Delete pytorch_mnist.py

update samples from Release-88 as a part of  SDK release 1.23.0

NBSETUP.md

@@ -28,7 +28,7 @@ git clone https://github.com/Azure/MachineLearningNotebooks.git
 pip install azureml-sdk[notebooks,tensorboard]
 
 # install model explainability component
-pip install azureml-sdk[explain]
+pip install azureml-sdk[interpret]
 
 # install automated ml components
 pip install azureml-sdk[automl]
@@ -86,7 +86,7 @@ If you need additional Azure ML SDK components, you can either modify the Docker
 pip install azureml-sdk[automl]
 
 # install the core SDK and model explainability component
-pip install azureml-sdk[explain]
+pip install azureml-sdk[interpret]
 
 # install the core SDK and experimental components
 pip install azureml-sdk[contrib]

README.md

@@ -1,6 +1,8 @@
 # Azure Machine Learning service example notebooks
 
-This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
+> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
+
+This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
 ![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)
 
@@ -18,10 +20,10 @@ This [index](./index.md) should assist in navigating the Azure Machine Learning
 If you want to...
 
  * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb).
- * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
+ * ...learn about experimentation and tracking run history: [track and monitor experiments](./how-to-use-azureml/track-and-monitor-experiments).
- * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
+ * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
- * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as a realtime scoring service, first learn the basics by [deploying to Azure Container Instance](./how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
- * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
+ * ...deploy models as a batch scoring service: [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
  * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
 
 ## Tutorials
@@ -33,13 +35,12 @@ The [Tutorials](./tutorials) folder contains notebooks for the tutorials describ
 The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
 
 - [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
-- [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
+- [Training with ML and DL frameworks](./how-to-use-azureml/ml-frameworks) - Examples demonstrating how to build and train machine learning models at scale on Azure ML and perform hyperparameter tuning.
 - [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
 - [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
-- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
 - [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
 
 ---
@@ -58,7 +59,6 @@ Visit this [community repository](https://github.com/microsoft/MLOps/tree/master
 ## Projects using Azure Machine Learning
 
 Visit following repos to see projects contributed by Azure ML users:
- - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
  - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
  - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
  - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 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.ipynb

@@ -38,7 +38,7 @@
         "## Introduction\n",
         "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.github.io) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.github.io/).\n",
         "\n",
-        "We will apply the [grid search algorithm](https://fairlearn.github.io/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
+        "We will apply the [grid search algorithm](https://fairlearn.github.io/master/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
         "\n",
         "### Setup\n",
         "\n",
@@ -46,7 +46,7 @@
         "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
         "This notebook also requires the following packages:\n",
         "* `azureml-contrib-fairness`\n",
-        "* `fairlearn==0.4.6`\n",
+        "* `fairlearn==0.4.6` (v0.5.0 will work with minor modifications)\n",
         "* `joblib`\n",
         "* `shap`\n",
         "\n",
@@ -62,13 +62,20 @@
         "# !pip install --upgrade scikit-learn>=0.22.1"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "<a id=\"LoadingData\"></a>\n",
         "## Loading the Data\n",
-        "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
+        "We use the well-known `adult` census dataset, which we will fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
       ]
     },
     {
@@ -79,89 +86,141 @@
       "source": [
         "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
         "from fairlearn.widget import FairlearnDashboard\n",
-        "from sklearn import svm\n",
+        "\n",
-        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.datasets import fetch_openml\n",
+        "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
-        "import pandas as pd\n",
-        "import shap"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can now load and inspect the data from the `shap` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_raw, Y = shap.datasets.adult()\n",
-        "X_raw[\"Race\"].value_counts().to_dict()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). We also separate out the Race column, but we will not perform any mitigation based on it. Finally, we perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "A = X_raw[['Sex','Race']]\n",
-        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
-        "X = pd.get_dummies(X)\n",
-        "\n",
-        "\n",
-        "le = LabelEncoder()\n",
-        "Y = le.fit_transform(Y)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "With our data prepared, we can make the conventional split in to 'test' and 'train' subsets:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
         "from sklearn.model_selection import train_test_split\n",
-        "X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_raw, \n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-        "                                                    Y, \n",
+        "from sklearn.compose import make_column_selector as selector\n",
-        "                                                    A,\n",
+        "from sklearn.pipeline import Pipeline\n",
-        "                                                    test_size = 0.2,\n",
+        "\n",
-        "                                                    random_state=0,\n",
+        "import pandas as pd"
-        "                                                    stratify=Y)\n",
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now load and inspect the data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairness_nb_utils import fetch_openml_with_retries\n",
+        "\n",
+        "data = fetch_openml_with_retries(data_id=1590)\n",
+        "    \n",
+        "# Extract the items we want\n",
+        "X_raw = data.data\n",
+        "y = (data.target == '>50K') * 1\n",
+        "\n",
+        "X_raw[\"race\"].value_counts().to_dict()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We are going to treat the sex and race of each individual as protected attributes, and in this particular case we are going to remove these attributes from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). Protected attributes are often denoted by 'A' in the literature, and we follow that convention here:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['sex','race']]\n",
+        "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
+        "    X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
+        ")\n",
+        "\n",
+        "# Ensure indices are aligned between X, y and A,\n",
+        "# after all the slicing and splitting of DataFrames\n",
+        "# and Series\n",
         "\n",
-        "# Work around indexing issue\n",
         "X_train = X_train.reset_index(drop=True)\n",
-        "A_train = A_train.reset_index(drop=True)\n",
         "X_test = X_test.reset_index(drop=True)\n",
-        "A_test = A_test.reset_index(drop=True)\n",
+        "y_train = y_train.reset_index(drop=True)\n",
+        "y_test = y_test.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
         "\n",
-        "# Improve labels\n",
+        "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
-        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
+      ]
-        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "numeric_transformer = Pipeline(\n",
+        "    steps=[\n",
+        "        (\"impute\", SimpleImputer()),\n",
+        "        (\"scaler\", StandardScaler()),\n",
+        "    ]\n",
+        ")\n",
         "\n",
+        "categorical_transformer = Pipeline(\n",
+        "    [\n",
+        "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "        (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n",
+        "    ]\n",
+        ")\n",
         "\n",
-        "A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
+        "preprocessor = ColumnTransformer(\n",
-        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
+        "    transformers=[\n",
-        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
+        "        (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
-        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
+        "        (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
-        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = preprocessor.fit_transform(X_train)\n",
+        "X_test = preprocessor.transform(X_test)"
       ]
     },
     {
@@ -182,7 +241,7 @@
       "source": [
         "unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
         "\n",
-        "unmitigated_predictor.fit(X_train, Y_train)"
+        "unmitigated_predictor.fit(X_train, y_train)"
       ]
     },
     {
@@ -199,7 +258,7 @@
       "outputs": [],
       "source": [
         "FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
-        "                   y_true=Y_test,\n",
+        "                   y_true=y_test,\n",
         "                   y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
       ]
     },
@@ -250,9 +309,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "sweep.fit(X_train, Y_train,\n",
+        "sweep.fit(X_train, y_train,\n",
-        "          sensitive_features=A_train.Sex)\n",
+        "          sensitive_features=A_train.sex)\n",
         "\n",
+        "# For Fairlearn v0.5.0, need sweep.predictors_\n",
         "predictors = sweep._predictors"
       ]
     },
@@ -274,9 +334,9 @@
         "    classifier = lambda X: m.predict(X)\n",
         "    \n",
         "    error = ErrorRate()\n",
-        "    error.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.Sex)\n",
+        "    error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
         "    disparity = DemographicParity()\n",
-        "    disparity.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.Sex)\n",
+        "    disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
         "    \n",
         "    errors.append(error.gamma(classifier)[0])\n",
         "    disparities.append(disparity.gamma(classifier).max())\n",
@@ -330,7 +390,7 @@
       "source": [
         "FairlearnDashboard(sensitive_features=A_test, \n",
         "                   sensitive_feature_names=['Sex', 'Race'],\n",
-        "                   y_true=Y_test.tolist(),\n",
+        "                   y_true=y_test.tolist(),\n",
         "                   y_pred=predictions_dominant)"
       ]
     },
@@ -338,7 +398,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "When using sex as the sensitive feature, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
+        "When using sex as the sensitive feature and accuracy as the metric, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
         "\n",
         "By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
       ]
@@ -440,12 +500,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "sf = { 'sex': A_test.Sex, 'race': A_test.Race }\n",
+        "sf = { 'sex': A_test.sex, 'race': A_test.race }\n",
         "\n",
         "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
         "\n",
         "\n",
-        "dash_dict = _create_group_metric_set(y_true=Y_test,\n",
+        "dash_dict = _create_group_metric_set(y_true=y_test,\n",
         "                                     predictions=predictions_dominant_ids,\n",
         "                                     sensitive_features=sf,\n",
         "                                     prediction_type='binary_classification')"

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -0,0 +1,7 @@
+name: fairlearn-azureml-mitigation
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-fairness
+  - fairlearn==0.4.6
+  - joblib

contrib/fairness/fairness_nb_utils.py

@@ -0,0 +1,28 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Utilities for azureml-contrib-fairness notebooks."""
+
+from sklearn.datasets import fetch_openml
+import time
+
+
+def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
+    """Fetch a given dataset from OpenML with retries as specified."""
+    for i in range(max_retries):
+        try:
+            print("Download attempt {0} of {1}".format(i + 1, max_retries))
+            data = fetch_openml(data_id=data_id, as_frame=True)
+            break
+        except Exception as e:
+            print("Download attempt failed with exception:")
+            print(e)
+            if i + 1 != max_retries:
+                print("Will retry after {0} seconds".format(retry_delay))
+                time.sleep(retry_delay)
+                retry_delay = retry_delay * 2
+    else:
+        raise RuntimeError("Unable to download dataset from OpenML")
+
+    return data

