction and actuals data.
+    forecast_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    forecast_df.dropna(subset=[ACTUALS, PREDICTIONS], inplace=True)
    metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER])
    metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False)

--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb
@@ -86,7 +86,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Default datastore name\"] = dstore.name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -322,10 +323,10 @@
        "| **iterations**                     | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n",
        "| **experiment_timeout_hours**       | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
        "| **label_column_name**              | The name of the label column. |\n",
-        "| **max_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
+        "| **forecast_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
        "| **n_cross_validations**            | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
        "| **time_column_name**               | The name of your time column. |\n",
-        "| **grain_column_names**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
+        "| **time_series_id_column_names**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
        "| **track_child_runs**               | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
        "| **partition_column_names**         | The names of columns used to group your models. For timeseries, the groups must not split up individual time-series. That is, each group must contain one or more whole time-series. |"
      ]
@@ -354,8 +355,8 @@
        "    \"label_column_name\": TARGET_COLNAME,\n",
        "    \"n_cross_validations\": 3,\n",
        "    \"time_column_name\": TIME_COLNAME,\n",
-        "    \"max_horizon\": 6,\n",
-        "    \"grain_column_names\": partition_column_names,\n",
+        "    \"forecast_horizon\": 6,\n",
+        "    \"time_series_id_column_names\": partition_column_names,\n",
        "    \"track_child_runs\": False,\n",
        "}\n",
        "\n",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py
@@ -5,6 +5,7 @@ import json
 import os
 import re

+import numpy as np
 import pandas as pd

 from matplotlib import pyplot as plt
@@ -146,6 +147,9 @@ def calculate_scores_and_build_plots(
        _draw_one_plot(one_forecast, time_column_name, grains, pdf)
    pdf.close()
    forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False)
+    # Remove np.NaN and np.inf from the prediction and actuals data.
+    forecast_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    forecast_df.dropna(subset=[ACTUALS, PREDICTIONS], inplace=True)
    metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER])
    metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False)

--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb
@@ -100,7 +100,8 @@
        "output[\"SKU\"] = ws.sku\n",
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_test.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_test.csv
@@ -1,20 +0,0 @@
-DATE,grain,BeerProduction
-2017-01-01,grain,9049
-2017-02-01,grain,10458
-2017-03-01,grain,12489
-2017-04-01,grain,11499
-2017-05-01,grain,13553
-2017-06-01,grain,14740
-2017-07-01,grain,11424
-2017-08-01,grain,13412
-2017-09-01,grain,11917
-2017-10-01,grain,12721
-2017-11-01,grain,13272
-2017-12-01,grain,14278
-2018-01-01,grain,9572
-2018-02-01,grain,10423
-2018-03-01,grain,12667
-2018-04-01,grain,11904
-2018-05-01,grain,14120
-2018-06-01,grain,14565
-2018-07-01,grain,12622
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_train.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_train.csv
@@ -1,301 +0,0 @@
-DATE,grain,BeerProduction
-1992-01-01,grain,3459
-1992-02-01,grain,3458
-1992-03-01,grain,4002
-1992-04-01,grain,4564
-1992-05-01,grain,4221
-1992-06-01,grain,4529
-1992-07-01,grain,4466
-1992-08-01,grain,4137
-1992-09-01,grain,4126
-1992-10-01,grain,4259
-1992-11-01,grain,4240
-1992-12-01,grain,4936
-1993-01-01,grain,3031
-1993-02-01,grain,3261
-1993-03-01,grain,4160
-1993-04-01,grain,4377
-1993-05-01,grain,4307
-1993-06-01,grain,4696
-1993-07-01,grain,4458
-1993-08-01,grain,4457
-1993-09-01,grain,4364
-1993-10-01,grain,4236
-1993-11-01,grain,4500
-1993-12-01,grain,4974
-1994-01-01,grain,3075
-1994-02-01,grain,3377
-1994-03-01,grain,4443
-1994-04-01,grain,4261
-1994-05-01,grain,4460
-1994-06-01,grain,4985
-1994-07-01,grain,4324
-1994-08-01,grain,4719
-1994-09-01,grain,4374
-1994-10-01,grain,4248
-1994-11-01,grain,4784
-1994-12-01,grain,4971
-1995-01-01,grain,3370
-1995-02-01,grain,3484
-1995-03-01,grain,4269
-1995-04-01,grain,3994
-1995-05-01,grain,4715
-1995-06-01,grain,4974
-1995-07-01,grain,4223
-1995-08-01,grain,5000
-1995-09-01,grain,4235
-1995-10-01,grain,4554
-1995-11-01,grain,4851
-1995-12-01,grain,4826
-1996-01-01,grain,3699
-1996-02-01,grain,3983
-1996-03-01,grain,4262
-1996-04-01,grain,4619
-1996-05-01,grain,5219
-1996-06-01,grain,4836
-1996-07-01,grain,4941
-1996-08-01,grain,5062
-1996-09-01,grain,4365
-1996-10-01,grain,5012
-1996-11-01,grain,4850
-1996-12-01,grain,5097
-1997-01-01,grain,3758
-1997-02-01,grain,3825
-1997-03-01,grain,4454
-1997-04-01,grain,4635
-1997-05-01,grain,5210
-1997-06-01,grain,5057
-1997-07-01,grain,5231
-1997-08-01,grain,5034
-1997-09-01,grain,4970
-1997-10-01,grain,5342
-1997-11-01,grain,4831
-1997-12-01,grain,5965
-1998-01-01,grain,3796
-1998-02-01,grain,4019
-1998-03-01,grain,4898
-1998-04-01,grain,5090
-1998-05-01,grain,5237
-1998-06-01,grain,5447
-1998-07-01,grain,5435
-1998-08-01,grain,5107
-1998-09-01,grain,5515
-1998-10-01,grain,5583
-1998-11-01,grain,5346
-1998-12-01,grain,6286
-1999-01-01,grain,4032
-1999-02-01,grain,4435
-1999-03-01,grain,5479
-1999-04-01,grain,5483
-1999-05-01,grain,5587
-1999-06-01,grain,6176
-1999-07-01,grain,5621
-1999-08-01,grain,5889
-1999-09-01,grain,5828
-1999-10-01,grain,5849
-1999-11-01,grain,6180
-1999-12-01,grain,6771
-2000-01-01,grain,4243
-2000-02-01,grain,4952
-2000-03-01,grain,6008
-2000-04-01,grain,5353
-2000-05-01,grain,6435
-2000-06-01,grain,6673
-2000-07-01,grain,5636
-2000-08-01,grain,6630
-2000-09-01,grain,5887
-2000-10-01,grain,6322
-2000-11-01,grain,6520
-2000-12-01,grain,6678
-2001-01-01,grain,5082
-2001-02-01,grain,5216
-2001-03-01,grain,5893
-2001-04-01,grain,5894
-2001-05-01,grain,6799
-2001-06-01,grain,6667
-2001-07-01,grain,6374
-2001-08-01,grain,6840
-2001-09-01,grain,5575
-2001-10-01,grain,6545
-2001-11-01,grain,6789
-2001-12-01,grain,7180
-2002-01-01,grain,5117
-2002-02-01,grain,5442
-2002-03-01,grain,6337
-2002-04-01,grain,6525
-2002-05-01,grain,7216
-2002-06-01,grain,6761
-2002-07-01,grain,6958
-2002-08-01,grain,7070
-2002-09-01,grain,6148
-2002-10-01,grain,6924
-2002-11-01,grain,6716
-2002-12-01,grain,7975
-2003-01-01,grain,5326
-2003-02-01,grain,5609
-2003-03-01,grain,6414
-2003-04-01,grain,6741
-2003-05-01,grain,7144
-2003-06-01,grain,7133
-2003-07-01,grain,7568
-2003-08-01,grain,7266
-2003-09-01,grain,6634
-2003-10-01,grain,7626
-2003-11-01,grain,6843
-2003-12-01,grain,8540
-2004-01-01,grain,5629
-2004-02-01,grain,5898
-2004-03-01,grain,7045
-2004-04-01,grain,7094
-2004-05-01,grain,7333
-2004-06-01,grain,7918
-2004-07-01,grain,7289
-2004-08-01,grain,7396
-2004-09-01,grain,7259
-2004-10-01,grain,7268
-2004-11-01,grain,7731
-2004-12-01,grain,9058
-2005-01-01,grain,5557
-2005-02-01,grain,6237
-2005-03-01,grain,7723
-2005-04-01,grain,7262
-2005-05-01,grain,8241
-2005-06-01,grain,8757
-2005-07-01,grain,7352
-2005-08-01,grain,8496
-2005-09-01,grain,7741
-2005-10-01,grain,7710
-2005-11-01,grain,8247
-2005-12-01,grain,8902
-2006-01-01,grain,6066
-2006-02-01,grain,6590
-2006-03-01,grain,7923
-2006-04-01,grain,7335
-2006-05-01,grain,8843
-2006-06-01,grain,9327
-2006-07-01,grain,7792
-2006-08-01,grain,9156
-2006-09-01,grain,8037
-2006-10-01,grain,8640
-2006-11-01,grain,9128
-2006-12-01,grain,9545
-2007-01-01,grain,6627
-2007-02-01,grain,6743
-2007-03-01,grain,8195
-2007-04-01,grain,7828
-2007-05-01,grain,9570
-2007-06-01,grain,9484
-2007-07-01,grain,8608
-2007-08-01,grain,9543
-2007-09-01,grain,8123
-2007-10-01,grain,9649
-2007-11-01,grain,9390
-2007-12-01,grain,10065
-2008-01-01,grain,7093
-2008-02-01,grain,7483
-2008-03-01,grain,8365
-2008-04-01,grain,8895
-2008-05-01,grain,9794
-2008-06-01,grain,9977
-2008-07-01,grain,9553
-2008-08-01,grain,9375
-2008-09-01,grain,9225
-2008-10-01,grain,9948
-2008-11-01,grain,8758
-2008-12-01,grain,10839
-2009-01-01,grain,7266
-2009-02-01,grain,7578
-2009-03-01,grain,8688
-2009-04-01,grain,9162
-2009-05-01,grain,9369
-2009-06-01,grain,10167
-2009-07-01,grain,9507
-2009-08-01,grain,8923
-2009-09-01,grain,9272
-2009-10-01,grain,9075
-2009-11-01,grain,8949
-2009-12-01,grain,10843
-2010-01-01,grain,6558
-2010-02-01,grain,7481
-2010-03-01,grain,9475
-2010-04-01,grain,9424
-2010-05-01,grain,9351
-2010-06-01,grain,10552
-2010-07-01,grain,9077
-2010-08-01,grain,9273
-2010-09-01,grain,9420
-2010-10-01,grain,9413
-2010-11-01,grain,9866
-2010-12-01,grain,11455
-2011-01-01,grain,6901
-2011-02-01,grain,8014
-2011-03-01,grain,9832
-2011-04-01,grain,9281
-2011-05-01,grain,9967
-2011-06-01,grain,11344
-2011-07-01,grain,9106
-2011-08-01,grain,10469
-2011-09-01,grain,10085
-2011-10-01,grain,9612
-2011-11-01,grain,10328
-2011-12-01,grain,11483
-2012-01-01,grain,7486
-2012-02-01,grain,8641
-2012-03-01,grain,9709
-2012-04-01,grain,9423
-2012-05-01,grain,11342
-2012-06-01,grain,11274
-2012-07-01,grain,9845
-2012-08-01,grain,11163
-2012-09-01,grain,9532
-2012-10-01,grain,10754
-2012-11-01,grain,10953
-2012-12-01,grain,11922
-2013-01-01,grain,8395
-2013-02-01,grain,8888
-2013-03-01,grain,10110
-2013-04-01,grain,10493
-2013-05-01,grain,12218
-2013-06-01,grain,11385
-2013-07-01,grain,11186
-2013-08-01,grain,11462
-2013-09-01,grain,10494
-2013-10-01,grain,11540
-2013-11-01,grain,11138
-2013-12-01,grain,12709
-2014-01-01,grain,8557
-2014-02-01,grain,9059
-2014-03-01,grain,10055
-2014-04-01,grain,10977
-2014-05-01,grain,11792
-2014-06-01,grain,11904
-2014-07-01,grain,10965
-2014-08-01,grain,10981
-2014-09-01,grain,10828
-2014-10-01,grain,11817
-2014-11-01,grain,10470
-2014-12-01,grain,13310
-2015-01-01,grain,8400
-2015-02-01,grain,9062
-2015-03-01,grain,10722
-2015-04-01,grain,11107
-2015-05-01,grain,11508
-2015-06-01,grain,12904
-2015-07-01,grain,11869
-2015-08-01,grain,11224
-2015-09-01,grain,12022
-2015-10-01,grain,11983
-2015-11-01,grain,11506
-2015-12-01,grain,14183
-2016-01-01,grain,8650
-2016-02-01,grain,10323
-2016-03-01,grain,12110
-2016-04-01,grain,11424
-2016-05-01,grain,12243
-2016-06-01,grain,13686
-2016-07-01,grain,10956
-2016-08-01,grain,12706
-2016-09-01,grain,12279
-2016-10-01,grain,11914
-2016-11-01,grain,13025
-2016-12-01,grain,14431
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml
@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-beer-remote
-dependencies:
- pip:
-  - azureml-sdk
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -64,22 +64,23 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
+        "import json\n",
        "import logging\n",
-        "\n",
-        "from azureml.core import Workspace, Experiment, Dataset\n",
-        "from azureml.train.automl import AutoMLConfig\n",
        "from datetime import datetime\n",
-        "from azureml.automl.core.featurization import FeaturizationConfig"
+        "\n",
+        "import azureml.core\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig\n",
+        "from azureml.core import Dataset, Experiment, Workspace\n",
+        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
      ]
    },
    {
@@ -88,7 +89,6 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -119,7 +119,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Run History Name\"] = experiment_name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -398,8 +399,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Retrieve the Best Model\n",
-        "Below we select the best model from all the training iterations using get_output method."
+        "### Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
      ]
    },
    {
@@ -408,8 +409,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "fitted_model.steps"
+        "best_run = remote_run.get_best_child()\n",
+        "best_run"
      ]
    },
    {
@@ -418,7 +419,7 @@
      "source": [
        "## Featurization\n",
        "\n",
-        "You can access the engineered feature names generated in time-series featurization. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization."
+        "We can look at the engineered feature names generated in time-series featurization via. the JSON file named 'engineered_feature_names.json' under the run outputs. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization."
      ]
    },
    {
@@ -427,7 +428,14 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "fitted_model.named_steps[\"timeseriestransformer\"].get_engineered_feature_names()"
+        "# Download the JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/engineered_feature_names.json\", \"engineered_feature_names.json\"\n",
+        ")\n",
+        "with open(\"engineered_feature_names.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "\n",
+        "records"
      ]
    },
    {
@@ -451,12 +459,26 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps[\n",
-        "    \"timeseriestransformer\"\n",
-        "].get_featurization_summary()\n",
-        "# View the featurization summary as a pandas dataframe\n",
-        "pd.DataFrame.from_records(featurization_summary)"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "fs = pd.DataFrame.from_records(records)\n",
+        "\n",
+        "# View a summary of the featurization\n",
+        "fs[\n",
+        "    [\n",
+        "        \"RawFeatureName\",\n",
+        "        \"TypeDetected\",\n",
+        "        \"Dropped\",\n",
+        "        \"EngineeredFeatureCount\",\n",
+        "        \"Transformations\",\n",
+        "    ]\n",
+        "]"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -68,6 +68,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
+        "import json\n",
        "import logging\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
@@ -90,7 +91,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
      ]
    },
    {
@@ -99,7 +100,6 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -132,7 +132,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Run History Name\"] = experiment_name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -398,8 +399,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Retrieve the Best Model\n",
-        "Below we select the best model from all the training iterations using get_output method."
+        "## Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
      ]
    },
    {
@@ -408,8 +409,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "fitted_model.steps"
+        "best_run = remote_run.get_best_child()\n",
+        "best_run"
      ]
    },
    {
@@ -417,7 +418,7 @@
      "metadata": {},
      "source": [
        "## Featurization\n",
-        "You can access the engineered feature names generated in time-series featurization."
+        "We can look at the engineered feature names generated in time-series featurization via. the JSON file named 'engineered_feature_names.json' under the run outputs."
      ]
    },
    {
@@ -426,7 +427,14 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "fitted_model.named_steps[\"timeseriestransformer\"].get_engineered_feature_names()"
+        "# Download the JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/engineered_feature_names.json\", \"engineered_feature_names.json\"\n",
+        ")\n",
+        "with open(\"engineered_feature_names.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "\n",
+        "records"
      ]
    },
    {
@@ -449,12 +457,26 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps[\n",
-        "    \"timeseriestransformer\"\n",
-        "].get_featurization_summary()\n",
-        "# View the featurization summary as a pandas dataframe\n",
-        "pd.DataFrame.from_records(featurization_summary)"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "fs = pd.DataFrame.from_records(records)\n",
+        "\n",
+        "# View a summary of the featurization\n",
+        "fs[\n",
+        "    [\n",
+        "        \"RawFeatureName\",\n",
+        "        \"TypeDetected\",\n",
+        "        \"Dropped\",\n",
+        "        \"EngineeredFeatureCount\",\n",
+        "        \"Transformations\",\n",
+        "    ]\n",
+        "]"
      ]
    },
    {
@@ -481,7 +503,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Retreiving forecasts from the model\n",
+        "### Retrieving forecasts from the model\n",
        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
      ]
    },
@@ -641,7 +663,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Retrieve the Best Model"
+        "### Retrieve the Best Run details"
      ]
    },
    {
@@ -650,7 +672,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "best_run_lags, fitted_model_lags = advanced_remote_run.get_output()"
+        "best_run_lags = remote_run.get_best_child()\n",
+        "best_run_lags"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
@@ -85,7 +85,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
      ]
    },
    {
@@ -94,7 +94,6 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -122,7 +121,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Run History Name\"] = experiment_name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
@@ -30,7 +30,7 @@
      },
      "source": [
        "# Automated Machine Learning\n",
-        "**Beer Production Forecasting**\n",
+        "**Github DAU Forecasting**\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
@@ -48,7 +48,7 @@
      },
      "source": [
        "## Introduction\n",
-        "This notebook demonstrates demand forecasting for Beer Production Dataset using AutoML.\n",
+        "This notebook demonstrates demand forecasting for Github Daily Active Users Dataset using AutoML.\n",
        "\n",
        "AutoML highlights here include using Deep Learning forecasts, Arima, Prophet,  Remote Execution and Remote Inferencing, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
        "\n",
@@ -57,7 +57,7 @@
        "Notebook synopsis:\n",
        "\n",
        "1. Creating an Experiment in an existing Workspace\n",
-        "2. Configuration and remote run of AutoML for a time-series model exploring Regression learners, Arima, Prophet and DNNs\n",
+        "2. Configuration and remote run of AutoML for a time-series model exploring DNNs\n",
        "4. Evaluating the fitted model using a rolling test "
      ]
    },
@@ -92,8 +92,7 @@
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core import Workspace, Experiment, Dataset\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
@@ -104,7 +103,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
      ]
    },
    {
@@ -113,7 +112,6 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -139,7 +137,7 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
-        "experiment_name = \"beer-remote-cpu\"\n",
+        "experiment_name = \"github-remote-cpu\"\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
@@ -149,7 +147,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Run History Name\"] = experiment_name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -180,7 +179,7 @@
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"beer-cluster\"\n",
+        "cpu_cluster_name = \"github-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
@@ -203,7 +202,7 @@
      },
      "source": [
        "## Data\n",
-        "Read Beer demand data from file, and preview data."
+        "Read Github DAU data from file, and preview data."
      ]
    },
    {
@@ -246,21 +245,19 @@
        "plt.tight_layout()\n",
        "\n",
        "plt.subplot(2, 1, 1)\n",
-        "plt.title(\"Beer Production By Year\")\n",
-        "df = pd.read_csv(\n",
-        "    \"Beer_no_valid_split_train.csv\", parse_dates=True, index_col=\"DATE\"\n",
-        ").drop(columns=\"grain\")\n",
+        "plt.title(\"Github Daily Active User By Year\")\n",
+        "df = pd.read_csv(\"github_dau_2011-2018_train.csv\", parse_dates=True, index_col=\"date\")\n",
        "test_df = pd.read_csv(\n",
-        "    \"Beer_no_valid_split_test.csv\", parse_dates=True, index_col=\"DATE\"\n",
-        ").drop(columns=\"grain\")\n",
+        "    \"github_dau_2011-2018_test.csv\", parse_dates=True, index_col=\"date\"\n",
+        ")\n",
        "plt.plot(df)\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
-        "plt.title(\"Beer Production By Month\")\n",
+        "plt.title(\"Github Daily Active User By Month\")\n",
        "groups = df.groupby(df.index.month)\n",
        "months = concat([DataFrame(x[1].values) for x in groups], axis=1)\n",
        "months = DataFrame(months)\n",
-        "months.columns = range(1, 13)\n",
+        "months.columns = range(1, 49)\n",
        "months.boxplot()\n",
        "\n",
        "plt.show()"
@@ -275,10 +272,10 @@
      },
      "outputs": [],
      "source": [
-        "target_column_name = \"BeerProduction\"\n",
-        "time_column_name = \"DATE\"\n",
+        "target_column_name = \"count\"\n",
+        "time_column_name = \"date\"\n",
        "time_series_id_column_names = []\n",
-        "freq = \"M\"  # Monthly data"
+        "freq = \"D\"  # Daily data"
      ]
    },
    {
@@ -301,40 +298,21 @@
        "from helper import split_full_for_forecasting\n",
        "\n",
        "train, valid = split_full_for_forecasting(df, time_column_name)\n",
-        "train.to_csv(\"train.csv\")\n",
-        "valid.to_csv(\"valid.csv\")\n",
-        "test_df.to_csv(\"test.csv\")\n",
+        "\n",
+        "# Reset index to create a Tabualr Dataset.\n",
+        "train.reset_index(inplace=True)\n",
+        "valid.reset_index(inplace=True)\n",
+        "test_df.reset_index(inplace=True)\n",
        "\n",
        "datastore = ws.get_default_datastore()\n",
-        "datastore.upload_files(\n",
-        "    files=[\"./train.csv\"],\n",
-        "    target_path=\"beer-dataset/tabular/\",\n",
-        "    overwrite=True,\n",
-        "    show_progress=True,\n",
+        "train_dataset = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    train, target=(datastore, \"dataset/\"), name=\"Github_DAU_train\"\n",
        ")\n",
-        "datastore.upload_files(\n",
-        "    files=[\"./valid.csv\"],\n",
-        "    target_path=\"beer-dataset/tabular/\",\n",
-        "    overwrite=True,\n",
-        "    show_progress=True,\n",
+        "valid_dataset = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    valid, target=(datastore, \"dataset/\"), name=\"Github_DAU_valid\"\n",
        ")\n",
-        "datastore.upload_files(\n",
-        "    files=[\"./test.csv\"],\n",
-        "    target_path=\"beer-dataset/tabular/\",\n",
-        "    overwrite=True,\n",
-        "    show_progress=True,\n",
-        ")\n",
-        "\n",
-        "from azureml.core import Dataset\n",
-        "\n",
-        "train_dataset = Dataset.Tabular.from_delimited_files(\n",
-        "    path=[(datastore, \"beer-dataset/tabular/train.csv\")]\n",
-        ")\n",
-        "valid_dataset = Dataset.Tabular.from_delimited_files(\n",
-        "    path=[(datastore, \"beer-dataset/tabular/valid.csv\")]\n",
-        ")\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
-        "    path=[(datastore, \"beer-dataset/tabular/test.csv\")]\n",
+        "test_dataset = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    test_df, target=(datastore, \"dataset/\"), name=\"Github_DAU_test\"\n",
        ")"
      ]
    },
@@ -397,10 +375,10 @@
        "forecasting_parameters = ForecastingParameters(\n",
        "    time_column_name=time_column_name,\n",
        "    forecast_horizon=forecast_horizon,\n",
-        "    freq=\"MS\",  # Set the forecast frequency to be monthly (start of the month)\n",
+        "    freq=\"D\",  # Set the forecast frequency to be daily\n",
        ")\n",
        "\n",
-        "# We will disable the enable_early_stopping flag to ensure the DNN model is recommended for demonstration purpose.\n",
+        "# To only allow the TCNForecaster we set the allowed_models parameter to reflect this.\n",
        "automl_config = AutoMLConfig(\n",
        "    task=\"forecasting\",\n",
        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
@@ -413,7 +391,7 @@
        "    max_concurrent_iterations=4,\n",
        "    max_cores_per_iteration=-1,\n",
        "    enable_dnn=True,\n",
-        "    enable_early_stopping=False,\n",
+        "    allowed_models=[\"TCNForecaster\"],\n",
        "    forecasting_parameters=forecasting_parameters,\n",
        ")"
      ]
@@ -506,7 +484,9 @@
        "if not forecast_model in summary_df[\"run_id\"]:\n",
        "    forecast_model = \"ForecastTCN\"\n",
        "\n",
-        "best_dnn_run_id = summary_df[\"run_id\"][forecast_model]\n",
+        "best_dnn_run_id = summary_df[summary_df[\"Score\"] == summary_df[\"Score\"].min()][\n",
+        "    \"run_id\"\n",
+        "][forecast_model]\n",
        "best_dnn_run = Run(experiment, best_dnn_run_id)"
      ]
    },
@@ -567,11 +547,6 @@
      },
      "outputs": [],
      "source": [
-        "from azureml.core import Dataset\n",
-        "\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
-        "    path=[(datastore, \"beer-dataset/tabular/test.csv\")]\n",
-        ")\n",
        "# preview the first 3 rows of the dataset\n",
        "test_dataset.take(5).to_pandas_dataframe()"
      ]
@@ -582,7 +557,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "compute_target = ws.compute_targets[\"beer-cluster\"]\n",
+        "compute_target = ws.compute_targets[\"github-cluster\"]\n",
        "test_experiment = Experiment(ws, experiment_name + \"_test\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml
@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-github-dau
+dependencies:
+- pip:
+  - azureml-sdk
--- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv
@@ -0,0 +1,455 @@
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--- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_train.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_train.csv
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py
@@ -79,9 +79,7 @@ def get_result_df(remote_run):
            if "goal" in run.properties:
                goal_minimize = run.properties["goal"].split("_")[-1] == "min"

