update samples from Release-156 as a part of SDK release

update samples from Release-155 as a part of  SDK release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -6,6 +6,7 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - protobuf==3.20.0

contrib/fairness/upload-fairness-dashboard.yml

@@ -6,6 +6,7 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - protobuf==3.20.0

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -13,19 +13,21 @@ dependencies:
 - pytorch::pytorch=1.4.0
 - conda-forge::fbprophet==0.7.1
 - cudatoolkit=10.1.243
-- scipy==1.5.2
+- scipy==1.5.3
 - notebook
 - pywin32==227
 - PySocks==1.7.1
-- Pygments==2.11.2
 - conda-forge::pyqt==5.12.3
+- jsonschema==4.9.1
+- Pygments==2.12.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.41.0
+  - azureml-widgets~=1.44.0
   - pytorch-transformers==1.0.0
   - 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/1.41.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.44.0/validated_win32_requirements.txt [--no-deps]
   - arch==4.14
+  - wasabi==0.9.1

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -11,10 +11,10 @@ dependencies:
 - boto3==1.20.19
 - botocore<=1.23.19
 - matplotlib==3.2.1
-- numpy==1.19.5
+- numpy>=1.21.6,<=1.22.3
 - cython==0.29.14
 - urllib3==1.26.7
-- scipy>=1.4.1,<=1.5.2
+- scipy>=1.4.1,<=1.5.3
 - scikit-learn==0.22.1
 - py-xgboost<=1.3.3
 - holidays==0.10.3
@@ -24,10 +24,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.41.0
+  - azureml-widgets~=1.44.0
   - pytorch-transformers==1.0.0
   - 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/1.41.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.44.0/validated_linux_requirements.txt [--no-deps]
   - arch==4.14

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -12,10 +12,10 @@ dependencies:
 - boto3==1.20.19
 - botocore<=1.23.19
 - matplotlib==3.2.1
-- numpy==1.19.5
+- numpy>=1.21.6,<=1.22.3
 - cython==0.29.14
 - urllib3==1.26.7
-- scipy>=1.4.1,<=1.5.2
+- scipy>=1.4.1,<=1.5.3
 - scikit-learn==0.22.1
 - py-xgboost<=1.3.3
 - holidays==0.10.3
@@ -25,10 +25,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.41.0
+  - azureml-widgets~=1.44.0
   - pytorch-transformers==1.0.0
   - 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/1.41.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.44.0/validated_darwin_requirements.txt [--no-deps]
   - arch==4.14

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

@@ -228,8 +228,8 @@
         "n_missing_samples = int(np.floor(data.shape[0] * missing_rate))\n",
         "missing_samples = np.hstack(\n",
         "    (\n",
-        "        np.zeros(data.shape[0] - n_missing_samples, dtype=np.bool),\n",
+        "        np.zeros(data.shape[0] - n_missing_samples, dtype=bool),\n",
-        "        np.ones(n_missing_samples, dtype=np.bool),\n",
+        "        np.ones(n_missing_samples, dtype=bool),\n",
         "    )\n",
         ")\n",
         "rng = np.random.RandomState(0)\n",
@@ -1074,7 +1074,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.8.12"
     },
     "nteract": {
       "version": "nteract-front-end@1.0.0"

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

@@ -207,11 +207,11 @@
         "\n",
         "def remove_blanks_20news(data, feature_column_name, target_column_name):\n",
         "\n",
-        "    data[feature_column_name] = (\n",
+        "    for index, row in data.iterrows():\n",
-        "        data[feature_column_name]\n",
+        "        data.at[index, feature_column_name] = (\n",
-        "        .replace(r\"\\n\", \" \", regex=True)\n",
+        "            row[feature_column_name].replace(\"\\n\", \" \").strip()\n",
-        "        .apply(lambda x: x.strip())\n",
         "        )\n",
+        "\n",
         "    data = data[data[feature_column_name] != \"\"]\n",
         "\n",
         "    return data"

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -1,6 +1,5 @@
 import pandas as pd
-from azureml.core import Environment
+from azureml.core import Environment, ScriptRunConfig
-from azureml.train.estimator import Estimator
 from azureml.core.run import Run
 
 
@@ -16,16 +15,19 @@ def run_inference(
 
     inference_env = train_run.get_environment()
 
-    est = Estimator(
+    est = ScriptRunConfig(
         source_directory=script_folder,
-        entry_script="infer.py",
+        script="infer.py",
-        script_params={
+        arguments=[
-            "--target_column_name": target_column_name,
+            "--target_column_name",
-            "--model_name": model_name,
+            target_column_name,
-        },
+            "--model_name",
-        inputs=[test_dataset.as_named_input("test_data")],
+            model_name,
+            "--input-data",
+            test_dataset.as_named_input("data"),
+        ],
         compute_target=compute_target,
-        environment_definition=inference_env,
+        environment=inference_env,
     )
 
     run = test_experiment.submit(

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -6,7 +6,7 @@ import numpy as np
 from sklearn.externals import joblib
 
 from azureml.automl.runtime.shared.score import scoring, constants
-from azureml.core import Run
+from azureml.core import Run, Dataset
 from azureml.core.model import Model
 
 
@@ -21,6 +21,8 @@ parser.add_argument(
     "--model_name", type=str, dest="model_name", help="Name of registered model"
 )
 
+parser.add_argument("--input-data", type=str, dest="input_data", help="Dataset")
+
 args = parser.parse_args()
 target_column_name = args.target_column_name
 model_name = args.model_name
@@ -34,8 +36,8 @@ model_path = Model.get_model_path(model_name)
 model = joblib.load(model_path)
 
 run = Run.get_context()
-# get input dataset by name
+
-test_dataset = run.input_datasets["test_data"]
+test_dataset = Dataset.get_by_id(run.experiment.workspace, id=args.input_data)
 
 X_test_df = test_dataset.drop_columns(
     columns=[target_column_name]

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

@@ -179,7 +179,7 @@
         "        \"azureml-opendatasets\",\n",
         "        \"azureml-defaults\",\n",
         "    ],\n",
-        "    conda_packages=[\"numpy==1.16.2\"],\n",
+        "    conda_packages=[\"numpy==1.19.5\"],\n",
         "    pin_sdk_version=False,\n",
         ")\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -120,9 +120,13 @@ except Exception:
     end_time = datetime(2021, 5, 1, 0, 0)
     end_time_last_slice = end_time - relativedelta(weeks=2)
 
-train_df = get_noaa_data(end_time_last_slice, end_time)
+try:
+    train_df = get_noaa_data(end_time_last_slice, end_time)
+except Exception as ex:
+    print("get_noaa_data failed:", ex)
+    train_df = None
 
-if train_df.size > 0:
+if train_df is not None and train_df.size > 0:
     print(
         "Received {0} rows of new data after {1}.".format(
             train_df.shape[0], end_time_last_slice

how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb

@@ -0,0 +1,346 @@
+{
+  "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/custom-model-training-from-autofeaturization-run.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning - Codegen for AutoFeaturization \n",
+        "_**Autofeaturization of credit card fraudulent transactions dataset on remote compute and codegen functionality**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Autofeaturization](#Autofeaturization)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Introduction'></a>\n",
+        "## Introduction"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Autofeaturization** lets you run an AutoML experiment to only featurize the datasets. These datasets along with the transformer are stored in AML Storage and linked to the run which can later be retrieved and used to train models. \n",
+        "\n",
+        "**To run Autofeaturization, set the number of iterations to zero and featurization as auto.**\n",
+        "\n",
+        "Please refer to [Autofeaturization and custom model training](../autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb) for more details on the same.\n",
+        "\n",
+        "[Codegen](https://github.com/Azure/automl-codegen-preview) is a feature, which when enabled, provides a user with the script of the underlying functionality and a notebook to tweak inputs or code and rerun the same.\n",
+        "\n",
+        "In this example we use the credit card fraudulent transactions dataset to showcase how you can use AutoML for autofeaturization and further how you can enable the `Codegen` feature.\n",
+        "\n",
+        "This notebook is using remote compute to complete the featurization.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../configuration.ipynb) notebook first if you haven't already, to establish your connection to the AzureML Workspace. \n",
+        "\n",
+        "Here you will learn how to create an autofeaturization experiment using an existing workspace with codegen feature enabled."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Setup'></a>\n",
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML 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 logging\n",
+        "import pandas as pd\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\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 1.44.0 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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# choose a name for experiment\n",
+        "experiment_name = 'automl-autofeaturization-ccard-codegen-remote'\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['Experiment Name'] = experiment.name\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing AmlCompute\n",
+        "A compute target is required to execute the Automated ML 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",
+        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "# 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",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "compute_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Data'></a>\n",
+        "## Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Data\n",
+        "\n",
+        "Load the credit card fraudulent transactions dataset from a CSV file, containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. \n",
+        "\n",
+        "Here the autofeaturization run will featurize the training data passed in."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Training Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard_train.csv\"\n",
+        "training_dataset = Dataset.Tabular.from_delimited_files(training_data) # Tabular dataset\n",
+        "\n",
+        "label_column_name = 'Class' # output label"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Autofeaturization'></a>\n",
+        "## AutoFeaturization\n",
+        "\n",
+        "Instantiate an AutoMLConfig object. This defines the settings and data used to run the autofeaturization experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification or regression or forecasting|\n",
+        "|**training_data**|Input training dataset, containing both features and label column.|\n",
+        "|**iterations**|For an autofeaturization run, iterations will be 0.|\n",
+        "|**featurization**|For an autofeaturization run, featurization can be 'auto' or 'custom'.|\n",
+        "|**label_column_name**|The name of the label column.|\n",
+        "|**enable_code_generation**|For enabling codegen for the run, value would be True|\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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             iterations = 0, # autofeaturization run can be triggered by setting iterations to 0\n",
+        "                             compute_target = compute_target,\n",
+        "                             training_data = training_dataset,\n",
+        "                             label_column_name = label_column_name,\n",
+        "                             featurization = 'auto',\n",
+        "                             verbosity = logging.INFO,\n",
+        "                             enable_code_generation = True # enable codegen\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Widget for Monitoring Runs\n",
+        "\n",
+        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+        "\n",
+        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(remote_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Codegen Script and Notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Codegen script and notebook can be found under the `Outputs + logs` section from the details page of the remote run. Please check for the `autofeaturization_notebook.ipynb` under `/outputs/generated_code`. To modify the featurization code, open `script.py` and make changes. The codegen notebook can be run with the same environment configuration as the above AutoML run."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Experiment Complete!"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "bhavanatumma"
+      }
+    ],
+    "interpreter": {
+      "hash": "adb464b67752e4577e3dc163235ced27038d19b7d88def00d75d1975bde5d9ab"
+    },
+    "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"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.yml