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -48,7 +48,7 @@
         "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
         "This notebook also requires the following packages:\n",
         "* `azureml-contrib-fairness`\n",
-        "* `fairlearn==0.4.6`\n",
+        "* `fairlearn==0.4.6` (should also work with v0.5.0)\n",
         "* `joblib`\n",
         "* `shap`\n",
         "\n",
@@ -64,13 +64,20 @@
         "# !pip install --upgrade scikit-learn>=0.22.1"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "<a id=\"LoadingData\"></a>\n",
         "## Loading the Data\n",
-        "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
+        "We use the well-known `adult` census dataset, which we fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
       ]
     },
     {
@@ -80,10 +87,14 @@
       "outputs": [],
       "source": [
         "from sklearn import svm\n",
-        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.datasets import fetch_openml\n",
+        "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
-        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
-        "import shap"
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.compose import make_column_selector as selector\n",
+        "from sklearn.pipeline import Pipeline"
       ]
     },
     {
@@ -99,7 +110,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_raw, Y = shap.datasets.adult()"
+        "from fairness_nb_utils import fetch_openml_with_retries\n",
+        "\n",
+        "data = fetch_openml_with_retries(data_id=1590)\n",
+        "    \n",
+        "# Extract the items we want\n",
+        "X_raw = data.data\n",
+        "y = (data.target == '>50K') * 1"
       ]
     },
     {
@@ -115,7 +132,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(X_raw[\"Race\"].value_counts().to_dict())"
+        "print(X_raw[\"race\"].value_counts().to_dict())"
       ]
     },
     {
@@ -125,7 +142,7 @@
         "<a id=\"ProcessingData\"></a>\n",
         "## Processing the Data\n",
         "\n",
-        "With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and put the rest of the feature data into `X`:"
+        "With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and leave the rest of the feature data in `X_raw`:"
       ]
     },
     {
@@ -134,16 +151,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "A = X_raw[['Sex','Race']]\n",
+        "A = X_raw[['sex','race']]\n",
-        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
+        "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
-        "X = pd.get_dummies(X)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Next, we apply a standard set of scalings:"
+        "We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
       ]
     },
     {
@@ -152,51 +168,74 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "sc = StandardScaler()\n",
+        "(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
-        "X_scaled = sc.fit_transform(X)\n",
+        "    X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
-        "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
+        ")\n",
         "\n",
-        "le = LabelEncoder()\n",
+        "# Ensure indices are aligned between X, y and A,\n",
-        "Y = le.fit_transform(Y)"
+        "# after all the slicing and splitting of DataFrames\n",
-      ]
+        "# and Series\n",
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Finally, we can then split our data into training and test sets, and also make the labels on our test portion of `A` human-readable:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled, \n",
-        "                                                    Y, \n",
-        "                                                    A,\n",
-        "                                                    test_size = 0.2,\n",
-        "                                                    random_state=0,\n",
-        "                                                    stratify=Y)\n",
         "\n",
-        "# Work around indexing issue\n",
         "X_train = X_train.reset_index(drop=True)\n",
-        "A_train = A_train.reset_index(drop=True)\n",
         "X_test = X_test.reset_index(drop=True)\n",
-        "A_test = A_test.reset_index(drop=True)\n",
+        "y_train = y_train.reset_index(drop=True)\n",
+        "y_test = y_test.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
         "\n",
-        "# Improve labels\n",
+        "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
-        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
+      ]
-        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "numeric_transformer = Pipeline(\n",
+        "    steps=[\n",
+        "        (\"impute\", SimpleImputer()),\n",
+        "        (\"scaler\", StandardScaler()),\n",
+        "    ]\n",
+        ")\n",
         "\n",
+        "categorical_transformer = Pipeline(\n",
+        "    [\n",
+        "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "        (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n",
+        "    ]\n",
+        ")\n",
         "\n",
-        "A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
+        "preprocessor = ColumnTransformer(\n",
-        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
+        "    transformers=[\n",
-        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
+        "        (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
-        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
+        "        (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
-        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = preprocessor.fit_transform(X_train)\n",
+        "X_test = preprocessor.transform(X_test)"
       ]
     },
     {
@@ -217,7 +256,7 @@
       "source": [
         "lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
         "\n",
-        "lr_predictor.fit(X_train, Y_train)"
+        "lr_predictor.fit(X_train, y_train)"
       ]
     },
     {
@@ -235,7 +274,7 @@
       "source": [
         "svm_predictor = svm.SVC()\n",
         "\n",
-        "svm_predictor.fit(X_train, Y_train)"
+        "svm_predictor.fit(X_train, y_train)"
       ]
     },
     {
@@ -354,7 +393,7 @@
         "\n",
         "FairlearnDashboard(sensitive_features=A_test, \n",
         "                   sensitive_feature_names=['Sex', 'Race'],\n",
-        "                   y_true=Y_test.tolist(),\n",
+        "                   y_true=y_test.tolist(),\n",
         "                   y_pred=ys_pred)"
       ]
     },
@@ -380,11 +419,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "sf = { 'Race': A_test.Race, 'Sex': A_test.Sex }\n",
+        "sf = { 'Race': A_test.race, 'Sex': A_test.sex }\n",
         "\n",
         "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
         "\n",
-        "dash_dict = _create_group_metric_set(y_true=Y_test,\n",
+        "dash_dict = _create_group_metric_set(y_true=y_test,\n",
         "                                     predictions=ys_pred,\n",
         "                                     sensitive_features=sf,\n",
         "                                     prediction_type='binary_classification')"
@@ -499,7 +538,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
+      "version": "3.6.10"
     }
   },
   "nbformat": 4,

contrib/fairness/upload-fairness-dashboard.yml

@@ -0,0 +1,7 @@
+name: upload-fairness-dashboard
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-fairness
+  - fairlearn==0.4.6
+  - joblib

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

@@ -97,62 +97,96 @@ jupyter notebook
  <a name="databricks"></a>
 ## Setup using Azure Databricks
 
-**NOTE**: Please create your Azure Databricks cluster as v6.0 (high concurrency preferred) with **Python 3** (dropdown).
+**NOTE**: Please create your Azure Databricks cluster as v7.1 (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
-- Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl]** as a PyPi library in Azure Databricks workspace.
+- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl).
-- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
+- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl) and import into the Azure databricks workspace.
-- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 
-- [auto-ml-classification-credit-card-fraud.ipynb](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)
+## Classification
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+- **Classify Credit Card Fraud**
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
+    - Dataset: [Kaggle's credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
-    - Uses azure compute for training
+      - **[Jupyter Notebook (remote run)](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)**
+          - run the experiment remotely on AML Compute cluster
+          - test the performance of the best model in the local environment
+      - **[Jupyter Notebook (local run)](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)**
+          - run experiment in the local environment
+          - use Mimic Explainer for computing feature importance
+          - deploy the best model along with the explainer to an Azure Kubernetes (AKS) cluster, which will compute the raw and engineered feature importances at inference time
+- **Predict Term Deposit Subscriptions in a Bank**
+    - Dataset: [UCI's bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
+        - **[Jupyter Notebook](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)**
+          - run experiment remotely on AML Compute cluster to generate ONNX compatible models
+          - view the featurization steps that were applied during training
+          - view feature importance for the best model
+          - download the best model in ONNX format and use it for inferencing using ONNXRuntime
+          - deploy the best model in PKL format to Azure Container Instance (ACI)
+- **Predict Newsgroup based on Text from News Article**
+    - Dataset: [20 newsgroups text dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html)
+        - **[Jupyter Notebook](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)**
+          - AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data
+          - AutoML will use Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used
+          - Bidirectional Long-Short Term neural network (BiLSTM) will be utilized when a CPU compute is used, thereby optimizing the choice of DNN
 
-- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
+## Regression
+- **Predict Performance of Hardware Parts**
     - Dataset: Hardware Performance Dataset
-    - Simple example of using automated ML for regression
+        - **[Jupyter Notebook](regression/auto-ml-regression.ipynb)**
-    - Uses azure compute for training
+            - run the experiment remotely on AML Compute cluster
+            - get best trained model for a different metric than the one the experiment was optimized for
+            - test the performance of the best model in the local environment
+        - **[Jupyter Notebook (advanced)](regression/auto-ml-regression.ipynb)**
+            - run the experiment remotely on AML Compute cluster
+            - customize featurization: override column purpose within the dataset, configure transformer parameters
+            - get best trained model for a different metric than the one the experiment was optimized for
+            - run a model explanation experiment on the remote cluster
+            - deploy the model along the explainer and run online inferencing
 
-- [auto-ml-regression-explanation-featurization.ipynb](regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb)
+## Time Series Forecasting
-    - Dataset: Hardware Performance Dataset
+- **Forecast Energy Demand**
-    - Shows featurization and excplanation
+    - Dataset: [NYC energy demand data](http://mis.nyiso.com/public/P-58Blist.htm)
-    - Uses azure compute for training
+        - **[Jupyter Notebook](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)**
-
+          - run experiment remotely on AML Compute cluster
-- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
+          - use lags and rolling window features
-    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
+          - view the featurization steps that were applied during training
-    - Example of using automated ML for training a forecasting model
+          - get the best model, use it to forecast on test data and compare the accuracy of predictions against real data
-
+- **Forecast Orange Juice Sales (Multi-Series)**
-- [auto-ml-classification-credit-card-fraud-local.ipynb](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)
+    - Dataset: [Dominick's grocery sales of orange juice](forecasting-orange-juice-sales/dominicks_OJ.csv)
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+        - **[Jupyter Notebook](forecasting-orange-juice-sales/dominicks_OJ.csv)**
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
+          - run experiment remotely on AML Compute cluster
-    - Uses local compute for training
+          - customize time-series featurization, change column purpose and override transformer hyper parameters
-
+          - evaluate locally the performance of the generated best model
-- [auto-ml-classification-bank-marketing-all-features.ipynb](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)
+          - deploy the best model as a webservice on Azure Container Instance (ACI)
-    - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
+          - get online predictions from the deployed model
-    - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
+- **Forecast Demand of a Bike-Sharing Service**
-    - Uses azure compute for training
+    - Dataset: [Bike demand data](forecasting-bike-share/bike-no.csv)
-
+        - **[Jupyter Notebook](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)**
-- [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
+          - run experiment remotely on AML Compute cluster
-    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
+          - integrate holiday features
-    - Example of training an automated ML forecasting model on multiple time-series
+          - run rolling forecast for test set that is longer than the forecast horizon
-
+          - compute metrics on the predictions from the remote forecast
-- [auto-ml-forecasting-bike-share.ipynb](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
+- **The Forecast Function Interface**
-    - Dataset: forecasting for a bike-sharing
+    - Dataset: Generated for sample purposes
-    - Example of training an automated ML forecasting model on multiple time-series
+        - **[Jupyter Notebook](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)**
-
+          - train a forecaster using a remote AML Compute cluster
-- [auto-ml-forecasting-function.ipynb](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
+          - capabilities of forecast function (e.g. forecast farther into the horizon)
-    - Example of training an automated ML forecasting model on multiple time-series
+          - generate confidence intervals
-
+- **Forecast Beverage Production**
-- [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
+    - Dataset: [Monthly beer production data](forecasting-beer-remote/Beer_no_valid_split_train.csv)
-    - Example of training an automated ML forecasting model on multiple time-series
+        - **[Jupyter Notebook](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)**
-    - Beer Production Forecasting
+          - train using a remote AML Compute cluster
-
+          - enable the DNN learning model
-- [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
+          - forecast on a remote compute cluster and compare different model performance
-    - Continuous retraining using Pipelines and Time-Series TabularDataset
+- **Continuous Retraining with NOAA Weather Data**
+    - Dataset: [NOAA weather data from Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/)
+        - **[Jupyter Notebook](continuous-retraining/auto-ml-continuous-retraining.ipynb)**
+          - continuously retrain a model using Pipelines and AutoML
+          - create a Pipeline to upload a time series dataset to an Azure blob
+          - create a Pipeline to run an AutoML experiment and register the best resulting model in the Workspace
+          - publish the training pipeline created and schedule it to run daily
 
 <a name="documentation"></a>
 See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
@@ -173,7 +207,7 @@ The main code of the file must be indented so that it is under this condition.
 ## automl_setup fails
 1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
-3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
+3. Check that you have conda 4.7.8 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
 4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
 5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
 

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

@@ -2,14 +2,15 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip<=19.3.1
+- pip==20.2.4
-- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.8
 - nb_conda
+- boto3==1.15.18
 - matplotlib==2.1.0
-- numpy~=1.18.0
+- numpy==1.18.5
 - cython
 - urllib3<1.24
-- scipy==1.4.1
+- scipy>=1.4.1,<=1.5.2
 - scikit-learn==0.22.1
 - pandas==0.25.1
 - py-xgboost<=0.90
@@ -20,9 +21,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets
+  - azureml-widgets~=1.23.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.14.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_win32_requirements.txt [--no-deps]
-
+  - PyJWT < 2.0.0

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

@@ -2,14 +2,15 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip<=19.3.1
+- pip==20.2.4
-- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.8
 - nb_conda
+- boto3==1.15.18
 - matplotlib==2.1.0
-- numpy~=1.18.0
+- numpy==1.18.5
 - cython
 - urllib3<1.24
-- scipy==1.4.1
+- scipy>=1.4.1,<=1.5.2
 - scikit-learn==0.22.1
 - pandas==0.25.1
 - py-xgboost<=0.90
@@ -20,9 +21,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets
+  - azureml-widgets~=1.23.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.14.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_linux_requirements.txt [--no-deps]
+  - PyJWT < 2.0.0
 