-    summary_df = summary_df.T.sort_values(
-        "Score", ascending=goal_minimize
-    ).drop_duplicates(["run_algorithm"])
+    summary_df = summary_df.T.sort_values("Score", ascending=goal_minimize)
    summary_df = summary_df.set_index("run_algorithm")
    return summary_df

@@ -105,13 +103,8 @@ def run_inference(
    train_run.download_file(
        "outputs/{}".format(model_base_name), "inference/{}".format(model_base_name)
    )
-    train_run.download_file("outputs/conda_env_v_1_0_0.yml", "inference/condafile.yml")

-    inference_env = Environment("myenv")
-    inference_env.docker.enabled = True
-    inference_env.python.conda_dependencies = CondaDependencies(
-        conda_dependencies_file_path="inference/condafile.yml"
-    )
+    inference_env = train_run.get_environment()

    est = Estimator(
        source_directory=script_folder,
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py
--- a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
@@ -78,7 +78,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Default datastore name\"] = dstore.name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -381,7 +382,7 @@
      "metadata": {},
      "source": [
        "### Submit the pipeline to run\n",
-        "Next we submit our pipeline to run. The whole training pipeline takes about 1h 11m using a Standard_D12_V2 VM with our current ParallelRunConfig setting."
+        "Next we submit our pipeline to run. The whole training pipeline takes about 1h using a Standard_D16_V3 VM with our current ParallelRunConfig setting."
      ]
    },
    {
@@ -571,7 +572,7 @@
      "source": [
        "## Retrieve results\n",
        "\n",
-        "Forecast results can be retrieved through the following code. The prediction results summary and the actual predictions are downloaded the \"forecast_results\" folder"
+        "Forecast results can be retrieved through the following code. The prediction results summary and the actual predictions are downloaded in forecast_results folder"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb
@@ -30,7 +30,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "For this notebook we are using a synthetic dataset portraying sales data to predict the quantity of a vartiety of product SKUs across several states, stores, and product categories.\n",
+        "For this notebook we are using a synthetic dataset portraying sales data to predict the the quantity of a vartiety of product skus across several states, stores, and product categories.\n",
        "\n",
        "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**"
      ]
@@ -78,7 +78,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Default datastore name\"] = dstore.name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -241,6 +242,34 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 2.4 Configure data with ``OutputFileDatasetConfig`` objects\n",
+        "This step shows how to configure output data from a pipeline step. One of the use cases for this step is when you want to do some preprocessing before feeding the data to training step. Intermediate data (or output of a step) is represented by an ``OutputFileDatasetConfig`` object. ``output_data`` is produced as the output of a step. Optionally, this data can be registered as a dataset by calling the ``register_on_complete`` method. If you create an ``OutputFileDatasetConfig`` in one step and use it as an input to another step, that data dependency between steps creates an implicit execution order in the pipeline.\n",
+        "\n",
+        "``OutputFileDatasetConfig`` objects return a directory, and by default write output to the default datastore of the workspace.\n",
+        "\n",
+        "Since instance creation for class ``OutputTabularDatasetConfig`` is not allowed, we first create an instance of this class. Then we use the ``read_parquet_files`` method to read the parquet file into ``OutputTabularDatasetConfig``."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data.output_dataset_config import OutputFileDatasetConfig\n",
+        "\n",
+        "output_data = OutputFileDatasetConfig(\n",
+        "    name=\"processed_data\", destination=(dstore, \"outputdataset/{run-id}/{output-name}\")\n",
+        ").as_upload()\n",
+        "# output_data_dataset = output_data.register_on_complete(\n",
+        "#     name='processed_data', description = 'files from prev step')\n",
+        "output_data = output_data.read_parquet_files()"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -302,13 +331,55 @@
        "    print(compute_target.status.serialize())"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure the training run's environment\n",
+        "The next step is making sure that the remote training run has all the dependencies needed by the training steps. Dependencies and the runtime context are set by creating and configuring a RunConfiguration object.\n",
+        "\n",
+        "The code below shows two options for handling dependencies. As presented, with ``USE_CURATED_ENV = True``, the configuration is based on a [curated environment](https://docs.microsoft.com/en-us/azure/machine-learning/resource-curated-environments). Curated environments have prebuilt Docker images in the [Microsoft Container Registry](https://hub.docker.com/publishers/microsoftowner). For more information, see [Azure Machine Learning curated environments](https://docs.microsoft.com/en-us/azure/machine-learning/resource-curated-environments).\n",
+        "\n",
+        "The path taken if you change ``USE_CURATED_ENV`` to False shows the pattern for explicitly setting your dependencies. In that scenario, a new custom Docker image will be created and registered in an Azure Container Registry within your resource group (see [Introduction to private Docker container registries in Azure](https://docs.microsoft.com/en-us/azure/container-registry/container-registry-intro)). Building and registering this image can take quite a few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core import Environment\n",
+        "\n",
+        "aml_run_config = RunConfiguration()\n",
+        "aml_run_config.target = compute_target\n",
+        "\n",
+        "USE_CURATED_ENV = True\n",
+        "if USE_CURATED_ENV:\n",
+        "    curated_environment = Environment.get(\n",
+        "        workspace=ws, name=\"AzureML-sklearn-0.24-ubuntu18.04-py37-cpu\"\n",
+        "    )\n",
+        "    aml_run_config.environment = curated_environment\n",
+        "else:\n",
+        "    aml_run_config.environment.python.user_managed_dependencies = False\n",
+        "\n",
+        "    # Add some packages relied on by data prep step\n",
+        "    aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
+        "        conda_packages=[\"pandas\", \"scikit-learn\"],\n",
+        "        pip_packages=[\"azureml-sdk\", \"azureml-dataset-runtime[fuse,pandas]\"],\n",
+        "        pin_sdk_version=False,\n",
+        "    )"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Set up training parameters\n",
        "\n",
-        "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings including the name of the time column, the maximum forecast horizon, and the partition column name definition.\n",
+        "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings inncluding the name of the time column, the maximum forecast horizon, and the partition column name definition.\n",
        "\n",
        "| Property                           | Description|\n",
        "| :---------------                   | :------------------- |\n",
@@ -324,7 +395,7 @@
        "| **enable_early_stopping**          | Flag to enable early termination if the score is not improving in the short term. |\n",
        "| **time_column_name**               | The name of your time column. |\n",
        "| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n",
-        "| **time_series_id_column_name**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
+        "| **time_series_id_column_names**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
        "| **track_child_runs**               | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
        "| **pipeline_fetch_max_batch_size**  | Determines how many pipelines (training algorithms) to fetch at a time for training, this helps reduce throttling when training at large scale. |\n",
        "| **partition_column_names**         | The names of columns used to group your models. For timeseries, the groups must not split up individual time-series. That is, each group must contain one or more whole time-series. |"
@@ -355,8 +426,8 @@
        "    \"n_cross_validations\": 3,\n",
        "    \"time_column_name\": \"WeekStarting\",\n",
        "    \"drop_column_names\": \"Revenue\",\n",
-        "    \"max_horizon\": 6,\n",
-        "    \"grain_column_names\": partition_column_names,\n",
+        "    \"forecast_horizon\": 6,\n",
+        "    \"time_series_id_column_names\": partition_column_names,\n",
        "    \"track_child_runs\": False,\n",
        "}\n",
        "\n",
@@ -365,6 +436,46 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Construct your pipeline steps\n",
+        "Once you have the compute resource and environment created, you're ready to define your pipeline's steps. There are many built-in steps available via the Azure Machine Learning SDK, as you can see on the [reference documentation for the azureml.pipeline.steps package](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps?view=azure-ml-py). The most flexible class is [PythonScriptStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py), which runs a Python script.\n",
+        "\n",
+        "Your data preparation code is in a subdirectory (in this example, \"data_preprocessing_tabular.py\" in the directory \"./scripts\"). As part of the pipeline creation process, this directory is zipped and uploaded to the compute_target and the step runs the script specified as the value for ``script_name``.\n",
+        "\n",
+        "The ``arguments`` values specify the inputs and outputs of the step. In the example below, the baseline data is the ``input_ds_small`` dataset. The script data_preprocessing_tabular.py does whatever data-transformation tasks are appropriate to the task at hand and outputs the data to ``output_data``, of type ``OutputFileDatasetConfig``. For more information, see [Moving data into and between ML pipeline steps (Python)](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-move-data-in-out-of-pipelines). The step will run on the machine defined by ``compute_target``, using the configuration ``aml_run_config``.\n",
+        "\n",
+        "Reuse of previous results (``allow_reuse``) is key when using pipelines in a collaborative environment since eliminating unnecessary reruns offers agility. Reuse is the default behavior when the ``script_name``, ``inputs``, and the parameters of a step remain the same. When reuse is allowed, results from the previous run are immediately sent to the next step. If ``allow_reuse`` is set to False, a new run will always be generated for this step during pipeline execution.\n",
+        "\n",
+        "> Note that we only support partitioned FileDataset and TabularDataset without partition when using such output as input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "dataprep_source_dir = \"./scripts\"\n",
+        "entry_point = \"data_preprocessing_tabular.py\"\n",
+        "ds_input = input_ds_small.as_named_input(\"train_10_models\")\n",
+        "\n",
+        "data_prep_step = PythonScriptStep(\n",
+        "    script_name=entry_point,\n",
+        "    source_directory=dataprep_source_dir,\n",
+        "    arguments=[\"--input\", ds_input, \"--output\", output_data],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=aml_run_config,\n",
+        "    allow_reuse=False,\n",
+        ")\n",
+        "\n",
+        "input_ds_small = output_data"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -554,12 +665,12 @@
        "| :---------------                   | :------------------- |\n",
        "| **experiment**                     | The experiment used for inference run. |\n",
        "| **inference_data**                 | The data to use for inferencing. It should be the same schema as used for training.\n",
-        "| **compute_target**                 | The compute target that runs the inference pipeline.|\n",
+        "| **compute_target** The compute target that runs the inference pipeline.|\n",
        "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku). |\n",
-        "| **process_count_per_node**         | The number of processes per node.\n",
-        "| **train_run_id**                   | \\[Optional\\] The run id of the hierarchy training, by default it is the latest successful training many model run in the experiment. |\n",
-        "| **train_experiment_name**          | \\[Optional\\] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n",
-        "| **process_count_per_node**         | \\[Optional\\] The number of processes per node, by default it's 4. |"
+        "| **process_count_per_node** The number of processes per node.\n",
+        "| **train_run_id**                   | \\[Optional] The run id of the hierarchy training, by default it is the latest successful training many model run in the experiment. |\n",
+        "| **train_experiment_name**          | \\[Optional] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n",
+        "| **process_count_per_node**         | \\[Optional] The number of processes per node, by default it's 4. |"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -58,21 +58,22 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
+        "import json\n",
        "import logging\n",
        "\n",
-        "from azureml.core.workspace import Workspace\n",
+        "import azureml.core\n",
+        "import pandas as pd\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig\n",
        "from azureml.core.experiment import Experiment\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.automl.core.featurization import FeaturizationConfig"
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
      ]
    },
    {
@@ -81,7 +82,6 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -112,7 +112,8 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Run History Name\"] = experiment_name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
@@ -472,8 +473,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Retrieve the Best Model\n",
-        "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
+        "### Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
      ]
    },
    {
@@ -482,9 +483,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "print(fitted_model.steps)\n",
-        "model_name = best_run.properties[\"model_name\"]"
+        "best_run = remote_run.get_best_child()\n",
+        "model_name = best_run.properties[\"model_name\"]\n",
+        "best_run"
      ]
    },
    {
@@ -502,16 +503,26 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "custom_featurizer = fitted_model.named_steps[\"timeseriestransformer\"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "custom_featurizer.get_featurization_summary()"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "fs = pd.DataFrame.from_records(records)\n",
+        "\n",
+        "# View a summary of the featurization\n",
+        "fs[\n",
+        "    [\n",
+        "        \"RawFeatureName\",\n",
+        "        \"TypeDetected\",\n",
+        "        \"Dropped\",\n",
+        "        \"EngineeredFeatureCount\",\n",
+        "        \"Transformations\",\n",
+        "    ]\n",
+        "]"
      ]
    },
    {
@@ -538,7 +549,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Retreiving forecasts from the model\n",
+        "### Retrieving forecasts from the model\n",
        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb
@@ -0,0 +1,823 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Training and Inferencing AutoML Forecasting Model Using Pipelines"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "In this notebook, we demonstrate how to use piplines to train and inference on AutoML Forecasting model. Two pipelines will be created: one for training AutoML model, and the other is for inference on AutoML model. We'll also demonstrate how to schedule the inference pipeline so you can get inference results periodically (with refreshed test dataset). Make sure you have executed the configuration notebook before running this notebook. In this notebook you will learn how to:\n",
+        "\n",
+        "- Configure AutoML using AutoMLConfig for forecasting tasks using pipeline AutoMLSteps.\n",
+        "- Create and register an AutoML model using AzureML pipeline.\n",
+        "- Inference and schdelue the pipeline using registered model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "import logging\n",
+        "import os\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "import pandas as pd\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Accessing the Azure ML workspace requires authentication with Azure.\n",
+        "\n",
+        "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",
+        "\n",
+        "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",
+        "```\n",
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "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",
+        "```\n",
+        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
+        "auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "For more details, see aka.ms/aml-notebook-auth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "dstor = ws.get_default_datastore()\n",
+        "\n",
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = \"forecasting-pipeline\"\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute \n",
+        "\n",
+        "#### Create or Attach existing AmlCompute\n",
+        "\n",
+        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
+        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"forecast-step-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
+        "compute_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n",
+        "You are now ready to load the historical orange juice sales data. For demonstration purposes, we extract sales time-series for just a few of the stores. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "time_column_name = \"WeekStarting\"\n",
+        "train = pd.read_csv(\"oj-train.csv\", parse_dates=[time_column_name])\n",
+        "\n",
+        "train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
+        "\n",
+        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "time_series_id_column_names = [\"Store\", \"Brand\"]\n",
+        "nseries = train.groupby(time_series_id_column_names).ngroups\n",
+        "print(\"Data contains {0} individual time-series.\".format(nseries))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test Splitting\n",
+        "We now split the data into a training and a testing set for later forecast prediction. The test set will contain the final 4 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "n_test_periods = 4\n",
+        "\n",
+        "test = pd.read_csv(\"oj-test.csv\", parse_dates=[time_column_name])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Upload data to datastore\n",
+        "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the train and test data and create [tabular datasets](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training and testing. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
+        "\n",
+        "datastore = ws.get_default_datastore()\n",
+        "train_dataset = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    train, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_train\"\n",
+        ")\n",
+        "\n",
+        "test_dataset = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    test, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_test\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Modeling\n",
+        "\n",
+        "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
+        "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
+        "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
+        "* Create features based on time series identifiers to enable fixed effects across different series\n",
+        "* Create time-based features to assist in learning seasonal patterns\n",
+        "* Encode categorical variables to numeric quantities\n",
+        "\n",
+        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n",
+        "\n",
+        "You are almost ready to start an AutoML training job. First, we need to define the target column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "target_column_name = \"Quantity\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|\n",
+        "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
+        "\n",
+        "forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=n_test_periods,\n",
+        "    time_series_id_column_names=time_series_id_column_names,\n",
+        "    freq=\"W-THU\",  # Set the forecast frequency to be weekly (start on each Thursday)\n",
+        ")\n",
+        "\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    debug_log=\"automl_oj_sales_errors.log\",\n",
+        "    primary_metric=\"normalized_mean_absolute_error\",\n",
+        "    experiment_timeout_hours=0.25,\n",
+        "    training_data=train_dataset,\n",
+        "    label_column_name=target_column_name,\n",
+        "    compute_target=compute_target,\n",
+        "    enable_early_stopping=True,\n",
+        "    n_cross_validations=5,\n",
+        "    verbosity=logging.INFO,\n",
+        "    max_cores_per_iteration=-1,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
+        "from azureml.pipeline.core import Pipeline, PipelineParameter\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "metrics_output_name = \"metrics_output\"\n",
+        "best_model_output_name = \"best_model_output\"\n",
+        "model_file_name = \"model_file\"\n",
+        "metrics_data_name = \"metrics_data\"\n",
+        "\n",
+        "metrics_data = PipelineData(\n",
+        "    name=metrics_data_name,\n",
+        "    datastore=datastore,\n",
+        "    pipeline_output_name=metrics_output_name,\n",
+        "    training_output=TrainingOutput(type=\"Metrics\"),\n",
+        ")\n",
+        "model_data = PipelineData(\n",
+        "    name=model_file_name,\n",
+        "    datastore=datastore,\n",
+        "    pipeline_output_name=best_model_output_name,\n",
+        "    training_output=TrainingOutput(type=\"Model\"),\n",
+        ")\n",
+        "\n",
+        "automl_step = AutoMLStep(\n",
+        "    name=\"automl_module\",\n",
+        "    automl_config=automl_config,\n",
+        "    outputs=[metrics_data, model_data],\n",
+        "    allow_reuse=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Model Step"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Run Configuration and Environment\n",
+        "To have a pipeline step run, we first need an environment to run the jobs. The environment can be build using the following code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute\n",
+        "conda_run_config.target = compute_target\n",
+        "\n",
+        "conda_run_config.docker.use_docker = True\n",
+        "\n",
+        "cd = CondaDependencies.create(\n",
+        "    pip_packages=[\n",
+        "        \"azureml-sdk[automl]\",\n",
+        "        \"applicationinsights\",\n",
+        "        \"azureml-opendatasets\",\n",
+        "        \"azureml-defaults\",\n",
+        "    ],\n",
+        "    conda_packages=[\"numpy==1.19.5\"],\n",
+        "    pin_sdk_version=False,\n",
+        ")\n",
+        "conda_run_config.environment.python.conda_dependencies = cd\n",
+        "\n",
+        "print(\"run config is ready\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Step to register the model.\n",
+        "The following code generates a step to register the model to the workspace from previous step. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineData\n",
+        "\n",
+        "# The model name with which to register the trained model in the workspace.\n",
+        "model_name_str = \"ojmodel\"\n",
+        "model_name = PipelineParameter(\"model_name\", default_value=model_name_str)\n",
+        "\n",
+        "\n",
+        "register_model_step = PythonScriptStep(\n",
+        "    script_name=\"register_model.py\",\n",
+        "    name=\"register_model\",\n",
+        "    source_directory=\"scripts\",\n",
+        "    allow_reuse=False,\n",
+        "    arguments=[\n",
+        "        \"--model_name\",\n",
+        "        model_name,\n",
+        "        \"--model_path\",\n",
+        "        model_data,\n",
+        "        \"--ds_name\",\n",
+        "        \"dominicks_OJ_train\",\n",
+        "    ],\n",
+        "    inputs=[model_data],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=conda_run_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Build the Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_pipeline = Pipeline(\n",
+        "    description=\"training_pipeline\",\n",
+        "    workspace=ws,\n",
+        "    steps=[automl_step, register_model_step],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit Pipeline Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_pipeline_run = experiment.submit(training_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_pipeline_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get metrics for each runs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "output_dir = \"train_output\"\n",
+        "pipeline_output = training_pipeline_run.get_pipeline_output(\"metrics_output\")\n",
+        "pipeline_output.download(output_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "file_path = os.path.join(output_dir, pipeline_output.path_on_datastore)\n",
+        "with open(file_path) as f:\n",
+        "    metrics = json.load(f)\n",
+        "for run_id, metrics in metrics.items():\n",
+        "    print(\"{}: {}\".format(run_id, metrics[\"normalized_root_mean_squared_error\"][0]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "There are several ways to do the inference, for here we will demonstrate how to use the registered model and pipeline to do the inference. (how to register a model https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get Inference Pipeline Environment\n",
+        "To trigger an inference pipeline run, we first need a running environment for run that contains all the appropriate packages for the model unpickling. This environment can be either assess from the training run or using the `yml` file that comes with the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Model\n",
+        "\n",
+        "model = Model(ws, model_name_str)\n",
+        "download_path = model.download(model_name_str, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After all the files are downloaded, we can generate the run config for inference runs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment, RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "env_file = os.path.join(download_path, \"conda_env_v_1_0_0.yml\")\n",
+        "inference_env = Environment(\"oj-inference-env\")\n",
+        "inference_env.python.conda_dependencies = CondaDependencies(\n",
+        "    conda_dependencies_file_path=env_file\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "[Optional] The enviroment can also be assessed from the training run using `get_environment()` API."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After we have the environment for the inference, we could build run config based on this environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run_config = RunConfiguration()\n",
+        "run_config.environment = inference_env"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Build and submit the inference pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The inference pipeline will create two different format of outputs, 1) a tabular dataset that contains the prediction and 2) an `OutputFileDatasetConfig` that can be used for the sequential pipeline steps."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data import OutputFileDatasetConfig\n",
+        "\n",
+        "output_data = OutputFileDatasetConfig(name=\"prediction_result\")\n",
+        "\n",
+        "output_ds_name = \"oj-output\"\n",
+        "\n",
+        "inference_step = PythonScriptStep(\n",
+        "    name=\"infer-results\",\n",
+        "    source_directory=\"scripts\",\n",
+        "    script_name=\"infer.py\",\n",
+        "    arguments=[\n",
+        "        \"--model_name\",\n",
+        "        model_name_str,\n",
+        "        \"--ouput_dataset_name\",\n",
+        "        output_ds_name,\n",
+        "        \"--test_dataset_name\",\n",
+        "        test_dataset.name,\n",
+        "        \"--target_column_name\",\n",
+        "        target_column_name,\n",
+        "        \"--output_path\",\n",
+        "        output_data,\n",
+        "    ],\n",
+        "    compute_target=compute_target,\n",
+        "    allow_reuse=False,\n",
+        "    runconfig=run_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_pipeline = Pipeline(ws, [inference_step])\n",
+        "inference_run = experiment.submit(inference_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get the predicted data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "\n",
+        "inference_ds = Dataset.get_by_name(ws, output_ds_name)\n",
+        "inference_df = inference_ds.to_pandas_dataframe()\n",
+        "inference_df.tail(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Schedule Pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This section is about how to schedule a pipeline for periodically predictions. For more info about pipeline schedule and pipeline endpoint, please follow this [notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_published_pipeline = inference_pipeline.publish(\n",
+        "    name=\"OJ Inference Test\", description=\"OJ Inference Test\"\n",
+        ")\n",
+        "print(\"Newly published pipeline id: {}\".format(inference_published_pipeline.id))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If `test_dataset` is going to refresh every 4 weeks before Friday 16:00 and we want to predict every 4 weeks (forecast_horizon), we can schedule our pipeline to run every 4 weeks at 16:00 to get daily inference results. You can refresh your test dataset (a newer version will be created) periodically when new data is available (i.e. target column in test dataset would have values in the beginning as context data, and followed by NaNs to be predicted). The inference pipeline will pick up context to further improve the forecast accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# schedule\n",
+        "\n",
+        "from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule\n",
+        "\n",
+        "recurrence = ScheduleRecurrence(\n",
+        "    frequency=\"Week\", interval=4, week_days=[\"Friday\"], hours=[16], minutes=[0]\n",
+        ")\n",
+        "\n",
+        "schedule = Schedule.create(\n",
+        "    workspace=ws,\n",
+        "    name=\"OJ_Inference_schedule\",\n",
+        "    pipeline_id=inference_published_pipeline.id,\n",
+        "    experiment_name=\"Schedule-run-OJ\",\n",
+        "    recurrence=recurrence,\n",
+        "    wait_for_provisioning=True,\n",
+        "    description=\"Schedule Run\",\n",
+        ")\n",
+        "\n",
+        "# You may want to make sure that the schedule is provisioned properly\n",
+        "# before making any further changes to the schedule\n",
+        "\n",
+        "print(\"Created schedule with id: {}\".format(schedule.id))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### [Optional] Disable schedule"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "schedule.disable()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jialiu"
+      }
+    ],
+    "category": "tutorial",
+    "celltoolbar": "Raw Cell Format",
+    "compute": [
+      "Remote"
+    ],
+    "datasets": [
+      "Orange Juice Sales"
+    ],
+    "deployment": [
+      "Azure Container Instance"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML AutoML"
+    ],
+    "friendly_name": "Forecasting orange juice sales with deployment",
+    "index_order": 1,
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    },
+    "tags": [
+      "None"
+    ],
+    "task": "Forecasting"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.yml
@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-pipelines
+dependencies:
+- pip:
+  - azureml-sdk
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-test.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-test.csv
@@ -0,0 +1,37 @@
+WeekStarting,Store,Brand,Advert,Price,Age60,COLLEGE,INCOME,Hincome150,Large HH,Minorities,WorkingWoman,SSTRDIST,SSTRVOL,CPDIST5,CPWVOL5
+1992-09-10,2,dominicks,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-10,2,minute.maid,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-10,2,tropicana,0,2.64,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-10,5,dominicks,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-10,5,minute.maid,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-10,5,tropicana,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-10,8,dominicks,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-10,8,minute.maid,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-10,8,tropicana,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-17,2,dominicks,0,1.77,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-17,2,minute.maid,0,2.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-17,2,tropicana,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-17,5,dominicks,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-17,5,minute.maid,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-17,5,tropicana,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-17,8,dominicks,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-17,8,minute.maid,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-17,8,tropicana,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-24,2,dominicks,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-24,2,minute.maid,0,2.67,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-24,2,tropicana,1,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-24,5,dominicks,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-24,5,minute.maid,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
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+1992-10-01,8,minute.maid,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-10-01,8,tropicana,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-train.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-train.csv
@@ -0,0 +1,997 @@
+WeekStarting,Store,Brand,Quantity,Advert,Price,Age60,COLLEGE,INCOME,Hincome150,Large HH,Minorities,WorkingWoman,SSTRDIST,SSTRVOL,CPDIST5,CPWVOL5
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+1992-07-30,8,dominicks,76352,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-07-30,8,minute.maid,6528,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-07-30,8,tropicana,5632,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-06,2,dominicks,3776,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-06,2,minute.maid,3968,1,2.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-06,2,tropicana,7168,1,3.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-06,5,dominicks,6592,1,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-06,5,minute.maid,5888,1,2.65,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-06,5,tropicana,8384,1,2.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-06,8,dominicks,17408,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-06,8,minute.maid,6208,1,2.45,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-06,8,tropicana,8960,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-13,2,dominicks,3328,0,1.97,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-13,2,minute.maid,49600,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-13,2,tropicana,5056,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-13,5,dominicks,2112,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-13,5,minute.maid,56384,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-13,5,tropicana,8832,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-13,8,dominicks,17536,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-13,8,minute.maid,94720,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-13,8,tropicana,6080,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-20,2,dominicks,13824,0,1.36,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-20,2,minute.maid,23488,1,1.94,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-20,2,tropicana,13376,1,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-20,5,dominicks,21248,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-20,5,minute.maid,27072,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-20,5,tropicana,17728,1,2.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-20,8,dominicks,31232,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-20,8,minute.maid,55552,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-20,8,tropicana,8576,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-27,2,dominicks,9024,0,1.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-27,2,minute.maid,19008,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-27,2,tropicana,8128,0,2.75,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-08-27,5,dominicks,1856,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-27,5,minute.maid,3840,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-27,5,tropicana,9600,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-08-27,8,dominicks,19200,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-27,8,minute.maid,18688,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-08-27,8,tropicana,8000,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-03,2,dominicks,2048,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-03,2,minute.maid,11584,0,1.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-03,2,tropicana,19456,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-09-03,5,dominicks,3712,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-03,5,minute.maid,6144,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-03,5,tropicana,25664,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-09-03,8,dominicks,12800,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-03,8,minute.maid,14656,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
+1992-09-03,8,tropicana,21760,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
@@ -0,0 +1,170 @@
+import argparse
+from datetime import datetime
+import os
+import uuid
+import numpy as np
+import pandas as pd
+
+from pandas.tseries.frequencies import to_offset
+from sklearn.externals import joblib
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+from azureml.data.dataset_factory import TabularDatasetFactory
+from azureml.automl.runtime.shared.score import scoring, constants as metrics_constants
+import azureml.automl.core.shared.constants as constants
+from azureml.core import Run, Dataset, Model
+
+try:
+    import torch
+
+    _torch_present = True
+except ImportError:
+    _torch_present = False
+
+
+def infer_forecasting_dataset_tcn(
+    X_test,
+    y_test,
+    model,
+    output_path,
+    output_dataset_name="results",
+):
+
+    y_pred, df_all = model.forecast(X_test, y_test)
+
+    run = Run.get_context()
+
+    registered_train = TabularDatasetFactory.register_pandas_dataframe(
+        df_all,
+        target=(
+            run.experiment.workspace.get_default_datastore(),
+            datetime.now().strftime("%Y-%m-%d-") + str(uuid.uuid4())[:6],
+        ),
+        name=output_dataset_name,
+    )
+    df_all.to_csv(os.path.join(output_path, output_dataset_name + ".csv"), index=False)
+
+
+def map_location_cuda(storage, loc):
+    return storage.cuda()
+
+
+def get_model(model_path, model_file_name):
+    # _, ext = os.path.splitext(model_path)
+    model_full_path = os.path.join(model_path, model_file_name)
+    print(model_full_path)
+    if model_file_name.endswith("pt"):
+        # Load the fc-tcn torch model.
+        assert _torch_present, "Loading DNN models needs torch to be presented."
+        if torch.cuda.is_available():
+            map_location = map_location_cuda
+        else:
+            map_location = "cpu"
+        with open(model_full_path, "rb") as fh:
+            fitted_model = torch.load(fh, map_location=map_location)
+    else:
+        # Load the sklearn pipeline.
+        fitted_model = joblib.load(model_full_path)
+    return fitted_model
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name",
+        type=str,
+        dest="model_name",
+        help="Model to be loaded",
+    )
+
+    parser.add_argument(
+        "--ouput_dataset_name",
+        type=str,
+        dest="ouput_dataset_name",
+        default="results",
+        help="Dataset name of the final output",
+    )
+    parser.add_argument(
+        "--target_column_name",
+        type=str,
+        dest="target_column_name",
+        help="The target column name.",
+    )
+    parser.add_argument(
+        "--test_dataset_name",
+        type=str,
+        dest="test_dataset_name",
+        default="results",
+        help="Dataset name of the final output",
+    )
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        dest="output_path",
+        default="results",
+        help="The output path",
+    )
+    args = parser.parse_args()
+    return args
+
+
+def get_data(
+    run,
+    fitted_model,
+    target_column_name,
+    test_dataset_name,
+):
+
+    # get input dataset by name
+    test_dataset = Dataset.get_by_name(run.experiment.workspace, test_dataset_name)
+    test_df = test_dataset.to_pandas_dataframe()
+    if target_column_name in test_df:
+        y_test = test_df.pop(target_column_name)
+    else:
+        y_test = np.full(test_df.shape[0], np.nan)
+
+    return test_df, y_test
+
+
+def get_model_filename(run, model_name, model_path):
+    model = Model(run.experiment.workspace, model_name)
+    if "model_file_name" in model.tags:
+        return model.tags["model_file_name"]
+    is_pkl = True
+    if model.tags.get("algorithm") == "TCNForecaster" or os.path.exists(
+        os.path.join(model_path, "model.pt")
+    ):
+        is_pkl = False
+    return "model.pkl" if is_pkl else "model.pt"
+
+
+if __name__ == "__main__":
+    run = Run.get_context()
+
+    args = get_args()
+    model_name = args.model_name
+    ouput_dataset_name = args.ouput_dataset_name
+    test_dataset_name = args.test_dataset_name
+    target_column_name = args.target_column_name
+    print("args passed are: ")
+
+    print(model_name)
+    print(test_dataset_name)
+    print(ouput_dataset_name)
+    print(target_column_name)
+
+    model_path = Model.get_model_path(model_name)