@@ -0,0 +1,4 @@
+name: codegen-for-autofeaturization
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb

@@ -0,0 +1,735 @@
+{
+  "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/custom-model-training-from-autofeaturization-run.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning - AutoFeaturization (Part 1)\n",
+        "_**Autofeaturization of credit card fraudulent transactions dataset on remote compute**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Autofeaturization](#Autofeaturization)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Introduction'></a>\n",
+        "## Introduction"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Autofeaturization is a new feature to let you as the user run an AutoML experiment to only featurize the datasets. These datasets along with the transformer will be stored in the experiment which can later be retrieved and used to train models, either via AutoML or custom training. \n",
+        "\n",
+        "**To run Autofeaturization, pass in zero iterations and featurization as auto. This will featurize the datasets and terminate the experiment. Training will not occur.**\n",
+        "\n",
+        "*Limitations - Sparse data cannot be supported at the moment. Any dataset that has extensive categorical data might be featurized into sparse data which will not be allowed as input to AutoML. Efforts are underway to support sparse data and will be updated soon.* \n",
+        "\n",
+        "In this example we use the credit card fraudulent transactions dataset to showcase how you can use AutoML for autofeaturization. The goal is to clean and featurize the training dataset.\n",
+        "\n",
+        "This notebook is using remote compute to complete the featurization.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../configuration.ipynb) notebook first if you haven't already, to establish your connection to the AzureML Workspace. \n",
+        "\n",
+        "In the below steps, you will learn how to:\n",
+        "1. Create an autofeaturization experiment using an existing workspace.\n",
+        "2. View the featurized datasets and transformer"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Setup'></a>\n",
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML 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 logging\n",
+        "import pandas as pd\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\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 1.44.0 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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# choose a name for experiment\n",
+        "experiment_name = 'automl-autofeaturization-ccard-remote'\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['Experiment Name'] = experiment.name\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing AmlCompute\n",
+        "A compute target is required to execute the Automated ML 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",
+        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "# 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",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "compute_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Data'></a>\n",
+        "## Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Data\n",
+        "\n",
+        "Load the credit card fraudulent transactions dataset from a CSV file, containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. \n",
+        "\n",
+        "Here the autofeaturization run will featurize the training data passed in."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Training Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard_train.csv\"\n",
+        "training_dataset = Dataset.Tabular.from_delimited_files(training_data) # Tabular dataset\n",
+        "\n",
+        "label_column_name = 'Class' # output label"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Autofeaturization'></a>\n",
+        "## AutoFeaturization\n",
+        "\n",
+        "Instantiate an AutoMLConfig object. This defines the settings and data used to run the autofeaturization experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification or regression|\n",
+        "|**training_data**|Input training dataset, containing both features and label column.|\n",
+        "|**iterations**|For an autofeaturization run, iterations will be 0.|\n",
+        "|**featurization**|For an autofeaturization run, featurization will be 'auto'.|\n",
+        "|**label_column_name**|The name of the label column.|\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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             iterations = 0, # autofeaturization run can be triggered by setting iterations to 0\n",
+        "                             compute_target = compute_target,\n",
+        "                             training_data = training_dataset,\n",
+        "                             label_column_name = label_column_name,\n",
+        "                             featurization = 'auto',\n",
+        "                             verbosity = logging.INFO\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transformer and Featurized Datasets\n",
+        "The given datasets have been featurized and stored under `Outputs + logs` from the details page of the remote run. The structure is shown below. The featurized dataset is stored under `/outputs/featurization/data` and the transformer is saved under `/outputs/featurization/pipeline` \n",
+        "\n",
+        "Below you will learn how to refer to the data saved in your run and retrieve the same."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Featurized Data](https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/autofeaturization_img.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Widget for Monitoring Runs\n",
+        "\n",
+        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+        "\n",
+        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(remote_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning - AutoFeaturization (Part 2)\n",
+        "_**Training using a custom model with the featurized data from Autofeaturization run of credit card fraudulent transactions dataset**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Data Setup](#DataSetup)\n",
+        "1. [Autofeaturization Data](#AutofeaturizationData)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Introduction'></a>\n",
+        "## Introduction\n",
+        "\n",
+        "Here we use the featurized dataset saved in the above run to showcase how you can perform custom training by using the transformer from an autofeaturization run to transform validation / test datasets. \n",
+        "\n",
+        "The goal is to use autofeaturized run data and transformer to transform and run a custom training experiment independently\n",
+        "\n",
+        "In the below steps, you will learn how to:\n",
+        "1. Read transformer from a completed autofeaturization run and transform data\n",
+        "2. Pull featurized data from a completed autofeaturization run\n",
+        "3. Run a custom training experiment with the above data\n",
+        "4. Check results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='DataSetup'></a>\n",
+        "## Data Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will load the featurized training data and also load the transformer from the above autofeaturized run. This transformer can then be used to transform the test data to check the accuracy of the custom model after training."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Test Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "load test dataset from CSV and split into X and y columns to featurize with the transformer going forward."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard_test.csv\"\n",
+        "\n",
+        "test_dataset = pd.read_csv(test_data)\n",
+        "label_column_name = 'Class'\n",
+        "\n",
+        "X_test_data = test_dataset[test_dataset.columns.difference([label_column_name])]\n",
+        "y_test_data = test_dataset[label_column_name].values\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load data_transformer from the above remote run artifact"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### (Method 1)\n",
+        "\n",
+        "Method 1 allows you to read the transformer from the remote storage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import mlflow\n",
+        "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())\n",
+        "\n",
+        "# Set uri to fetch data transformer from remote parent run.\n",
+        "artifact_path = \"/outputs/featurization/pipeline/\"\n",
+        "uri = \"runs:/\" + remote_run.id + artifact_path\n",
+        "\n",
+        "print(uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### (Method 2)\n",
+        "\n",
+        "Method 2 downloads the transformer to the local directory and then can be used to transform the data. Uncomment to use."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "''' import pathlib\n",
+        "\n",
+        "# Download the transformer to the local directory\n",
+        "transformers_file_path = \"/outputs/featurization/pipeline/\"\n",
+        "local_path = \"./transformer\"\n",
+        "remote_run.download_files(prefix=transformers_file_path, output_directory=local_path, batch_size=500)\n",
+        "\n",
+        "path = pathlib.Path(\"transformer\") \n",
+        "path = str(path.absolute()) + transformers_file_path\n",
+        "str_uri = \"file:///\" + path\n",
+        "\n",
+        "print(str_uri) '''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transform Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Note:** Not all datasets produce a y_transformer. The dataset used in the current notebook requires a transformer as the y column data is categorical."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.shared.constants import Transformers\n",
+        "\n",
+        "transformers = mlflow.sklearn.load_model(uri) # Using method 1\n",
+        "data_transformers = transformers.get_transformers()\n",
+        "x_transformer = data_transformers[Transformers.X_TRANSFORMER]\n",
+        "y_transformer = data_transformers[Transformers.Y_TRANSFORMER]\n",
+        "\n",
+        "X_test = x_transformer.transform(X_test_data)\n",
+        "y_test = y_transformer.transform(y_test_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the following cell to see the featurization summary of X and y transformers.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_data_summary = x_transformer.get_featurization_summary(is_user_friendly=False)\n",
+        "\n",
+        "summary_df = pd.DataFrame.from_records(X_data_summary)\n",
+        "summary_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Datastore\n",
+        "\n",
+        "The below data store holds the featurized datasets, hence we load and access the data. Check the path and file names according to the saved structure in your experiment `Outputs + logs` as seen in <i>Autofeaturization Part 1</i>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.datastore import Datastore\n",
+        "\n",
+        "ds = Datastore.get(ws, \"workspaceartifactstore\")\n",
+        "experiment_loc = \"ExperimentRun/dcid.\" + remote_run.id\n",
+        "\n",
+        "remote_data_path = \"/outputs/featurization/data/\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='AutofeaturizationData'></a>\n",
+        "## Autofeaturization Data\n",
+        "\n",
+        "We will load the training data from the previously completed Autofeaturization experiment. The resulting featurized dataframe can be passed into the custom model for training. Here we are saving the file to local from the experiment storage and reading the data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_data_file_path = \"full_training_dataset.df.parquet\"\n",
+        "local_data_path = \"./data/\" + train_data_file_path\n",
+        "\n",
+        "remote_run.download_file(remote_data_path + train_data_file_path, local_data_path)\n",
+        "\n",
+        "full_training_data = pd.read_parquet(local_data_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Another way to load the data is to go to the above autofeaturization experiment and check for the featurized dataset ids under `Output datasets`. Uncomment and replace them accordingly below to use."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# train_data = Dataset.get_by_id(ws, 'cb4418ee-bac4-45ac-b055-600653bdf83a') # replace the featurized full_training_dataset id\n",
+        "# full_training_data = train_data.to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Training Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We are dropping the y column and weights column from the featurized training dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Y_COLUMN = \"automl_y\"\n",
+        "SW_COLUMN = \"automl_weights\"\n",
+        "\n",
+        "X_train = full_training_data[full_training_data.columns.difference([Y_COLUMN, SW_COLUMN])]\n",
+        "y_train = full_training_data[Y_COLUMN].values\n",
+        "sample_weight = full_training_data[SW_COLUMN].values"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Train'></a>\n",
+        "## Train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we are passing our training data to the lightgbm classifier, any custom model can be used with your data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import lightgbm as lgb\n",
+        "\n",
+        "model = lgb.LGBMClassifier(learning_rate=0.08,max_depth=-5,random_state=42)\n",
+        "model.fit(X_train, y_train, sample_weight=sample_weight, eval_set=[(X_test, y_test),(X_train, y_train)],\n",
+        "          verbose=20,eval_metric='logloss')\n",
+        "\n",
+        "print('Training accuracy {:.4f}'.format(model.score(X_train, y_train)))\n",
+        "print('Testing accuracy {:.4f}'.format(model.score(X_test, y_test)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Results'></a>\n",
+        "## Analyze results\n",
+        "\n",
+        "### Retrieve the Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='Test'></a>\n",
+        "## Test the fitted model\n",
+        "\n",
+        "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred = model.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Calculate metrics for the prediction\n",
+        "\n",
+        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
+        "from the trained model that was returned."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import itertools\n",
+        "\n",
+        "cf =confusion_matrix(y_test,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",
+        "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",
+        "# 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",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Experiment Complete!"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "bhavanatumma"
+      }
+    ],
+    "interpreter": {
+      "hash": "adb464b67752e4577e3dc163235ced27038d19b7d88def00d75d1975bde5d9ab"
+    },
+    "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"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.yml