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

@@ -2,15 +2,16 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip<=19.3.1
+- pip==20.2.4
 - nomkl
-- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.8
 - nb_conda
+- boto3==1.15.18
 - matplotlib==2.1.0
-- numpy~=1.18.0
+- numpy==1.18.5
 - cython
 - urllib3<1.24
-- scipy==1.4.1
+- scipy>=1.4.1,<=1.5.2
 - scikit-learn==0.22.1
 - pandas==0.25.1
 - py-xgboost<=0.90
@@ -21,8 +22,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets
+  - azureml-widgets~=1.23.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.14.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_darwin_requirements.txt [--no-deps]
+  - PyJWT < 2.0.0

how-to-use-azureml/automated-machine-learning/automl_setup.cmd

@@ -6,11 +6,22 @@ set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
 IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+SET check_conda_version_script="check_conda_version.py"
 
 IF NOT EXIST %automl_env_file% GOTO YmlMissing
 
 IF "%CONDA_EXE%"=="" GOTO CondaMissing
 
+IF NOT EXIST %check_conda_version_script% GOTO VersionCheckMissing
+
+python "%check_conda_version_script%"
+IF errorlevel 1 GOTO ErrorExit:
+
+SET replace_version_script="replace_latest_version.ps1"
+IF EXIST %replace_version_script% (
+  powershell -file %replace_version_script% %automl_env_file%
+)
+
 call conda activate %conda_env_name% 2>nul:
 
 if not errorlevel 1 (
@@ -54,6 +65,10 @@ echo If you are running an older version of Miniconda or Anaconda,
 echo you can upgrade using the command: conda update conda
 goto End
 
+:VersionCheckMissing
+echo File %check_conda_version_script% not found.
+goto End
+
 :YmlMissing
 echo File %automl_env_file% not found.
 

how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh

@@ -4,6 +4,7 @@ CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
+CHECK_CONDA_VERSION_SCRIPT="check_conda_version.py"
 
 if [ "$CONDA_ENV_NAME" == "" ]
 then
@@ -20,6 +21,18 @@ if [ ! -f $AUTOML_ENV_FILE ]; then
     exit 1
 fi
 
+if [ ! -f $CHECK_CONDA_VERSION_SCRIPT ]; then
+    echo "File $CHECK_CONDA_VERSION_SCRIPT not found"
+    exit 1
+fi
+
+python "$CHECK_CONDA_VERSION_SCRIPT"
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+
+sed -i 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE
+
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading existing conda environment" $CONDA_ENV_NAME

how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh

@@ -4,6 +4,7 @@ CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
+CHECK_CONDA_VERSION_SCRIPT="check_conda_version.py"
 
 if [ "$CONDA_ENV_NAME" == "" ]
 then
@@ -20,6 +21,18 @@ if [ ! -f $AUTOML_ENV_FILE ]; then
     exit 1
 fi
 
+if [ ! -f $CHECK_CONDA_VERSION_SCRIPT ]; then
+    echo "File $CHECK_CONDA_VERSION_SCRIPT not found"
+    exit 1
+fi
+
+python "$CHECK_CONDA_VERSION_SCRIPT"
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+
+sed -i '' 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE
+
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading existing conda environment" $CONDA_ENV_NAME

how-to-use-azureml/automated-machine-learning/check_conda_version.py

@@ -0,0 +1,26 @@
+from distutils.version import LooseVersion
+import platform
+
+try:
+    import conda
+except:
+    print('Failed to import conda.')
+    print('This setup is usually run from the base conda environment.')
+    print('You can activate the base environment using the command "conda activate base"')
+    exit(1)
+
+architecture = platform.architecture()[0]
+
+if architecture != "64bit":
+    print('This setup requires 64bit Anaconda or Miniconda.  Found: ' + architecture)
+    exit(1)
+
+minimumVersion = "4.7.8"
+
+versionInvalid = (LooseVersion(conda.__version__) < LooseVersion(minimumVersion))
+
+if versionInvalid:
+    print('Setup requires conda version ' + minimumVersion + ' or higher.')
+    print('You can use the command "conda update conda" to upgrade conda.')
+
+exit(versionInvalid)

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

@@ -89,7 +89,7 @@
         "from azureml.automl.core.featurization import FeaturizationConfig\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.interpret._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret import ExplanationClient"
       ]
     },
     {
@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -167,7 +167,7 @@
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\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 on the default limits and how to request more quota."
+        "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."
       ]
     },
     {
@@ -899,7 +899,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "anumamah"
+        "name": "ratanase"
       }
     ],
     "category": "tutorial",

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

@@ -1,4 +0,0 @@
-name: auto-ml-classification-bank-marketing-all-features
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -424,22 +424,33 @@
       "source": [
         "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
         "\n",
+        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u00a9 Libre de Bruxelles) on big data mining and fraud detection.\n",
+        "More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
         "\n",
-        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
+        "Please cite the following works:\n",
-        "Please cite the following works: \n",
+        "\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
+        "Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
+        "\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
+        "Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
-        "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
+        "\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
+        "Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing"
+        "\n",
+        "Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
+        "\n",
+        "Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
+        "\n",
+        "Carcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing\n",
+        "\n",
+        "Bertrand Lebichot, Yann-A\u00c3\u00abl Le Borgne, Liyun He, Frederic Obl\u00c3\u00a9, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019\n",
+        "\n",
+        "Fabrizio Carcillo, Yann-A\u00c3\u00abl Le Borgne, Olivier Caelen, Frederic Obl\u00c3\u00a9, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "tzvikei"
+        "name": "ratanase"
       }
     ],
     "category": "tutorial",

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

@@ -1,4 +0,0 @@
-name: auto-ml-classification-credit-card-fraud
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -0,0 +1,589 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Text Classification Using Deep Learning**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Evaluate](#Evaluate)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "This notebook demonstrates classification with text data using deep learning in AutoML.\n",
+        "\n",
+        "AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data. Depending on the compute cluster the user provides, AutoML tried out Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used, and Bidirectional Long-Short Term neural network (BiLSTM) when a CPU compute is used, thereby optimizing the choice of DNN for the uesr's setup.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "Notebook synopsis:\n",
+        "\n",
+        "1. Creating an Experiment in an existing Workspace\n",
+        "2. Configuration and remote run of AutoML for a text dataset (20 Newsgroups dataset from scikit-learn) for classification\n",
+        "3. Registering the best model for future use\n",
+        "4. Evaluating the final model on a test set"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import shutil\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",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.run import Run\n",
+        "from azureml.widgets import RunDetails\n",
+        "from azureml.core.model import Model \n",
+        "from helper import run_inference, get_result_df\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from sklearn.datasets import fetch_20newsgroups"
+      ]
+    },
+    {
+      "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 1.23.0 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": [
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose an experiment name.\n",
+        "experiment_name = 'automl-classification-text-dnn'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace Name'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up a compute cluster\n",
+        "This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
+        "\n",
+        "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively.  If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers."
+      ]
+    },
+    {
+      "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",
+        "num_nodes = 2\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"dnntext-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(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
+        "                                                           # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\" \n",
+        "                                                           # or similar GPU option\n",
+        "                                                           # available in your workspace\n",
+        "                                                           max_nodes = num_nodes)\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": [
+        "### Get data\n",
+        "For this notebook we will use 20 Newsgroups data from scikit-learn. We filter the data to contain four classes and take a sample as training data. Please note that for accuracy improvement, more data is needed. For this notebook we provide a small-data example so that you can use this template to use with your larger sized data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_dir = \"text-dnn-data\" # Local directory to store data\n",
+        "blobstore_datadir = data_dir # Blob store directory to store data in\n",
+        "target_column_name = 'y'\n",
+        "feature_column_name = 'X'\n",
+        "\n",
+        "def get_20newsgroups_data():\n",
+        "    '''Fetches 20 Newsgroups data from scikit-learn\n",
+        "       Returns them in form of pandas dataframes\n",
+        "    '''\n",
+        "    remove = ('headers', 'footers', 'quotes')\n",
+        "    categories = [\n",
+        "        'rec.sport.baseball',\n",
+        "        'rec.sport.hockey',\n",
+        "        'comp.graphics',\n",
+        "        'sci.space',\n",
+        "        ]\n",
+        "\n",
+        "    data = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+        "                                    shuffle = True, random_state = 42,\n",
+        "                                    remove = remove)\n",
+        "    data = pd.DataFrame({feature_column_name: data.data, target_column_name: data.target})\n",
+        "\n",
+        "    data_train = data[:200]\n",
+        "    data_test = data[200:300]    \n",
+        "\n",
+        "    data_train = remove_blanks_20news(data_train, feature_column_name, target_column_name)\n",
+        "    data_test = remove_blanks_20news(data_test, feature_column_name, target_column_name)\n",
+        "    \n",
+        "    return data_train, data_test\n",
+        "    \n",
+        "def remove_blanks_20news(data, feature_column_name, target_column_name):\n",
+        "    \n",
+        "    data[feature_column_name] = data[feature_column_name].replace(r'\\n', ' ', regex=True).apply(lambda x: x.strip())\n",
+        "    data = data[data[feature_column_name] != '']\n",
+        "    \n",
+        "    return data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Fetch data and upload to datastore for use in training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_train, data_test = get_20newsgroups_data()\n",
+        "\n",
+        "if not os.path.isdir(data_dir):\n",
+        "    os.mkdir(data_dir)\n",
+        "    \n",
+        "train_data_fname = data_dir + '/train_data.csv'\n",
+        "test_data_fname = data_dir + '/test_data.csv'\n",
+        "\n",
+        "data_train.to_csv(train_data_fname, index=False)\n",
+        "data_test.to_csv(test_data_fname, index=False)\n",
+        "\n",
+        "datastore = ws.get_default_datastore()\n",
+        "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir,\n",
+        "                    overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/train_data.csv')])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare AutoML run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook uses the blocked_models parameter to exclude some models that can take a longer time to train on some text datasets. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"experiment_timeout_minutes\": 20,\n",
+        "    \"primary_metric\": 'accuracy',\n",
+        "    \"max_concurrent_iterations\": num_nodes, \n",
+        "    \"max_cores_per_iteration\": -1,\n",
+        "    \"enable_dnn\": True,\n",
+        "    \"enable_early_stopping\": True,\n",
+        "    \"validation_size\": 0.3,\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "    \"enable_voting_ensemble\": False,\n",
+        "    \"enable_stack_ensemble\": False,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             compute_target=compute_target,\n",
+        "                             training_data=train_dataset,\n",
+        "                             label_column_name=target_column_name,\n",
+        "                             blocked_models = ['LightGBM', 'XGBoostClassifier'],\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Submit AutoML Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run = experiment.submit(automl_config, show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Displaying the run objects gives you links to the visual tools in the Azure Portal. Go try them!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model\n",
+        "Below we select the best model pipeline from our iterations, use it to test on test data on the same compute cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can test the model locally to get a feel of the input/output. When the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your MachineLearningNotebooks folder here:\n",
+        "MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl_env.yml"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = automl_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now see what text transformations are used to convert text data to features for this dataset, including deep learning transformations based on BiLSTM or Transformer (BERT is one implementation of a Transformer) models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "text_transformations_used = []\n",
+        "for column_group in fitted_model.named_steps['datatransformer'].get_featurization_summary():\n",
+        "    text_transformations_used.extend(column_group['Transformations'])\n",
+        "text_transformations_used"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Registering the best model\n",
+        "We now register the best fitted model from the AutoML Run for use in future deployments.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get results stats, extract the best model from AutoML run, download and register the resultant best model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "summary_df = get_result_df(automl_run)\n",
+        "best_dnn_run_id = summary_df['run_id'].iloc[0]\n",
+        "best_dnn_run = Run(experiment, best_dnn_run_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_dir = 'Model' # Local folder where the model will be stored temporarily\n",
+        "if not os.path.isdir(model_dir):\n",
+        "    os.mkdir(model_dir)\n",
+        "    \n",
+        "best_dnn_run.download_file('outputs/model.pkl', model_dir + '/model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the model in your Azure Machine Learning Workspace. If you previously registered a model, please make sure to delete it so as to replace it with this new model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Register the model\n",
+        "model_name = 'textDNN-20News'\n",
+        "model = Model.register(model_path = model_dir + '/model.pkl',\n",
+        "                       model_name = model_name,\n",
+        "                       tags=None,\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluate on Test Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now use the best fitted model from the AutoML Run to make predictions on the test set.  \n",
+        "\n",
+        "Test set schema should match that of the training set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/test_data.csv')])\n",
+        "\n",
+        "# preview the first 3 rows of the dataset\n",
+        "test_dataset.take(3).to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_experiment = Experiment(ws, experiment_name + \"_test\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_folder = os.path.join(os.getcwd(), 'inference')\n",
+        "os.makedirs(script_folder, exist_ok=True)\n",
+        "shutil.copy('infer.py', script_folder)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
+        "                         train_dataset, test_dataset, target_column_name, model_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Display computed metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(test_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pd.Series(test_run.get_metrics())"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "anshirga"
+      }
+    ],
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "None"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "None"
+    ],
+    "friendly_name": "DNN Text Featurization",
+    "index_order": 2,
+    "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": "3.6.7"
+    },
+    "tags": [
+      "None"
+    ],
+    "task": "Text featurization using DNNs for classification"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,56 @@
+import pandas as pd
+from azureml.core import Environment
+from azureml.train.estimator import Estimator
+from azureml.core.run import Run
+
+
+def run_inference(test_experiment, compute_target, script_folder, train_run,
+                  train_dataset, test_dataset, target_column_name, model_name):
+
+    inference_env = train_run.get_environment()
+
+    est = Estimator(source_directory=script_folder,
+                    entry_script='infer.py',
+                    script_params={
+                        '--target_column_name': target_column_name,
+                        '--model_name': model_name
+                    },
+                    inputs=[
+                        train_dataset.as_named_input('train_data'),
+                        test_dataset.as_named_input('test_data')
+                    ],
+                    compute_target=compute_target,
+                    environment_definition=inference_env)
+
+    run = test_experiment.submit(
+        est, tags={
+            'training_run_id': train_run.id,
+            'run_algorithm': train_run.properties['run_algorithm'],
+            'valid_score': train_run.properties['score'],
+            'primary_metric': train_run.properties['primary_metric']
+        })
+
+    run.log("run_algorithm", run.tags['run_algorithm'])
+    return run
+
+
+def get_result_df(remote_run):
+
+    children = list(remote_run.get_children(recursive=True))
+    summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
+                                     'primary_metric', 'Score'])
+    goal_minimize = False
+    for run in children:
+        if('run_algorithm' in run.properties and 'score' in run.properties):
+            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
+                                  run.properties['primary_metric'],
+                                  float(run.properties['score'])]
+            if('goal' in run.properties):
+                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
+
+    summary_df = summary_df.T.sort_values(
+        'Score',
+        ascending=goal_minimize).drop_duplicates(['run_algorithm'])
+    summary_df = summary_df.set_index('run_algorithm')
+
+    return summary_df