+    model_file_name = get_model_filename(run, model_name, model_path)
+    print(model_file_name)
+    fitted_model = get_model(model_path, model_file_name)
+
+    X_test_df, y_test = get_data(
+        run,
+        fitted_model,
+        target_column_name,
+        test_dataset_name,
+    )
+
+    infer_forecasting_dataset_tcn(
+        X_test_df, y_test, fitted_model, args.output_path, ouput_dataset_name
+    )
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/register_model.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/register_model.py
@@ -0,0 +1,64 @@
+import argparse
+import os
+import uuid
+import shutil
+from azureml.core.model import Model, Dataset
+from azureml.core.run import Run, _OfflineRun
+from azureml.core import Workspace
+import azureml.automl.core.shared.constants as constants
+from azureml.train.automl.run import AutoMLRun
+
+
+def get_best_automl_run(pipeline_run):
+    all_children = [c for c in pipeline_run.get_children()]
+    automl_step = [
+        c for c in all_children if c.properties.get("runTemplate") == "AutoML"
+    ]
+    for c in all_children:
+        print(c, c.properties)
+    automlrun = AutoMLRun(pipeline_run.experiment, automl_step[0].id)
+    best = automlrun.get_best_child()
+    return best
+
+
+def get_model_path(model_artifact_path):
+    return model_artifact_path.split("/")[1]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_name")
+    parser.add_argument("--model_path")
+    parser.add_argument("--ds_name")
+    args = parser.parse_args()
+
+    print("Argument 1(model_name): %s" % args.model_name)
+    print("Argument 2(model_path): %s" % args.model_path)
+    print("Argument 3(ds_name): %s" % args.ds_name)
+
+    run = Run.get_context()
+    ws = None
+    if type(run) == _OfflineRun:
+        ws = Workspace.from_config()
+    else:
+        ws = run.experiment.workspace
+
+    train_ds = Dataset.get_by_name(ws, args.ds_name)
+    datasets = [(Dataset.Scenario.TRAINING, train_ds)]
+    new_dir = str(uuid.uuid4())
+    os.makedirs(new_dir)
+
+    # Register model with training dataset
+    best_run = get_best_automl_run(run.parent)
+    model_artifact_path = best_run.properties[constants.PROPERTY_KEY_OF_MODEL_PATH]
+    algo = best_run.properties.get("run_algorithm")
+    model_artifact_dir = model_artifact_path.split("/")[0]
+    model_file_name = model_artifact_path.split("/")[1]
+    model = best_run.register_model(
+        args.model_name,
+        model_path=model_artifact_dir,
+        datasets=datasets,
+        tags={"algorithm": algo, "model_file_name": model_file_name},
+    )
+
+    print("Registered version {0} of model {1}".format(model.version, model.name))
--- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb
@@ -229,7 +229,7 @@
        "output[\"Resource Group\"] = ws.resource_group\n",
        "output[\"Location\"] = ws.location\n",
        "output[\"Run History Name\"] = experiment_name\n",
-        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "print(outputDf.T)"
      ]
@@ -387,8 +387,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Retrieve the best model\n",
-        "Below we select the best model from all the training iterations using get_output method."
+        "### Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
      ]
    },
    {
@@ -397,8 +397,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "fitted_model.steps"
+        "best_run = remote_run.get_best_child()\n",
+        "best_run"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/helper_functions.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/helper_functions.py
@@ -46,11 +46,11 @@ def kpss_test(series, **kw):
    """
    if kw["store"]:
        statistic, p_value, critical_values, rstore = stattools.kpss(
-            series, regression=kw["reg_type"], lags=kw["lags"], store=kw["store"]
+            series, regression=kw["reg_type"], nlags=kw["lags"], store=kw["store"]
        )
    else:
        statistic, p_value, lags, critical_values = stattools.kpss(
-            series, regression=kw["reg_type"], lags=kw["lags"]
+            series, regression=kw["reg_type"], nlags=kw["lags"]
        )
    output = {
        "statistic": statistic,
--- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -90,16 +74,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -109,18 +83,19 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-ccard-local'\n",
+        "experiment_name = \"automl-classification-ccard-local\"\n",
        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
+        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -142,7 +117,7 @@
        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
-        "label_column_name = 'Class'"
+        "label_column_name = \"Class\""
      ]
    },
    {
@@ -168,23 +143,26 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "enable-ensemble"
+      },
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"experiment_timeout_hours\": 0.25, # 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",
+        "    \"primary_metric\": \"average_precision_score_weighted\",\n",
+        "    \"experiment_timeout_hours\": 0.25,  # 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",
        "    \"verbosity\": logging.INFO,\n",
-        "    \"enable_stack_ensemble\": False\n",
+        "    \"enable_stack_ensemble\": False,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             training_data = training_data,\n",
-        "                             label_column_name = label_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    training_data=training_data,\n",
+        "    label_column_name=label_column_name,\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
@@ -201,7 +179,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
+        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
@@ -211,8 +189,8 @@
      "outputs": [],
      "source": [
        "# If you need to retrieve a run that already started, use the following code\n",
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#local_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# local_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
      ]
    },
    {
@@ -240,6 +218,7 @@
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
+        "\n",
        "RunDetails(local_run).show()"
      ]
    },
@@ -288,8 +267,12 @@
      "outputs": [],
      "source": [
        "# convert the test data to dataframe\n",
-        "X_test_df = validation_data.drop_columns(columns=[label_column_name]).to_pandas_dataframe()\n",
-        "y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True).to_pandas_dataframe()"
+        "X_test_df = validation_data.drop_columns(\n",
+        "    columns=[label_column_name]\n",
+        ").to_pandas_dataframe()\n",
+        "y_test_df = validation_data.keep_columns(\n",
+        "    columns=[label_column_name], validate=True\n",
+        ").to_pandas_dataframe()"
      ]
    },
    {
@@ -323,20 +306,26 @@
        "import numpy as np\n",
        "import itertools\n",
        "\n",
-        "cf =confusion_matrix(y_test_df.values,y_pred)\n",
-        "plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
+        "cf = confusion_matrix(y_test_df.values, y_pred)\n",
+        "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n",
        "plt.colorbar()\n",
-        "plt.title('Confusion Matrix')\n",
-        "plt.xlabel('Predicted')\n",
-        "plt.ylabel('Actual')\n",
-        "class_labels = ['False','True']\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Actual\")\n",
+        "class_labels = [\"False\", \"True\"]\n",
        "tick_marks = np.arange(len(class_labels))\n",
-        "plt.xticks(tick_marks,class_labels)\n",
-        "plt.yticks([-0.5,0,1,1.5],['','False','True',''])\n",
+        "plt.xticks(tick_marks, class_labels)\n",
+        "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"False\", \"True\", \"\"])\n",
        "# plotting text value inside cells\n",
-        "thresh = cf.max() / 2.\n",
-        "for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
-        "    plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
+        "thresh = cf.max() / 2.0\n",
+        "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n",
+        "    plt.text(\n",
+        "        j,\n",
+        "        i,\n",
+        "        format(cf[i, j], \"d\"),\n",
+        "        horizontalalignment=\"center\",\n",
+        "        color=\"white\" if cf[i, j] > thresh else \"black\",\n",
+        "    )\n",
        "plt.show()"
      ]
    },
@@ -363,7 +352,10 @@
        "client = ExplanationClient.from_run(best_run)\n",
        "engineered_explanations = client.download_model_explanation(raw=False)\n",
        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
+        "print(\n",
+        "    \"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n",
+        "    + best_run.get_portal_url()\n",
+        ")"
      ]
    },
    {
@@ -382,7 +374,10 @@
      "source": [
        "raw_explanations = client.download_model_explanation(raw=True)\n",
        "print(raw_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
+        "print(\n",
+        "    \"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n",
+        "    + best_run.get_portal_url()\n",
+        ")"
      ]
    },
    {
@@ -398,7 +393,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "automl_run, fitted_model = local_run.get_output(metric='accuracy')"
+        "automl_run, fitted_model = local_run.get_output(metric=\"accuracy\")"
      ]
    },
    {
@@ -432,12 +427,18 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
+        "from azureml.train.automl.runtime.automl_explain_utilities import (\n",
+        "    automl_setup_model_explanations,\n",
+        ")\n",
        "\n",
-        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
-        "                                                             X_test=X_test, y=y_train, \n",
-        "                                                             task='classification',\n",
-        "                                                             automl_run=automl_run)"
+        "automl_explainer_setup_obj = automl_setup_model_explanations(\n",
+        "    fitted_model,\n",
+        "    X=X_train,\n",
+        "    X_test=X_test,\n",
+        "    y=y_train,\n",
+        "    task=\"classification\",\n",
+        "    automl_run=automl_run,\n",
+        ")"
      ]
    },
    {
@@ -455,13 +456,18 @@
      "outputs": [],
      "source": [
        "from azureml.interpret.mimic_wrapper import MimicWrapper\n",
-        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator,\n",
-        "                         explainable_model=automl_explainer_setup_obj.surrogate_model, \n",
-        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_explainer_setup_obj.automl_run,\n",
-        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
-        "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
-        "                         classes=automl_explainer_setup_obj.classes,\n",
-        "                         explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params)"
+        "\n",
+        "explainer = MimicWrapper(\n",
+        "    ws,\n",
+        "    automl_explainer_setup_obj.automl_estimator,\n",
+        "    explainable_model=automl_explainer_setup_obj.surrogate_model,\n",
+        "    init_dataset=automl_explainer_setup_obj.X_transform,\n",
+        "    run=automl_explainer_setup_obj.automl_run,\n",
+        "    features=automl_explainer_setup_obj.engineered_feature_names,\n",
+        "    feature_maps=[automl_explainer_setup_obj.feature_map],\n",
+        "    classes=automl_explainer_setup_obj.classes,\n",
+        "    explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params,\n",
+        ")"
      ]
    },
    {
@@ -479,9 +485,14 @@
      "outputs": [],
      "source": [
        "# Compute the engineered explanations\n",
-        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "engineered_explanations = explainer.explain(\n",
+        "    [\"local\", \"global\"], eval_dataset=automl_explainer_setup_obj.X_test_transform\n",
+        ")\n",
        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+        "print(\n",
+        "    \"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n",
+        "    + automl_run.get_portal_url()\n",
+        ")"
      ]
    },
    {
@@ -499,12 +510,18 @@
      "outputs": [],
      "source": [
        "# Compute the raw explanations\n",
-        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True,\n",
-        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
-        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform,\n",
-        "                                     raw_eval_dataset=automl_explainer_setup_obj.X_test_raw)\n",
+        "raw_explanations = explainer.explain(\n",
+        "    [\"local\", \"global\"],\n",
+        "    get_raw=True,\n",
+        "    raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
+        "    eval_dataset=automl_explainer_setup_obj.X_test_transform,\n",
+        "    raw_eval_dataset=automl_explainer_setup_obj.X_test_raw,\n",
+        ")\n",
        "print(raw_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+        "print(\n",
+        "    \"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n",
+        "    + automl_run.get_portal_url()\n",
+        ")"
      ]
    },
    {
@@ -524,15 +541,17 @@
        "import joblib\n",
        "\n",
        "# Initialize the ScoringExplainer\n",
-        "scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
+        "scoring_explainer = TreeScoringExplainer(\n",
+        "    explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map]\n",
+        ")\n",
        "\n",
        "# Pickle scoring explainer locally to './scoring_explainer.pkl'\n",
-        "scoring_explainer_file_name = 'scoring_explainer.pkl'\n",
-        "with open(scoring_explainer_file_name, 'wb') as stream:\n",
+        "scoring_explainer_file_name = \"scoring_explainer.pkl\"\n",
+        "with open(scoring_explainer_file_name, \"wb\") as stream:\n",
        "    joblib.dump(scoring_explainer, stream)\n",
        "\n",
        "# Upload the scoring explainer to the automl run\n",
-        "automl_run.upload_file('outputs/scoring_explainer.pkl', scoring_explainer_file_name)"
+        "automl_run.upload_file(\"outputs/scoring_explainer.pkl\", scoring_explainer_file_name)"
      ]
    },
    {
@@ -551,10 +570,12 @@
      "outputs": [],
      "source": [
        "# Register trained automl model present in the 'outputs' folder in the artifacts\n",
-        "original_model = automl_run.register_model(model_name='automl_model', \n",
-        "                                           model_path='outputs/model.pkl')\n",
-        "scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer',\n",
-        "                                                    model_path='outputs/scoring_explainer.pkl')"
+        "original_model = automl_run.register_model(\n",
+        "    model_name=\"automl_model\", model_path=\"outputs/model.pkl\"\n",
+        ")\n",
+        "scoring_explainer_model = automl_run.register_model(\n",
+        "    model_name=\"scoring_explainer\", model_path=\"outputs/scoring_explainer.pkl\"\n",
+        ")"
      ]
    },
    {
@@ -575,7 +596,7 @@
        "from azureml.automl.core.shared import constants\n",
        "from azureml.core.environment import Environment\n",
        "\n",
-        "automl_run.download_file(constants.CONDA_ENV_FILE_PATH, 'myenv.yml')\n",
+        "automl_run.download_file(constants.CONDA_ENV_FILE_PATH, \"myenv.yml\")\n",
        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
        "myenv"
      ]
@@ -598,7 +619,9 @@
        "import joblib\n",
        "import pandas as pd\n",
        "from azureml.core.model import Model\n",
-        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
+        "from azureml.train.automl.runtime.automl_explain_utilities import (\n",
+        "    automl_setup_model_explanations,\n",
+        ")\n",
        "\n",
        "\n",
        "def init():\n",
@@ -607,28 +630,35 @@
        "\n",
        "    # Retrieve the path to the model file using the model name\n",
        "    # Assume original model is named original_prediction_model\n",
-        "    automl_model_path = Model.get_model_path('automl_model')\n",
-        "    scoring_explainer_path = Model.get_model_path('scoring_explainer')\n",
+        "    automl_model_path = Model.get_model_path(\"automl_model\")\n",
+        "    scoring_explainer_path = Model.get_model_path(\"scoring_explainer\")\n",
        "\n",
        "    automl_model = joblib.load(automl_model_path)\n",
        "    scoring_explainer = joblib.load(scoring_explainer_path)\n",
        "\n",
        "\n",
        "def run(raw_data):\n",
-        "    data = pd.read_json(raw_data, orient='records')    \n",
+        "    data = pd.read_json(raw_data, orient=\"records\")\n",
        "    # Make prediction\n",
        "    predictions = automl_model.predict(data)\n",
        "    # Setup for inferencing explanations\n",
-        "    automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,\n",
-        "                                                                 X_test=data, task='classification')\n",
+        "    automl_explainer_setup_obj = automl_setup_model_explanations(\n",
+        "        automl_model, X_test=data, task=\"classification\"\n",
+        "    )\n",
        "    # Retrieve model explanations for engineered explanations\n",
-        "    engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)\n",
+        "    engineered_local_importance_values = scoring_explainer.explain(\n",
+        "        automl_explainer_setup_obj.X_test_transform\n",
+        "    )\n",
        "    # Retrieve model explanations for raw explanations\n",
-        "    raw_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform, get_raw=True)\n",
+        "    raw_local_importance_values = scoring_explainer.explain(\n",
+        "        automl_explainer_setup_obj.X_test_transform, get_raw=True\n",
+        "    )\n",
        "    # You can return any data type as long as it is JSON-serializable\n",
-        "    return {'predictions': predictions.tolist(),\n",
-        "            'engineered_local_importance_values': engineered_local_importance_values,\n",
-        "            'raw_local_importance_values': raw_local_importance_values}\n"
+        "    return {\n",
+        "        \"predictions\": predictions.tolist(),\n",
+        "        \"engineered_local_importance_values\": engineered_local_importance_values,\n",
+        "        \"raw_local_importance_values\": raw_local_importance_values,\n",
+        "    }"
      ]
    },
    {
@@ -647,7 +677,7 @@
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "\n",
-        "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
+        "inf_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
@@ -668,17 +698,17 @@
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your cluster.\n",
-        "aks_name = 'scoring-explain'\n",
+        "aks_name = \"scoring-explain\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    prov_config = AksCompute.provisioning_configuration(vm_size='STANDARD_D3_V2')\n",
-        "    aks_target = ComputeTarget.create(workspace=ws, \n",
-        "                                      name=aks_name,\n",
-        "                                      provisioning_configuration=prov_config)\n",
+        "    prov_config = AksCompute.provisioning_configuration(vm_size=\"STANDARD_D3_V2\")\n",
+        "    aks_target = ComputeTarget.create(\n",
+        "        workspace=ws, name=aks_name, provisioning_configuration=prov_config\n",
+        "    )\n",
        "aks_target.wait_for_completion(show_output=True)"
      ]
    },
@@ -708,16 +738,18 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "aks_service_name ='model-scoring-local-aks'\n",
+        "aks_service_name = \"model-scoring-local-aks\"\n",
        "\n",
-        "aks_service = Model.deploy(workspace=ws,\n",
-        "                           name=aks_service_name,\n",
-        "                           models=[scoring_explainer_model, original_model],\n",
-        "                           inference_config=inf_config,\n",
-        "                           deployment_config=aks_config,\n",
-        "                           deployment_target=aks_target)\n",
+        "aks_service = Model.deploy(\n",
+        "    workspace=ws,\n",
+        "    name=aks_service_name,\n",
+        "    models=[scoring_explainer_model, original_model],\n",
+        "    inference_config=inf_config,\n",
+        "    deployment_config=aks_config,\n",
+        "    deployment_target=aks_target,\n",
+        ")\n",
        "\n",
-        "aks_service.wait_for_deployment(show_output = True)\n",
+        "aks_service.wait_for_deployment(show_output=True)\n",
        "print(aks_service.state)"
      ]
    },
@@ -752,18 +784,24 @@
      "outputs": [],
      "source": [
        "# Serialize the first row of the test data into json\n",
-        "X_test_json = X_test_df[:1].to_json(orient='records')\n",
+        "X_test_json = X_test_df[:1].to_json(orient=\"records\")\n",
        "print(X_test_json)\n",
        "\n",
        "# Call the service to get the predictions and the engineered and raw explanations\n",
        "output = aks_service.run(X_test_json)\n",
        "\n",
        "# Print the predicted value\n",
-        "print('predictions:\\n{}\\n'.format(output['predictions']))\n",
+        "print(\"predictions:\\n{}\\n\".format(output[\"predictions\"]))\n",
        "# Print the engineered feature importances for the predicted value\n",
-        "print('engineered_local_importance_values:\\n{}\\n'.format(output['engineered_local_importance_values']))\n",
+        "print(\n",
+        "    \"engineered_local_importance_values:\\n{}\\n\".format(\n",
+        "        output[\"engineered_local_importance_values\"]\n",
+        "    )\n",
+        ")\n",
        "# Print the raw feature importances for the predicted value\n",
-        "print('raw_local_importance_values:\\n{}\\n'.format(output['raw_local_importance_values']))\n"
+        "print(\n",
+        "    \"raw_local_importance_values:\\n{}\\n\".format(output[\"raw_local_importance_values\"])\n",
+        ")"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression-car-price-model-explaination-and-featurization/auto-ml-regression.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -78,6 +62,7 @@
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
+        "\n",
        "from azureml.automl.core.featurization import FeaturizationConfig\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core.dataset import Dataset"
@@ -90,16 +75,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -109,17 +84,18 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment.\n",
-        "experiment_name = 'automl-regression-hardware-explain'\n",
+        "experiment_name = \"automl-regression-hardware-explain\"\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace Name\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -152,12 +128,12 @@
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "                                                           max_nodes=4)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
-        "\n",
        "compute_target.wait_for_completion(show_output=True)"
      ]
    },
@@ -176,7 +152,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "data = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv'\n",
+        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
        "\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "\n",
@@ -185,14 +161,22 @@
        "\n",
        "\n",
        "# Register the train dataset with your workspace\n",
-        "train_data.register(workspace = ws, name = 'machineData_train_dataset',\n",
-        "                       description = 'hardware performance training data',\n",
-        "                      create_new_version=True)\n",
+        "train_data.register(\n",
+        "    workspace=ws,\n",
+        "    name=\"machineData_train_dataset\",\n",
+        "    description=\"hardware performance training data\",\n",
+        "    create_new_version=True,\n",
+        ")\n",
        "\n",
        "# Register the test dataset with your workspace\n",
-        "test_data.register(workspace = ws, name = 'machineData_test_dataset', description = 'hardware performance test data', create_new_version=True)\n",
+        "test_data.register(\n",
+        "    workspace=ws,\n",
+        "    name=\"machineData_test_dataset\",\n",
+        "    description=\"hardware performance test data\",\n",
+        "    create_new_version=True,\n",
+        ")\n",
        "\n",
-        "label =\"ERP\"\n",
+        "label = \"ERP\"\n",
        "\n",
        "train_data.to_pandas_dataframe().head()"
      ]
@@ -249,15 +233,19 @@
      "outputs": [],
      "source": [
        "featurization_config = FeaturizationConfig()\n",
-        "featurization_config.blocked_transformers = ['LabelEncoder']\n",
-        "#featurization_config.drop_columns = ['MMIN']\n",
-        "featurization_config.add_column_purpose('MYCT', 'Numeric')\n",
-        "featurization_config.add_column_purpose('VendorName', 'CategoricalHash')\n",
-        "#default strategy mean, add transformer param for for 3 columns\n",
-        "featurization_config.add_transformer_params('Imputer', ['CACH'], {\"strategy\": \"median\"})\n",
-        "featurization_config.add_transformer_params('Imputer', ['CHMIN'], {\"strategy\": \"median\"})\n",
-        "featurization_config.add_transformer_params('Imputer', ['PRP'], {\"strategy\": \"most_frequent\"})\n",
-        "#featurization_config.add_transformer_params('HashOneHotEncoder', [], {\"number_of_bits\": 3})"
+        "featurization_config.blocked_transformers = [\"LabelEncoder\"]\n",
+        "# featurization_config.drop_columns = ['MMIN']\n",
+        "featurization_config.add_column_purpose(\"MYCT\", \"Numeric\")\n",
+        "featurization_config.add_column_purpose(\"VendorName\", \"CategoricalHash\")\n",
+        "# default strategy mean, add transformer param for for 3 columns\n",
+        "featurization_config.add_transformer_params(\"Imputer\", [\"CACH\"], {\"strategy\": \"median\"})\n",
+        "featurization_config.add_transformer_params(\n",
+        "    \"Imputer\", [\"CHMIN\"], {\"strategy\": \"median\"}\n",
+        ")\n",
+        "featurization_config.add_transformer_params(\n",
+        "    \"Imputer\", [\"PRP\"], {\"strategy\": \"most_frequent\"}\n",
+        ")\n",
+        "# featurization_config.add_transformer_params('HashOneHotEncoder', [], {\"number_of_bits\": 3})"
      ]
    },
    {
@@ -271,23 +259,24 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_hours\" : 0.25,\n",
+        "    \"enable_early_stopping\": True,\n",
+        "    \"experiment_timeout_hours\": 0.25,\n",
        "    \"max_concurrent_iterations\": 4,\n",
        "    \"max_cores_per_iteration\": -1,\n",
        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'normalized_root_mean_squared_error',\n",
-        "    \"verbosity\": logging.INFO\n",
+        "    \"primary_metric\": \"normalized_root_mean_squared_error\",\n",
+        "    \"verbosity\": logging.INFO,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             compute_target=compute_target,\n",
-        "                             featurization=featurization_config,\n",
-        "                             training_data = train_data,\n",
-        "                             label_column_name = label,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"regression\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    compute_target=compute_target,\n",
+        "    featurization=featurization_config,\n",
+        "    training_data=train_data,\n",
+        "    label_column_name=label,\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
@@ -304,7 +293,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
      ]
    },
    {
@@ -320,9 +309,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
-        "#remote_run"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
+        "# remote_run"
      ]
    },
    {
@@ -359,8 +348,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Download the featuurization summary JSON file locally\n",
-        "best_run.download_file(\"outputs/featurization_summary.json\", \"featurization_summary.json\")\n",
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
        "\n",
        "# Render the JSON as a pandas DataFrame\n",
        "with open(\"featurization_summary.json\", \"r\") as f:\n",
@@ -394,7 +385,8 @@
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
+        "\n",
+        "RunDetails(remote_run).show()"
      ]
    },
    {
@@ -415,7 +407,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#automl_run, fitted_model = remote_run.get_output(metric='r2_score')\n",
+        "# automl_run, fitted_model = remote_run.get_output(metric='r2_score')\n",
        "automl_run, fitted_model = remote_run.get_output(iteration=2)"
      ]
    },
@@ -441,7 +433,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "with open('train_explainer.py', 'r') as cefr:\n",
+        "with open(\"train_explainer.py\", \"r\") as cefr:\n",
        "    print(cefr.read())"
      ]
    },
@@ -463,32 +455,36 @@
        "import os\n",
        "\n",
        "# create script folder\n",
-        "script_folder = './sample_projects/automl-regression-hardware'\n",
+        "script_folder = \"./sample_projects/automl-regression-hardware\"\n",
        "if not os.path.exists(script_folder):\n",
        "    os.makedirs(script_folder)\n",
        "\n",
        "# Copy the sample script to script folder.\n",
-        "shutil.copy('train_explainer.py', script_folder)\n",
+        "shutil.copy(\"train_explainer.py\", script_folder)\n",
        "\n",
        "# Create the explainer script that will run on the remote compute.\n",
-        "script_file_name = script_folder + '/train_explainer.py'\n",
+        "script_file_name = script_folder + \"/train_explainer.py\"\n",
        "\n",
        "# Open the sample script for modification\n",
-        "with open(script_file_name, 'r') as cefr:\n",
+        "with open(script_file_name, \"r\") as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "# Replace the values in train_explainer.py file with the appropriate values\n",
-        "content = content.replace('<<experiment_name>>', automl_run.experiment.name) # your experiment name.\n",
-        "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
-        "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
-        "content = content.replace('<<task>>', 'regression') # Training task type\n",
+        "content = content.replace(\n",
+        "    \"<<experiment_name>>\", automl_run.experiment.name\n",
+        ")  # your experiment name.\n",
+        "content = content.replace(\n",
+        "    \"<<run_id>>\", automl_run.id\n",
+        ")  # Run-id of the AutoML run for which you want to explain the model.\n",
+        "content = content.replace(\"<<target_column_name>>\", \"ERP\")  # Your target column name\n",
+        "content = content.replace(\"<<task>>\", \"regression\")  # Training task type\n",
        "# Name of your training dataset register with your workspace\n",
-        "content = content.replace('<<train_dataset_name>>', 'machineData_train_dataset') \n",
+        "content = content.replace(\"<<train_dataset_name>>\", \"machineData_train_dataset\")\n",
        "# Name of your test dataset register with your workspace\n",
-        "content = content.replace('<<test_dataset_name>>', 'machineData_test_dataset')\n",
+        "content = content.replace(\"<<test_dataset_name>>\", \"machineData_test_dataset\")\n",
        "\n",
        "# Write sample file into your script folder.\n",
-        "with open(script_file_name, 'w') as cefw:\n",
+        "with open(script_file_name, \"w\") as cefw:\n",
        "    cefw.write(content)"
      ]
    },
@@ -506,6 +502,8 @@
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import pkg_resources\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -515,7 +513,7 @@
        "conda_run_config.environment.docker.enabled = True\n",
        "\n",
        "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = automl_run.get_environment().python.conda_dependencies"
+        "conda_run_config.environment = automl_run.get_environment()"
      ]
    },
    {
@@ -535,9 +533,11 @@
        "# Now submit a run on AmlCompute for model explanations\n",
        "from azureml.core.script_run_config import ScriptRunConfig\n",
        "\n",
-        "script_run_config = ScriptRunConfig(source_directory=script_folder,\n",
-        "                                    script='train_explainer.py',\n",
-        "                                    run_config=conda_run_config)\n",
+        "script_run_config = ScriptRunConfig(\n",
+        "    source_directory=script_folder,\n",
+        "    script=\"train_explainer.py\",\n",
+        "    run_config=conda_run_config,\n",
+        ")\n",
        "\n",
        "run = experiment.submit(script_run_config)\n",
        "\n",
@@ -579,10 +579,16 @@
      "outputs": [],
      "source": [
        "from azureml.interpret import ExplanationClient\n",
+        "\n",
        "client = ExplanationClient.from_run(automl_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
+        "engineered_explanations = client.download_model_explanation(\n",
+        "    raw=False, comment=\"engineered explanations\"\n",
+        ")\n",
        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+        "print(\n",
+        "    \"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n",
+        "    + automl_run.get_portal_url()\n",
+        ")"
      ]
    },
    {
@@ -599,9 +605,14 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "raw_explanations = client.download_model_explanation(raw=True, comment='raw explanations')\n",
+        "raw_explanations = client.download_model_explanation(\n",
+        "    raw=True, comment=\"raw explanations\"\n",
+        ")\n",
        "print(raw_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+        "print(\n",
+        "    \"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n",
+        "    + automl_run.get_portal_url()\n",
+        ")"
      ]
    },
    {
@@ -623,33 +634,12 @@
      "outputs": [],
      "source": [
        "# Register trained automl model present in the 'outputs' folder in the artifacts\n",
-        "original_model = automl_run.register_model(model_name='automl_model', \n",
-        "                                           model_path='outputs/model.pkl')\n",
-        "scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer',\n",
-        "                                                    model_path='outputs/scoring_explainer.pkl')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create the conda dependencies for setting up the service\n",
-        "We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "conda_dep = automl_run.get_environment().python.conda_dependencies\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(conda_dep.serialize_to_string())\n",
-        "\n",
-        "with open(\"myenv.yml\",\"r\") as f:\n",
-        "    print(f.read())"
+        "original_model = automl_run.register_model(\n",
+        "    model_name=\"automl_model\", model_path=\"outputs/model.pkl\"\n",
+        ")\n",
+        "scoring_explainer_model = automl_run.register_model(\n",
+        "    model_name=\"scoring_explainer\", model_path=\"outputs/scoring_explainer.pkl\"\n",
+        ")"
      ]
    },
    {
@@ -665,7 +655,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "with open(\"score_explain.py\",\"r\") as f:\n",
+        "with open(\"score_explain.py\", \"r\") as f:\n",
        "    print(f.read())"
      ]
    },
@@ -674,7 +664,7 @@
      "metadata": {},
      "source": [
        "### Deploy the service\n",
-        "In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. "
+        "In the cell below, we deploy the service using the automl training environment and the scoring file from the previous steps. "
      ]
    },
    {
@@ -683,22 +673,30 @@
      "metadata": {},
      "outputs": [],
      "source": [
+        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "from azureml.core.model import Model\n",
        "from azureml.core.environment import Environment\n",
        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=2, \n",
-        "                                               memory_gb=2, \n",
-        "                                               tags={\"data\": \"Machine Data\",  \n",
-        "                                                     \"method\" : \"local_explanation\"}, \n",
-        "                                               description='Get local explanations for Machine test data')\n",
+        "aciconfig = AciWebservice.deploy_configuration(\n",
+        "    cpu_cores=2,\n",
+        "    memory_gb=2,\n",
+        "    tags={\"data\": \"Machine Data\", \"method\": \"local_explanation\"},\n",
+        "    description=\"Get local explanations for Machine test data\",\n",
+        ")\n",
        "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "myenv = automl_run.get_environment()\n",
        "inference_config = InferenceConfig(entry_script=\"score_explain.py\", environment=myenv)\n",
        "\n",
        "# Use configs and models generated above\n",
-        "service = Model.deploy(ws, 'model-scoring', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
+        "service = Model.deploy(\n",
+        "    ws,\n",
+        "    \"model-scoring\",\n",
+        "    [scoring_explainer_model, original_model],\n",
+        "    inference_config,\n",
+        "    aciconfig,\n",
+        ")\n",
        "service.wait_for_deployment(show_output=True)"
      ]
    },
@@ -732,19 +730,19 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "if service.state == 'Healthy':\n",
+        "if service.state == \"Healthy\":\n",
        "    X_test = test_data.drop_columns([label]).to_pandas_dataframe()\n",
        "    # Serialize the first row of the test data into json\n",
-        "    X_test_json = X_test[:1].to_json(orient='records')\n",
+        "    X_test_json = X_test[:1].to_json(orient=\"records\")\n",
        "    print(X_test_json)\n",
        "    # Call the service to get the predictions and the engineered and raw explanations\n",
        "    output = service.run(X_test_json)\n",
        "    # Print the predicted value\n",
-        "    print(output['predictions'])\n",
+        "    print(output[\"predictions\"])\n",
        "    # Print the engineered feature importances for the predicted value\n",
-        "    print(output['engineered_local_importance_values'])\n",
+        "    print(output[\"engineered_local_importance_values\"])\n",
        "    # Print the raw feature importances for the predicted value\n",
-        "    print(output['raw_local_importance_values'])"
+        "    print(output[\"raw_local_importance_values\"])"
      ]
    },
    {
@@ -780,14 +778,14 @@
        "# preview the first 3 rows of the dataset\n",
        "\n",
        "test_data = test_data.to_pandas_dataframe()\n",
-        "y_test = test_data['ERP'].fillna(0)\n",
-        "test_data = test_data.drop('ERP', 1)\n",
+        "y_test = test_data[\"ERP\"].fillna(0)\n",
+        "test_data = test_data.drop(\"ERP\", 1)\n",
        "test_data = test_data.fillna(0)\n",
        "\n",
        "\n",
        "train_data = train_data.to_pandas_dataframe()\n",
-        "y_train = train_data['ERP'].fillna(0)\n",
-        "train_data = train_data.drop('ERP', 1)\n",
+        "y_train = train_data[\"ERP\"].fillna(0)\n",
+        "train_data = train_data.drop(\"ERP\", 1)\n",
        "train_data = train_data.fillna(0)"
      ]
    },
@@ -814,27 +812,41 @@
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Set up a multi-plot chart.\n",
-        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
-        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
+        "f, (a0, a1) = plt.subplots(\n",
+        "    1, 2, gridspec_kw={\"width_ratios\": [1, 1], \"wspace\": 0, \"hspace\": 0}\n",
+        ")\n",
+        "f.suptitle(\"Regression Residual Values\", fontsize=18)\n",
        "f.set_figheight(6)\n",
        "f.set_figwidth(16)\n",
        "\n",
        "# Plot residual values of training set.\n",
        "a0.axis([0, 360, -100, 100])\n",
-        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
-        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
-        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
-        "a0.set_xlabel('Training samples', fontsize = 12)\n",
-        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
+        "a0.plot(y_residual_train, \"bo\", alpha=0.5)\n",
+        "a0.plot([-10, 360], [0, 0], \"r-\", lw=3)\n",
+        "a0.text(\n",
+        "    16,\n",
+        "    170,\n",
+        "    \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_train, y_pred_train))),\n",
+        "    fontsize=12,\n",
+        ")\n",
+        "a0.text(\n",
+        "    16, 140, \"R2 score = {0:.2f}\".format(r2_score(y_train, y_pred_train)), fontsize=12\n",
+        ")\n",
+        "a0.set_xlabel(\"Training samples\", fontsize=12)\n",
+        "a0.set_ylabel(\"Residual Values\", fontsize=12)\n",
        "\n",
        "# Plot residual values of test set.\n",
        "a1.axis([0, 90, -100, 100])\n",
-        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
-        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
-        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
-        "a1.set_xlabel('Test samples', fontsize = 12)\n",
+        "a1.plot(y_residual_test, \"bo\", alpha=0.5)\n",
+        "a1.plot([-10, 360], [0, 0], \"r-\", lw=3)\n",
+        "a1.text(\n",
+        "    5,\n",
+        "    170,\n",
+        "    \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_test, y_pred_test))),\n",
+        "    fontsize=12,\n",
+        ")\n",
+        "a1.text(5, 140, \"R2 score = {0:.2f}\".format(r2_score(y_test, y_pred_test)), fontsize=12)\n",
+        "a1.set_xlabel(\"Test samples\", fontsize=12)\n",
        "a1.set_yticklabels([])\n",
        "\n",
        "plt.show()"
@@ -847,9 +859,11 @@
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
-        "test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "test_pred = plt.scatter(y_test, y_pred_test, color=\"\")\n",
+        "test_test = plt.scatter(y_test, y_test, color=\"g\")\n",
+        "plt.legend(\n",
+        "    (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n",
+        ")\n",
        "plt.show()"
      ]
    }
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py
@@ -1,7 +1,10 @@
 import pandas as pd
 import joblib
 from azureml.core.model import Model
-from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations
+from azureml.train.automl.runtime.automl_explain_utilities import (
+    automl_setup_model_explanations,
+)
+import scipy as sp