@@ -0,0 +1,4 @@
+name: custom-model-training-from-autofeaturization-run
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml

@@ -7,12 +7,13 @@ dependencies:
 - cython==0.29.14
 - urllib3==1.26.7
 - PyJWT < 2.0.0
-- numpy==1.18.5
+- numpy==1.21.6
 - pywin32==227
+- cryptography<37.0.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azure-core==1.21.1
+  - azure-core==1.24.1
   - azure-identity==1.7.0
   - azureml-defaults
   - azureml-sdk

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml

@@ -10,11 +10,12 @@ dependencies:
 - python>=3.6.0,<3.9
 - urllib3==1.26.7
 - PyJWT < 2.0.0
-- numpy==1.19.5
+- numpy>=1.21.6,<=1.22.3
+- cryptography<37.0.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azure-core==1.21.1
+  - azure-core==1.24.1
   - azure-identity==1.7.0
   - azureml-defaults
   - azureml-sdk

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 1.41.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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 1.41.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb

@@ -524,7 +524,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_list = Model.list(ws, tags={\"experiment\": \"automl-backtesting\"})\n",
+        "model_list = Model.list(ws, tags=[[\"experiment\", \"automl-backtesting\"]])\n",
         "model_data = {\"name\": [], \"last_training_date\": []}\n",
         "for model in model_list:\n",
         "    if (\n",

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py

@@ -72,6 +72,8 @@ def get_backtest_pipeline(
     run_config.docker.use_docker = True
     run_config.environment = env
 
+    utilities.set_environment_variables_for_run(run_config)
+
     split_data = PipelineData(name="split_data_output", datastore=None).as_dataset()
     split_step = PythonScriptStep(
         name="split_data_for_backtest",
@@ -114,6 +116,7 @@ def get_backtest_pipeline(
         run_invocation_timeout=3600,
         node_count=node_count,
     )
+    utilities.set_environment_variables_for_run(back_test_config)
     forecasts = PipelineData(name="forecasts", datastore=None)
     if model_name:
         parallel_step_name = "{}-backtest".format(model_name.replace("_", "-"))
@@ -149,12 +152,7 @@ def get_backtest_pipeline(
         inputs=[forecasts.as_mount()],
         outputs=[data_results],
         source_directory=PROJECT_FOLDER,
-        arguments=[
+        arguments=["--forecasts", forecasts, "--output-dir", data_results],
-            "--forecasts",
-            forecasts,
-            "--output-dir",
-            data_results,
-        ],
         runconfig=run_config,
         compute_target=compute_target,
         allow_reuse=False,

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb

@@ -647,13 +647,11 @@
         "        & (fulldata[time_column_name] <= forecast_origin + horizon)\n",
         "    ]\n",
         "\n",
-        "    y_past = X_past.pop(target_column_name).values.astype(np.float)\n",
+        "    y_past = X_past.pop(target_column_name).values.astype(float)\n",
-        "    y_future = X_future.pop(target_column_name).values.astype(np.float)\n",
+        "    y_future = X_future.pop(target_column_name).values.astype(float)\n",
         "\n",
         "    # Now take y_future and turn it into question marks\n",
-        "    y_query = y_future.copy().astype(\n",
+        "    y_query = y_future.copy().astype(float)  # because sometimes life hands you an int\n",
-        "        np.float\n",
-        "    )  # because sometimes life hands you an int\n",
         "    y_query.fill(np.NaN)\n",
         "\n",
         "    print(\"X_past is \" + str(X_past.shape) + \" - shaped\")\n",

how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py

@@ -95,7 +95,7 @@ def do_rolling_forecast_with_lookback(
         # Extract test data from an expanding window up-to the horizon
         expand_wind = X[time_column_name] < horizon_time
         X_test_expand = X[expand_wind]
-        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
+        y_query_expand = np.zeros(len(X_test_expand)).astype(float)
         y_query_expand.fill(np.NaN)
 
         if origin_time != X[time_column_name].min():
@@ -176,7 +176,7 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq="D"):
         # Extract test data from an expanding window up-to the horizon
         expand_wind = X_test[time_column_name] < horizon_time
         X_test_expand = X_test[expand_wind]
-        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
+        y_query_expand = np.zeros(len(X_test_expand)).astype(float)
         y_query_expand.fill(np.NaN)
 
         if origin_time != X_test[time_column_name].min():