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

@@ -0,0 +1,60 @@
+import argparse
+
+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.model import Model
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    '--target_column_name', type=str, dest='target_column_name',
+    help='Target Column Name')
+parser.add_argument(
+    '--model_name', type=str, dest='model_name',
+    help='Name of registered model')
+
+args = parser.parse_args()
+target_column_name = args.target_column_name
+model_name = args.model_name
+
+print('args passed are: ')
+print('Target column name: ', target_column_name)
+print('Name of registered model: ', model_name)
+
+model_path = Model.get_model_path(model_name)
+# deserialize the model file back into a sklearn model
+model = joblib.load(model_path)
+
+run = Run.get_context()
+# get input dataset by name
+test_dataset = run.input_datasets['test_data']
+train_dataset = run.input_datasets['train_data']
+
+X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
+                        .to_pandas_dataframe()
+y_test_df = test_dataset.with_timestamp_columns(None) \
+                        .keep_columns(columns=[target_column_name]) \
+                        .to_pandas_dataframe()
+y_train_df = test_dataset.with_timestamp_columns(None) \
+                         .keep_columns(columns=[target_column_name]) \
+                         .to_pandas_dataframe()
+
+predicted = model.predict_proba(X_test_df)
+
+# Use the AutoML scoring module
+class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
+train_labels = model.classes_
+classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
+scores = scoring.score_classification(y_test_df.values, predicted,
+                                      classification_metrics,
+                                      class_labels, train_labels)
+
+print("scores:")
+print(scores)
+
+for key, value in scores.items():
+    run.log(key, value)

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

@@ -32,13 +32,6 @@
         "8. [Test Retraining](#Test-Retraining)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -88,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -150,7 +143,7 @@
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\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 on the default limits and how to request more quota."
+        "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."
       ]
     },
     {
@@ -190,7 +183,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE, RunConfiguration\n",
+        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -199,7 +192,6 @@
         "conda_run_config.target = compute_target\n",
         "\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
         "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n",
         "                              conda_packages=['numpy==1.16.2'], \n",

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

@@ -1,4 +0,0 @@
-name: auto-ml-continuous-retraining
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/automated-machine-learning/experimental/README.md

@@ -17,16 +17,16 @@ There's no need to install mini-conda specifically.
 - Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
 
 ### 3. Setup a new conda environment
-The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
+The **automl_setup_thin_client** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl_experimental.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
 
 Packages installed by the **automl_setup** script:
     <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>pandas</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
 
-For more details refer to the [automl_env.yml](./automl_env.yml)
+For more details refer to the [automl_env_thin_client.yml](./automl_env_thin_client.yml)
 ## Windows
 Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
 ```
-automl_setup
+automl_setup_thin_client
 ```
 ## Mac
 Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
@@ -34,14 +34,14 @@ Install "Command line developer tools" if it is not already installed (you can u
 Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
 
 ```
-bash automl_setup_mac.sh
+bash automl_setup_thin_client_mac.sh
 ```
 
 ## Linux
 cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
 
 ```
-bash automl_setup_linux.sh
+bash automl_setup_thin_client_linux.sh
 ```
 
 ### 4. Running configuration.ipynb
@@ -49,7 +49,7 @@ bash automl_setup_linux.sh
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 
 ### 5. Running Samples
-- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
+- Please make sure you use the Python [conda env:azure_automl_experimental] kernel when trying the sample Notebooks.
 - Follow the instructions in the individual notebooks to explore various features in automated ML.
 
 ### 6. Starting jupyter notebook manually
@@ -71,7 +71,7 @@ jupyter notebook
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 
-- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
+- [auto-ml-regression-model-proxy.ipynb](regression-model-proxy/auto-ml-regression-model-proxy.ipynb)
     - Dataset: Hardware Performance Dataset
     - Simple example of using automated ML for regression
     - Uses azure compute for training

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

@@ -5,16 +5,13 @@ dependencies:
 - pip<=19.3.1
 - python>=3.5.2,<3.8
 - nb_conda
-- matplotlib==2.1.0
-- numpy~=1.18.0
 - cython
 - urllib3<1.24
-- scikit-learn==0.22.1
-- pandas==0.25.1
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
   - azureml-sdk
   - azureml-widgets
-  - azureml-explain-model
+  - pandas
+  - PyJWT < 2.0.0

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

@@ -6,16 +6,13 @@ dependencies:
 - nomkl
 - python>=3.5.2,<3.8
 - nb_conda
-- matplotlib==2.1.0
-- numpy~=1.18.0
 - cython
 - urllib3<1.24
-- scikit-learn==0.22.1
-- pandas==0.25.1
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
   - azureml-sdk
   - azureml-widgets
-  - azureml-explain-model
+  - pandas
+  - PyJWT < 2.0.0

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

@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/regression/auto-ml-regression.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.png)"
       ]
     },
     {
@@ -38,7 +38,7 @@
       "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",
+        "In this example we use an experimental feature, Model Proxy, to do a predict on the best generated model without downloading the model locally. The prediction will happen on same compute and environment that was used to train the model. This feature is currently in the experimental state, which means that the API is prone to changing, please make sure to run on the latest version of this notebook if you face any issues.\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",
@@ -67,10 +67,8 @@
       "source": [
         "import logging\n",
         "\n",
-        "from matplotlib import pyplot as plt\n",
+        "import json\n",
-        "import numpy as np\n",
+        "\n",
-        "import pandas as pd\n",
-        " \n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
@@ -92,7 +90,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -115,9 +113,7 @@
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
         "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
+        "output"
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
       ]
     },
     {
@@ -138,7 +134,8 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"reg-cluster\"\n",
+        "# Try to ensure that the cluster name is unique across the notebooks\n",
+        "cpu_cluster_name = \"reg-model-proxy\"\n",
         "\n",
         "# Verify that cluster does not exist already\n",
         "try:\n",
@@ -197,6 +194,7 @@
         "|**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.|\n",
+        "|**scenario**|We need to set this parameter to 'Latest' to enable some experimental features. This parameter should not be set outside of this experimental notebook.|\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)"
       ]
@@ -225,6 +223,7 @@
         "                             compute_target = compute_target,\n",
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
+        "                             scenario='Latest',\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -272,34 +271,13 @@
         "## 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",
+        "remote_run.wait_for_completion(show_output=True)"
-        "RunDetails(remote_run).show() "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run.wait_for_completion()"
       ]
     },
     {
@@ -321,6 +299,24 @@
         "print(best_run)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Show hyperparameters\n",
+        "Show the model pipeline used for the best run with its hyperparameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run_properties = json.loads(best_run.get_details()['properties']['pipeline_script'])\n",
+        "print(json.dumps(run_properties, indent = 1)) "
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -346,18 +342,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# preview the first 3 rows of the dataset\n",
+        "y_test = test_data.keep_columns('ERP')\n",
-        "\n",
+        "test_data = test_data.drop_columns('ERP')\n",
-        "test_data = test_data.to_pandas_dataframe()\n",
-        "y_test = test_data['ERP'].fillna(0)\n",
-        "test_data = test_data.drop('ERP', 1)\n",
-        "test_data = test_data.fillna(0)\n",
         "\n",
         "\n",
-        "train_data = train_data.to_pandas_dataframe()\n",
+        "y_train = train_data.keep_columns('ERP')\n",
-        "y_train = train_data['ERP'].fillna(0)\n",
+        "train_data = train_data.drop_columns('ERP')\n"
-        "train_data = train_data.drop('ERP', 1)\n",
-        "train_data = train_data.fillna(0)\n"
       ]
     },
     {
@@ -375,7 +365,16 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl.model_proxy import ModelProxy\n",
-        "best_model_proxy = ModelProxy(best_run)"
+        "best_model_proxy = ModelProxy(best_run)\n",
+        "y_pred_train = best_model_proxy.predict(train_data)\n",
+        "y_pred_test = best_model_proxy.predict(test_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Exploring results"
       ]
     },
     {
@@ -384,60 +383,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_pred_train = best_model_proxy.predict(train_data).to_pandas_dataframe()\n",
+        "y_pred_train = y_pred_train.to_pandas_dataframe().values.flatten()\n",
+        "y_train = y_train.to_pandas_dataframe().values.flatten()\n",
         "y_residual_train = y_train - y_pred_train\n",
         "\n",
-        "y_pred_test = best_model_proxy.predict(test_data).to_pandas_dataframe()\n",
+        "y_pred_test = y_pred_test.to_pandas_dataframe().values.flatten()\n",
-        "y_residual_test = y_test - y_pred_test"
+        "y_test = y_test.to_pandas_dataframe().values.flatten()\n",
-      ]
+        "y_residual_test = y_test - y_pred_test\n",
-    },
+        "print(y_residual_train)\n",
-    {
+        "print(y_residual_test)"
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%matplotlib inline\n",
-        "from sklearn.metrics import mean_squared_error, r2_score\n",
-        "\n",
-        "# Set up a multi-plot chart.\n",
-        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
-        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
-        "f.set_figheight(6)\n",
-        "f.set_figwidth(16)\n",
-        "\n",
-        "# Plot residual values of training set.\n",
-        "a0.axis([0, 360, -100, 100])\n",
-        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
-        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
-        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
-        "a0.set_xlabel('Training samples', fontsize = 12)\n",
-        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
-        "\n",
-        "# Plot residual values of test set.\n",
-        "a1.axis([0, 90, -100, 100])\n",
-        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
-        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
-        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
-        "a1.set_xlabel('Test samples', fontsize = 12)\n",
-        "a1.set_yticklabels([])\n",
-        "\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%matplotlib inline\n",
-        "test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
-        "plt.show()"
       ]
     },
     {
@@ -451,7 +405,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "rakellam"
+        "name": "sekrupa"
       }
     ],
     "categories": [