 def init():
@@ -11,26 +14,55 @@ def init():

    # Retrieve the path to the model file using the model name
    # Assume original model is named original_prediction_model
-    automl_model_path = Model.get_model_path('automl_model')
-    scoring_explainer_path = Model.get_model_path('scoring_explainer')
+    automl_model_path = Model.get_model_path("automl_model")
+    scoring_explainer_path = Model.get_model_path("scoring_explainer")

    automl_model = joblib.load(automl_model_path)
    scoring_explainer = joblib.load(scoring_explainer_path)


+def is_multi_dimensional(matrix):
+    if hasattr(matrix, "ndim") and matrix.ndim > 1:
+        return True
+    if hasattr(matrix, "shape") and matrix.shape[1]:
+        return True
+    return False
+
+
+def convert_matrix(matrix):
+    if sp.sparse.issparse(matrix):
+        matrix = matrix.todense()
+    if is_multi_dimensional(matrix):
+        matrix = matrix.tolist()
+    return matrix
+
+
 def run(raw_data):
    # Get predictions and explanations for each data point
-    data = pd.read_json(raw_data, orient='records')
+    data = pd.read_json(raw_data, orient="records")
    # Make prediction
    predictions = automl_model.predict(data)
    # Setup for inferencing explanations
-    automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,
-                                                                 X_test=data, task='regression')
+    automl_explainer_setup_obj = automl_setup_model_explanations(
+        automl_model, X_test=data, task="regression"
+    )
    # Retrieve model explanations for engineered explanations
-    engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)
+    engineered_local_importance_values = scoring_explainer.explain(
+        automl_explainer_setup_obj.X_test_transform
+    )
+    engineered_local_importance_values = convert_matrix(
+        engineered_local_importance_values
+    )
+
    # Retrieve model explanations for raw explanations
-    raw_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform, get_raw=True)
+    raw_local_importance_values = scoring_explainer.explain(
+        automl_explainer_setup_obj.X_test_transform, get_raw=True
+    )
+    raw_local_importance_values = convert_matrix(raw_local_importance_values)
+
    # You can return any data type as long as it is JSON-serializable
-    return {'predictions': predictions.tolist(),
-            'engineered_local_importance_values': engineered_local_importance_values,
-            'raw_local_importance_values': raw_local_importance_values}
+    return {
+        "predictions": predictions.tolist(),
+        "engineered_local_importance_values": engineered_local_importance_values,
+        "raw_local_importance_values": raw_local_importance_values,
+    }
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py
@@ -10,11 +10,13 @@ from azureml.core.dataset import Dataset
 from azureml.core.run import Run
 from azureml.interpret.mimic_wrapper import MimicWrapper
 from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer
-from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations, \
-    automl_check_model_if_explainable
+from azureml.train.automl.runtime.automl_explain_utilities import (
+    automl_setup_model_explanations,
+    automl_check_model_if_explainable,
+)


-OUTPUT_DIR = './outputs/'
+OUTPUT_DIR = "./outputs/"
 os.makedirs(OUTPUT_DIR, exist_ok=True)

 # Get workspace from the run context
@@ -22,63 +24,77 @@ run = Run.get_context()
 ws = run.experiment.workspace

 # Get the AutoML run object from the experiment name and the workspace
-experiment = Experiment(ws, '<<experiment_name>>')
-automl_run = Run(experiment=experiment, run_id='<<run_id>>')
+experiment = Experiment(ws, "<<experiment_name>>")
+automl_run = Run(experiment=experiment, run_id="<<run_id>>")

 # Check if this AutoML model is explainable
 if not automl_check_model_if_explainable(automl_run):
-    raise Exception("Model explanations are currently not supported for " + automl_run.get_properties().get(
-        'run_algorithm'))
+    raise Exception(
+        "Model explanations are currently not supported for "
+        + automl_run.get_properties().get("run_algorithm")
+    )

 # Download the best model from the artifact store
-automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
+automl_run.download_file(name=MODEL_PATH, output_file_path="model.pkl")

 # Load the AutoML model into memory
-fitted_model = joblib.load('model.pkl')
+fitted_model = joblib.load("model.pkl")

 # Get the train dataset from the workspace
-train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
+train_dataset = Dataset.get_by_name(workspace=ws, name="<<train_dataset_name>>")
 # Drop the labeled column to get the training set.
-X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
-y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
+X_train = train_dataset.drop_columns(columns=["<<target_column_name>>"])
+y_train = train_dataset.keep_columns(columns=["<<target_column_name>>"], validate=True)

 # Get the test dataset from the workspace
-test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
+test_dataset = Dataset.get_by_name(workspace=ws, name="<<test_dataset_name>>")
 # Drop the labeled column to get the testing set.
-X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
+X_test = test_dataset.drop_columns(columns=["<<target_column_name>>"])

 # Setup the class for explaining the AutoML models
-automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
-                                                             X=X_train, X_test=X_test,
-                                                             y=y_train,
-                                                             automl_run=automl_run)
+automl_explainer_setup_obj = automl_setup_model_explanations(
+    fitted_model, "<<task>>", X=X_train, X_test=X_test, y=y_train, automl_run=automl_run
+)

 # Initialize the Mimic Explainer
-explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
-                         init_dataset=automl_explainer_setup_obj.X_transform,
-                         run=automl_explainer_setup_obj.automl_run,
-                         features=automl_explainer_setup_obj.engineered_feature_names,
-                         feature_maps=[automl_explainer_setup_obj.feature_map],
-                         classes=automl_explainer_setup_obj.classes)
+explainer = MimicWrapper(
+    ws,
+    automl_explainer_setup_obj.automl_estimator,
+    LGBMExplainableModel,
+    init_dataset=automl_explainer_setup_obj.X_transform,
+    run=automl_explainer_setup_obj.automl_run,
+    features=automl_explainer_setup_obj.engineered_feature_names,
+    feature_maps=[automl_explainer_setup_obj.feature_map],
+    classes=automl_explainer_setup_obj.classes,
+)

 # Compute the engineered explanations
-engineered_explanations = explainer.explain(['local', 'global'], tag='engineered explanations',
-                                            eval_dataset=automl_explainer_setup_obj.X_test_transform)
+engineered_explanations = explainer.explain(
+    ["local", "global"],
+    tag="engineered explanations",
+    eval_dataset=automl_explainer_setup_obj.X_test_transform,
+)

 # Compute the raw explanations
-raw_explanations = explainer.explain(['local', 'global'], get_raw=True, tag='raw explanations',
-                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
-                                     eval_dataset=automl_explainer_setup_obj.X_test_transform,
-                                     raw_eval_dataset=automl_explainer_setup_obj.X_test_raw)
+raw_explanations = explainer.explain(
+    ["local", "global"],
+    get_raw=True,
+    tag="raw explanations",
+    raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
+    eval_dataset=automl_explainer_setup_obj.X_test_transform,
+    raw_eval_dataset=automl_explainer_setup_obj.X_test_raw,
+)

 print("Engineered and raw explanations computed successfully")

 # Initialize the ScoringExplainer
-scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])
+scoring_explainer = TreeScoringExplainer(
+    explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map]
+)