how-to-use-azureml/automated-machine-learning/forecasting-many-models/README.md

@@ -0,0 +1,122 @@
+---
+page_type: sample
+languages:
+- python
+products:
+- azure-machine-learning
+description: Tutorial showing how to solve a complex machine learning time series forecasting problems at scale by using Azure Automated ML and Many Models solution accelerator.
+---
+
+![Many Models Solution Accelerator Banner](images/mmsa.png)
+# Many Models Solution Accelerator
+
+<!-- 
+Guidelines on README format: https://review.docs.microsoft.com/help/onboard/admin/samples/concepts/readme-template?branch=master
+
+Guidance on onboarding samples to docs.microsoft.com/samples: https://review.docs.microsoft.com/help/onboard/admin/samples/process/onboarding?branch=master
+
+Taxonomies for products and languages: https://review.docs.microsoft.com/new-hope/information-architecture/metadata/taxonomies?branch=master
+-->
+
+In the real world, many problems can be too complex to be solved by a single machine learning model. Whether that be predicting sales for each individual store, building a predictive maintanence model for hundreds of oil wells, or tailoring an experience to individual users, building a model for each instance can lead to improved results on many machine learning problems.
+
+This Pattern is very common across a wide variety of industries and applicable to many real world use cases. Below are some examples we have seen where this pattern is being used.
+
+- Energy and utility companies building predictive maintenance models for thousands of oil wells, hundreds of wind turbines or hundreds of smart meters
+
+- Retail organizations building workforce optimization models for thousands of stores, campaign promotion propensity models, Price optimization models for hundreds of thousands of products they sell
+
+- Restaurant chains building demand forecasting models across thousands of restaurants  
+
+- Banks and financial institutes building models for cash replenishment for ATM Machine and for several ATMs or building personalized models for individuals
+
+- Enterprises building revenue forecasting models at each division level
+
+- Document management companies building text analytics and legal document search models per each state
+
+Azure Machine Learning (AML) makes it easy to train, operate, and manage hundreds or even thousands of models. This repo will walk you through the end to end process of creating a many models solution from training to scoring to monitoring.
+
+## Prerequisites
+
+To use this solution accelerator, all you need is access to an [Azure subscription](https://azure.microsoft.com/free/) and an [Azure Machine Learning Workspace](https://docs.microsoft.com/azure/machine-learning/how-to-manage-workspace) that you'll create below.
+
+While it's not required, a basic understanding of Azure Machine Learning will be helpful for understanding the solution. The following resources can help introduce you to AML:
+
+1. [Azure Machine Learning Overview](https://azure.microsoft.com/services/machine-learning/)
+2. [Azure Machine Learning Tutorials](https://docs.microsoft.com/azure/machine-learning/tutorial-1st-experiment-sdk-setup)
+3. [Azure Machine Learning Sample Notebooks on Github](https://github.com/Azure/azureml-examples)
+
+## Getting started
+
+### 1. Deploy Resources
+
+Start by deploying the resources to Azure. The button below will deploy Azure Machine Learning and its related resources:
+
+<a href="https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fmicrosoft%2Fsolution-accelerator-many-models%2Fmaster%2Fazuredeploy.json" target="_blank">
+    <img src="http://azuredeploy.net/deploybutton.png"/>
+</a>
+
+### 2. Configure Development Environment
+
+Next you'll need to configure your [development environment](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment) for Azure Machine Learning. We recommend using a [Compute Instance](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment#compute-instance) as it's the fastest way to get up and running.
+
+### 3. Run Notebooks
+
+Once your development environment is set up, run through the Jupyter Notebooks sequentially following the steps outlined. By the end, you'll know how to train, score, and make predictions using the many models pattern on Azure Machine Learning.
+
+![Sequence of Notebooks](./images/mmsa-overview.png)
+
+
+## Contents
+
+In this repo, you'll train and score a forecasting model for each orange juice brand and for each store at a (simulated) grocery chain. By the end, you'll have forecasted sales by using up to 11,973 models to predict sales for the next few weeks.
+
+The data used in this sample is simulated based on the [Dominick's Orange Juice Dataset](http://www.cs.unitn.it/~taufer/QMMA/L10-OJ-Data.html#(1)), sales data from a Chicago area grocery store.
+
+<img src="images/Flow_map.png" width="1000">
+
+### Using Automated ML to train the models:
+
+The [`auto-ml-forecasting-many-models.ipynb`](./auto-ml-forecasting-many-models.ipynb) noteboook is a guided solution accelerator that demonstrates steps from data preparation, to model training, and forecasting on train models as well as operationalizing the solution.
+
+## How-to-videos
+
+Watch these how-to-videos for a step by step walk-through of the many model solution accelerator to learn how to setup your models using Automated ML.
+
+### Automated ML
+
+[![Watch the video](https://media.giphy.com/media/dWUKfameudyNGRnp1t/giphy.gif)](https://channel9.msdn.com/Shows/Docs-AI/Building-Large-Scale-Machine-Learning-Forecasting-Models-using-Azure-Machine-Learnings-Automated-ML)
+
+## Key concepts
+
+### ParallelRunStep
+
+[ParallelRunStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallel_run_step.parallelrunstep?view=azure-ml-py) enables the parallel training of models and is commonly used for batch inferencing. This [document](https://docs.microsoft.com/azure/machine-learning/how-to-use-parallel-run-step) walks through some of the key concepts around ParallelRunStep.
+
+### Pipelines
+
+[Pipelines](https://docs.microsoft.com/azure/machine-learning/concept-ml-pipelines) allow you to create workflows in your machine learning projects. These workflows have a number of benefits including speed, simplicity, repeatability, and modularity.
+
+### Automated Machine Learning
+
+[Automated Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml) also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
+
+### Other Concepts
+
+In additional to ParallelRunStep, Pipelines and Automated Machine Learning, you'll also be working with the following concepts including [workspace](https://docs.microsoft.com/azure/machine-learning/concept-workspace), [datasets](https://docs.microsoft.com/azure/machine-learning/concept-data#datasets), [compute targets](https://docs.microsoft.com/azure/machine-learning/concept-compute-target#train), [python script steps](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py), and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/).
+
+## Contributing
+
+This project welcomes contributions and suggestions. To learn more visit the [contributing](../../../CONTRIBUTING.md) section.
+
+Most contributions require you to agree to a Contributor License Agreement (CLA)
+declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+
+When you submit a pull request, a CLA bot will automatically determine whether you need to provide
+a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
+provided by the bot. You will only need to do this once across all repos using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb

@@ -242,6 +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": {},
@@ -303,6 +331,48 @@
         "    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": {},
@@ -366,6 +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": {},

how-to-use-azureml/automated-machine-learning/forecasting-many-models/scripts/data_preprocessing_file.py

@@ -0,0 +1,39 @@
+from pathlib import Path
+from azureml.core import Run
+
+import argparse
+import os
+
+
+def main(args):
+    output = Path(args.output)
+    output.mkdir(parents=True, exist_ok=True)
+
+    run_context = Run.get_context()
+    input_path = run_context.input_datasets["train_10_models"]
+
+    for file_name in os.listdir(input_path):
+        input_file = os.path.join(input_path, file_name)
+        with open(input_file, "r") as f:
+            content = f.read()
+
+            # Apply any data pre-processing techniques here
+
+            output_file = os.path.join(output, file_name)
+            with open(output_file, "w") as f:
+                f.write(content)
+
+
+def my_parse_args():
+    parser = argparse.ArgumentParser("Test")
+
+    parser.add_argument("--input", type=str)
+    parser.add_argument("--output", type=str)
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = my_parse_args()
+    main(args)

how-to-use-azureml/automated-machine-learning/forecasting-many-models/scripts/data_preprocessing_tabular.py

@@ -0,0 +1,31 @@
+from pathlib import Path
+from azureml.core import Run
+import argparse
+
+
+def main(args):
+    output = Path(args.output)
+    output.mkdir(parents=True, exist_ok=True)
+
+    run_context = Run.get_context()
+    dataset = run_context.input_datasets["train_10_models"]
+    df = dataset.to_pandas_dataframe()
+
+    # Apply any data pre-processing techniques here
+
+    df.to_parquet(output / "data_prepared_result.parquet", compression=None)
+
+
+def my_parse_args():
+    parser = argparse.ArgumentParser("Test")
+
+    parser.add_argument("--input", type=str)
+    parser.add_argument("--output", type=str)
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = my_parse_args()
+    main(args)

how-to-use-azureml/automated-machine-learning/forecasting-many-models/update_env.yml

@@ -0,0 +1,3 @@
+dependencies:
+- pip:
+  - azureml-contrib-automl-pipeline-steps

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_pipeline\"\n",
+        ")\n",
+        "\n",
+        "test_dataset = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    test, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_test_pipeline\"\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
+}

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

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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+1992-09-24,8,tropicana,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
+1992-10-01,2,dominicks,0,1.82,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-10-01,2,minute.maid,1,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-10-01,2,tropicana,0,2.97,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1992-10-01,5,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
+1992-10-01,5,minute.maid,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-10-01,5,tropicana,0,2.78,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1992-10-01,8,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
+1992-10-01,8,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
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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
+1990-06-14,2,dominicks,10560,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1990-06-14,2,minute.maid,4480,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1990-06-14,2,tropicana,8256,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
+1990-06-14,5,dominicks,1792,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
+1990-06-14,5,minute.maid,4224,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
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+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

how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py

@@ -0,0 +1,155 @@
+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).values
+    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
+    )

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))

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

@@ -513,9 +513,7 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "\n",
         "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = (\n",
+        "conda_run_config.environment = automl_run.get_environment()"
-        "    automl_run.get_environment().python.conda_dependencies\n",
-        ")"
       ]
     },
     {
@@ -644,28 +642,6 @@
         ")"
       ]
     },
-    {
-      "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",
-        "with open(\"myenv.yml\", \"r\") as f:\n",
-        "    print(f.read())"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -688,7 +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. "
       ]
     },
     {
@@ -710,7 +686,7 @@
         "    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",

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -111,7 +111,7 @@
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'numpy',\n",
-        "    'scikit-learn==0.19.1',\n",
+        "    'scikit-learn==0.22.1',\n",
         "    'scipy'\n",
         "])"
       ]