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

@@ -1,4 +0,0 @@
-name: auto-ml-regression-model-proxy
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -54,9 +54,8 @@
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)\n",
-        "\n",
         "Notebook synopsis:\n",
+        "\n",
         "1. Creating an Experiment in an existing Workspace\n",
         "2. Configuration and remote run of AutoML for a time-series model exploring Regression learners, Arima, Prophet and DNNs\n",
         "4. Evaluating the fitted model using a rolling test "
@@ -114,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -219,6 +218,8 @@
         "\n",
         "**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
         "\n",
+        "**Forecast frequency (freq)** 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.\n",
+        "\n",
         "This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
       ]
     },
@@ -350,9 +351,7 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**enable_dnn**|Enable Forecasting DNNs|\n",
+        "|**enable_dnn**|Enable Forecasting DNNs|\n"
-        "\n",
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)."
       ]
     },
     {
@@ -650,7 +649,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "omkarm"
+        "name": "jialiu"
       }
     ],
     "hide_code_all_hidden": false,

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

@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-beer-remote
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -3,11 +3,11 @@ from azureml.core import Environment
 from azureml.core.conda_dependencies import CondaDependencies
 from azureml.train.estimator import Estimator
 from azureml.core.run import Run
+from azureml.automl.core.shared import constants
 
 
 def split_fraction_by_grain(df, fraction, time_column_name,
                             grain_column_names=None):
-
     if not grain_column_names:
         df['tmp_grain_column'] = 'grain'
         grain_column_names = ['tmp_grain_column']
@@ -17,10 +17,10 @@ def split_fraction_by_grain(df, fraction, time_column_name,
                   .groupby(grain_column_names, group_keys=False))
 
     df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-int(len(dfg) *
-                               fraction)] if fraction > 0 else dfg)
+                                                          fraction)] if fraction > 0 else dfg)
 
     df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-int(len(dfg) *
-                               fraction):] if fraction > 0 else dfg[:0])
+                                                         fraction):] if fraction > 0 else dfg[:0])
 
     if 'tmp_grain_column' in grain_column_names:
         for df2 in (df, df_head, df_tail):
@@ -59,11 +59,13 @@ def get_result_df(remote_run):
                                      'primary_metric', 'Score'])
     goal_minimize = False
     for run in children:
-        if('run_algorithm' in run.properties and 'score' in run.properties):
+        if run.get_status().lower() == constants.RunState.COMPLETE_RUN \
+                and 'run_algorithm' in run.properties and 'score' in run.properties:
+            # We only count in the completed child runs.
             summary_df[run.id] = [run.id, run.properties['run_algorithm'],
                                   run.properties['primary_metric'],
                                   float(run.properties['score'])]
-            if('goal' in run.properties):
+            if ('goal' in run.properties):
                 goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
 
     summary_df = summary_df.T.sort_values(
@@ -118,7 +120,6 @@ def run_multiple_inferences(summary_df, train_experiment, test_experiment,
                             compute_target, script_folder, test_dataset,
                             lookback_dataset, max_horizon, target_column_name,
                             time_column_name, freq):
-
     for run_name, run_summary in summary_df.iterrows():
         print(run_name)
         print(run_summary)

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

@@ -1,4 +1,5 @@
 import argparse
+import os
 
 import numpy as np
 import pandas as pd
@@ -10,6 +11,13 @@ from sklearn.metrics import mean_absolute_error, mean_squared_error
 from azureml.automl.runtime.shared.score import scoring, constants
 from azureml.core import Run
 
+try:
+    import torch
+
+    _torch_present = True
+except ImportError:
+    _torch_present = False
+
 
 def align_outputs(y_predicted, X_trans, X_test, y_test,
                   predicted_column_name='predicted',
@@ -48,7 +56,7 @@ def align_outputs(y_predicted, X_trans, X_test, y_test,
     # or at edges of time due to lags/rolling windows
     clean = together[together[[target_column_name,
                                predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
+    return (clean)
 
 
 def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
@@ -83,8 +91,7 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         if origin_time != X[time_column_name].min():
             # Set the context by including actuals up-to the origin time
             test_context_expand_wind = (X[time_column_name] < origin_time)
-            context_expand_wind = (
+            context_expand_wind = (X_test_expand[time_column_name] < origin_time)
-                X_test_expand[time_column_name] < origin_time)
             y_query_expand[context_expand_wind] = y[test_context_expand_wind]
 
         # Print some debug info
@@ -115,8 +122,7 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         # Align forecast with test set for dates within
         # the current rolling window
         trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)
-            trans_tindex < horizon_time)
         test_roll_wind = expand_wind & (X[time_column_name] >= origin_time)
         df_list.append(align_outputs(
             y_fcst[trans_roll_wind], X_trans[trans_roll_wind],
@@ -155,8 +161,7 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         if origin_time != X_test[time_column_name].min():
             # Set the context by including actuals up-to the origin time
             test_context_expand_wind = (X_test[time_column_name] < origin_time)
-            context_expand_wind = (
+            context_expand_wind = (X_test_expand[time_column_name] < origin_time)
-                X_test_expand[time_column_name] < origin_time)
             y_query_expand[context_expand_wind] = y_test[
                 test_context_expand_wind]
 
@@ -186,10 +191,8 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         # Align forecast with test set for dates within the
         # current rolling window
         trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)
-            trans_tindex < horizon_time)
+        test_roll_wind = expand_wind & (X_test[time_column_name] >= origin_time)
-        test_roll_wind = expand_wind & (
-            X_test[time_column_name] >= origin_time)
         df_list.append(align_outputs(y_fcst[trans_roll_wind],
                                      X_trans[trans_roll_wind],
                                      X_test[test_roll_wind],
@@ -221,6 +224,10 @@ def MAPE(actual, pred):
     return np.mean(APE(actual_safe, pred_safe))
 
 
+def map_location_cuda(storage, loc):
+    return storage.cuda()
+
+
 parser = argparse.ArgumentParser()
 parser.add_argument(
     '--max_horizon', type=int, dest='max_horizon',
@@ -238,7 +245,6 @@ parser.add_argument(
     '--model_path', type=str, dest='model_path',
     default='model.pkl', help='Filename of model to be loaded')
 
-
 args = parser.parse_args()
 max_horizon = args.max_horizon
 target_column_name = args.target_column_name
@@ -246,7 +252,6 @@ time_column_name = args.time_column_name
 freq = args.freq
 model_path = args.model_path
 
-
 print('args passed are: ')
 print(max_horizon)
 print(target_column_name)
@@ -274,8 +279,19 @@ X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name])
 y_lookback_df = lookback_dataset.with_timestamp_columns(
     None).keep_columns(columns=[target_column_name])
 
-fitted_model = joblib.load(model_path)
+_, ext = os.path.splitext(model_path)
-
+if ext == '.pt':
+    # Load the fc-tcn torch model.
+    assert _torch_present
+    if torch.cuda.is_available():
+        map_location = map_location_cuda
+    else:
+        map_location = 'cpu'
+    with open(model_path, 'rb') as fh:
+        fitted_model = torch.load(fh, map_location=map_location)
+else:
+    # Load the sklearn pipeline.
+    fitted_model = joblib.load(model_path)
 
 if hasattr(fitted_model, 'get_lookback'):
     lookback = fitted_model.get_lookback()

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

@@ -87,7 +87,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -131,7 +131,7 @@
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\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 on the default limits and how to request more quota."
+        "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."
       ]
     },
     {
@@ -205,6 +205,10 @@
       "outputs": [],
       "source": [
         "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'dataset/bike-no.csv')]).with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
+        "\n",
+        "# Drop the columns 'casual' and 'registered' as these columns are a breakdown of the total and therefore a leak.\n",
+        "dataset = dataset.drop_columns(columns=['casual', 'registered'])\n",
+        "\n",
         "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
     },
@@ -251,7 +255,7 @@
         "|**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",
         "|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
         "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
-        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|"
+        "|**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."
       ]
     },
     {
@@ -314,8 +318,7 @@
         "    time_column_name=time_column_name,\n",
         "    forecast_horizon=forecast_horizon,\n",
         "    country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
-        "    target_lags='auto', # use heuristic based lag setting    \n",
+        "    target_lags='auto' # use heuristic based lag setting    \n",
-        "    drop_column_names=['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -548,6 +551,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
+        "For more details on what metrics are included and how they are calculated, please refer to [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics). You could also calculate residuals, like described [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
+        "\n",
+        "\n",
         "Since we did a rolling evaluation on the test set, we can analyze the predictions by their forecast horizon relative to the rolling origin. The model was initially trained at a forecast horizon of 14, so each prediction from the model is associated with a horizon value from 1 to 14. The horizon values are in a column named, \"horizon_origin,\" in the prediction set. For example, we can calculate some of the error metrics grouped by the horizon:"
       ]
     },
@@ -594,7 +600,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "category": "tutorial",

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

@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-bike-share
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py

@@ -1,22 +1,24 @@
 import argparse
-import azureml.train.automl
+from azureml.core import Dataset, Run
-from azureml.core import Run
 from sklearn.externals import joblib
 
-
 parser = argparse.ArgumentParser()
 parser.add_argument(
     '--target_column_name', type=str, dest='target_column_name',
     help='Target Column Name')
+parser.add_argument(
+    '--test_dataset', type=str, dest='test_dataset',
+    help='Test Dataset')
 
 args = parser.parse_args()
 target_column_name = args.target_column_name
+test_dataset_id = args.test_dataset
 
 run = Run.get_context()
-# get input dataset by name
+ws = run.experiment.workspace
-test_dataset = run.input_datasets['test_data']
 
-df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
+# get the input dataset by id
+test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
 y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py

@@ -1,37 +1,32 @@
-from azureml.core import Environment
+from azureml.core import ScriptRunConfig
-from azureml.core.conda_dependencies import CondaDependencies
-from azureml.train.estimator import Estimator
-from azureml.core.run import Run
 