 # Pickle scoring explainer locally
-with open('scoring_explainer.pkl', 'wb') as stream:
+with open("scoring_explainer.pkl", "wb") as stream:
    joblib.dump(scoring_explainer, stream)

 # Upload the scoring explainer to the automl run
-automl_run.upload_file('outputs/scoring_explainer.pkl', 'scoring_explainer.pkl')
+automl_run.upload_file("outputs/scoring_explainer.pkl", "scoring_explainer.pkl")
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.png)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -70,7 +54,7 @@
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
-        " \n",
+        "\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
@@ -86,16 +70,6 @@
        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -105,18 +79,19 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment.\n",
-        "experiment_name = 'automl-regression'\n",
+        "experiment_name = \"automl-regression\"\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
        "outputDf.T"
      ]
    },
@@ -143,10 +118,11 @@
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "                                                           max_nodes=4)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True)"
@@ -179,7 +155,7 @@
        "# Split the dataset into train and test datasets\n",
        "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n",
        "\n",
-        "label = \"ERP\"\n"
+        "label = \"ERP\""
      ]
    },
    {
@@ -213,20 +189,21 @@
      "source": [
        "automl_settings = {\n",
        "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'normalized_root_mean_squared_error',\n",
-        "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
+        "    \"primary_metric\": \"r2_score\",\n",
+        "    \"enable_early_stopping\": True,\n",
+        "    \"experiment_timeout_hours\": 0.3,  # for real scenarios we reccommend a timeout of at least one hour\n",
        "    \"max_concurrent_iterations\": 4,\n",
        "    \"max_cores_per_iteration\": -1,\n",
        "    \"verbosity\": logging.INFO,\n",
        "}\n",
        "\n",
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             compute_target = compute_target,\n",
-        "                             training_data = train_data,\n",
-        "                             label_column_name = label,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"regression\",\n",
+        "    compute_target=compute_target,\n",
+        "    training_data=train_data,\n",
+        "    label_column_name=label,\n",
+        "    **automl_settings,\n",
+        ")"
      ]
    },
    {
@@ -242,7 +219,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
      ]
    },
    {
@@ -252,8 +229,8 @@
      "outputs": [],
      "source": [
        "# If you need to retrieve a run that already started, use the following code\n",
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
      ]
    },
    {
@@ -281,7 +258,8 @@
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
+        "\n",
+        "RunDetails(remote_run).show()"
      ]
    },
    {
@@ -328,7 +306,7 @@
      "outputs": [],
      "source": [
        "lookup_metric = \"root_mean_squared_error\"\n",
-        "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+        "best_run, fitted_model = remote_run.get_output(metric=lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
@@ -348,7 +326,7 @@
      "outputs": [],
      "source": [
        "iteration = 3\n",
-        "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
+        "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
        "print(third_run)\n",
        "print(third_model)"
      ]
@@ -366,12 +344,12 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "y_test = test_data.keep_columns('ERP').to_pandas_dataframe()\n",
-        "test_data = test_data.drop_columns('ERP').to_pandas_dataframe()\n",
+        "y_test = test_data.keep_columns(\"ERP\").to_pandas_dataframe()\n",
+        "test_data = test_data.drop_columns(\"ERP\").to_pandas_dataframe()\n",
        "\n",
        "\n",
-        "y_train = train_data.keep_columns('ERP').to_pandas_dataframe()\n",
-        "train_data = train_data.drop_columns('ERP').to_pandas_dataframe()\n"
+        "y_train = train_data.keep_columns(\"ERP\").to_pandas_dataframe()\n",
+        "train_data = train_data.drop_columns(\"ERP\").to_pandas_dataframe()"
      ]
    },
    {
@@ -397,27 +375,41 @@
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Set up a multi-plot chart.\n",
-        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
-        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
+        "f, (a0, a1) = plt.subplots(\n",
+        "    1, 2, gridspec_kw={\"width_ratios\": [1, 1], \"wspace\": 0, \"hspace\": 0}\n",
+        ")\n",
+        "f.suptitle(\"Regression Residual Values\", fontsize=18)\n",
        "f.set_figheight(6)\n",
        "f.set_figwidth(16)\n",
        "\n",
        "# Plot residual values of training set.\n",
        "a0.axis([0, 360, -100, 100])\n",
-        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
-        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
-        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
-        "a0.set_xlabel('Training samples', fontsize = 12)\n",
-        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
+        "a0.plot(y_residual_train, \"bo\", alpha=0.5)\n",
+        "a0.plot([-10, 360], [0, 0], \"r-\", lw=3)\n",
+        "a0.text(\n",
+        "    16,\n",
+        "    170,\n",
+        "    \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_train, y_pred_train))),\n",
+        "    fontsize=12,\n",
+        ")\n",
+        "a0.text(\n",
+        "    16, 140, \"R2 score = {0:.2f}\".format(r2_score(y_train, y_pred_train)), fontsize=12\n",
+        ")\n",
+        "a0.set_xlabel(\"Training samples\", fontsize=12)\n",
+        "a0.set_ylabel(\"Residual Values\", fontsize=12)\n",
        "\n",
        "# Plot residual values of test set.\n",
        "a1.axis([0, 90, -100, 100])\n",
-        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
-        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
-        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
-        "a1.set_xlabel('Test samples', fontsize = 12)\n",
+        "a1.plot(y_residual_test, \"bo\", alpha=0.5)\n",
+        "a1.plot([-10, 360], [0, 0], \"r-\", lw=3)\n",
+        "a1.text(\n",
+        "    5,\n",
+        "    170,\n",
+        "    \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_test, y_pred_test))),\n",
+        "    fontsize=12,\n",
+        ")\n",
+        "a1.text(5, 140, \"R2 score = {0:.2f}\".format(r2_score(y_test, y_pred_test)), fontsize=12)\n",
+        "a1.set_xlabel(\"Test samples\", fontsize=12)\n",
        "a1.set_yticklabels([])\n",
        "\n",
        "plt.show()"
@@ -430,9 +422,11 @@
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
-        "test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "test_pred = plt.scatter(y_test, y_pred_test, color=\"\")\n",
+        "test_test = plt.scatter(y_test, y_test, color=\"g\")\n",
+        "plt.legend(\n",
+        "    (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n",
+        ")\n",
        "plt.show()"
      ]
    },
--- a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
+++ b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
@@ -82,7 +82,7 @@
      "source": [
        "## Create trained model\n",
        "\n",
-        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html). "
      ]
    },
    {
@@ -279,7 +279,9 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
@@ -478,7 +480,9 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
--- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
+++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
@@ -81,7 +81,7 @@
      "source": [
        "## Create trained model\n",
        "\n",
-        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
      ]
    },
    {
@@ -263,7 +263,7 @@
        "\n",
        "# explicitly set base_image to None when setting base_dockerfile\n",
        "myenv.docker.base_image = None\n",
-        "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\\nRUN echo \\\"this is test\\\"\"\n",
+        "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\\nRUN echo \\\"this is test\\\"\"\n",
        "myenv.inferencing_stack_version = \"latest\"\n",
        "\n",
        "inference_config = InferenceConfig(source_directory=source_directory,\n",
--- a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb
+++ b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb
@@ -105,11 +105,13 @@
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "environment=Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'numpy',\n",
-        "    'scikit-learn==0.19.1',\n",
+        "    'scikit-learn==0.22.1',\n",
        "    'scipy'\n",
        "])"
      ]
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
+++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
@@ -172,7 +172,7 @@
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy==1.19.5','scikit-learn==0.22.1'],\n",
        "                                 pip_packages=['azureml-defaults'])\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
@@ -70,7 +70,7 @@
        "\n",
        "import urllib.request\n",
        "\n",
-        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
+        "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
        "\n",
        "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
        "\n",
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
@@ -70,7 +70,7 @@
        "\n",
        "import urllib.request\n",
        "\n",
-        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
+        "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
        "\n",
        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
      ]
--- a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb
@@ -240,8 +240,9 @@
        "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n",
        "# for open-sourced GPU base images.\n",
        "env.docker.base_image = DEFAULT_GPU_IMAGE\n",
-        "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n",
-        "                                 pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
+        "env.python.conda_dependencies = CondaDependencies.create(python_version=\"3.6.2\", \n",
+        "                                    conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n",
+        "                                    pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
        "\n",
        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
        "aks_config = AksWebservice.deploy_configuration()\n",
--- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb
@@ -109,7 +109,7 @@
        "from azureml.core import Environment\n",
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
-        "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.19.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
+        "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.22.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
        "myenv = Environment(name='myenv')\n",
        "myenv.python.conda_dependencies = conda_deps"
      ]
--- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
@@ -109,7 +109,7 @@
        "from azureml.core import Environment\n",
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
-        "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
+        "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.22.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
        "myenv = Environment(name='myenv')\n",
        "myenv.python.conda_dependencies = conda_deps"
      ]
@@ -295,12 +295,14 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
        "    'numpy',\n",
-        "    'scikit-learn==0.19.1',\n",
+        "    'scikit-learn==0.22.1',\n",
        "    'scipy'\n",
        "])\n",
        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
--- a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb
@@ -106,7 +106,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
@@ -249,6 +249,7 @@
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import sys\n",
        "\n",
        "# Create a new RunConfig object\n",
        "run_config = RunConfiguration(framework=\"python\")\n",
@@ -260,6 +261,8 @@
        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
        "]\n",
        "\n",
+        "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n",
+        "\n",
        "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
        "# In production scenario user would choose their dependencies\n",
        "import pkg_resources\n",
@@ -283,7 +286,7 @@
        "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
        "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
        "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages,  python_version=python_version)\n",
        "\n",
        "from azureml.core import ScriptRunConfig\n",
        "\n",
--- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml
+++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml
@@ -11,4 +11,6 @@ dependencies:
  - matplotlib
  - azureml-dataset-runtime
  - ipywidgets
-  - raiwidgets~=0.15.0
+  - raiwidgets~=0.18.1
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
--- a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
+++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
@@ -10,4 +10,7 @@ dependencies:
  - ipython
  - matplotlib
  - ipywidgets
-  - raiwidgets~=0.15.0
+  - raiwidgets~=0.18.1
+  - packaging>=20.9
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
@@ -358,6 +358,7 @@
        "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
        "myenv = CondaDependencies.create(\n",
        "    python_version=python_version,\n",
+        "    conda_packages=['pip==20.2.4'],\n",
        "    pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
@@ -391,7 +392,7 @@
        "\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-        "                                               memory_gb=1, \n",
+        "                                               memory_gb=2, \n",
        "                                               tags={\"data\": \"IBM_Attrition\",  \n",
        "                                                     \"method\" : \"local_explanation\"}, \n",
        "                                               description='Get local explanations for IBM Employee Attrition data')\n",
@@ -415,8 +416,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import requests\n",
        "import json\n",
+        "from raiutils.webservice import post_with_retries\n",
        "\n",
        "\n",
        "# Create data to test service with\n",
@@ -428,7 +429,7 @@
        "\n",
        "# Send request to service\n",
        "print(\"POST to url\", service.scoring_uri)\n",
-        "resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n",
+        "resp = post_with_retries(service.scoring_uri, sample_data, headers)\n",
        "\n",
        "# Can covert back to Python objects from json string if desired\n",
        "print(\"prediction:\", resp.text)\n",
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
@@ -10,4 +10,7 @@ dependencies:
  - ipython
  - matplotlib
  - ipywidgets
-  - raiwidgets~=0.15.0
+  - raiwidgets~=0.18.1
+  - packaging>=20.9
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
@@ -513,7 +513,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import requests\n",
+        "from raiutils.webservice import post_with_retries\n",
        "\n",
        "# Create data to test service with\n",
        "examples = x_test[:4]\n",
@@ -523,7 +523,7 @@
        "\n",
        "# Send request to service\n",
        "print(\"POST to url\", service.scoring_uri)\n",
-        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "resp = post_with_retries(service.scoring_uri, input_data, headers)\n",
        "\n",
        "# Can covert back to Python objects from json string if desired\n",
        "print(\"prediction:\", resp.text)"
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml
@@ -12,4 +12,6 @@ dependencies:
  - azureml-dataset-runtime
  - azureml-core
  - ipywidgets
-  - raiwidgets~=0.15.0
+  - raiwidgets~=0.18.1
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
--- a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/cleanse.py
+++ b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/cleanse.py
@@ -5,17 +5,6 @@ import argparse
 import os
 from azureml.core import Run

-
-def get_dict(dict_str):
-    pairs = dict_str.strip("{}").split(r'\;')
-    new_dict = {}
-    for pair in pairs:
-        key, value = pair.strip().split(":")
-        new_dict[key.strip().strip("'")] = value.strip().strip("'")
-
-    return new_dict
-
-
 print("Cleans the input data")

 # Get the input green_taxi_data. To learn more about how to access dataset in your script, please
@@ -23,7 +12,6 @@ print("Cleans the input data")
 run = Run.get_context()
 raw_data = run.input_datasets["raw_data"]

-
 parser = argparse.ArgumentParser("cleanse")
 parser.add_argument("--output_cleanse", type=str, help="cleaned taxi data directory")
 parser.add_argument("--useful_columns", type=str, help="useful columns to keep")
@@ -38,8 +26,8 @@ print("Argument 3(output cleansed taxi data path): %s" % args.output_cleanse)
 # These functions ensure that null data is removed from the dataset,
 # which will help increase machine learning model accuracy.

-useful_columns = [s.strip().strip("'") for s in args.useful_columns.strip("[]").split(r'\;')]
-columns = get_dict(args.columns)
+useful_columns = eval(args.useful_columns.replace(';', ','))
+columns = eval(args.columns.replace(';', ','))

 new_df = (raw_data.to_pandas_dataframe()
          .dropna(how='all')
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
@@ -359,7 +359,9 @@
        "from azureml.core import Environment\n",
        "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n",
        "\n",
-        "batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n",
+        "batch_conda_deps = CondaDependencies.create(python_version=\"3.7\",\n",
+        "                                            conda_packages=['pip==20.2.4'],\n",
+        "                                            pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \"protobuf==3.20.1\",\n",
        "                                                          \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
        "batch_env = Environment(name=\"batch_environment\")\n",
        "batch_env.python.conda_dependencies = batch_conda_deps\n",
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
@@ -308,7 +308,9 @@
        "from azureml.core import Environment\n",
        "from azureml.core.runconfig import CondaDependencies\n",
        "\n",
-        "predict_conda_deps = CondaDependencies.create(pip_packages=[\"scikit-learn==0.20.3\",\n",
+        "predict_conda_deps = CondaDependencies.create(python_version=\"3.7\", \n",
+        "                                              conda_packages=['pip==20.2.4'],\n",
+        "                                              pip_packages=[\"scikit-learn==0.20.3\",\n",
        "                                                            \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n",
        "\n",
        "predict_env = Environment(name=\"predict_environment\")\n",
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb
@@ -308,7 +308,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "cd = CondaDependencies()\n",
+        "cd = CondaDependencies.create(python_version=\"3.7\", conda_packages=['pip==20.2.4'])\n",
        "\n",
        "cd.add_channel(\"conda-forge\")\n",
        "cd.add_conda_package(\"ffmpeg==4.0.2\")\n",
@@ -401,13 +401,12 @@
        "from azureml.core import Environment\n",
        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
        "\n",
-        "parallel_cd = CondaDependencies()\n",
+        "parallel_cd = CondaDependencies.create(python_version=\"3.7\", conda_packages=['pip==20.2.4', 'numpy==1.19'])\n",
        "\n",
        "parallel_cd.add_channel(\"pytorch\")\n",
        "parallel_cd.add_conda_package(\"pytorch\")\n",
        "parallel_cd.add_conda_package(\"torchvision\")\n",
        "parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n",
-        "parallel_cd.add_pip_package(\"azureml-core\")\n",
        "\n",
        "styleenvironment = Environment(name=\"styleenvironment\")\n",
        "styleenvironment.python.conda_dependencies=parallel_cd\n",
--- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
+++ b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
@@ -254,6 +254,7 @@
        "- conda-forge\n",
        "dependencies:\n",
        "- python=3.6.2\n",
+        "- pip=21.3.1\n",
        "- pip:\n",
        "  - azureml-defaults\n",
        "  - azureml-opendatasets\n",
@@ -587,7 +588,7 @@
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n",
        "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
-        "                                               memory_gb=1,\n",
+        "                                               memory_gb=2,\n",
        "                                               tags={'name': 'mnist', 'framework': 'Chainer'},\n",
        "                                               description='Chainer DNN with MNIST')\n",
        "\n",
--- a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb
+++ b/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb
@@ -163,7 +163,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "fastai_env.docker.base_image = \"fastdotai/fastai:latest\"\n",
+        "fastai_env.docker.base_image = \"fastdotai/fastai:2021-02-11\"\n",
        "fastai_env.python.user_managed_dependencies = True"
      ]
    },
@@ -199,7 +199,7 @@
        "Specify docker steps as a string:\n",
        "```python \n",
        "dockerfile = r\"\"\" \\\n",
-        "FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\n",
+        "FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\n",
        "RUN echo \"Hello from custom container!\" \\\n",
        "\"\"\"\n",
        "```\n",
--- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb
+++ b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb
@@ -430,7 +430,8 @@
        "channels:\n",
        "- conda-forge\n",
        "dependencies:\n",
-        "- python=3.6.2\n",
+        "- python=3.7\n",
+        "- pip=21.3.1\n",
        "- pip:\n",
        "  - h5py<=2.10.0\n",
        "  - azureml-defaults\n",
@@ -983,7 +984,7 @@
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
-        "cd = CondaDependencies.create()\n",
+        "cd = CondaDependencies.create(python_version=\"3.7\")\n",
        "cd.add_tensorflow_conda_package()\n",
        "cd.add_conda_package('h5py<=2.10.0')\n",
        "cd.add_conda_package('keras<=2.3.1')\n",
--- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
+++ b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
@@ -262,6 +262,7 @@
        "- conda-forge\n",
        "dependencies:\n",
        "- python=3.6.2\n",
+        "- pip=21.3.1\n",
        "- pip:\n",
        "  - azureml-defaults\n",
        "  - torch==1.6.0\n",
--- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml
+++ b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml
@@ -6,5 +6,5 @@ dependencies:
  - pillow==5.4.1
  - matplotlib
  - numpy==1.19.3
-  - https://download.pytorch.org/whl/cpu/torch-1.6.0%2Bcpu-cp36-cp36m-win_amd64.whl
-  - https://download.pytorch.org/whl/cpu/torchvision-0.7.0%2Bcpu-cp36-cp36m-win_amd64.whl
+  - pytorch==1.8.1
+  - torchvision==0.9.1
--- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
@@ -941,7 +941,7 @@
        "\n",
        "cd = CondaDependencies.create()\n",
        "cd.add_conda_package('numpy')\n",
-        "cd.add_pip_package('tensorflow==2.0.0')\n",
+        "cd.add_pip_package('tensorflow==2.2.0')\n",
        "cd.add_pip_package(\"azureml-defaults\")\n",
        "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
        "\n",
--- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py
+++ b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py
@@ -103,15 +103,14 @@ device = torch.device("cuda" if use_cuda else "cpu")

 kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
 # Use Azure Open Datasets for MNIST dataset
+datasets.MNIST.mirrors = [
+    "https://azureopendatastorage.azurefd.net/mnist/"
+]
 datasets.MNIST.resources = [
-    ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
-     "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-    ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
-     "d53e105ee54ea40749a09fcbcd1e9432"),
-    ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
-     "9fb629c4189551a2d022fa330f9573f3"),
-    ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
-     "ec29112dd5afa0611ce80d1b7f02629c")
+    ("train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
+    ("train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"),
+    ("t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"),
+    ("t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c")
 ]
 train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
--- a/how-to-use-azureml/reinforcement-learning/README.md
+++ b/how-to-use-azureml/reinforcement-learning/README.md
@@ -1,4 +1,7 @@

+# Important Note
+Azure Machine Learning reinforcement learning via the `azureml.contrib.train.rl` package that is used on this page will no longer be supported after June 2022. We recommend customers use Ray-on-AML library to facilitate execution of reinforcement learning experiments on Azure Machine Learning. The sample notebooks referenced in [this section](#contents) are updated accordingly to use Ray on AML library.
+
 # Azure Machine Learning - Reinforcement Learning (Public Preview)

 <!-- 
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu
@@ -0,0 +1,19 @@
+FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
+
+USER root
+RUN conda install -c anaconda python=3.7
+
+RUN pip install ray-on-aml==0.1.6
+RUN pip install gym[atari]==0.19.0
+RUN pip install gym[accept-rom-license]==0.19.0
+RUN pip install ale-py==0.7.0
+RUN pip install azureml-core
+RUN pip install ray==0.8.7
+RUN pip install ray[rllib,tune,serve]==0.8.7
+RUN pip install tensorflow==1.14.0
+RUN pip install 'msrest<0.7.0'
+RUN pip install protobuf==3.20.0
+
+RUN apt-get update
+RUN apt-get install -y jq
+RUN apt-get install -y rsync
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu
@@ -0,0 +1,77 @@
+FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.0.3-cudnn8-ubuntu18.04
+
+USER root
+RUN conda install -c anaconda python=3.7
+
+# CUDA repository key rotation: https://forums.developer.nvidia.com/t/notice-cuda-linux-repository-key-rotation/212771
+RUN apt-key del 7fa2af80
+ENV distro ubuntu1804
+ENV arch x86_64
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/3bf863cc.pub
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python-opengl \
+    rsync \
+    xvfb && \
+    apt-get clean -y && \
+    rm -rf /var/lib/apt/lists/* && \
+    rm -rf /usr/share/man/*
+
+ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/tensorflow-2.4
+
+# Create conda environment
+RUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \
+    python=3.7 pip=20.2.4
+
+# Prepend path to AzureML conda environment
+ENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH
+
+RUN pip --version
+RUN python --version
+
+# Install ray-on-aml
+RUN pip install 'ray-on-aml==0.1.6'
+
+RUN pip install ray==0.8.7
+RUN pip install gym[atari]==0.19.0
+RUN pip install gym[accept-rom-license]==0.19.0
+
+# Install pip dependencies
+RUN pip install 'matplotlib>=3.3,<3.4' \
+                'psutil>=5.8,<5.9' \
+                'tqdm>=4.59,<4.60' \
+                'pandas>=1.1,<1.2' \
+                'scipy>=1.5,<1.6' \
+                'numpy>=1.10,<1.20' \
+                'ipykernel~=6.0' \
+                'azureml-core==1.36.0.post2' \
+                'azureml-defaults==1.36.0' \
+                'azureml-mlflow==1.36.0' \
+                'azureml-telemetry==1.36.0' \
+                'tensorboard==2.4.0' \
+                'tensorflow-gpu==2.4.1' \
+                'tensorflow-datasets==4.3.0' \
+                'onnxruntime-gpu>=1.7,<1.8' \
+                'horovod[tensorflow-gpu]==0.21.3'
+
+RUN pip install --no-cache-dir \
+    azureml-defaults \
+    azureml-dataset-runtime[fuse,pandas] \
+    azureml-contrib-reinforcementlearning \
+    gputil \
+    cloudpickle==1.3.0 \
+    tabulate \
+    dm_tree \
+    lz4 \
+    psutil \
+    setproctitle
+
+# This is required for ray 0.8.7
+RUN pip install -U aiohttp==3.7.4
+
+RUN pip install 'msrest<0.7.0'
+RUN pip install protobuf==3.20.0
+
+# This is needed for mpi to locate libpython
+ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
+
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/pong_rllib.py
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/pong_rllib.py
@@ -1,40 +1,35 @@
-import ray
+from ray_on_aml.core import Ray_On_AML
+
 import ray.tune as tune
 from ray.rllib import train

-import os
-import sys
-
-from azureml.core import Run
 from utils import callbacks

-DEFAULT_RAY_ADDRESS = 'localhost:6379'
-
 if __name__ == "__main__":

-    # Parse arguments
-    train_parser = train.create_parser()
+    ray_on_aml = Ray_On_AML()
+    ray = ray_on_aml.getRay()
+    if ray:  # in the headnode
+        # Parse arguments
+        train_parser = train.create_parser()

-    args = train_parser.parse_args()
-    print("Algorithm config:", args.config)
+        args = train_parser.parse_args()
+        print("Algorithm config:", args.config)