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",

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb

@@ -69,17 +69,19 @@
         "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
         "\n",
         "import urllib.request\n",
+        "import os\n",
         "\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",
+        "os.mkdir(\"emotion_ferplus\")\n",
         "\n",
         "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
         "# code from the command line instead of the notebook kernel\n",
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf emotion-ferplus-7.tar.gz"
+        "!tar xvzf emotion-ferplus-7.tar.gz -C emotion_ferplus"
       ]
     },
     {
@@ -130,7 +132,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_dir = \"emotion_ferplus\" # replace this with the location of your model files\n",
+        "model_dir = \"emotion_ferplus/model\" # replace this with the location of your model files\n",
         "\n",
         "# leave as is if it's in the same folder as this notebook"
       ]
@@ -496,13 +498,12 @@
         "\n",
         "# to use parsers to read in our model/data\n",
         "import json\n",
-        "import os\n",
         "\n",
         "test_inputs = []\n",
         "test_outputs = []\n",
         "\n",
-        "# read in 3 testing images from .pb files\n",
+        "# read in 1 testing images from .pb files\n",
-        "test_data_size = 3\n",
+        "test_data_size = 1\n",
         "\n",
         "for num in np.arange(test_data_size):\n",
         "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'input_0.pb')\n",
@@ -533,7 +534,7 @@
       },
       "source": [
         "### Show some sample images\n",
-        "We use `matplotlib` to plot 3 test images from the dataset."
+        "We use `matplotlib` to plot 1 test images from the dataset."
       ]
     },
     {
@@ -547,7 +548,7 @@
       "outputs": [],
       "source": [
         "plt.figure(figsize = (20, 20))\n",
-        "for test_image in np.arange(3):\n",
+        "for test_image in np.arange(test_data_size):\n",
         "    test_inputs[test_image].reshape(1, 64, 64)\n",
         "    plt.subplot(1, 8, test_image+1)\n",
         "    plt.axhline('')\n",

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb

@@ -69,10 +69,12 @@
         "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
         "\n",
         "import urllib.request\n",
+        "import os\n",
         "\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\")"
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")\n",
+        "os.mkdir(\"mnist\")"
       ]
     },
     {
@@ -86,7 +88,7 @@
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf mnist-7.tar.gz"
+        "!tar xvzf mnist-7.tar.gz -C mnist"
       ]
     },
     {
@@ -137,7 +139,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_dir = \"mnist\" # replace this with the location of your model files\n",
+        "model_dir = \"mnist/model\" # replace this with the location of your model files\n",
         "\n",
         "# leave as is if it's in the same folder as this notebook"
       ]
@@ -447,13 +449,12 @@
         "\n",
         "# to use parsers to read in our model/data\n",
         "import json\n",
-        "import os\n",
         "\n",
         "test_inputs = []\n",
         "test_outputs = []\n",
         "\n",
-        "# read in 3 testing images from .pb files\n",
+        "# read in 1 testing images from .pb files\n",
-        "test_data_size = 3\n",
+        "test_data_size = 1\n",
         "\n",
         "for i in np.arange(test_data_size):\n",
         "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
@@ -486,7 +487,7 @@
       },
       "source": [
         "### Show some sample images\n",
-        "We use `matplotlib` to plot 3 test images from the dataset."
+        "We use `matplotlib` to plot 1 test images from the dataset."
       ]
     },
     {
@@ -500,7 +501,7 @@
       "outputs": [],
       "source": [
         "plt.figure(figsize = (16, 6))\n",
-        "for test_image in np.arange(3):\n",
+        "for test_image in np.arange(test_data_size):\n",
         "    plt.subplot(1, 15, test_image+1)\n",
         "    plt.axhline('')\n",
         "    plt.axvline('')\n",

how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb

@@ -240,7 +240,8 @@
         "# 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",
+        "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",

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"
       ]

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",

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/gpu_tree_explainer.py

@@ -2,6 +2,8 @@
 # Licensed under the MIT license.
 
 from azureml.core.run import Run
+from azureml.interpret import ExplanationClient
+from interpret_community.adapter import ExplanationAdapter
 import joblib
 import os
 import shap
@@ -11,9 +13,11 @@ OUTPUT_DIR = './outputs/'
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
 run = Run.get_context()
+client = ExplanationClient.from_run(run)
 
 # get a dataset on income prediction
 X, y = shap.datasets.adult()
+features = X.columns.values
 
 # train an XGBoost model (but any other tree model type should work)
 model = xgboost.XGBClassifier()
@@ -26,6 +30,12 @@ shap_values = explainer(X_shap)
 print("computed shap values:")
 print(shap_values)
 
+# Use the explanation adapter to convert the importances into an interpret-community
+# style explanation which can be uploaded to AzureML or visualized in the
+# ExplanationDashboard widget
+adapter = ExplanationAdapter(features, classification=True)
+global_explanation = adapter.create_global(shap_values.values, X_shap, expected_values=shap_values.base_values)
+
 # write X_shap out as a pickle file for later visualization
 x_shap_pkl = 'x_shap.pkl'
 with open(x_shap_pkl, 'wb') as file:
@@ -42,3 +52,8 @@ with open(model_file_name, 'wb') as file:
 run.upload_file('xgboost_model.pkl', os.path.join('./outputs/', model_file_name))
 original_model = run.register_model(model_name='xgboost_with_gpu_tree_explainer',
                                     model_path='xgboost_model.pkl')
+
+# Uploading model explanation data for storage or visualization in webUX
+# The explanation can then be downloaded on any compute
+comment = 'Global explanation on classification model trained on adult census income dataset'
+client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)

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 1.41.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -225,36 +225,68 @@
         "\n",
         "from azureml.core import Environment\n",
         "\n",
-        "environment_name = \"shap-gpu-tree\"\n",
+        "environment_name = \"shapgpu\"\n",
-        "\n",
         "env = Environment(environment_name)\n",
         "\n",
         "env.docker.enabled = True\n",
         "env.docker.base_image = None\n",
-        "env.docker.base_dockerfile = \"\"\"\n",
+        "\n",
-        "FROM rapidsai/rapidsai:cuda10.0-devel-ubuntu18.04\n",
+        "\n",
+        "# Note: this is to pin the pandas and xgboost versions to be same as notebook.\n",
+        "# In production scenario user would choose their dependencies\n",
+        "import pkg_resources\n",
+        "available_packages = pkg_resources.working_set\n",
+        "pandas_ver = None\n",
+        "for dist in list(available_packages):\n",
+        "    if dist.key == 'pandas':\n",
+        "        pandas_ver = dist.version\n",
+        "pandas_dep = 'pandas'\n",
+        "if pandas_ver:\n",
+        "    pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
+        "\n",
+        "# Note: we build shap at commit 690245 for Tesla K80 GPUs\n",
+        "env.docker.base_dockerfile = f\"\"\"\n",
+        "FROM nvidia/cuda:10.2-devel-ubuntu18.04\n",
+        "ENV PATH=\"/root/miniconda3/bin:${{PATH}}\"\n",
+        "ARG PATH=\"/root/miniconda3/bin:${{PATH}}\"\n",
         "RUN apt-get update && \\\n",
         "apt-get install -y fuse && \\\n",
         "apt-get install -y build-essential && \\\n",
         "apt-get install -y python3-dev && \\\n",
-        "source activate rapids && \\\n",
+        "apt-get install -y wget && \\\n",
+        "apt-get install -y git && \\\n",
+        "rm -rf /var/lib/apt/lists/* && \\\n",
+        "wget \\\n",
+        "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \\\n",
+        "mkdir /root/.conda && \\\n",
+        "bash Miniconda3-latest-Linux-x86_64.sh -b && \\\n",
+        "rm -f Miniconda3-latest-Linux-x86_64.sh && \\\n",
+        "conda init bash && \\\n",
+        ". ~/.bashrc && \\\n",
+        "conda create -n shapgpu python=3.8 && \\\n",
+        "conda activate shapgpu && \\\n",
         "apt-get install -y g++ && \\\n",
         "printenv && \\\n",
         "echo \"which nvcc: \" && \\\n",
         "which nvcc && \\\n",
         "pip install azureml-defaults && \\\n",
         "pip install azureml-telemetry && \\\n",
+        "pip install azureml-interpret && \\\n",
+        "pip install {pandas_dep} && \\\n",
         "cd /usr/local/src && \\\n",
-        "git clone https://github.com/slundberg/shap && \\\n",
+        "git clone https://github.com/slundberg/shap.git --single-branch && \\\n",
         "cd shap && \\\n",
+        "git reset --hard 690245c6ab043edf40cfce3d8438a62e29ab599f && \\\n",
         "mkdir build && \\\n",
         "python setup.py install --user && \\\n",
         "pip uninstall -y xgboost && \\\n",
-        "rm /conda/envs/rapids/lib/libxgboost.so && \\\n",
+        "conda install py-xgboost==1.3.3 && \\\n",
-        "pip install xgboost==1.4.2\n",
+        "pip uninstall -y numpy && \\\n",
+        "conda install numpy==1.20.3 \\\n",
         "\"\"\"\n",
         "\n",
         "env.python.user_managed_dependencies = True\n",
+        "env.python.interpreter_path = '/root/miniconda3/envs/shapgpu/bin/python'\n",
         "\n",
         "from azureml.core import Run\n",
         "from azureml.core import ScriptRunConfig\n",
@@ -266,6 +298,176 @@
         "run = experiment.submit(config=src)\n",
         "run"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# Shows output of the run on stdout.\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download \n",
+        "1. Download model explanation data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.interpret import ExplanationClient\n",
+        "\n",
+        "# Get model explanation data\n",
+        "client = ExplanationClient.from_run(run)\n",
+        "global_explanation = client.download_model_explanation()\n",
+        "local_importance_values = global_explanation.local_importance_values\n",
+        "expected_values = global_explanation.expected_values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the top k (e.g., 4) most important features with their importance values\n",
+        "global_explanation_topk = client.download_model_explanation(top_k=4)\n",
+        "global_importance_values = global_explanation_topk.get_ranked_global_values()\n",
+        "global_importance_names = global_explanation_topk.get_ranked_global_names()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print('global importance values: {}'.format(global_importance_values))\n",
+        "print('global importance names: {}'.format(global_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "2. Download model file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Retrieve model for visualization and deployment\n",
+        "from azureml.core.model import Model\n",
+        "import joblib\n",
+        "original_model = Model(ws, 'xgboost_with_gpu_tree_explainer')\n",
+        "model_path = original_model.download(exist_ok=True)\n",
+        "original_model = joblib.load(model_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "3. Download test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Retrieve x_test for visualization\n",
+        "x_test_path = './x_shap_adult_census.pkl'\n",
+        "run.download_file('x_shap_adult_census.pkl', output_file_path=x_test_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "x_test = joblib.load('x_shap_adult_census.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from raiwidgets import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from interpret_community.common.model_wrapper import wrap_model\n",
+        "from interpret_community.dataset.dataset_wrapper import DatasetWrapper\n",
+        "# note we need to wrap the XGBoost model to output predictions and probabilities in the scikit-learn format\n",
+        "class WrappedXGBoostModel(object):\n",
+        "    \"\"\"A class for wrapping an XGBoost model to output integer predicted classes.\"\"\"\n",
+        "\n",
+        "    def __init__(self, model):\n",
+        "        self.model = model\n",
+        "\n",
+        "    def predict(self, dataset):\n",
+        "        return self.model.predict(dataset).astype(int)\n",
+        "\n",
+        "    def predict_proba(self, dataset):\n",
+        "        return self.model.predict_proba(dataset)\n",
+        "\n",
+        "wrapped_model = WrappedXGBoostModel(wrap_model(original_model, DatasetWrapper(x_test), model_task='classification'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, wrapped_model, dataset=x_test)"
+      ]
     }
   ],
   "metadata": {