 
-def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
+def run_rolling_forecast(test_experiment, compute_target, train_run,
-                         target_column_name, inference_folder='./forecast'):
+                         test_dataset, target_column_name,
-    condafile = inference_folder + '/condafile.yml'
+                         inference_folder='./forecast'):
     train_run.download_file('outputs/model.pkl',
                             inference_folder + '/model.pkl')
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml', condafile)
 
-    inference_env = Environment("myenv")
+    inference_env = train_run.get_environment()
-    inference_env.docker.enabled = True
-    inference_env.python.conda_dependencies = CondaDependencies(
-        conda_dependencies_file_path=condafile)
 
-    est = Estimator(source_directory=inference_folder,
+    config = ScriptRunConfig(source_directory=inference_folder,
-                    entry_script='forecasting_script.py',
+                             script='forecasting_script.py',
-                    script_params={
+                             arguments=['--target_column_name',
-                        '--target_column_name': target_column_name
+                                        target_column_name,
-                    },
+                                        '--test_dataset',
-                    inputs=[test_dataset.as_named_input('test_data')],
+                                        test_dataset.as_named_input(test_dataset.name)],
-                    compute_target=compute_target,
+                             compute_target=compute_target,
-                    environment_definition=inference_env)
+                             environment=inference_env)
 
-    run = test_experiment.submit(est,
+    run = test_experiment.submit(config,
-                                 tags={
+                                 tags={'training_run_id':
-                                     'training_run_id': train_run.id,
+                                       train_run.id,
-                                     'run_algorithm': train_run.properties['run_algorithm'],
+                                       'run_algorithm':
-                                     'valid_score': train_run.properties['score'],
+                                       train_run.properties['run_algorithm'],
-                                     'primary_metric': train_run.properties['primary_metric']
+                                       'valid_score':
-                                 })
+                                       train_run.properties['score'],
+                                       'primary_metric':
+                                       train_run.properties['primary_metric']})
 
     run.log("run_algorithm", run.tags['run_algorithm'])
     return run

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

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -301,7 +301,8 @@
         "|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).|"
+        "|**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",
+        "|**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."
       ]
     },
     {
@@ -497,7 +498,7 @@
       "metadata": {},
       "source": [
         "### Evaluate\n",
-        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE).\n",
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
         "\n",
         "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
       ]
@@ -703,7 +704,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "categories": [

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

@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-energy-demand
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -24,7 +24,7 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "This notebook demonstrates the full interface to the `forecast()` function. \n",
+        "This notebook demonstrates the full interface of the `forecast()` function. \n",
         "\n",
         "The best known and most frequent usage of `forecast` enables forecasting on test sets that immediately follows training data. \n",
         "\n",
@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -302,7 +302,8 @@
         "* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
         "* Set limitations on the length of experiment run to 15 minutes.\n",
         "* Finally, we set the task to be forecasting.\n",
-        "* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones."
+        "* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones.\n",
+        "* [Optional] Forecast frequency parameter (freq) 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."
       ]
     },
     {
@@ -809,7 +810,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "category": "tutorial",

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

@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-function
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -82,7 +82,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -126,7 +126,7 @@
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\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 on the default limits and how to request more quota."
+        "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."
       ]
     },
     {
@@ -169,6 +169,10 @@
       "source": [
         "time_column_name = 'WeekStarting'\n",
         "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
+        "\n",
+        "# Drop the columns 'logQuantity' as it is a leaky feature.\n",
+        "data.drop('logQuantity', axis=1, inplace=True)\n",
+        "\n",
         "data.head()"
       ]
     },
@@ -325,12 +329,11 @@
       "source": [
         "## Customization\n",
         "\n",
-        "The featurization customization in forecasting is an advanced feature in AutoML which allows our customers to change the default forecasting featurization behaviors and column types through `FeaturizationConfig`. The supported scenarios include,\n",
+        "The featurization customization in forecasting is an advanced feature in AutoML which allows our customers to change the default forecasting featurization behaviors and column types through `FeaturizationConfig`. The supported scenarios include:\n",
+        "\n",
         "1. Column purposes update: Override feature type for the specified column. Currently supports DateTime, Categorical and Numeric. This customization can be used in the scenario that the type of the column cannot correctly reflect its purpose. Some numerical columns, for instance, can be treated as Categorical columns which need to be converted to categorical while some can be treated as epoch timestamp which need to be converted to datetime. To tell our SDK to correctly preprocess these columns, a configuration need to be add with the columns and their desired types.\n",
         "2. Transformer parameters update: Currently supports parameter change for Imputer only. User can customize imputation methods. The supported imputing methods for target column are constant and ffill (forward fill). The supported imputing methods for feature columns are mean, median, most frequent, constant and ffill (forward fill). This customization can be used for the scenario that our customers know which imputation methods fit best to the input data. For instance, some datasets use NaN to represent 0 which the correct behavior should impute all the missing value with 0. To achieve this behavior, these columns need to be configured as constant imputation with `fill_value` 0.\n",
-        "3. Drop columns: Columns to drop from being featurized. These usually are the columns which are leaky or the columns contain no useful data.\n",
+        "3. Drop columns: Columns to drop from being featurized. These usually are the columns which are leaky or the columns contain no useful data."
-        "\n",
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)"
       ]
     },
     {
@@ -344,7 +347,6 @@
       "outputs": [],
       "source": [
         "featurization_config = FeaturizationConfig()\n",
-        "featurization_config.drop_columns = ['logQuantity']  # 'logQuantity' is a leaky feature, so we remove it.\n",
         "# Force the CPWVOL5 feature to be numeric type.\n",
         "featurization_config.add_column_purpose('CPWVOL5', 'Numeric')\n",
         "# Fill missing values in the target column, Quantity, with zeros.\n",
@@ -367,7 +369,8 @@
         "|-|-|\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.|"
+        "|**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."
       ]
     },
     {
@@ -383,7 +386,7 @@
         "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the forecast horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning application that estimates the next month of sales should set the horizon according to suitable planning time-scales. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
         "\n",
         "We note here that AutoML can sweep over two types of time-series models:\n",
-        "* Models that are trained for each series such as ARIMA and Facebook's Prophet. Note that these models are only available for [Enterprise Edition Workspaces](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace#upgrade).\n",
+        "* Models that are trained for each series such as ARIMA and Facebook's Prophet.\n",
         "* Models trained across multiple time-series using a regression approach.\n",
         "\n",
         "In the first case, AutoML loops over all time-series in your dataset and trains one model (e.g. AutoArima or Prophet, as the case may be) for each series. This can result in long runtimes to train these models if there are a lot of series in the data. One way to mitigate this problem is to fit models for different series in parallel if you have multiple compute cores available. To enable this behavior, set the `max_cores_per_iteration` parameter in your AutoMLConfig as shown in the example in the next cell. \n",
@@ -572,7 +575,7 @@
       "source": [
         "# Evaluate\n",
         "\n",
-        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
         "\n",
         "We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics."
       ]
@@ -764,7 +767,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "category": "tutorial",

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

@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-orange-juice-sales
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -80,7 +80,7 @@
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.interpret._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret import ExplanationClient"
       ]
     },
     {
@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -359,7 +359,7 @@
         "Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance based on your test data.\n",
         "\n",
         "### Run the explanation\n",
-        "#### Download engineered feature importance from artifact store\n",
+        "#### Download the engineered feature importance from artifact store\n",
         "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
       ]
     },
@@ -375,6 +375,25 @@
         "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download the raw feature importance from artifact store\n",
+        "You can use ExplanationClient to download the raw feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the raw features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "raw_explanations = client.download_model_explanation(raw=True)\n",
+        "print(raw_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -474,6 +493,29 @@
         "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
+        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the original features in your data. You can also use azure portal url to view the dash board visualization of the feature importance values of the original/raw features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Compute the raw explanations\n",
+        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True,\n",
+        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
+        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform,\n",
+        "                                     raw_eval_dataset=automl_explainer_setup_obj.X_test_raw)\n",
+        "print(raw_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -589,10 +631,13 @@
         "    automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,\n",
         "                                                                 X_test=data, task='classification')\n",
         "    # Retrieve model explanations for engineered explanations\n",
-        "    engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)    \n",
+        "    engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)\n",
+        "    # Retrieve model explanations for raw explanations\n",
+        "    raw_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform, get_raw=True)\n",
         "    # You can return any data type as long as it is JSON-serializable\n",
         "    return {'predictions': predictions.tolist(),\n",
-        "            'engineered_local_importance_values': engineered_local_importance_values}\n"
+        "            'engineered_local_importance_values': engineered_local_importance_values,\n",
+        "            'raw_local_importance_values': raw_local_importance_values}\n"
       ]
     },
     {
@@ -725,7 +770,9 @@
         "# Print the predicted value\n",
         "print('predictions:\\n{}\\n'.format(output['predictions']))\n",
         "# Print the engineered feature importances for the predicted value\n",
-        "print('engineered_local_importance_values:\\n{}\\n'.format(output['engineered_local_importance_values']))"
+        "print('engineered_local_importance_values:\\n{}\\n'.format(output['engineered_local_importance_values']))\n",
+        "# Print the raw feature importances for the predicted value\n",
+        "print('raw_local_importance_values:\\n{}\\n'.format(output['raw_local_importance_values']))\n"
       ]
     },
     {
@@ -773,7 +820,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "anumamah"
+        "name": "ratanase"
       }
     ],
     "category": "tutorial",

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

@@ -1,4 +0,0 @@
-name: auto-ml-classification-credit-card-fraud-local
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -42,8 +42,6 @@
         "\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",
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade) \n",
-        "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
         "2. Instantiating AutoMLConfig with FeaturizationConfig for customization\n",
@@ -98,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -223,9 +221,8 @@
       "source": [
         "## Customization\n",
         "\n",
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade). \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",
@@ -447,7 +444,6 @@
       "metadata": {},
       "source": [
         "## Explanations\n",
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade). \n",
         "This section will walk you through the workflow to compute model explanations for an AutoML model on your remote compute.\n",
         "\n",
         "### Retrieve any AutoML Model for explanations\n",
@@ -625,7 +621,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.interpret._internal.explanation_client import ExplanationClient\n",
+        "from azureml.interpret import ExplanationClient\n",
         "client = ExplanationClient.from_run(automl_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
@@ -655,7 +651,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Operationailze\n",
+        "## Operationalize\n",
         "In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n",
         "\n",
         "### Register the AutoML model and the scoring explainer\n",
@@ -905,7 +901,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "anumamah"
+        "name": "anshirga"
       }
     ],
     "categories": [

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

@@ -1,4 +0,0 @@
-name: auto-ml-regression-explanation-featurization
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py

@@ -4,7 +4,7 @@ import os
 import joblib
 
 from interpret.ext.glassbox import LGBMExplainableModel
-from automl.client.core.common.constants import MODEL_PATH
+from azureml.automl.core.shared.constants import MODEL_PATH
 from azureml.core.experiment import Experiment
 from azureml.core.dataset import Dataset
 from azureml.core.run import Run
@@ -66,7 +66,8 @@ engineered_explanations = explainer.explain(['local', 'global'], tag='engineered
 # Compute the raw explanations
 raw_explanations = explainer.explain(['local', 'global'], get_raw=True, tag='raw explanations',
                                      raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
-                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)
+                                     eval_dataset=automl_explainer_setup_obj.X_test_transform,
+                                     raw_eval_dataset=automl_explainer_setup_obj.X_test_raw)
 
 print("Engineered and raw explanations computed successfully")
 