-    if args.ray_address is None:
-        args.ray_address = DEFAULT_RAY_ADDRESS
-
-    ray.init(address=args.ray_address)
-
-    tune.run(
-        run_or_experiment=args.run,
-        config={
-            "env": args.env,
-            "num_gpus": args.config["num_gpus"],
-            "num_workers": args.config["num_workers"],
-            "callbacks": {"on_train_result": callbacks.on_train_result},
-            "sample_batch_size": 50,
-            "train_batch_size": 1000,
-            "num_sgd_iter": 2,
-            "num_data_loader_buffers": 2,
-            "model": {"dim": 42},
-        },
-        stop=args.stop,
-        local_dir='./logs')
+        tune.run(
+            run_or_experiment=args.run,
+            config={
+                "env": args.env,
+                "num_gpus": args.config["num_gpus"],
+                "num_workers": args.config["num_workers"],
+                "callbacks": {"on_train_result": callbacks.on_train_result},
+                "sample_batch_size": 50,
+                "train_batch_size": 1000,
+                "num_sgd_iter": 2,
+                "num_data_loader_buffers": 2,
+                "model": {"dim": 42},
+            },
+            stop=args.stop,
+            local_dir='./logs')
+    else:
+        print("in worker node")
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/utils/callbacks.py
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/utils/callbacks.py
@@ -8,7 +8,7 @@ from azureml.core import Run
 def on_train_result(info):
    '''Callback on train result to record metrics returned by trainer.
    '''
-    run = Run.get_context().parent
+    run = Run.get_context()
    run.log(
        name='episode_reward_mean',
        value=info["result"]["episode_reward_mean"])
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb
@@ -84,7 +84,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646081765827
+        }
+      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
@@ -93,7 +97,7 @@
        "import azureml.core\n",
        "\n",
        "# Check core SDK version number\n",
-        "print(\"Azure Machine Learning SDK Version: \", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version: \", azureml.core.VERSION)"
      ]
    },
    {
@@ -107,7 +111,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646081772340
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
@@ -127,7 +135,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646081775643
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core.experiment import Experiment\n",
@@ -137,180 +149,13 @@
        "exp = Experiment(workspace=ws, name=experiment_name)"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Virtual Network and Network Security Group\n",
-        "\n",
-        "**If you are using separate compute targets for the Ray head and worker, as we do in this notebook**, a virtual network must be created in the resource group.  If you have already created a virtual network in the resource group, you can skip this step.\n",
-        "\n",
-        "> Note that your user role must have permissions to create and manage virtual networks to run the cells below. Talk to your IT admin if you do not have these permissions.\n",
-        "\n",
-        "#### Create Virtual Network\n",
-        "To create the virtual network you first must install the [Azure Networking Python API](https://docs.microsoft.com/python/api/overview/azure/network?view=azure-python).\n",
-        "\n",
-        "`pip install --upgrade azure-mgmt-network`\n",
-        "\n",
-        "Note: In this section we are using [DefaultAzureCredential](https://docs.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python)\n",
-        "class for authentication which, by default, examines several options in turn, and stops on the first option that provides\n",
-        "a token. You will need to log in using Azure CLI, if none of the other options are available (please find more details [here](https://docs.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python))."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# If you need to install the Azure Networking SDK, uncomment the following line.\n",
-        "#!pip install --upgrade azure-mgmt-network"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azure.mgmt.network import NetworkManagementClient\n",
-        "from azure.identity import DefaultAzureCredential\n",
-        "\n",
-        "# Virtual network name\n",
-        "vnet_name =\"rl_pong_vnet\"\n",
-        "\n",
-        "# Default subnet\n",
-        "subnet_name =\"default\"\n",
-        "\n",
-        "# The Azure subscription you are using\n",
-        "subscription_id=ws.subscription_id\n",
-        "\n",
-        "# The resource group for the reinforcement learning cluster\n",
-        "resource_group=ws.resource_group\n",
-        "\n",
-        "# Azure region of the resource group\n",
-        "location=ws.location\n",
-        "\n",
-        "network_client = NetworkManagementClient(credential=DefaultAzureCredential(), subscription_id=subscription_id)\n",
-        "\n",
-        "async_vnet_creation = network_client.virtual_networks.begin_create_or_update(\n",
-        "    resource_group,\n",
-        "    vnet_name,\n",
-        "    {\n",
-        "        'location': location,\n",
-        "        'address_space': {\n",
-        "            'address_prefixes': ['10.0.0.0/16']\n",
-        "        }\n",
-        "    }\n",
-        ")\n",
-        "\n",
-        "async_vnet_creation.wait()\n",
-        "print(\"Virtual network created successfully: \", async_vnet_creation.result())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Set up Network Security Group on Virtual Network\n",
-        "\n",
-        "Depending on your Azure setup, you may need to open certain ports to make it possible for Azure to manage the compute targets that you create.  The ports that need to be opened are described [here](https://docs.microsoft.com/azure/machine-learning/how-to-enable-virtual-network).\n",
-        "\n",
-        "A common situation is that ports `29876-29877` are closed.  The following code will add a security rule to open these ports.    Or you can do this manually in the [Azure portal](https://portal.azure.com).\n",
-        "\n",
-        "You may need to modify the code below to match your scenario."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azure.mgmt.network.models\n",
-        "\n",
-        "security_group_name = vnet_name + '-' + \"nsg\"\n",
-        "security_rule_name = \"AllowAML\"\n",
-        "\n",
-        "# Create a network security group\n",
-        "nsg_params = azure.mgmt.network.models.NetworkSecurityGroup(\n",
-        "    location=location,\n",
-        "    security_rules=[\n",
-        "        azure.mgmt.network.models.SecurityRule(\n",
-        "            name=security_rule_name,\n",
-        "            access=azure.mgmt.network.models.SecurityRuleAccess.allow,\n",
-        "            description='Reinforcement Learning in Azure Machine Learning rule',\n",
-        "            destination_address_prefix='*',\n",
-        "            destination_port_range='29876-29877',\n",
-        "            direction=azure.mgmt.network.models.SecurityRuleDirection.inbound,\n",
-        "            priority=400,\n",
-        "            protocol=azure.mgmt.network.models.SecurityRuleProtocol.tcp,\n",
-        "            source_address_prefix='BatchNodeManagement',\n",
-        "            source_port_range='*'\n",
-        "        ),\n",
-        "    ],\n",
-        ")\n",
-        "\n",
-        "async_nsg_creation = network_client.network_security_groups.begin_create_or_update(\n",
-        "    resource_group,\n",
-        "    security_group_name,\n",
-        "    nsg_params,\n",
-        ")\n",
-        "\n",
-        "async_nsg_creation.wait() \n",
-        "print(\"Network security group created successfully:\", async_nsg_creation.result())\n",
-        "\n",
-        "network_security_group = network_client.network_security_groups.get(\n",
-        "    resource_group,\n",
-        "    security_group_name,\n",
-        ")\n",
-        "\n",
-        "# Define a subnet to be created with network security group\n",
-        "subnet = azure.mgmt.network.models.Subnet(\n",
-        "            id='default',\n",
-        "            address_prefix='10.0.0.0/24',\n",
-        "            network_security_group=network_security_group\n",
-        "            )\n",
-        "    \n",
-        "# Create subnet on virtual network\n",
-        "async_subnet_creation = network_client.subnets.begin_create_or_update(\n",
-        "    resource_group_name=resource_group,\n",
-        "    virtual_network_name=vnet_name,\n",
-        "    subnet_name=subnet_name,\n",
-        "    subnet_parameters=subnet\n",
-        ")\n",
-        "\n",
-        "async_subnet_creation.wait()\n",
-        "print(\"Subnet created successfully:\", async_subnet_creation.result())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Review the virtual network security rules\n",
-        "Ensure that the virtual network is configured correctly with required ports open. It is possible that you have configured rules with broader range of ports that allows ports 29876-29877 to be opened. Kindly review your network security group rules.  "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from files.networkutils import *\n",
-        "from azure.identity import DefaultAzureCredential\n",
-        "\n",
-        "check_vnet_security_rules(DefaultAzureCredential(), ws.subscription_id, ws.resource_group, vnet_name, True)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create compute targets\n",
        "\n",
-        "In this example, we show how to set up separate compute targets for the Ray head and Ray worker nodes.\n",
+        "In this example, we show how to set up separate compute targets for the Ray nodes.\n",
        "\n",
        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
        "\n",
@@ -322,149 +167,126 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646086081229
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
        "\n",
-        "# Choose a name for the Ray head cluster\n",
-        "head_compute_name = 'head-gpu'\n",
-        "head_compute_min_nodes = 0\n",
-        "head_compute_max_nodes = 2\n",
+        "# Choose a name for the Ray cluster\n",
+        "compute_name = 'compute-gpu'\n",
+        "compute_min_nodes = 0\n",
+        "compute_max_nodes = 2\n",
        "\n",
        "# This example uses GPU VM. For using CPU VM, set SKU to STANDARD_D2_V2\n",
-        "head_vm_size = 'STANDARD_NC6'\n",
+        "vm_size = 'STANDARD_NC6'\n",
        "\n",
-        "if head_compute_name in ws.compute_targets:\n",
-        "    head_compute_target = ws.compute_targets[head_compute_name]\n",
-        "    if head_compute_target and type(head_compute_target) is AmlCompute:\n",
-        "        if head_compute_target.provisioning_state == 'Succeeded':\n",
-        "            print('found head compute target. just use it', head_compute_name)\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        if compute_target.provisioning_state == 'Succeeded':\n",
+        "            print('found compute target. just use it', compute_name)\n",
        "        else: \n",
        "            raise Exception(\n",
-        "                'found head compute target but it is in state', head_compute_target.provisioning_state)\n",
+        "                'found compute target but it is in state', compute_target.provisioning_state)\n",
        "else:\n",
-        "    print('creating a new head compute target...')\n",
+        "    print('creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(\n",
-        "        vm_size=head_vm_size,\n",
-        "        min_nodes=head_compute_min_nodes, \n",
-        "        max_nodes=head_compute_max_nodes,\n",
-        "        vnet_resourcegroup_name=ws.resource_group,\n",
-        "        vnet_name=vnet_name,\n",
-        "        subnet_name='default')\n",
+        "        vm_size=vm_size,\n",
+        "        min_nodes=compute_min_nodes, \n",
+        "        max_nodes=compute_max_nodes,\n",
+        "    )\n",
        "\n",
        "    # Create the cluster\n",
-        "    head_compute_target = ComputeTarget.create(ws, head_compute_name, provisioning_config)\n",
+        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
        "    \n",
        "    # Can poll for a minimum number of nodes and for a specific timeout. \n",
        "    # If no min node count is provided it will use the scale settings for the cluster\n",
-        "    head_compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
        "    \n",
        "    # For a more detailed view of current AmlCompute status, use get_status()\n",
-        "    print(head_compute_target.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create worker compute target\n",
-        "\n",
-        "Now we create a compute target with CPUs for the additional Ray worker nodes. CPUs in these worker nodes are used by Ray worker processes. Each Ray worker node, depending on the CPUs on the node, may have multiple Ray worker processes. There can be multiple worker tasks on each worker process (core)."
+        "    print(compute_target.get_status().serialize())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646093795069
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
      "outputs": [],
      "source": [
-        "# Choose a name for your Ray worker compute target\n",
-        "worker_compute_name = 'worker-cpu'\n",
-        "worker_compute_min_nodes = 0 \n",
-        "worker_compute_max_nodes = 4\n",
+        "from azureml.core import Environment\n",
+        "import os\n",
        "\n",
-        "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
-        "worker_vm_size = 'STANDARD_D2_V2'\n",
+        "ray_environment_name = 'pong-cpu'\n",
+        "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'docker', 'Dockerfile-cpu')\n",
        "\n",
-        "# Create the compute target if it hasn't been created already\n",
-        "if worker_compute_name in ws.compute_targets:\n",
-        "    worker_compute_target = ws.compute_targets[worker_compute_name]\n",
-        "    if worker_compute_target and type(worker_compute_target) is AmlCompute:\n",
-        "        if worker_compute_target.provisioning_state == 'Succeeded':\n",
-        "            print('found worker compute target. just use it', worker_compute_name)\n",
-        "        else: \n",
-        "            raise Exception(\n",
-        "                'found worker compute target but it is in state', head_compute_target.provisioning_state)\n",
-        "else:\n",
-        "    print('creating a new worker compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(\n",
-        "        vm_size=worker_vm_size,\n",
-        "        min_nodes=worker_compute_min_nodes,\n",
-        "        max_nodes=worker_compute_max_nodes,\n",
-        "        vnet_resourcegroup_name=ws.resource_group,\n",
-        "        vnet_name=vnet_name,\n",
-        "        subnet_name='default')\n",
+        "# Build CPU image\n",
+        "ray_cpu_env = Environment. \\\n",
+        "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
+        "    register(workspace=ws)\n",
+        "ray_cpu_build_details = ray_cpu_env.build(workspace=ws)\n",
        "\n",
-        "    # Create the compute target\n",
-        "    worker_compute_target = ComputeTarget.create(ws, worker_compute_name, provisioning_config)\n",
-        "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # If no min node count is provided it will use the scale settings for the cluster\n",
-        "    worker_compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "    \n",
-        "    # For a more detailed view of current AmlCompute status, use get_status()\n",
-        "    print(worker_compute_target.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train Pong Agent\n",
-        "To facilitate reinforcement learning, Azure Machine Learning Python SDK provides a high level abstraction, the _ReinforcementLearningEstimator_ class, which allows users to easily construct  reinforcement learning run configurations for the underlying reinforcement learning framework.  Reinforcement Learning in Azure Machine Learning supports the open source [Ray framework](https://ray.io/) and its highly customizable [RLLib](https://ray.readthedocs.io/en/latest/rllib.html#rllib-scalable-reinforcement-learning). In this section we show how to use _ReinforcementLearningEstimator_ and Ray/RLLib framework to train a Pong playing agent.\n",
-        "\n",
-        "\n",
-        "### Define worker configuration\n",
-        "Define a `WorkerConfiguration` using your worker compute target. We specify the number of nodes in the worker compute target to be used for training and additional PIP packages to install on those nodes as a part of setup.\n",
-        "In this case, we define the PIP packages as dependencies for both head and worker nodes. With this setup, the game simulations will run directly on the worker compute nodes."
+        "ray_cpu_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646160884910
+        },
+        "jupyter": {
+          "outputs_hidden": true,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
      "outputs": [],
      "source": [
-        "from azureml.contrib.train.rl import WorkerConfiguration\n",
+        "from azureml.core import Environment\n",
        "\n",
-        "# Specify the Ray worker configuration\n",
-        "worker_conf = WorkerConfiguration(\n",
-        "    \n",
-        "    # Azure Machine Learning compute target to run Ray workers\n",
-        "    compute_target=worker_compute_target, \n",
-        "    \n",
-        "    # Number of worker nodes\n",
-        "    node_count=4,\n",
-        "    \n",
-        "    # GPU\n",
-        "    use_gpu=False, \n",
-        "    \n",
-        "    # Shared memory size\n",
-        "    # Uncomment line below to set shm_size for workers (requires Azure Machine Learning SDK 1.33 or greater)\n",
-        "    # shm_size=1024*1024*1024, \n",
-        "    \n",
-        "    # PIP packages to use\n",
-        ")"
+        "ray_environment_name = 'pong-gpu'\n",
+        "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'docker', 'Dockerfile-gpu')\n",
+        "\n",
+        "# Build GPU image\n",
+        "ray_gpu_env = Environment. \\\n",
+        "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
+        "    register(workspace=ws)\n",
+        "ray_gpu_build_details = ray_gpu_env.build(workspace=ws)\n",
+        "\n",
+        "ray_gpu_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Create reinforcement learning estimator\n",
+        "### Create reinforcement learning training run\n",
        "\n",
-        "The `ReinforcementLearningEstimator` is used to submit a job to Azure Machine Learning to start the Ray experiment run. We define the training script parameters here that will be passed to the estimator. \n",
+        "The code below submits the training run using a `ScriptRunConfig`. By providing the\n",
+        "command to run the training, and a `RunConfig` object configured with your\n",
+        "compute target, number of nodes, and environment image to use.\n",
        "\n",
        "We specify `episode_reward_mean` to 18 as we want to stop the training as soon as the trained agent reaches an average win margin of at least 18 point over opponent over all episodes in the training epoch.\n",
        "Number of Ray worker processes are defined by parameter `num_workers`. We set it to 13 as we have 13 CPUs available in our compute targets. Multiple Ray worker processes parallelizes agent training and helps in achieving our goal faster. \n",
@@ -479,70 +301,44 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646162435310
+        }
+      },
      "outputs": [],
      "source": [
-        "from azureml.contrib.train.rl import ReinforcementLearningEstimator, Ray\n",
+        "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n",
+        "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n",
+        "\n",
+        "experiment_name = 'rllib-pong-multi-node'\n",
+        "\n",
+        "experiment = Experiment(workspace=ws, name=experiment_name)\n",
+        "ray_environment = Environment.get(workspace=ws, name=ray_environment_name)\n",
+        "\n",
+        "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n",
+        "aml_run_config_ml.target = compute_target\n",
+        "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n",
+        "aml_run_config_ml.node_count = 2\n",
+        "aml_run_config_ml.environment = ray_environment\n",
        "\n",
        "training_algorithm = \"IMPALA\"\n",
        "rl_environment = \"PongNoFrameskip-v4\"\n",
+        "script_name='pong_rllib.py'\n",
        "\n",
-        "# Training script parameters\n",
-        "script_params = {\n",
-        "    \n",
-        "    # Training algorithm, IMPALA in this case\n",
-        "    \"--run\": training_algorithm,\n",
-        "    \n",
-        "    # Environment, Pong in this case\n",
-        "    \"--env\": rl_environment,\n",
-        "    \n",
-        "    # Add additional single quotes at the both ends of string values as we have spaces in the \n",
-        "    # string parameters, outermost quotes are not passed to scripts as they are not actually part of string\n",
-        "    # Number of GPUs\n",
-        "    # Number of ray workers\n",
-        "    \"--config\": '\\'{\"num_gpus\": 1, \"num_workers\": 13}\\'',\n",
-        "    \n",
-        "    # Target episode reward mean to stop the training\n",
-        "    # Total training time in seconds\n",
-        "    \"--stop\": '\\'{\"episode_reward_mean\": 18, \"time_total_s\": 3600}\\'',\n",
-        "}\n",
+        "command=[\n",
+        "    'python', script_name,\n",
+        "    '--run', training_algorithm,\n",
+        "    '--env', rl_environment,\n",
+        "    '--config', '\\'{\"num_gpus\": 1, \"num_workers\": 11}\\'',\n",
+        "    '--stop', '\\'{\"episode_reward_mean\": 18, \"time_total_s\": 3600}\\''\n",
+        "]\n",
        "\n",
-        "#  Reinforcement learning estimator\n",
-        "rl_estimator = ReinforcementLearningEstimator(\n",
-        "    \n",
-        "    # Location of source files\n",
-        "    source_directory='files',\n",
-        "    \n",
-        "    # Python script file\n",
-        "    entry_script=\"pong_rllib.py\",\n",
-        "    \n",
-        "    # Parameters to pass to the script file\n",
-        "    # Defined above.\n",
-        "    script_params=script_params,\n",
-        "    \n",
-        "    # The Azure Machine Learning compute target set up for Ray head nodes\n",
-        "    compute_target=head_compute_target,\n",
-        "    \n",
-        "    # GPU usage\n",
-        "    use_gpu=True,\n",
-        "    \n",
-        "    # Reinforcement learning framework. Currently must be Ray.\n",
-        "    rl_framework=Ray('0.8.3'),\n",
-        "    \n",
-        "    # Ray worker configuration defined above.\n",
-        "    worker_configuration=worker_conf,\n",
-        "    \n",
-        "    # How long to wait for whole cluster to start\n",
-        "    cluster_coordination_timeout_seconds=3600,\n",
-        "    \n",
-        "    # Maximum time for the whole Ray job to run\n",
-        "    # This will cut off the run after an hour\n",
-        "    max_run_duration_seconds=3600,\n",
-        "    \n",
-        "    # Allow the docker container Ray runs in to make full use\n",
-        "    # of the shared memory available from the host OS.\n",
-        "    shm_size=24*1024*1024*1024\n",
-        ")"
+        "config = ScriptRunConfig(source_directory='./files',\n",
+        "                    command=command,\n",
+        "                    run_config = aml_run_config_ml\n",
+        "                   )\n",
+        "training_run = experiment.submit(config)"
      ]
    },
    {
@@ -571,23 +367,6 @@
        "```"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit the estimator to start a run\n",
-        "Now we use the rl_estimator configured above to submit a run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = exp.submit(config=rl_estimator)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -605,7 +384,7 @@
      "source": [
        "from azureml.widgets import RunDetails\n",
        "\n",
-        "RunDetails(run).show()"
+        "RunDetails(training_run).show()"
      ]
    },
    {
@@ -614,7 +393,7 @@
      "source": [
        "### Stop the run\n",
        "\n",
-        "To stop the run, call `run.cancel()`."
+        "To stop the run, call `training_run.cancel()`."
      ]
    },
    {
@@ -624,7 +403,7 @@
      "outputs": [],
      "source": [
        "# Uncomment line below to cancel the run\n",
-        "# run.cancel()"
+        "# training_run.cancel()"
      ]
    },
    {
@@ -643,7 +422,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "run.wait_for_completion()"
+        "training_run.wait_for_completion()"
      ]
    },
    {
@@ -663,8 +442,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Get the reward metrics from worker run\n",
-        "episode_reward_mean = run.get_metrics(name='episode_reward_mean')"
+        "# Get the reward metrics from training_run\n",
+        "episode_reward_mean = training_run.get_metrics(name='episode_reward_mean')"
      ]
    },
    {
@@ -733,6 +512,16 @@
        "name": "vineetg"
      }
    ],
+    "categories": [
+      "how-to-use-azureml",
+      "reinforcement-learning"
+    ],
+    "interpreter": {
+      "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab"
+    },
+    "kernel_info": {
+      "name": "python3-azureml"
+    },
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
@@ -748,10 +537,13 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.8.0"
    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00e2\u20ac\u00afLicensed under the MIT License.\u00e2\u20ac\u00af "
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00afLicensed under the MIT License.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00af ",
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    }
  },
  "nbformat": 4,
-  "nbformat_minor": 4
+  "nbformat_minor": 0
 }
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb
@@ -82,11 +82,15 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646344676671
+        }
+      },
      "outputs": [],
      "source": [
        "import azureml.core\n",
-        "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
@@ -100,7 +104,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646344680982
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
@@ -123,7 +131,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646344684217
+        }
+      },
      "outputs": [],
      "source": [
        "import os.path\n",
@@ -146,7 +158,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646344690768
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeInstance\n",
@@ -194,13 +210,52 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646344835579
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core.experiment import Experiment\n",
        "\n",
        "experiment_name = 'CartPole-v0-CI'\n",
-        "exp = Experiment(workspace=ws, name=experiment_name)"
+        "experiment = Experiment(workspace=ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1646346293902
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "import os\n",
+        "import time\n",
+        "\n",
+        "ray_environment_name = 'cartpole-ray-ci'\n",
+        "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'files', 'docker', 'Dockerfile')\n",
+        "\n",
+        "# Build environment image\n",
+        "ray_environment = Environment. \\\n",
+        "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
+        "    register(workspace=ws)\n",
+        "ray_env_build_details = ray_environment.build(workspace=ws)\n",
+        "\n",
+        "ray_env_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
@@ -208,80 +263,69 @@
      "metadata": {},
      "source": [
        "## Train Cartpole Agent\n",
-        "To facilitate reinforcement learning, Azure Machine Learning Python SDK provides a high level abstraction, the _ReinforcementLearningEstimator_ class, which allows users to easily construct reinforcement learning run configurations for the underlying reinforcement learning framework. Reinforcement Learning in Azure Machine Learning supports the open source [Ray framework](https://ray.io/) and its highly customizable [RLlib](https://ray.readthedocs.io/en/latest/rllib.html#rllib-scalable-reinforcement-learning). In this section we show how to use _ReinforcementLearningEstimator_ and Ray/RLlib framework to train a cartpole playing agent. "
+        "In this section, we show how to use Azure Machine Learning jobs and Ray/RLlib framework to train a cartpole playing agent. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Create reinforcement learning estimator\n",
+        "### Create reinforcement learning training run\n",
        "\n",
-        "The code below creates an instance of *ReinforcementLearningEstimator*, `training_estimator`, which then will be used to submit a job to Azure Machine Learning to start the Ray experiment run.\n",
-        "\n",
-        "Note that this example is purposely simplified to the minimum. Here is a short description of the parameters we are passing into the constructor:\n",
-        "\n",
-        "- `source_directory`, local directory containing your training script(s) and helper modules,\n",
-        "- `entry_script`, path to your entry script relative to the source directory,\n",
-        "- `script_params`, constant parameters to be passed to each run of training script,\n",
-        "- `compute_target`, reference to the compute target in which the trainer and worker(s) jobs will be executed,\n",
-        "- `rl_framework`, the reinforcement learning framework to be used (currently must be Ray).\n",
-        "\n",
-        "We use the `script_params` parameter to pass in general and algorithm-specific parameters to the training script.\n"
+        "The code below submits the training run using a `ScriptRunConfig`. By providing the\n",
+        "command to run the training, and a `RunConfig` object configured with your\n",
+        "compute target, number of nodes, and environment image to use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347120585
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
      "outputs": [],
      "source": [
-        "from azureml.contrib.train.rl import ReinforcementLearningEstimator, Ray\n",
+        "from azureml.core import Environment\n",
+        "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n",
+        "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n",
        "\n",
-        "training_algorithm = \"PPO\"\n",
-        "rl_environment = \"CartPole-v0\"\n",
+        "training_algorithm = 'PPO'\n",
+        "rl_environment = 'CartPole-v0'\n",
        "\n",
-        "script_params = {\n",
+        "script_name = 'cartpole_training.py'\n",
+        "script_arguments = [\n",
+        "    '--run', training_algorithm,\n",
+        "    '--env', rl_environment,\n",
+        "    '--config', '{\"num_gpus\": 0, \"num_workers\": 1}',\n",
+        "    '--stop', '{\"episode_reward_mean\": 200, \"time_total_s\": 300}',\n",
+        "    '--checkpoint-freq', '2',\n",
+        "    '--checkpoint-at-end',\n",
+        "    '--local-dir', './logs'\n",
+        "]\n",
        "\n",
-        "    # Training algorithm\n",
-        "    \"--run\": training_algorithm,\n",
-        "    \n",
-        "    # Training environment\n",
-        "    \"--env\": rl_environment,\n",
-        "    \n",
-        "    # Algorithm-specific parameters\n",
-        "    \"--config\": '\\'{\"num_gpus\": 0, \"num_workers\": 1}\\'',\n",
-        "    \n",
-        "    # Stop conditions\n",
-        "    \"--stop\": '\\'{\"episode_reward_mean\": 200, \"time_total_s\": 300}\\'',\n",
-        "    \n",
-        "    # Frequency of taking checkpoints\n",
-        "    \"--checkpoint-freq\": 2,\n",
-        "    \n",
-        "    # If a checkpoint should be taken at the end - optional argument with no value\n",
-        "    \"--checkpoint-at-end\": \"\",\n",
-        "    \n",
-        "    # Log directory\n",
-        "    \"--local-dir\": './logs'\n",
-        "}\n",
+        "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n",
+        "aml_run_config_ml.target = compute_target\n",
+        "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n",
+        "aml_run_config_ml.node_count = 1\n",
+        "aml_run_config_ml.environment = ray_environment\n",
        "\n",
-        "training_estimator = ReinforcementLearningEstimator(\n",
-        "\n",
-        "    # Location of source files\n",
-        "    source_directory='files',\n",
-        "    \n",
-        "    # Python script file\n",
-        "    entry_script='cartpole_training.py',\n",
-        "    \n",
-        "    # A dictionary of arguments to pass to the training script specified in ``entry_script``\n",
-        "    script_params=script_params,\n",
-        "    \n",
-        "    # The Azure Machine Learning compute target set up for Ray head nodes\n",
-        "    compute_target=compute_target,\n",
-        "    \n",
-        "    # Reinforcement learning framework. Currently must be Ray.\n",
-        "    rl_framework=Ray()\n",
-        ")"
+        "training_config = ScriptRunConfig(source_directory='./files',\n",
+        "                    script=script_name,\n",
+        "                    arguments=script_arguments,\n",
+        "                    run_config = aml_run_config_ml\n",
+        "                   )\n",
+        "training_run = experiment.submit(training_config)"
      ]
    },
    {
@@ -304,6 +348,7 @@
        "See [RLlib Training APIs](https://ray.readthedocs.io/en/latest/rllib-training.html#rllib-training-apis) for more details, and also [Training (tune.run, tune.Experiment)](https://ray.readthedocs.io/en/latest/tune/api_docs/execution.html#training-tune-run-tune-experiment) for the complete list of parameters.\n",
        "\n",
        "```python\n",
+        "import os\n",
        "import ray\n",
        "import ray.tune as tune\n",
        "\n",
@@ -311,8 +356,9 @@
        "\n",
        "    # parse arguments ...\n",
        "    \n",
-        "    # Intitialize ray\n",
-        "    ay.init(address=args.ray_address)\n",
+        "    # Start ray head (single node)\n",
+        "    os.system('ray start --head')\n",
+        "    ray.init(address='auto')\n",
        "\n",
        "    # Run training task using tune.run\n",
        "    tune.run(\n",
@@ -326,23 +372,6 @@
        "```"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit the estimator to start experiment\n",
-        "Now we use the *training_estimator* to submit a run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "training_run = exp.submit(training_estimator)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -350,15 +379,17 @@
        "### Monitor experiment\n",
        "Azure Machine Learning provides a Jupyter widget to show the status of an experiment run. You could use this widget to monitor the status of the runs.\n",
        "\n",
-        "Note that _ReinforcementLearningEstimator_ creates at least two runs: (a) A parent run, i.e. the run returned above, and (b) a collection of child runs. The number of the child runs depends on the configuration of the reinforcement learning estimator. In our simple scenario, configured above, only one child run will be created.\n",
-        "\n",
-        "The widget will show a list of the child runs as well. You can click on the link under **Status** to see the details of a child run. It will also show the metrics being logged."
+        "You can click on the link under **Status** to see the details of a child run. It will also show the metrics being logged."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347127671
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
@@ -398,50 +429,23 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347318682
+        }
+      },
      "outputs": [],
      "source": [
        "training_run.wait_for_completion()"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Get a handle to the child run\n",
-        "You can obtain a handle to the child run as follows. In our scenario, there is only one child run, we have it called `child_run_0`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import time\n",
-        "\n",
-        "child_run_0 = None\n",
-        "timeout = 30\n",
-        "while timeout > 0 and not child_run_0:\n",
-        "    child_runs = list(training_run.get_children())\n",
-        "    print('Number of child runs:', len(child_runs))\n",
-        "    if len(child_runs) > 0:\n",
-        "        child_run_0 = child_runs[0]\n",
-        "        break\n",
-        "    time.sleep(2) # Wait for 2 seconds\n",
-        "    timeout -= 2\n",
-        "\n",
-        "print('Child run info:')\n",
-        "print(child_run_0)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluate Trained Agent and See Results\n",
        "\n",
-        "We can evaluate a previously trained policy using the `rollout.py` helper script provided by RLlib (see [Evaluating Trained Policies](https://ray.readthedocs.io/en/latest/rllib-training.html#evaluating-trained-policies) for more details). Here we use an adaptation of this script to reconstruct a policy from a checkpoint taken and saved during training. We took these checkpoints by setting `checkpoint-freq` and `checkpoint-at-end` parameters above.\n",
+        "We can evaluate a previously trained policy using the `cartpole_rollout.py` helper script provided by RLlib (see [Evaluating Trained Policies](https://ray.readthedocs.io/en/latest/rllib-training.html#evaluating-trained-policies) for more details). Here we use an adaptation of this script to reconstruct a policy from a checkpoint taken and saved during training. We took these checkpoints by setting `checkpoint-freq` and `checkpoint-at-end` parameters above.\n",
        "\n",
        "In this section we show how to get access to these checkpoints data, and then how to use them to evaluate the trained policy."
      ]
@@ -458,7 +462,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347328505
+        }
+      },
      "outputs": [],
      "source": [
        "from os import path\n",
@@ -471,7 +479,7 @@
        "    dir_util.remove_tree(training_artifacts_path)\n",
        "\n",
        "# Download run artifacts to local compute\n",
-        "child_run_0.download_files(training_artifacts_path)"
+        "training_run.download_files(training_artifacts_path)"
      ]
    },
    {
@@ -484,7 +492,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347334571
+        }
+      },
      "outputs": [],
      "source": [
        "# A helper function to find checkpoint files in a directory\n",
@@ -501,7 +513,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347337724
+        }
+      },
      "outputs": [],
      "source": [
        "# Find checkpoints and last checkpoint number\n",
@@ -529,14 +545,18 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347346085
+        }
+      },
      "outputs": [],
      "source": [
        "# Upload the checkpoint files and create a DataSet\n",
        "from azureml.core import Dataset\n",
        "\n",
        "datastore = ws.get_default_datastore()\n",
-        "checkpoint_dataref = datastore.upload_files(checkpoint_files, target_path='cartpole_checkpoints_' + run_id, overwrite=True)\n",
+        "checkpoint_dataref = datastore.upload_files(checkpoint_files, target_path='cartpole_checkpoints_' + training_run.id, overwrite=True)\n",
        "checkpoint_ds = Dataset.File.from_files(checkpoint_dataref)"
      ]
    },
@@ -550,7 +570,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347354726
+        }
+      },
      "outputs": [],
      "source": [
        "artifacts_paths = checkpoint_ds.to_path()\n",
@@ -564,82 +588,67 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Evaluate a trained policy\n",
-        "We need to configure another reinforcement learning estimator, `rollout_estimator`, and then use it to submit another run. Note that the entry script for this estimator now points to `cartpole-rollout.py` script.\n",
-        "Also note how we pass the checkpoints dataset to this script using `inputs` parameter of the _ReinforcementLearningEstimator_.\n",
+        "## Evaluate Trained Agent and See Results\n",
        "\n",
-        "We are using script parameters to pass in the same algorithm and the same environment used during training. We also specify the checkpoint number of the checkpoint we wish to evaluate, `checkpoint-number`, and number of the steps we shall run the rollout, `steps`.\n",
-        "\n",
-        "The checkpoints dataset will be accessible to the rollout script as a mounted folder. The mounted folder and the checkpoint number, passed in via `checkpoint-number`, will be used to create a path to the checkpoint we are going to evaluate. The created checkpoint path then will be passed into RLlib rollout script for evaluation.\n",
-        "\n",
-        "Now let's configure rollout estimator. Note that we use the last checkpoint for evaluation. The assumption is that the last checkpoint points to our best trained agent. You may change this to any of the checkpoint numbers printed above and observe the effect."
+        "We can evaluate a previously trained policy using the `cartpole_rollout.py` helper script provided by RLlib (see [Evaluating Trained Policies](https://ray.readthedocs.io/en/latest/rllib-training.html#evaluating-trained-policies) for more details). Here we use an adaptation of this script to reconstruct a policy from a checkpoint taken and saved during training. We took these checkpoints by setting `checkpoint-freq` and `checkpoint-at-end` parameters above.\n",
+        "In this section we show how to use these checkpoints to evaluate the trained policy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347414835
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
      "outputs": [],
      "source": [
-        "script_params = {    \n",
-        "    # Checkpoint number of the checkpoint from which to roll out\n",
-        "    \"--checkpoint-number\": last_checkpoint_number,\n",
+        "ray_environment_name = 'cartpole-ray-ci'\n",
        "\n",
-        "    # Training algorithm\n",
-        "    \"--run\": training_algorithm,\n",
-        "    \n",
-        "    # Training environment\n",
-        "    \"--env\": rl_environment,\n",
-        "    \n",
-        "    # Algorithm-specific parameters\n",
-        "    \"--config\": '{}',\n",
-        "    \n",
-        "    # Number of rollout steps \n",
-        "    \"--steps\": 2000,\n",
-        "    \n",
-        "    # If should repress rendering of the environment\n",
-        "    \"--no-render\": \"\"\n",
-        "}\n",
+        "experiment_name = 'CartPole-v0-CI'\n",
+        "training_algorithm = 'PPO'\n",
+        "rl_environment = 'CartPole-v0'\n",
        "\n",
-        "rollout_estimator = ReinforcementLearningEstimator(\n",
-        "    # Location of source files\n",
-        "    source_directory='files',\n",
-        "    \n",
-        "    # Python script file\n",
-        "    entry_script='cartpole_rollout.py',\n",
-        "    \n",
-        "    # A dictionary of arguments to pass to the rollout script specified in ``entry_script``\n",
-        "    script_params = script_params,\n",
-        "    \n",
-        "    # Data inputs\n",
-        "    inputs=[\n",
-        "        checkpoint_ds.as_named_input('artifacts_dataset'),\n",
-        "        checkpoint_ds.as_named_input('artifacts_path').as_mount()],\n",
-        "    \n",
-        "    # The Azure Machine Learning compute target\n",
-        "    compute_target=compute_target,\n",
-        "    \n",
-        "    # Reinforcement learning framework. Currently must be Ray.\n",
-        "    rl_framework=Ray(),\n",
-        "    \n",
-        "    # Additional pip packages to install\n",
-        "    pip_packages = ['azureml-dataset-runtime[fuse,pandas]'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Same as before, we use the *rollout_estimator* to submit a run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rollout_run = exp.submit(rollout_estimator)"
+        "experiment = Experiment(workspace=ws, name=experiment_name)\n",
+        "ray_environment = Environment.get(workspace=ws, name=ray_environment_name)\n",
+        "\n",
+        "script_name = 'cartpole_rollout.py'\n",
+        "script_arguments = [\n",
+        "    '--run', training_algorithm,\n",
+        "    '--env', rl_environment,\n",
+        "    '--config', '{}',\n",
+        "    '--steps', '2000',\n",
+        "    '--checkpoint-number', str(last_checkpoint_number),\n",
+        "    '--no-render',\n",
+        "    '--artifacts-dataset', checkpoint_ds.as_named_input('artifacts_dataset'),\n",
+        "    '--artifacts-path', checkpoint_ds.as_named_input('artifacts_path').as_mount()\n",
+        "]\n",
+        "\n",
+        "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n",
+        "aml_run_config_ml.target = compute_target\n",
+        "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n",
+        "aml_run_config_ml.node_count = 1\n",
+        "aml_run_config_ml.environment = ray_environment\n",
+        "aml_run_config_ml.data\n",
+        "\n",
+        "rollout_config = ScriptRunConfig(\n",
+        "                    source_directory='./files',\n",
+        "                    script=script_name,\n",
+        "                    arguments=script_arguments,\n",
+        "                    run_config = aml_run_config_ml\n",
+        "                   )\n",
+        "                   \n",
+        "rollout_run = experiment.submit(rollout_config)"
      ]
    },
    {
@@ -652,7 +661,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347429626
+        }
+      },
      "outputs": [],
      "source": [
        "RunDetails(rollout_run).show()"
@@ -717,6 +730,16 @@
        "name": "hoazari"
      }
    ],
+    "categories": [
+      "how-to-use-azureml",
+      "reinforcement-learning"
+    ],
+    "interpreter": {
+      "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab"
+    },
+    "kernel_info": {
+      "name": "python3-azureml"
+    },
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
@@ -732,10 +755,20 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.7.9"
    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
+    "microsoft": {
+      "host": {
+        "AzureML": {
+          "notebookHasBeenCompleted": true
+        }
+      }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    }
  },
  "nbformat": 4,
-  "nbformat_minor": 4
+  "nbformat_minor": 0
 }
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_rollout.py
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_rollout.py
@@ -1,4 +1,3 @@
-import argparse
 import os
 import sys