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml

@@ -1,5 +1,18 @@
 name: train-explain-model-gpu-tree-explainer
 dependencies:
+- py-xgboost==1.3.3
 - pip:
   - azureml-sdk
   - azureml-interpret
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
+  - matplotlib
+  - ipywidgets
+  - raiwidgets~=0.19.0
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml

@@ -11,6 +11,8 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml

@@ -10,7 +10,9 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - packaging>=20.9
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/score_local_explain.py

@@ -18,7 +18,9 @@ def init():
     original_model_path = Model.get_model_path('local_deploy_model')
     scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
 
+    # Load the original model into the environment
     original_model = joblib.load(original_model_path)
+    # Load the scoring explainer into the environment
     scoring_explainer = joblib.load(scoring_explainer_path)
 
 
@@ -29,5 +31,15 @@ def run(raw_data):
     predictions = original_model.predict(data)
     # Retrieve model explanations
     local_importance_values = scoring_explainer.explain(data)
+    # Retrieve the feature names, which we may want to return to the user.
+    # Note: you can also get the raw_features and engineered_features
+    # by calling scoring_explainer.raw_features and
+    # scoring_explainer.engineered_features but you may need to pass
+    # the raw or engineered feature names in the ScoringExplainer
+    # constructor, depending on if you are using feature maps or
+    # transformations on the original explainer.
+    features = scoring_explainer.features
     # You can return any data type as long as it is JSON-serializable
-    return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}
+    return {'predictions': predictions.tolist(),
+            'local_importance_values': local_importance_values,
+            'features': features}

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -340,17 +340,29 @@
         "available_packages = pkg_resources.working_set\n",
         "sklearn_ver = None\n",
         "pandas_ver = None\n",
+        "numpy_ver = None\n",
+        "numba_ver = None\n",
         "for dist in available_packages:\n",
         "    if dist.key == 'scikit-learn':\n",
         "        sklearn_ver = dist.version\n",
+        "    elif dist.key == 'numpy':\n",
+        "        numpy_ver = dist.version\n",
+        "    elif dist.key == 'numba':\n",
+        "        numba_ver = dist.version\n",
         "    elif dist.key == 'pandas':\n",
         "        pandas_ver = dist.version\n",
         "sklearn_dep = 'scikit-learn'\n",
         "pandas_dep = 'pandas'\n",
+        "numpy_dep = 'numpy'\n",
+        "numba_dep = 'numba'\n",
         "if sklearn_ver:\n",
         "    sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
         "if pandas_ver:\n",
         "    pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
+        "if numpy_ver:\n",
+        "    numpy_dep = 'numpy=={}'.format(numpy_ver)\n",
+        "if numba_ver:\n",
+        "    numba_dep = 'numba=={}'.format(numba_ver)\n",
         "# Specify CondaDependencies obj\n",
         "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
@@ -358,8 +370,8 @@
         "# 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",
+        "    conda_packages=['pip==20.2.4', numpy_dep],\n",
-        "    pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n",
+        "    pip_packages=['pyyaml', sklearn_dep, pandas_dep, numba_dep] + azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml

@@ -10,8 +10,9 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - packaging>=20.9
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
-  - raiutils
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml

@@ -12,7 +12,8 @@ dependencies:
   - azureml-dataset-runtime
   - azureml-core
   - ipywidgets
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
-  - raiutils
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.yml

@@ -3,3 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-widgets
+  - protobuf==3.20.0

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/commandstep_r/Dockerfile

@@ -1,3 +1,4 @@
+# DisableDockerDetector "Disabled to unblock PRs until the owner can fix the file. Not used in any prod deployments - only as a documentation for the customers"
 FROM rocker/tidyverse:4.0.0-ubuntu18.04
  
 # Install python

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",

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",

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",

how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb

@@ -554,7 +554,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_conda_package('numpy')\n",
         "cd.add_pip_package('chainer==5.1.0')\n",
-        "cd.add_pip_package(\"azureml-defaults\")\n",
+        "cd.add_pip_package(\"azureml-defaults==1.43.0\")\n",
         "cd.add_pip_package(\"azureml-opendatasets\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",

how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb

@@ -430,14 +430,15 @@
         "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",
         "  - tensorflow-gpu==2.0.0\n",
         "  - keras<=2.3.1\n",
-        "  - matplotlib"
+        "  - matplotlib\n",
+        "  - protobuf==3.20.1"
       ]
     },
     {
@@ -984,11 +985,12 @@
       "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",
         "cd.add_pip_package(\"azureml-defaults\")\n",
+        "cd.add_pip_package(\"protobuf==3.20.1\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"

how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb

@@ -264,7 +264,7 @@
         "- python=3.6.2\n",
         "- pip=21.3.1\n",
         "- pip:\n",
-        "  - azureml-defaults\n",
+        "  - azureml-defaults==1.43.0\n",
         "  - torch==1.6.0\n",
         "  - torchvision==0.7.0\n",
         "  - future==0.17.1\n",
@@ -539,7 +539,7 @@
       "metadata": {},
       "source": [
         "## Deploy model as web service\n",
-        "Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where)."
+        "Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-deploy-and-where)."
       ]
     },
     {

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-cp38-cp38-win_amd64.whl
+  - pytorch==1.8.1
-  - https://download.pytorch.org/whl/cpu/torchvision-0.7.0%2Bcpu-cp38-cp38-win_amd64.whl
+  - torchvision==0.9.1

how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb

@@ -941,8 +941,9 @@
         "\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.add_pip_package(\"protobuf==3.20.1\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu

@@ -1,5 +1,8 @@
 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
@@ -8,8 +11,8 @@ 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'
-USER root
+RUN pip install protobuf==3.20.0
 
 RUN apt-get update
 RUN apt-get install -y jq

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu

@@ -1,4 +1,7 @@
-FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.0.3-cudnn8-ubuntu18.04:20211111.v1
+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
@@ -34,8 +37,7 @@ RUN pip install gym[atari]==0.19.0
 RUN pip install gym[accept-rom-license]==0.19.0
 
 # Install pip dependencies
-RUN HOROVOD_WITH_TENSORFLOW=1 \
+RUN pip install 'matplotlib>=3.3,<3.4' \
-    pip install 'matplotlib>=3.3,<3.4' \
                 'psutil>=5.8,<5.9' \
                 'tqdm>=4.59,<4.60' \
                 'pandas>=1.1,<1.2' \
@@ -67,6 +69,9 @@ RUN pip install --no-cache-dir \
 # 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
 

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb

@@ -90,7 +90,7 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile

@@ -1,5 +1,8 @@
 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
@@ -9,8 +12,7 @@ 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'
-USER root
 