@@ -11,10 +10,7 @@ from azureml.core import Run
 from utils import callbacks


-DEFAULT_RAY_ADDRESS = 'localhost:6379'
-
-
-def run_rollout(args, parser, ray_address):
+def run_rollout(args, parser):

    config = args.config
    if not args.env:
@@ -22,8 +18,6 @@ def run_rollout(args, parser, ray_address):
            parser.error("the following arguments are required: --env")
        args.env = config.get("env")

-    ray.init(address=ray_address)
-
    # Create the Trainer from config.
    cls = get_trainable_cls(args.run)
    agent = cls(env=args.env, config=config)
@@ -76,6 +70,10 @@ def run_rollout(args, parser, ray_address):

 if __name__ == "__main__":

+    # Start ray head (single node)
+    os.system('ray start --head')
+    ray.init(address='auto')
+
    # Add positional argument - serves as placeholder for checkpoint
    argvc = sys.argv[1:]
    argvc.insert(0, 'checkpoint-placeholder')
@@ -88,8 +86,12 @@ if __name__ == "__main__":
        help='Checkpoint number of the checkpoint from which to roll out')

    rollout_parser.add_argument(
-        '--ray-address', required=False, default=DEFAULT_RAY_ADDRESS,
-        help='The address of the Ray cluster to connect to')
+        '--artifacts-dataset', required=True,
+        help='The checkpoints artifacts dataset')
+
+    rollout_parser.add_argument(
+        '--artifacts-path', required=True,
+        help='The checkpoints artifacts path')

    args = rollout_parser.parse_args(argvc)

@@ -116,4 +118,4 @@ if __name__ == "__main__":
    args.checkpoint = checkpoint

    # Start rollout
-    run_rollout(args, rollout_parser, args.ray_address)
+    run_rollout(args, rollout_parser)
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_training.py
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_training.py
@@ -1,17 +1,10 @@
-import argparse
-import os
-import sys
-
 import ray
 from ray.rllib import train
 from ray import tune
+import os

 from utils import callbacks

-
-DEFAULT_RAY_ADDRESS = 'localhost:6379'
-
-
 if __name__ == "__main__":

    # Parse arguments and add callbacks to config
@@ -24,11 +17,9 @@ if __name__ == "__main__":
    if 'monitor' in args.config and args.config['monitor']:
        print("Video capturing is ON!")

-    # Start (connect to) Ray cluster
-    if args.ray_address is None:
-        args.ray_address = DEFAULT_RAY_ADDRESS
-
-    ray.init(address=args.ray_address)
+    # Start ray head (single node)
+    os.system('ray start --head')
+    ray.init(address='auto')

    # Run training task using tune.run
    tune.run(
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile
@@ -0,0 +1,19 @@
+FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
+
+USER root
+RUN conda install -c anaconda python=3.7
+
+RUN pip install ray-on-aml==0.1.6
+RUN pip install gym[atari]==0.19.0
+RUN pip install gym[accept-rom-license]==0.19.0
+RUN pip install ale-py==0.7.0
+RUN pip install azureml-core
+RUN pip install azureml-dataset-runtime
+RUN pip install ray==0.8.7
+RUN pip install ray[rllib,tune,serve]==0.8.7
+RUN pip install tensorflow==1.14.0
+RUN pip install 'msrest<0.7.0'
+
+RUN apt-get update
+RUN apt-get install -y jq
+RUN apt-get install -y rsync
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/callbacks.py
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/callbacks.py
@@ -8,7 +8,7 @@ from azureml.core import Run
 def on_train_result(info):
    '''Callback on train result to record metrics returned by trainer.
    '''
-    run = Run.get_context().parent
+    run = Run.get_context()
    run.log(
        name='episode_reward_mean',
        value=info["result"]["episode_reward_mean"])
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
@@ -82,12 +82,16 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646347616697
+        }
+      },
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
@@ -101,7 +105,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646429058500
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
@@ -126,7 +134,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646359152101
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
@@ -167,13 +179,51 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646348040613
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.core.experiment import Experiment\n",
        "\n",
        "experiment_name = 'CartPole-v0-SC'\n",
-        "exp = Experiment(workspace=ws, name=experiment_name)"
+        "experiment = Experiment(workspace=ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1646417962898
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "import os\n",
+        "\n",
+        "ray_environment_name = 'cartpole-ray-sc'\n",
+        "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'files', 'docker', 'Dockerfile')\n",
+        "\n",
+        "# Build environment image\n",
+        "ray_environment = Environment. \\\n",
+        "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
+        "    register(workspace=ws)\n",
+        "ray_env_build_details = ray_environment.build(workspace=ws)\n",
+        "\n",
+        "ray_env_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
@@ -181,109 +231,79 @@
      "metadata": {},
      "source": [
        "## Train Cartpole Agent\n",
-        "To facilitate reinforcement learning, Azure Machine Learning Python SDK provides a high level abstraction, the _ReinforcementLearningEstimator_ class, which allows users to easily construct reinforcement learning run configurations for the underlying reinforcement learning framework. Reinforcement Learning in Azure Machine Learning supports the open source [Ray framework](https://ray.io/) and its highly customizable [RLlib](https://ray.readthedocs.io/en/latest/rllib.html#rllib-scalable-reinforcement-learning). In this section we show how to use _ReinforcementLearningEstimator_ and Ray/RLlib framework to train a cartpole playing agent. "
+        "In this section, we show how to use Azure Machine Learning jobs and Ray/RLlib framework to train a cartpole playing agent. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Create reinforcement learning estimator\n",
+        "### Create reinforcement learning training run\n",
        "\n",
-        "The code below creates an instance of *ReinforcementLearningEstimator*, `training_estimator`, which then will be used to submit a job to Azure Machine Learning to start the Ray experiment run.\n",
-        "\n",
-        "Note that this example is purposely simplified to the minimum. Here is a short description of the parameters we are passing into the constructor:\n",
-        "\n",
-        "- `source_directory`, local directory containing your training script(s) and helper modules,\n",
-        "- `entry_script`, path to your entry script relative to the source directory,\n",
-        "- `script_params`, constant parameters to be passed to each run of training script,\n",
-        "- `compute_target`, reference to the compute target in which the trainer and worker(s) jobs will be executed,\n",
-        "- `rl_framework`, the reinforcement learning framework to be used (currently must be Ray).\n",
-        "\n",
-        "We use the `script_params` parameter to pass in general and algorithm-specific parameters to the training script.\n"
+        "The code below submits the training run using a `ScriptRunConfig`. By providing the\n",
+        "command to run the training, and a `RunConfig` object configured with your\n",
+        "compute target, number of nodes, and environment image to use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437786449
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
      "outputs": [],
      "source": [
-        "from azureml.contrib.train.rl import ReinforcementLearningEstimator, Ray\n",
-        "from azureml.core.environment import Environment\n",
+        "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n",
+        "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n",
        "\n",
        "training_algorithm = \"PPO\"\n",
        "rl_environment = \"CartPole-v0\"\n",
        "video_capture = True\n",
-        "\n",
        "if video_capture:\n",
        "    algorithm_config = '\\'{\"num_gpus\": 0, \"num_workers\": 1, \"monitor\": true}\\''\n",
        "else:\n",
        "    algorithm_config = '\\'{\"num_gpus\": 0, \"num_workers\": 1, \"monitor\": false}\\''\n",
        "\n",
-        "script_params = {\n",
+        "script_name = 'cartpole_training.py'\n",
+        "script_arguments = [\n",
+        "    '--run', training_algorithm,\n",
+        "    '--env', rl_environment,\n",
+        "    '--stop', '\\'{\"episode_reward_mean\": 200, \"time_total_s\": 300}\\'',\n",
+        "    '--config', algorithm_config,\n",
+        "    '--checkpoint-freq', '2',\n",
+        "    '--checkpoint-at-end',\n",
+        "    '--local-dir', './logs'\n",
+        "]\n",
        "\n",
-        "    # Training algorithm\n",
-        "    \"--run\": training_algorithm,\n",
-        "    \n",
-        "    # Training environment\n",
-        "    \"--env\": rl_environment,\n",
-        "    \n",
-        "    # Algorithm-specific parameters\n",
-        "    \"--config\": algorithm_config,\n",
-        "    \n",
-        "    # Stop conditions\n",
-        "    \"--stop\": '\\'{\"episode_reward_mean\": 200, \"time_total_s\": 300}\\'',\n",
-        "    \n",
-        "    # Frequency of taking checkpoints\n",
-        "    \"--checkpoint-freq\": 2,\n",
-        "    \n",
-        "    # If a checkpoint should be taken at the end - optional argument with no value\n",
-        "    \"--checkpoint-at-end\": \"\",\n",
-        "    \n",
-        "    # Log directory\n",
-        "    \"--local-dir\": './logs'\n",
-        "}\n",
+        "ray_environment = Environment.get(ws, name=ray_environment_name)\n",
+        "run_config = RunConfiguration(communicator='OpenMpi')\n",
+        "run_config.target = compute_target\n",
+        "run_config.docker = DockerConfiguration(use_docker=True)\n",
+        "run_config.node_count = 1\n",
+        "run_config.environment = ray_environment\n",
+        "command=[\"python\", script_name, *script_arguments]\n",
        "\n",
-        "xvfb_env = None\n",
        "if video_capture:\n",
-        "    # Ray's video capture support requires to run everything under a headless display driver called (xvfb).\n",
-        "    # There are two parts to this:\n",
-        "    # 1. Use a custom docker file with proper instructions to install xvfb, ffmpeg, python-opengl\n",
-        "    # and other dependencies.\n",
-        "   \n",
-        "    with open(\"files/docker/Dockerfile\", \"r\") as f:\n",
-        "        dockerfile=f.read()\n",
+        "    command = [\"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' \"] + command\n",
+        "    run_config.environment_variables[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
-        "    xvfb_env = Environment(name='xvfb-vdisplay')\n",
-        "    xvfb_env.docker.base_image = None\n",
-        "    xvfb_env.docker.base_dockerfile = dockerfile\n",
-        "    \n",
-        "    # 2.  Execute the Python process via the xvfb-run command to set up the headless display driver.\n",
-        "    xvfb_env.python.user_managed_dependencies = True\n",
-        "    xvfb_env.python.interpreter_path = \"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' python\"\n",
+        "trainint_config = ScriptRunConfig(source_directory='./files',\n",
+        "            command=command,\n",
+        "            run_config = run_config\n",
+        "            )\n",
        "\n",
-        "\n",
-        "training_estimator = ReinforcementLearningEstimator(\n",
-        "\n",
-        "    # Location of source files\n",
-        "    source_directory='files',\n",
-        "    \n",
-        "    # Python script file\n",
-        "    entry_script='cartpole_training.py',\n",
-        "    \n",
-        "    # A dictionary of arguments to pass to the training script specified in ``entry_script``\n",
-        "    script_params=script_params,\n",
-        "    \n",
-        "    # The Azure Machine Learning compute target set up for Ray head nodes\n",
-        "    compute_target=compute_target,\n",
-        "    \n",
-        "    # Reinforcement learning framework. Currently must be Ray.\n",
-        "    rl_framework=Ray(),\n",
-        "    \n",
-        "    # Custom environmnet for Xvfb\n",
-        "    environment=xvfb_env\n",
-        ")"
+        "training_run = experiment.submit(trainint_config)"
      ]
    },
    {
@@ -313,8 +333,9 @@
        "\n",
        "    # parse arguments ...\n",
        "    \n",
-        "    # Intitialize ray\n",
-        "    ray.init(address=args.ray_address)\n",
+        "    # Start ray head (single node)\n",
+        "    os.system('ray start --head')\n",
+        "    ray.init(address='auto')\n",
        "\n",
        "    # Run training task using tune.run\n",
        "    tune.run(\n",
@@ -328,40 +349,23 @@
        "```"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit the estimator to start experiment\n",
-        "Now we use the *training_estimator* to submit a run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "training_run = exp.submit(training_estimator)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Monitor experiment\n",
        "\n",
-        "Azure Machine Learning provides a Jupyter widget to show the status of an experiment run. You could use this widget to monitor the status of the runs.\n",
-        "\n",
-        "Note that _ReinforcementLearningEstimator_ creates at least two runs: (a) A parent run, i.e. the run returned above, and (b) a collection of child runs. The number of the child runs depends on the configuration of the reinforcement learning estimator. In our simple scenario, configured above, only one child run will be created.\n",
-        "\n",
-        "The widget will show a list of the child runs as well. You can click on the link under **Status** to see the details of a child run. It will also show the metrics being logged."
+        "Azure Machine Learning provides a Jupyter widget to show the status of an experiment run. You could use this widget to monitor the status of the runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437627002
+        }
+      },
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
@@ -384,7 +388,7 @@
      "outputs": [],
      "source": [
        "# Uncomment line below to cancel the run\n",
-        "#training_run.cancel()"
+        "# training_run.cancel()"
      ]
    },
    {
@@ -406,37 +410,6 @@
        "training_run.wait_for_completion()"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Get a handle to the child run\n",
-        "You can obtain a handle to the child run as follows. In our scenario, there is only one child run, we have it called `child_run_0`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import time\n",
-        "\n",
-        "child_run_0 = None\n",
-        "timeout = 30\n",
-        "while timeout > 0 and not child_run_0:\n",
-        "    child_runs = list(training_run.get_children())\n",
-        "    print('Number of child runs:', len(child_runs))\n",
-        "    if len(child_runs) > 0:\n",
-        "        child_run_0 = child_runs[0]\n",
-        "        break\n",
-        "    time.sleep(2) # Wait for 2 seconds\n",
-        "    timeout -= 2\n",
-        "\n",
-        "print('Child run info:')\n",
-        "print(child_run_0)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -453,8 +426,8 @@
      "source": [
        "from azureml.core import Run\n",
        "\n",
-        "run_id = child_run_0.id # Or set to run id of a completed run (e.g. 'rl-cartpole-v0_1587572312_06e04ace_head')\n",
-        "child_run_0 = Run(exp, run_id=run_id)"
+        "run_id = training_run.id # Or set to run id of a completed run (e.g. 'rl-cartpole-v0_1587572312_06e04ace_head')\n",
+        "run = Run(experiment, run_id=run_id)"
      ]
    },
    {
@@ -467,7 +440,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437652309
+        }
+      },
      "outputs": [],
      "source": [
        "from os import path\n",
@@ -480,7 +457,7 @@
        "    dir_util.remove_tree(training_artifacts_path)\n",
        "\n",
        "# Download run artifacts to local compute\n",
-        "child_run_0.download_files(training_artifacts_path)"
+        "training_run.download_files(training_artifacts_path)"
      ]
    },
    {
@@ -497,7 +474,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437657045
+        }
+      },
      "outputs": [],
      "source": [
        "import shutil\n",
@@ -516,15 +497,10 @@
        "\n",
        "\n",
        "# A helper function to display a movie\n",
-        "from IPython.core.display import display, HTML\n",
+        "from IPython.core.display import Video\n",
+        "from IPython.display import display\n",
        "def display_movie(movie_file):\n",
-        "    display(\n",
-        "        HTML('\\\n",
-        "            <video alt=\"cannot display video\" autoplay loop> \\\n",
-        "                <source src=\"{}\" type=\"video/mp4\"> \\\n",
-        "            </video>'.format(movie_file)\n",
-        "        )\n",
-        "    )"
+        "    display(Video(movie_file, embed=True, html_attributes='controls'))"
      ]
    },
    {
@@ -537,7 +513,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437690241
+        }
+      },
      "outputs": [],
      "source": [
        "mp4_files = find_movies(training_artifacts_path)\n",
@@ -554,7 +534,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437692954
+        }
+      },
      "outputs": [],
      "source": [
        "first_movie = mp4_files[0] if len(mp4_files) > 0 else None\n",
@@ -573,7 +557,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1646437717147
+        }
+      },
      "outputs": [],
      "source": [
        "last_movie = mp4_files[-1] if len(mp4_files) > 0 else None\n",
@@ -597,8 +585,8 @@
      "metadata": {},
      "source": [
        "### Evaluate a trained policy\n",
-        "We need to configure another reinforcement learning estimator, `rollout_estimator`, and then use it to submit another run. Note that the entry script for this estimator now points to `cartpole-rollout.py` script.\n",
-        "Also note how we pass the checkpoints dataset to this script using `inputs` parameter of the _ReinforcementLearningEstimator_.\n",
+        "In this section, we submit another job, to evalute a trained policy. The entrypoint for this job is\n",
+        "`cartpole-rollout.py` script, and we we pass the checkpoints dataset to this script as a dataset refrence.\n",
        "\n",
        "We are using script parameters to pass in the same algorithm and the same environment used during training. We also specify the checkpoint number of the checkpoint we wish to evaluate, `checkpoint-number`, and number of the steps we shall run the rollout, `steps`.\n",
        "\n",
@@ -663,7 +651,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "Now let's configure rollout estimator. Note that we use the last checkpoint for evaluation. The assumption is that the last checkpoint points to our best trained agent. You may change this to any of the checkpoint numbers printed above and observe the effect."
+        "You can submit the training run using a `ScriptRunConfig`. By providing the\n",
+        "command to run the training, and a `RunConfig` object configured w"
      ]
    },
    {
@@ -672,94 +661,51 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "script_params = {    \n",
-        "    # Checkpoint number of the checkpoint from which to roll out\n",
-        "    \"--checkpoint-number\": last_checkpoint_number,\n",
+        "ray_environment_name = 'cartpole-ray-sc'\n",
        "\n",
-        "    # Training algorithm\n",
-        "    \"--run\": training_algorithm,\n",
-        "    \n",
-        "    # Training environment\n",
-        "    \"--env\": rl_environment,\n",
-        "    \n",
-        "    # Algorithm-specific parameters\n",
-        "    \"--config\": '{}',\n",
-        "    \n",
-        "    # Number of rollout steps \n",
-        "    \"--steps\": 2000,\n",
-        "    \n",
-        "    # If should repress rendering of the environment\n",
-        "    \"--no-render\": \"\",\n",
-        "    \n",
-        "    # The place where recorded videos will be stored\n",
-        "    \"--video-dir\": \"./logs/video\"\n",
-        "}\n",
+        "experiment_name = 'CartPole-v0-SC'\n",
+        "training_algorithm = 'PPO'\n",
+        "rl_environment = 'CartPole-v0'\n",
        "\n",
+        "experiment = Experiment(workspace=ws, name=experiment_name)\n",
+        "ray_environment = Environment.get(workspace=ws, name=ray_environment_name)\n",
+        "\n",
+        "script_name = 'cartpole_rollout.py'\n",
+        "video_capture = True\n",
        "if video_capture:\n",
-        "    script_params.pop(\"--no-render\")\n",
+        "    script_arguments = ['--video-dir', './logs/video']\n",
        "else:\n",
-        "    script_params.pop(\"--video-dir\")\n",
+        "    script_arguments = ['--no-render']\n",
+        "script_arguments = script_arguments + [\n",
+        "    '--run', training_algorithm,\n",
+        "    '--env', rl_environment,\n",
+        "    '--config', '{}',\n",
+        "    '--steps', '2000',\n",
+        "    '--checkpoint-number', str(last_checkpoint_number),\n",
+        "    '--artifacts-dataset', checkpoint_ds.as_named_input('artifacts_dataset'),\n",
+        "    '--artifacts-path', checkpoint_ds.as_named_input('artifacts_path').as_mount()\n",
+        "]\n",
        "\n",
+        "command = [\"python\", script_name, *script_arguments]\n",
        "\n",
-        "# Ray's video capture support requires to run everything under a headless display driver called (xvfb).\n",
-        "# There are two parts to this:\n",
-        "\n",
-        "# 1. Use a custom docker file with proper instructions to install xvfb, ffmpeg, python-opengl\n",
-        "# and other dependencies.\n",
-        "# Note: Even when the rendering is off pyhton-opengl is needed.\n",
-        "\n",
-        "with open(\"files/docker/Dockerfile\", \"r\") as f:\n",
-        "    dockerfile=f.read()\n",
-        "\n",
-        "xvfb_env = Environment(name='xvfb-vdisplay')\n",
-        "xvfb_env.docker.base_image = None\n",
-        "xvfb_env.docker.base_dockerfile = dockerfile\n",
-        "    \n",
-        "# 2.  Execute the Python process via the xvfb-run command to set up the headless display driver.\n",
-        "xvfb_env.python.user_managed_dependencies = True\n",
        "if video_capture:\n",
-        "    xvfb_env.python.interpreter_path = \"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' python\"\n",
+        "    command = [\"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' \"] + command\n",
+        "    run_config.environment_variables[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
+        "run_config = RunConfiguration(communicator='OpenMpi')\n",
+        "run_config.target = compute_target\n",
+        "run_config.docker = DockerConfiguration(use_docker=True)\n",
+        "run_config.node_count = 1\n",
+        "run_config.environment = ray_environment\n",
        "\n",
-        "rollout_estimator = ReinforcementLearningEstimator(\n",
-        "    # Location of source files\n",
-        "    source_directory='files',\n",
-        "    \n",
-        "    # Python script file\n",
-        "    entry_script='cartpole_rollout.py',\n",
-        "    \n",
-        "    # A dictionary of arguments to pass to the rollout script specified in ``entry_script``\n",
-        "    script_params = script_params,\n",
-        "    \n",
-        "    # Data inputs\n",
-        "    inputs=[\n",
-        "        checkpoint_ds.as_named_input('artifacts_dataset'),\n",
-        "        checkpoint_ds.as_named_input('artifacts_path').as_mount()],\n",
-        "    \n",
-        "    # The Azure Machine Learning compute target set up for Ray head nodes\n",
-        "    compute_target=compute_target,\n",
-        "    \n",
-        "    # Reinforcement learning framework. Currently must be Ray.\n",
-        "    rl_framework=Ray(),\n",
-        "    \n",
-        "    # Custom environmnet for Xvfb\n",
-        "    environment=xvfb_env)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Same as before, we use the *rollout_estimator* to submit a run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rollout_run = exp.submit(rollout_estimator)"
+        "rollout_config = ScriptRunConfig(\n",
+        "                    source_directory='./files',\n",
+        "                    command=command,\n",
+        "                    run_config=run_config\n",
+        "                   )\n",
+        "\n",
+        "rollout_run = experiment.submit(rollout_config)\n",
+        "rollout_run"
      ]
    },
    {
@@ -811,11 +757,6 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Get a handle to child run\n",
-        "child_runs = list(rollout_run.get_children())\n",
-        "print('Number of child runs:', len(child_runs))\n",
-        "child_run_0 = child_runs[0]\n",
-        "\n",
        "# Download rollout artifacts\n",
        "rollout_artifacts_path = path.join(\"logs\", \"rollout\")\n",
        "print(\"Rollout artifacts path:\", rollout_artifacts_path)\n",
@@ -824,7 +765,7 @@
        "    dir_util.remove_tree(rollout_artifacts_path)\n",
        "\n",
        "# Download videos to local compute\n",
-        "child_run_0.download_files(\"logs/video\", output_directory = rollout_artifacts_path)"
+        "rollout_run.download_files(\"logs/video\", output_directory = rollout_artifacts_path)"
      ]
    },
    {
@@ -914,6 +855,16 @@
        "name": "dasommer"
      }
    ],
+    "categories": [
+      "how-to-use-azureml",
+      "reinforcement-learning"
+    ],
+    "interpreter": {
+      "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab"
+    },
+    "kernel_info": {
+      "name": "python38-azureml"
+    },
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
@@ -929,10 +880,13 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.7.9"
    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    }
  },
  "nbformat": 4,
-  "nbformat_minor": 4
+  "nbformat_minor": 0
 }
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_rollout.py
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_rollout.py
@@ -1,4 +1,3 @@
-import argparse
 import os
 import sys

@@ -11,10 +10,7 @@ from azureml.core import Run
 from utils import callbacks


-DEFAULT_RAY_ADDRESS = 'localhost:6379'
-
-
-def run_rollout(args, parser, ray_address):
+def run_rollout(args, parser):

    config = args.config
    if not args.env:
@@ -22,8 +18,6 @@ def run_rollout(args, parser, ray_address):
            parser.error("the following arguments are required: --env")
        args.env = config.get("env")

-    ray.init(address=ray_address)
-
    # Create the Trainer from config.
    cls = get_trainable_cls(args.run)
    agent = cls(env=args.env, config=config)
@@ -76,6 +70,10 @@ def run_rollout(args, parser, ray_address):

 if __name__ == "__main__":

+    # Start ray head (single node)
+    os.system('ray start --head')
+    ray.init(address='auto')
+
    # Add positional argument - serves as placeholder for checkpoint
    argvc = sys.argv[1:]
    argvc.insert(0, 'checkpoint-placeholder')
@@ -88,8 +86,12 @@ if __name__ == "__main__":
        help='Checkpoint number of the checkpoint from which to roll out')

    rollout_parser.add_argument(
-        '--ray-address', required=False, default=DEFAULT_RAY_ADDRESS,
-        help='The address of the Ray cluster to connect to')
+        '--artifacts-dataset', required=True,
+        help='The checkpoints artifacts dataset')
+
+    rollout_parser.add_argument(
+        '--artifacts-path', required=True,
+        help='The checkpoints artifacts path')

    args = rollout_parser.parse_args(argvc)

@@ -116,4 +118,4 @@ if __name__ == "__main__":
    args.checkpoint = checkpoint

    # Start rollout
-    run_rollout(args, rollout_parser, args.ray_address)
+    run_rollout(args, rollout_parser)
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_training.py
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_training.py
@@ -1,17 +1,10 @@
-import argparse
 import os
-import sys
-
 import ray
 from ray.rllib import train
 from ray import tune

 from utils import callbacks

-
-DEFAULT_RAY_ADDRESS = 'localhost:6379'
-
-
 if __name__ == "__main__":

    # Parse arguments and add callbacks to config
@@ -24,11 +17,9 @@ if __name__ == "__main__":
    if 'monitor' in args.config and args.config['monitor']:
        print("Video capturing is ON!")

-    # Start (connect to) Ray cluster
-    if args.ray_address is None:
-        args.ray_address = DEFAULT_RAY_ADDRESS
-
-    ray.init(address=args.ray_address)
+    # Start ray head (single node)
+    os.system('ray start --head')
+    ray.init(address='auto')

    # Run training task using tune.run
    tune.run(
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
@@ -8,7 +8,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    rm -rf /var/lib/apt/lists/* && \
    rm -rf /usr/share/man/*

-RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
+RUN conda install -y conda=4.12.0 python=3.7 && conda clean -ay
+RUN pip install ray-on-aml==0.1.6 & \
    pip install --no-cache-dir \
    azureml-defaults \
    azureml-dataset-runtime[fuse,pandas] \
@@ -22,10 +23,16 @@ RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
    tabulate \
    dm_tree \
    lz4 \
-    ray==0.8.3 \
-    ray[rllib,dashboard,tune]==0.8.3 \
    psutil \
    setproctitle \
-    gym[atari] && \
+    pygame \
+    gym[classic_control]==0.19.0 && \
    conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
    conda install -c anaconda opencv
+
+RUN pip install protobuf==3.20.0
+
+RUN pip install --upgrade ray==0.8.3 \
+    ray[rllib,dashboard,tune]==0.8.3
+
+RUN pip install 'msrest<0.7.0'
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/callbacks.py
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/callbacks.py
@@ -8,7 +8,7 @@ from azureml.core import Run
 def on_train_result(info):
    '''Callback on train result to record metrics returned by trainer.
    '''
-    run = Run.get_context().parent
+    run = Run.get_context()
    run.log(
        name='episode_reward_mean',
        value=info["result"]["episode_reward_mean"])
--- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile
+++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile
@@ -1,36 +1,9 @@
-FROM mcr.microsoft.com/azureml/base:openmpi3.1.2-ubuntu18.04
+FROM akdmsft/particle-cpu

-# Install some basic utilities
-RUN apt-get update && apt-get install -y \
-    curl \
-    ca-certificates \
-    sudo \
-	cpio \
-    git \
-    bzip2 \
-    libx11-6 \
-    tmux \
-    htop \
-    gcc \
-    xvfb \
-    python-opengl \
-    x11-xserver-utils \
-    ffmpeg \
-    mesa-utils \
-    nano \
-    vim \
-    rsync \
- && rm -rf /var/lib/apt/lists/*
-
-# Install python 3.7
-RUN conda install python==3.7 
-
-# Create a working directory
-RUN mkdir /app
-WORKDIR /app
+RUN conda install -c anaconda python=3.7

 # Install required pip packages
-RUN pip install --upgrade pip setuptools && pip install --upgrade \
+RUN pip3 install --upgrade pip setuptools && pip3 install --upgrade \
    pandas \
    matplotlib \
    psutil \
@@ -43,18 +16,23 @@ RUN pip install --upgrade pip setuptools && pip install --upgrade \
    tensorflow-probability==0.8.0 \
    onnxruntime \
    tf2onnx \
-    cloudpickle==1.2.0 \
+    cloudpickle==1.1.1 \
    tabulate \
    dm_tree \
    lz4 \
-    opencv-python \
-    ray==0.8.3 \
-    ray[rllib]==0.8.3 \
-    ray[tune]==0.8.3
+    opencv-python

-# Install particle
-RUN git clone https://github.com/openai/multiagent-particle-envs.git
-COPY patch_files/* multiagent-particle-envs/multiagent/
 RUN cd multiagent-particle-envs && \
-    pip install -e . && \
-    pip install --upgrade pyglet==1.3.2
+    pip3 install -e . && \
+    pip3 install --upgrade pyglet==1.3.2
+
+RUN pip3 install ray-on-aml==0.1.6
+
+RUN pip install protobuf==3.20.0
+
+RUN pip3 install --upgrade \
+    ray==0.8.7 \
+    ray[rllib]==0.8.7 \
+    ray[tune]==0.8.7
+
+RUN pip install 'msrest<0.7.0'
--- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/particle_train.py
+++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/particle_train.py
@@ -1,8 +1,7 @@
-import argparse
-import re
 import os

-import ray
+from ray_on_aml.core import Ray_On_AML
+
 from ray.tune import run_experiments
 from ray.tune.registry import register_trainable, register_env, get_trainable_cls
 import ray.rllib.contrib.maddpg.maddpg as maddpg
@@ -12,7 +11,8 @@ from util import parse_args


 def setup_ray():
-    ray.init(address='auto')
+    ray_on_aml = Ray_On_AML()
+    ray_on_aml.getRay()

    register_env('particle', env_creator)

@@ -120,5 +120,4 @@ if __name__ == '__main__':
        'horizon': args.max_episode_len,
        'video_frequency': args.checkpoint_freq,
    }
-
    train(args, env_config)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
amlrelsa-ms	ee5d0239a3	update samples from Release-146 as a part of SDK release	2022-06-20 20:45:50 +00:00
Harneet Virk	388111cedc	Merge pull request #1763 from Azure/release_update/Release-144 update samples from Release-144 as a part of SDK release	2022-06-03 11:04:13 -07:00
amlrelsa-ms	b86191ed7f	update samples from Release-144 as a part of SDK release	2022-06-03 17:28:37 +00:00
Harneet Virk	22753486de	Merge pull request #1762 from Azure/release_update/Release-143 update samples from Release-143 as a part of SDK release	2022-06-01 11:29:19 -07:00
amlrelsa-ms	cf1d1dbf01	update samples from Release-143 as a part of SDK release	2022-06-01 17:26:59 +00:00
Harneet Virk	2e45d9800d	Merge pull request #1758 from Azure/release_update/Release-142 update samples from Release-142 as a part of SDK release	2022-05-27 15:44:52 -07:00
amlrelsa-ms	a9a8de02ec	update samples from Release-142 as a part of SDK release	2022-05-27 18:58:51 +00:00
Harneet Virk	dd8339e650	Merge pull request #1754 from Azure/release_update/Release-141 update samples from Release-141 as a part of SDK release	2022-05-25 10:12:10 -07:00
amlrelsa-ms	1594ee64a1	update samples from Release-141 as a part of SDK release	2022-05-25 16:56:26 +00:00
Harneet Virk	83ed8222d2	Merge pull request #1750 from Azure/release_update/Release-140 update samples from Release-140 as a part of SDK release	2022-05-04 16:16:28 -07:00
amlrelsa-ms	b0aa91acce	update samples from Release-140 as a part of SDK release	2022-05-04 23:01:56 +00:00
Harneet Virk	5928ba83bb	Merge pull request #1748 from Azure/release_update/Release-138 update samples from Release-138 as a part of SDK release	2022-04-29 10:40:01 -07:00
amlrelsa-ms	ffa3a43979	update samples from Release-138 as a part of SDK release	2022-04-29 17:09:13 +00:00
Harneet Virk	7ce79a43f1	Merge pull request #1746 from Azure/release_update/Release-137 update samples from Release-137 as a part of SDK release	2022-04-27 11:50:44 -07:00
amlrelsa-ms	edcc50ab0c	update samples from Release-137 as a part of SDK release	2022-04-27 17:59:44 +00:00
Harneet Virk	4a391522d0	Merge pull request #1742 from Azure/release_update/Release-136 update samples from Release-136 as a part of SDK release	2022-04-25 13:16:03 -07:00
amlrelsa-ms	1903f78285	update samples from Release-136 as a part of SDK release	2022-04-25 17:08:42 +00:00
Harneet Virk	a4dfcc4693	Merge pull request #1730 from Azure/release_update/Release-135 update samples from Release-135 as a part of SDK release	2022-04-04 14:47:18 -07:00
amlrelsa-ms	faffb3fef7	update samples from Release-135 as a part of SDK release	2022-04-04 20:15:29 +00:00
Harneet Virk	6c6227c403	Merge pull request #1729 from rezasherafat/rl_notebook_update add docker subfolder to pong notebook directly.	2022-03-30 16:05:10 -07:00
Reza Sherafat	e3be364e7a	add docker subfolder to pong notebook directly.	2022-03-30 22:47:50 +00:00
Harneet Virk	90e20a60e9	Merge pull request #1726 from Azure/release_update/Release-131 update samples from Release-131 as a part of SDK release	2022-03-29 19:32:11 -07:00
amlrelsa-ms	33a4eacf1d	update samples from Release-131 as a part of SDK release	2022-03-30 02:26:53 +00:00
Harneet Virk	e30b53fddc	Merge pull request #1725 from Azure/release_update/Release-130 update samples from Release-130 as a part of SDK release	2022-03-29 15:41:28 -07:00
amlrelsa-ms	95b0392ed2	update samples from Release-130 as a part of SDK release	2022-03-29 22:33:38 +00:00
Harneet Virk	796798cb49	Merge pull request #1724 from Azure/release_update/Release-129 update samples from Release-129 as a part of 1.40.0 SDK release	2022-03-29 12:18:30 -07:00
amlrelsa-ms	08b0ba7854	update samples from Release-129 as a part of SDK release	2022-03-29 18:28:35 +00:00
Harneet Virk	ceaf82acc6	Merge pull request #1720 from Azure/release_update/Release-128 update samples from Release-128 as a part of SDK release	2022-03-21 17:56:06 -07:00
amlrelsa-ms	dadc93cfe5	update samples from Release-128 as a part of SDK release	2022-03-22 00:51:19 +00:00
Harneet Virk	c7076bf95c	Merge pull request #1715 from Azure/release_update/Release-127 update samples from Release-127 as a part of SDK release	2022-03-15 17:02:41 -07:00
amlrelsa-ms	ebdffd5626	update samples from Release-127 as a part of SDK release	2022-03-16 00:00:00 +00:00
Harneet Virk	d123880562	Merge pull request #1711 from Azure/release_update/Release-126 update samples from Release-126 as a part of SDK release	2022-03-11 16:53:06 -08:00
amlrelsa-ms	4864e8ea60	update samples from Release-126 as a part of SDK release	2022-03-12 00:47:46 +00:00
Harneet Virk	c86db0d7fd	Merge pull request #1707 from Azure/release_update/Release-124 update samples from Release-124 as a part of SDK release	2022-03-08 09:15:45 -08:00
amlrelsa-ms	ccfbbb3b14	update samples from Release-124 as a part of SDK release	2022-03-08 00:37:35 +00:00
Harneet Virk	c42ba64b15	Merge pull request #1700 from Azure/release_update/Release-123 update samples from Release-123 as a part of SDK release	2022-03-01 16:33:02 -08:00
amlrelsa-ms	6d8bf32243	update samples from Release-123 as a part of SDK release	2022-02-28 17:20:57 +00:00
Harneet Virk	9094da4085	Merge pull request #1684 from Azure/release_update/Release-122 update samples from Release-122 as a part of SDK release	2022-02-14 11:38:49 -08:00
amlrelsa-ms	ebf9d2855c	update samples from Release-122 as a part of SDK release	2022-02-14 19:24:27 +00:00
v-pbavanari	1bbd78eb33	update samples from Release-121 as a part of SDK release (#1678 ) Co-authored-by: amlrelsa-ms <amlrelsa@microsoft.com>	2022-02-02 12:28:49 -05:00
v-pbavanari	77f5a69e04	update samples from Release-120 as a part of SDK release (#1676 ) Co-authored-by: amlrelsa-ms <amlrelsa@microsoft.com>	2022-01-28 12:51:49 -05:00
raja7592	ce82af2ab0	update samples from Release-118 as a part of SDK release (#1673 ) Co-authored-by: amlrelsa-ms <amlrelsa@microsoft.com>	2022-01-24 20:07:35 -05:00
Harneet Virk	2a2d2efa17	Merge pull request #1658 from Azure/release_update/Release-117 Update samples from Release sdk 1.37.0 as a part of SDK release	2021-12-13 10:36:08 -08:00