 RUN apt-get update
 RUN apt-get install -y jq

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb

@@ -91,7 +91,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile

@@ -8,8 +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=4.12.0 python=3.7 && conda clean -ay
+RUN conda install -y conda=4.13.0 python=3.7 && conda clean -ay
-RUN pip install ray-on-aml==0.1.6 & \
+RUN pip install ray-on-aml==0.2.1 & \
     pip install --no-cache-dir \
     azureml-defaults \
     azureml-dataset-runtime[fuse,pandas] \
@@ -30,5 +30,9 @@ RUN pip install ray-on-aml==0.1.6 & \
     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'

how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile

@@ -1,5 +1,8 @@
+# DisableDockerDetector "Disabled to unblock PRs until the owner can fix the file. Not used in any prod deployments - only as a documentation for the customers"
 FROM akdmsft/particle-cpu
 
+RUN conda install -c anaconda python=3.7
+
 # Install required pip packages
 RUN pip3 install --upgrade pip setuptools && pip3 install --upgrade \
     pandas \
@@ -26,7 +29,11 @@ RUN cd multiagent-particle-envs && \
 
 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'

how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb

@@ -85,7 +85,7 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "print('Azure Machine Learning SDK Version: ', azureml.core.VERSION)"
+        "print('Azure Machine Learning SDK version: ', azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb

@@ -1,699 +0,0 @@
-{
-  "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/responsible-ai/auto-ml-regresion-responsibleai/auto-ml-regresion-responsibleai.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Regression with Aml Compute**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The regression goal is to predict the performance of certain combinations of hardware parts.\n",
-        "After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n",
-        "\n",
-        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Instantiate AutoMLConfig with FeaturizationConfig for customization.\n",
-        "3. Train the model using remote compute.\n",
-        "4. Explore the results and featurization transparency options.\n",
-        "5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
-        "6. Start an AzureML experiment on your remote compute.\n",
-        "7. Submit model analysis, explain runs and counterfactual runs for a specific AutoML model.\n",
-        "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n",
-        "10. Download counterfactual examples and view them in the notebook.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML 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 logging\n",
-        "\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",
-        "import azureml.dataprep as dprep\n",
-        "from azureml.automl.core.featurization import FeaturizationConfig\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.core.dataset import Dataset"
-      ]
-    },
-    {
-      "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 1.41.0 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,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment.\n",
-        "experiment_name = 'automl-regression-rai'\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",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#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.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "\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 cluster.\n",
-        "amlcompute_cluster_name = \"hardware-rai\"\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(vm_size='STANDARD_D2_V2',\n",
-        "                                                           max_nodes=4)\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
-        "\n",
-        "compute_target.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Setup Training and Test Data for AutoML experiment\n",
-        "\n",
-        "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model.  We also register the datasets in your workspace using a name so that these datasets may be accessed from the remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv'\n",
-        "\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
-        "\n",
-        "# Split the dataset into train and test datasets\n",
-        "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n",
-        "\n",
-        "# Drop ModelName\n",
-        "train_data = train_data.drop_columns(['ModelName', 'VendorName'])\n",
-        "test_data = test_data.drop_columns(['ModelName', 'VendorName'])\n",
-        "\n",
-        "# Register the train dataset with your workspace\n",
-        "train_data.register(workspace = ws, name = 'rai_machine_train_dataset',\n",
-        "                    description = 'hardware performance training data',\n",
-        "                    create_new_version=True)\n",
-        "\n",
-        "# Register the test dataset with your workspace\n",
-        "test_data.register(workspace = ws, name = 'rai_machine_test_dataset', description = 'hardware performance test data', create_new_version=True)\n",
-        "\n",
-        "label =\"ERP\"\n",
-        "\n",
-        "train_data.to_pandas_dataframe().head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification, regression or forecasting|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
-        "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
-        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Customization\n",
-        "\n",
-        "Supported customization includes:\n",
-        "\n",
-        "1. Column purpose update: Override feature type for the specified column.\n",
-        "2. Transformer parameter update: Update parameters for the specified transformer. Currently supports Imputer and HashOneHotEncoder.\n",
-        "3. Drop columns: Columns to drop from being featurized.\n",
-        "4. Block transformers: Allow/Block transformers to be used on featurization process."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Create FeaturizationConfig object using API calls"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-featurizationconfig-remarks2"
-        ]
-      },
-      "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",
-        "#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})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-featurizationconfig-remarks3"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "automl_settings = {\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",
-        "}\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",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the `AutoMLConfig`. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output=False` to suppress output for each iteration.  You can monitor the run by clicking on the link in the output."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run the following cell to access previous runs. Uncomment the cell below and update the run_id."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_1723d4fe-c33d-41f7-83ad-c010215583b0')\n",
-        "#remote_run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run.wait_for_completion(wait_post_processing=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Generating Responsible AI insights for AutoML model\n",
-        "This section will walk you through the workflow to compute Responsible AI insights like model explanations and counterfactual examples using model analysis workflow for an AutoML model on your remote compute.\n",
-        "\n",
-        "### Retrieve any AutoML Model for explanations\n",
-        "\n",
-        "Below we select an AutoML pipeline from our iterations. The `get_best_child` method returns the a AutoML run with the best score for the specified metric"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_run = remote_run.get_best_child(metric='mean_absolute_error')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Setup model analysis on the remote compute\n",
-        "The following section provides details on how to setup an AzureML experiment to run model analysis for an AutoML model on your remote compute."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create conda configuration for model analysis and explanations runs from automl_run object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# create a new RunConfiguration object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "conda_run_config.target = compute_target\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "conda_run_config.environment = automl_run.get_environment()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Register the AutoML model and create a `PickleModelLoader` for the model analysis so that the model analysis can instantiate the model downloaded from AzureML."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Model\n",
-        "from azureml.responsibleai.common.pickle_model_loader import PickleModelLoader\n",
-        "from azureml.responsibleai.tools.model_analysis.model_analysis_config import ModelAnalysisConfig\n",
-        "from azureml.responsibleai.tools.model_analysis.explain_config import ExplainConfig\n",
-        "from azureml.automl.core.shared.constants import MODEL_PATH\n",
-        "\n",
-        "automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')\n",
-        "\n",
-        "model = automl_run.register_model(model_name='automl_rai', \n",
-        "                                  model_path='outputs/model.pkl')\n",
-        "\n",
-        "model_loader = PickleModelLoader('model.pkl')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Construct the list of the feature column names by dropping the name of the label column from the list of all column names."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_column_names = train_data.to_pandas_dataframe().columns.values\n",
-        "X_column_names = X_column_names[X_column_names!=label]\n",
-        "X_column_names"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get the train and test dataset for the model analysis."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "train_dataset = Dataset.get_by_name(workspace=ws, name='rai_machine_train_dataset')\n",
-        "test_dataset = Dataset.get_by_name(workspace=ws, name='rai_machine_test_dataset')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the `ModelAnalysisConfig` below, `confidential_datastore_name` is the name of the datastore where the analyses will be uploaded.  This example uses the default data store because the dataset is also in the default datastore.  If you have confidential data in the dataset, you should specify a different data store as the `confidential_datastore_name` because analysis makes a copy of the data in this data store."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_analysis_config = ModelAnalysisConfig(\n",
-        "    title=\"Model analysis\",\n",
-        "    model=model,\n",
-        "    model_type='regression',\n",
-        "    model_loader=model_loader,\n",
-        "    train_dataset=train_dataset,\n",
-        "    test_dataset=test_dataset,\n",
-        "    X_column_names=X_column_names,\n",
-        "    target_column_name=label,\n",
-        "    confidential_datastore_name=ws.get_default_datastore().name,\n",
-        "    run_configuration=conda_run_config,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Run model analysis\n",
-        "\n",
-        "The model analysis run takes a snapshot of the data in preparation for model explanation, error analysis, causal and counterfactual.\n",
-        "The model analysis run is the parent run for the model explanation, error analysis, causal and counterfactual runs.\n",
-        "In this example we will just generate an explanation and counterfactuals, but causal and error analyses may be performed as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_analysis_run = experiment.submit(model_analysis_config)\n",
-        "model_analysis_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Compute explanations"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run model explanation based on the model analysis.\n",
-        "The explanation run is a child run of the model analysis run.\n",
-        "In the future, the `add_request` method will allow extra parameters to configure the explanation generated."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "explain_config = ExplainConfig(model_analysis_run, conda_run_config)\n",
-        "explain_config.add_request()\n",
-        "explain_run = model_analysis_run.submit_child(explain_config)\n",
-        "explain_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `explanation_manager.list` method below returns a list of metadata dictionaries for each explain run.  In this case, there is a single explain run.  So, the list contains a single dictionary."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "explanations = model_analysis_run.explanation_manager.list()\n",
-        "explanation = explanations[0]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Feature importance  and  visualizing explanation dashboard\n",
-        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model on the azure portal."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "feature_explanations = model_analysis_run.explanation_manager.download_by_id(explanation['id'])\n",
-        "print(feature_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the explanations for your features under the 'Explanations (preview)' tab in the explain run at:-\\n\" + explain_run.get_portal_url())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Generate counterfactual examples\n",
-        "\n",
-        "Generate counterfactuals for all the samples in the `test_dataset` based on the model analysis.\n",
-        "The counterfactual run is a child run of the model analysis run.\n",
-        "In the future, the `add_request` method will allow extra parameters to configure the counterfactuals generated."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.responsibleai.tools.model_analysis.counterfactual_config import CounterfactualConfig\n",
-        "\n",
-        "cf_config = CounterfactualConfig(model_analysis_run, conda_run_config)\n",
-        "cf_config.add_request(total_CFs=10, desired_range=[10, 300])\n",
-        "cf_run = model_analysis_run.submit_child(cf_config)\n",
-        "cf_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Downloading counterfactual examples\n",
-        "The `counterfactual_manager.list` method below returns a list of metadata dictionaries for each counterfactual run.  In this case, there is a single counterfactual run.  So, the list contains a single dictionary.\n",
-        "\n",
-        "The `download_by_id()` method available in the `counterfactual_manager` can be used to download the counterfactual examples."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "cf_meta = model_analysis_run.counterfactual_manager.list()\n",
-        "counterfactual_object = model_analysis_run.counterfactual_manager.download_by_id(cf_meta[0]['id'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Visualizing the generated counterfactuals\n",
-        "You can use `visualize_as_dataframe()` method to view the generated counterfactual examples for the samples in `test_dataset`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "counterfactual_object.visualize_as_dataframe(show_only_changes=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Visualize counterfactual feature importance"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "counterfactual_object.summary_importance"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "categories": [
-      "how-to-use-azureml",
-      "automated-machine-learning"
-    ],
-    "category": "tutorial",
-    "compute": [
-      "AML"
-    ],
-    "datasets": [
-      "MachineData"
-    ],
-    "deployment": [
-      "ACI"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "None"
-    ],
-    "friendly_name": "Automated ML run with featurization and model explainability.",
-    "index_order": 5,
-    "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": "3.6.12"
-    },
-    "tags": [
-      "featurization",
-      "explainability",
-      "remote_run",
-      "AutomatedML"
-    ],
-    "task": "Regression"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml

@@ -1,5 +0,0 @@
-name: auto-ml-regression-responsibleai
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-responsibleai

how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml

@@ -8,8 +8,9 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.17.0
+  - raiwidgets~=0.19.0
   - liac-arff
   - packaging>=20.9
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - protobuf==3.20.0

how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -43,6 +43,7 @@
         "        1. Logging numeric metrics\n",
         "        1. Logging vectors\n",
         "        1. Logging tables\n",
+        "        1. Logging when additional Metric Names are required\n",
         "        1. Uploading files\n",
         "1. [Analyzing results](#Analyzing-results)\n",
         "    1. Tagging a run\n",
@@ -100,7 +101,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.41.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.44.0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -367,7 +368,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Logging for when more Metric Names are required\n",
+        "### Logging when additional Metric Names are required\n",
         "\n",
         "Limits on logging are internally enforced to ensure a smooth experience, however these can sometimes be limiting, particularly in terms of the limit on metric names.\n",
         "\n",

how-to-use-azureml/training/using-environments/using-environments.ipynb

@@ -160,7 +160,7 @@
         "\n",
         "myenv = Environment(name=\"myenv\")\n",
         "conda_dep = CondaDependencies()\n",
-        "conda_dep.add_conda_package(\"scikit-learn\")"
+        "conda_dep.add_conda_package(\"scikit-learn==0.22.1\")"
       ]
     },
     {
@@ -180,7 +180,7 @@
       },
       "outputs": [],
       "source": [
-        "conda_dep.add_pip_package(\"pillow==5.4.1\")\n",
+        "conda_dep.add_pip_package(\"pillow==6.2.1\")\n",
         "myenv.python.conda_dependencies=conda_dep"
       ]
     },

index.md

@@ -17,6 +17,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting |
 | [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None |
+| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None |
 | [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | None | Local | Local | None | None |
 | :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift |
 | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun |
@@ -27,7 +28,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML |
 | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb) | Classification | Creditcard | AML Compute | None | None | AutomatedML |
 | [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
-| [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
 | [auto-ml-forecasting-backtest-single-model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb) |  | None | Remote | None | Azure ML AutoML |  |
 | :star:[Azure Machine Learning Pipeline with DataTranferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) | Demonstrates the use of DataTranferStep | Custom | ADF | None | Azure ML | None |
 | [Getting Started with Azure Machine Learning Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) | Getting Started notebook for ANML Pipelines | Custom | AML Compute | None | Azure ML | None |
@@ -106,6 +106,8 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [upload-fairness-dashboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/fairness/upload-fairness-dashboard.ipynb) |  |  |  |  |  |  |
 | [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) |  |  |  |  |  |  |
 | [auto-ml-continuous-retraining](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) |  |  |  |  |  |  |
+| [codegen-for-autofeaturization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb) |  |  |  |  |  |  |
+| [custom-model-training-from-autofeaturization-run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb) |  |  |  |  |  |  |
 | [auto-ml-regression-model-proxy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb) |  |  |  |  |  |  |
 | [auto-ml-forecasting-backtest-many-models](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb) |  |  |  |  |  |  |
 | [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) |  |  |  |  |  |  |

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb

@@ -211,7 +211,7 @@
       "metadata": {},
       "source": [
         "## View Experiment\n",
-        "In the left-hand menu in Azure Machine Learning Studio, select __Experiments__ and then select your experiment (azure-ml-in10-mins-tutorial). An experiment is a grouping of many runs from a specified script or piece of code. Information for the run is stored under that experiment. If the name doesn't exist when you submit an experiment, if you select your run you will see various tabs containing metrics, logs, explanations, etc.\n",
+        "In the left-hand menu in Azure Machine Learning Studio, select __Jobs__ and then select your experiment (azure-ml-in10-mins-tutorial). An experiment is a grouping of many runs from a specified script or piece of code. Information for the run is stored under that experiment. If the name doesn't exist when you submit an experiment, if you select your run you will see various tabs containing metrics, logs, explanations, etc.\n",
         "\n",
         "## Version control your models with the model registry\n",
         "\n",

tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb

@@ -222,7 +222,7 @@
       "source": [
         "### Submit the job\n",
         "\n",
-        "Run the experiment by submitting the ScriptRunConfig object. After this there are many options for monitoring your run. Once submitted, you can either navigate to the experiment \"get-started-with-jobsubmission-tutorial\" in the left menu item __Experiments__ to monitor the run, or you can monitor the run inline as the `run.wait_for_completion(show_output=True)` will stream the logs of the run. You will see that the environment is built for you to ensure reproducibility - this adds a couple of minutes to the run time. On subsequent runs, the environment is re-used making the runtime shorter."
+        "Run the experiment by submitting the ScriptRunConfig object. After this there are many options for monitoring your run. Once submitted, you can either navigate to the experiment \"get-started-with-jobsubmission-tutorial\" in the left menu item __Jobs__ to monitor the run, or you can monitor the run inline as the `run.wait_for_completion(show_output=True)` will stream the logs of the run. You will see that the environment is built for you to ensure reproducibility - this adds a couple of minutes to the run time. On subsequent runs, the environment is re-used making the runtime shorter."
       ]
     },
     {

tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py

@@ -5,17 +5,19 @@ import os
 import argparse
 import datetime
 import time
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from math import ceil
 import numpy as np
+import sys
 import shutil
-from tensorflow.contrib.slim.python.slim.nets import inception_v3
+import subprocess
+import tf_slim
 
 from azureml.core import Run
 from azureml.core.model import Model
 from azureml.core.dataset import Dataset
 
-slim = tf.contrib.slim
+slim = tf_slim
 
 image_size = 299
 num_channel = 3
@@ -32,16 +34,18 @@ def get_class_label_dict(labels_dir):
 
 def init():
     global g_tf_sess, probabilities, label_dict, input_images
+    subprocess.run(["git", "clone", "https://github.com/tensorflow/models/"])
+    sys.path.append("./models/research/slim")
 
     parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
     parser.add_argument('--model_name', dest="model_name", required=True)
     parser.add_argument('--labels_dir', dest="labels_dir", required=True)
     args, _ = parser.parse_known_args()
-
+    from nets import inception_v3, inception_utils
     label_dict = get_class_label_dict(args.labels_dir)
     classes_num = len(label_dict)
-
+    tf.disable_v2_behavior()
-    with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
+    with slim.arg_scope(inception_utils.inception_arg_scope()):
         input_images = tf.placeholder(tf.float32, [1, image_size, image_size, num_channel])
         logits, _ = inception_v3.inception_v3(input_images,
                                               num_classes=classes_num,

tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb

@@ -247,7 +247,7 @@
         "    config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
         "                                                   vm_priority=\"lowpriority\", \n",
         "                                                   min_nodes=0, \n",
-        "                                                   max_nodes=1)\n",
+        "                                                   max_nodes=2)\n",
         "\n",
         "    compute_target = ComputeTarget.create(workspace=ws, name=compute_name, provisioning_configuration=config)\n",
         "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)"
@@ -305,7 +305,10 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.15.2\",\n",
+        "cd = CondaDependencies.create(python_version=\"3.8\",\n",
+        "                              conda_packages=['pip==20.2.4'],\n",
+        "                              pip_packages=[\"tensorflow-gpu==2.3.0\",\n",
+        "                                            \"tf_slim==1.1.0\", \"protobuf==3.20.1\",\n",
         "                                            \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "\n",
         "env = Environment(name=\"parallelenv\")\n",