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

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -375,18 +375,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# preview the first 3 rows of the dataset\n",
+        "y_test = test_data.keep_columns('ERP').to_pandas_dataframe()\n",
-        "\n",
+        "test_data = test_data.drop_columns('ERP').to_pandas_dataframe()\n",
-        "test_data = test_data.to_pandas_dataframe()\n",
-        "y_test = test_data['ERP'].fillna(0)\n",
-        "test_data = test_data.drop('ERP', 1)\n",
-        "test_data = test_data.fillna(0)\n",
         "\n",
         "\n",
-        "train_data = train_data.to_pandas_dataframe()\n",
+        "y_train = train_data.keep_columns('ERP').to_pandas_dataframe()\n",
-        "y_train = train_data['ERP'].fillna(0)\n",
+        "train_data = train_data.drop_columns('ERP').to_pandas_dataframe()\n"
-        "train_data = train_data.drop('ERP', 1)\n",
-        "train_data = train_data.fillna(0)\n"
       ]
     },
     {
@@ -396,10 +390,10 @@
       "outputs": [],
       "source": [
         "y_pred_train = fitted_model.predict(train_data)\n",
-        "y_residual_train = y_train - y_pred_train\n",
+        "y_residual_train = y_train.values - y_pred_train\n",
         "\n",
         "y_pred_test = fitted_model.predict(test_data)\n",
-        "y_residual_test = y_test - y_pred_test"
+        "y_residual_test = y_test.values - y_pred_test"
       ]
     },
     {
@@ -462,7 +456,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "rakellam"
+        "name": "ratanase"
       }
     ],
     "categories": [

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

@@ -1,4 +0,0 @@
-name: auto-ml-regression
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/azure-databricks/README.md

@@ -1,33 +0,0 @@
-Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
-
-In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks. 
-
-- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning. 
-- You can keep the data within the same cluster. 
-- You can leverage the local worker nodes with autoscale and auto termination capabilities. 
-- You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
-- You can further tune the model generated by automated machine learning if you chose to. 
-- Every run (including the best run) is available as a pipeline, which you can tune further if needed. 
-- The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
-
-Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
-
-**Single file** - 
-The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
-
-Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. 
-
-Notebook 6 is an Automated ML sample notebook for Classification.
-
-Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
-
-**Databricks as a Compute Target from AML Pipelines**
-You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb). 
-
-For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
-
-**Please let us know your feedback.**
-
- 
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/README.png) 

how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb

@@ -1,373 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Model Building"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import pprint\n",
-        "import numpy as np\n",
-        "\n",
-        "from pyspark.ml import Pipeline, PipelineModel\n",
-        "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
-        "from pyspark.ml.classification import LogisticRegression\n",
-        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
-        "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Set auth to be used by workspace related APIs.\n",
-        "# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
-        "# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
-        "auth = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# import the Workspace class and check the azureml SDK version\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#get the train and test datasets\n",
-        "train_data_path = \"AdultCensusIncomeTrain\"\n",
-        "test_data_path = \"AdultCensusIncomeTest\"\n",
-        "\n",
-        "train = spark.read.parquet(train_data_path)\n",
-        "test = spark.read.parquet(test_data_path)\n",
-        "\n",
-        "print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
-        "print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
-        "\n",
-        "train.printSchema()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Define Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "label = \"income\"\n",
-        "dtypes = dict(train.dtypes)\n",
-        "dtypes.pop(label)\n",
-        "\n",
-        "si_xvars = []\n",
-        "ohe_xvars = []\n",
-        "featureCols = []\n",
-        "for idx,key in enumerate(dtypes):\n",
-        "    if dtypes[key] == \"string\":\n",
-        "        featureCol = \"-\".join([key, \"encoded\"])\n",
-        "        featureCols.append(featureCol)\n",
-        "        \n",
-        "        tmpCol = \"-\".join([key, \"tmp\"])\n",
-        "        # string-index and one-hot encode the string column\n",
-        "        #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
-        "        #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
-        "        #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
-        "        #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
-        "        si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
-        "        ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
-        "    else:\n",
-        "        featureCols.append(key)\n",
-        "\n",
-        "# string-index the label column into a column named \"label\"\n",
-        "si_label = StringIndexer(inputCol=label, outputCol='label')\n",
-        "\n",
-        "# assemble the encoded feature columns in to a column named \"features\"\n",
-        "assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.run import Run\n",
-        "from azureml.core.experiment import Experiment\n",
-        "import numpy as np\n",
-        "import os\n",
-        "import shutil\n",
-        "\n",
-        "model_name = \"AdultCensus_runHistory.mml\"\n",
-        "model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
-        "run_history_name = 'spark-ml-notebook'\n",
-        "\n",
-        "# start a training run by defining an experiment\n",
-        "myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
-        "root_run = myexperiment.start_logging()\n",
-        "\n",
-        "# Regularization Rates - \n",
-        "regs = [0.0001, 0.001, 0.01, 0.1]\n",
-        " \n",
-        "# try a bunch of regularization rate in a Logistic Regression model\n",
-        "for reg in regs:\n",
-        "    print(\"Regularization rate: {}\".format(reg))\n",
-        "    # create a bunch of child runs\n",
-        "    with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
-        "        # create a new Logistic Regression model.\n",
-        "        lr = LogisticRegression(regParam=reg)\n",
-        "        \n",
-        "        # put together the pipeline\n",
-        "        pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
-        "\n",
-        "        # train the model\n",
-        "        model_p = pipe.fit(train)\n",
-        "        \n",
-        "        # make prediction\n",
-        "        pred = model_p.transform(test)\n",
-        "        \n",
-        "        # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
-        "        bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
-        "        au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
-        "        au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
-        "\n",
-        "        print(\"Area under ROC: {}\".format(au_roc))\n",
-        "        print(\"Area Under PR: {}\".format(au_prc))\n",
-        "      \n",
-        "        # log reg, au_roc, au_prc and feature names in run history\n",
-        "        run.log(\"reg\", reg)\n",
-        "        run.log(\"au_roc\", au_roc)\n",
-        "        run.log(\"au_prc\", au_prc)\n",
-        "        run.log_list(\"columns\", train.columns)\n",
-        "\n",
-        "        # save model\n",
-        "        model_p.write().overwrite().save(model_name)\n",
-        "        \n",
-        "        # upload the serialized model into run history record\n",
-        "        mdl, ext = model_name.split(\".\")\n",
-        "        model_zip = mdl + \".zip\"\n",
-        "        shutil.make_archive(mdl, 'zip', model_dbfs)\n",
-        "        run.upload_file(\"outputs/\" + model_name, model_zip)        \n",
-        "        #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
-        "\n",
-        "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
-        "        shutil.rmtree(model_dbfs)\n",
-        "        os.remove(model_zip)\n",
-        "        \n",
-        "# Declare run completed\n",
-        "root_run.complete()\n",
-        "root_run_id = root_run.id\n",
-        "print (\"run id:\", root_run.id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metrics = root_run.get_metrics(recursive=True)\n",
-        "best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
-        "print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Get the best run\n",
-        "child_runs = {}\n",
-        "\n",
-        "for r in root_run.get_children():\n",
-        "    child_runs[r.id] = r\n",
-        "   \n",
-        "best_run = child_runs[best_run_id]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Download the model from the best run to a local folder\n",
-        "best_model_file_name = \"best_model.zip\"\n",
-        "best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Model Evaluation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "##unzip the model to dbfs (as load() seems to require that) and load it.\n",
-        "if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
-        "    shutil.rmtree(model_dbfs)\n",
-        "shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
-        "\n",
-        "model_p_best = PipelineModel.load(model_name)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# make prediction\n",
-        "pred = model_p_best.transform(test)\n",
-        "output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
-        "display(output.limit(5))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
-        "bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
-        "au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
-        "au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
-        "\n",
-        "print(\"Area under ROC: {}\".format(au_roc))\n",
-        "print(\"Area Under PR: {}\".format(au_prc))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Model Persistence"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
-        "model_p_best.write().overwrite().save(model_name)\n",
-        "print(\"saved model to {}\".format(model_dbfs))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%sh\n",
-        "\n",
-        "ls -la /dbfs/AdultCensus_runHistory.mml/*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dbutils.notebook.exit(\"success\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "pasha"
-      }
-    ],
-    "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": "3.6.6"
-    },
-    "name": "build-model-run-history-03",
-    "notebookId": 3836944406456339
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb

@@ -1,320 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Register Azure Databricks trained model and deploy it to ACI\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please ensure you have run all previous notebooks in sequence before running this.\n",
-        "\n",
-        "Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Set auth to be used by workspace related APIs.\n",
-        "# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
-        "# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
-        "auth = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "##NOTE: service deployment always gets the model from the current working dir.\n",
-        "import os\n",
-        "\n",
-        "model_name = \"AdultCensus_runHistory.mml\" # \n",
-        "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
-        "\n",
-        "print(\"copy model from dbfs to local\")\n",
-        "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
-        "dbutils.fs.cp(model_name, model_local, True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Register the model\n",
-        "from azureml.core.model import Model\n",
-        "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
-        "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
-        "                       description = \"ADB trained model by Parashar\",\n",
-        "                       workspace = ws)\n",
-        "\n",
-        "print(mymodel.name, mymodel.description, mymodel.version)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#%%writefile score_sparkml.py\n",
-        "score_sparkml = \"\"\"\n",
-        " \n",
-        "import json\n",
-        " \n",
-        "def init():\n",
-        "    # One-time initialization of PySpark and predictive model\n",
-        "    import pyspark\n",
-        "    import os\n",
-        "    from azureml.core.model import Model\n",
-        "    from pyspark.ml import PipelineModel\n",
-        " \n",
-        "    global trainedModel\n",
-        "    global spark\n",
-        " \n",
-        "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
-        "    model_name = \"{model_name}\" #interpolated\n",
-        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), model_name)\n",
-        "    trainedModel = PipelineModel.load(model_path)\n",
-        "    \n",
-        "def run(input_json):\n",
-        "    if isinstance(trainedModel, Exception):\n",
-        "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
-        "      \n",
-        "    try:\n",
-        "        sc = spark.sparkContext\n",
-        "        input_list = json.loads(input_json)\n",
-        "        input_rdd = sc.parallelize(input_list)\n",
-        "        input_df = spark.read.json(input_rdd)\n",
-        "    \n",
-        "        # Compute prediction\n",
-        "        prediction = trainedModel.transform(input_df)\n",
-        "        #result = prediction.first().prediction\n",
-        "        predictions = prediction.collect()\n",
-        " \n",
-        "        #Get each scored result\n",
-        "        preds = [str(x['prediction']) for x in predictions]\n",
-        "        result = \",\".join(preds)\n",
-        "        # you can return any data type as long as it is JSON-serializable\n",
-        "        return result.tolist()\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return result\n",
-        "    \n",
-        "\"\"\".format(model_name=model_name)\n",
-        " \n",
-        "exec(score_sparkml)\n",
-        " \n",
-        "with open(\"score_sparkml.py\", \"w\") as file:\n",
-        "    file.write(score_sparkml)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) # showing how to add libs as an eg. - not needed for this model.\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myacienv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#deploy to ACI\n",
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "from azureml.core.model import InferenceConfig\n",
-        "from azureml.core.environment import Environment\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "\n",
-        "myaci_config = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
-        "                                                  memory_gb = 2, \n",
-        "                                                  tags = {'name':'Databricks Azure ML ACI'}, \n",
-        "                                                  description = 'This is for ADB and AML example.')\n",
-        "\n",
-        "service_name = 'aciws'\n",
-        "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
-        "# we need to add extra packages to procured environment\n",
-        "# in order to deploy amended environment we need to rename it\n",
-        "myenv.name = 'myenv'\n",
-        "model_dependencies = CondaDependencies('myenv.yml')\n",
-        "for pip_dep in model_dependencies.pip_packages:\n",
-        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
-        "for conda_dep in model_dependencies.conda_packages:\n",
-        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
-        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
-        "\n",
-        "myservice = Model.deploy(ws, service_name, [mymodel], inference_config, myaci_config)\n",
-        "myservice.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "help(Webservice)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#for using the Web HTTP API \n",
-        "print(myservice.scoring_uri)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "#get the some sample data\n",
-        "test_data_path = \"AdultCensusIncomeTest\"\n",
-        "test = spark.read.parquet(test_data_path).limit(5)\n",
-        "\n",
-        "test_json = json.dumps(test.toJSON().collect())\n",
-        "\n",
-        "print(test_json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
-        "myservice.run(input_data=test_json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#comment to not delete the web service\n",
-        "myservice.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploying to other types of computes\n",
-        "\n",
-        "In order to learn how to deploy to other types of compute targets, such as AKS, please take a look at the set of notebooks in the [deployment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment) folder."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "pasha"
-      }
-    ],
-    "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": "3.6.8"
-    },
-    "name": "deploy-to-aci-04",
-    "notebookId": 3836944406456376
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb

@@ -1,179 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Data Ingestion"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import urllib"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
-        "dataurl = \"https://amldockerdatasets.azureedge.net/AdultCensusIncome.csv\"\n",
-        "datafile = \"AdultCensusIncome.csv\"\n",
-        "datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
-        "\n",
-        "if os.path.isfile(datafile_dbfs):\n",
-        "    print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
-        "else:\n",
-        "    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
-        "    urllib.request.urlretrieve(dataurl, datafile_dbfs)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Create a Spark dataframe out of the csv file.\n",
-        "data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
-        "print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
-        "data_all.printSchema()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#renaming columns\n",
-        "columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
-        "data_all = data_all.toDF(*columns_new)\n",
-        "data_all.printSchema()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "display(data_all.limit(5))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Data Preparation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Choose feature columns and the label column.\n",
-        "label = \"income\"\n",
-        "xvars = set(data_all.columns) - {label}\n",
-        "\n",
-        "print(\"label = {}\".format(label))\n",
-        "print(\"features = {}\".format(xvars))\n",
-        "\n",
-        "data = data_all.select([*xvars, label])\n",
-        "\n",
-        "# Split data into train and test.\n",
-        "train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
-        "\n",
-        "print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
-        "print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#Data Persistence"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Write the train and test data sets to intermediate storage\n",
-        "train_data_path = \"AdultCensusIncomeTrain\"\n",
-        "test_data_path = \"AdultCensusIncomeTest\"\n",
-        "\n",
-        "train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
-        "test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
-        "\n",
-        "train.write.mode('overwrite').parquet(train_data_path)\n",
-        "test.write.mode('overwrite').parquet(test_data_path)\n",
-        "print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "pasha"
-      }
-    ],
-    "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": "3.6.6"
-    },
-    "name": "ingest-data-02",
-    "notebookId": 3836944406456362
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb

@@ -1,183 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
-        "\n",
-        "**install azureml-sdk**\n",
-        "* Source: Upload Python Egg or PyPi\n",
-        "* PyPi Name: `azureml-sdk[databricks]`\n",
-        "* Select Install Library"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "# Check core SDK version number - based on build number of preview/master.\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
-        "\n",
-        "You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
-        "\n",
-        "For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
-        "\n",
-        "NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# subscription_id = \"<your-subscription-id>\"\n",
-        "# resource_group = \"<your-existing-resource-group>\"\n",
-        "# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
-        "# workspace_region = \"<your-resource group-region>\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Set auth to be used by workspace related APIs.\n",
-        "# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
-        "# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
-        "auth = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# import the Workspace class and check the azureml SDK version\n",
-        "# exist_ok checks if workspace exists or not.\n",
-        "\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.create(name = workspace_name,\n",
-        "                      subscription_id = subscription_id,\n",
-        "                      resource_group = resource_group, \n",
-        "                      location = workspace_region,\n",
-        "                      auth = auth,\n",
-        "                      exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#get workspace details\n",
-        "ws.get_details()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace(workspace_name = workspace_name,\n",
-        "               subscription_id = subscription_id,\n",
-        "               resource_group = resource_group,\n",
-        "               auth = auth)\n",
-        "\n",
-        "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-        "ws.write_config()\n",
-        "#if you need to give a different path/filename please use this\n",
-        "#write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "help(Workspace)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# import the Workspace class and check the azureml SDK version\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "#ws = Workspace.from_config(<full path>)\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "pasha"
-      }
-    ],
-    "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": "3.6.6"
-    },
-    "name": "installation-and-configuration-01",
-    "notebookId": 3688394266452835
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

how-to-use-azureml/azure-databricks/automl/README.md

@@ -1,9 +1,21 @@
-# Adding an init script to an Azure Databricks cluster
+# Automated ML introduction
+Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
 
-The [azureml-cluster-init.sh](./azureml-cluster-init.sh) script configures the environment to
-1. Install the latest AutoML library
 
-To create the Azure Databricks cluster-scoped init script
+If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
+
+If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
+
+# Install Instructions using Azure Databricks :
+
+#### For Databricks non ML runtime 7.1(scala 2.21, spark 3.0.0) and up, Install Automated Machine Learning sdk by adding and running the following command as the first cell of your notebook. This will install AutoML dependencies specific for your notebook.
+
+%pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt
+
+
+#### For Databricks non ML runtime 7.0 and lower, Install Automated Machine Learning sdk using init script as shown below before running the notebook.**
+
+**Create the Azure Databricks cluster-scoped init script 'azureml-cluster-init.sh' as below
 
 1. Create the base directory you want to store the init script in if it does not exist.
     ```
@@ -15,7 +27,7 @@ To create the Azure Databricks cluster-scoped init script
     dbutils.fs.put("/databricks/init/azureml-cluster-init.sh","""
     #!/bin/bash
 	set -ex
-	/databricks/python/bin/pip install -r https://aka.ms/automl_linux_requirements.txt
+	/databricks/python/bin/pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt
     """, True)
     ```
 
@@ -24,6 +36,8 @@ To create the Azure Databricks cluster-scoped init script
     display(dbutils.fs.ls("dbfs:/databricks/init/azureml-cluster-init.sh"))
     ```
 
+**Install libraries to cluster using init script 'azureml-cluster-init.sh' created in previous step
+
 1. Configure the cluster to run the script.
     * Using the cluster configuration page
         1. On the cluster configuration page, click the Advanced Options toggle.

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb

@@ -17,9 +17,9 @@
         "\n",
         "**For Databricks non ML runtime 7.1(scala 2.21, spark 3.0.0) and up, Install AML sdk by running the following command in the first cell of the notebook.**\n",
         "\n",
-        "%pip install -r https://aka.ms/automl_linux_requirements.txt\n",
+        "%pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt\n",
         "\n",
-        "**For Databricks non ML runtime 7.0 and lower, Install AML sdk using init script as shown in [readme](readme.md) before running this notebook.**\n"
+        "**For Databricks non ML runtime 7.0 and lower, Install AML sdk using init script as shown in [readme](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/README.md) before running this notebook.**\n"
       ]
     },
     {

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb

@@ -17,9 +17,9 @@
         "\n",
         "**For Databricks non ML runtime 7.1(scala 2.21, spark 3.0.0) and up, Install AML sdk by running the following command in the first cell of the notebook.**\n",
         "\n",
-        "%pip install -r https://aka.ms/automl_linux_requirements.txt\n",
+        "%pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt\n",
         "\n",
-        "**For Databricks non ML runtime 7.0 and lower, Install AML sdk using init script as shown in [readme](readme.md) before running this notebook.**"
+        "**For Databricks non ML runtime 7.0 and lower, Install AML sdk using init script as shown in [readme](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/README.md) before running this notebook.**"
       ]
     },
     {

how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -1,719 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
-        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
-        "\n",
-        "The notebook will show:\n",
-        "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
-        "2. Running an arbitrary Python script that the customer has in DBFS\n",
-        "3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks) \n",
-        "4. Running a JAR job that the customer has in DBFS.\n",
-        "\n",
-        "## Before you begin:\n",
-        "\n",
-        "1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace. You will need details of this workspace later on to define DatabricksStep. [Click here](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces) for more information.\n",
-        "2. **Create PAT (access token)**: Manually create a Databricks access token at the Azure Databricks portal. See [this](https://docs.databricks.com/api/latest/authentication.html#generate-a-token) for more information.\n",
-        "3. **Add demo notebook to ADB**: This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook. \n",
-        "4. **Create/attach a Blob storage** for use from ADB"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Add demo notebook to ADB Workspace\n",
-        "Copy and paste the below code to create a new notebook in your ADB workspace."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```python\n",
-        "# direct access\n",
-        "dbutils.widgets.get(\"myparam\")\n",
-        "p = getArgument(\"myparam\")\n",
-        "print (\"Param -\\'myparam':\")\n",
-        "print (p)\n",
-        "\n",
-        "dbutils.widgets.get(\"input\")\n",
-        "i = getArgument(\"input\")\n",
-        "print (\"Param -\\'input':\")\n",
-        "print (i)\n",
-        "\n",
-        "dbutils.widgets.get(\"output\")\n",
-        "o = getArgument(\"output\")\n",
-        "print (\"Param -\\'output':\")\n",
-        "print (o)\n",
-        "\n",
-        "n = i + \"/testdata.txt\"\n",
-        "df = spark.read.csv(n)\n",
-        "\n",
-        "display (df)\n",
-        "\n",
-        "data = [('value1', 'value2')]\n",
-        "df2 = spark.createDataFrame(data)\n",
-        "\n",
-        "z = o + \"/output.txt\"\n",
-        "df2.write.csv(z)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Azure Machine Learning and Pipeline SDK-specific imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import azureml.core\n",
-        "from azureml.core.runconfig import JarLibrary\n",
-        "from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
-        "from azureml.exceptions import ComputeTargetException\n",
-        "from azureml.core import Workspace, Experiment\n",
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
-        "from azureml.pipeline.steps import DatabricksStep\n",
-        "from azureml.core.datastore import Datastore\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Attach Databricks compute target\n",
-        "Next, you need to add your Databricks workspace to Azure Machine Learning as a compute target and give it a name. You will use this name to refer to your Databricks workspace compute target inside Azure Machine Learning.\n",
-        "\n",
-        "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n",
-        "- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n",
-        "- **Databricks Access Token** - The access token you created in ADB\n",
-        "\n",
-        "**The Databricks workspace need to be present in the same subscription as your AML workspace**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Replace with your account info before running.\n",
-        " \n",
-        "db_compute_name=os.getenv(\"DATABRICKS_COMPUTE_NAME\", \"<my-databricks-compute-name>\") # Databricks compute name\n",
-        "db_resource_group=os.getenv(\"DATABRICKS_RESOURCE_GROUP\", \"<my-db-resource-group>\") # Databricks resource group\n",
-        "db_workspace_name=os.getenv(\"DATABRICKS_WORKSPACE_NAME\", \"<my-db-workspace-name>\") # Databricks workspace name\n",
-        "db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"<my-access-token>\") # Databricks access token\n",
-        " \n",
-        "try:\n",
-        "    databricks_compute = DatabricksCompute(workspace=ws, name=db_compute_name)\n",
-        "    print('Compute target {} already exists'.format(db_compute_name))\n",
-        "except ComputeTargetException:\n",
-        "    print('Compute not found, will use below parameters to attach new one')\n",
-        "    print('db_compute_name {}'.format(db_compute_name))\n",
-        "    print('db_resource_group {}'.format(db_resource_group))\n",
-        "    print('db_workspace_name {}'.format(db_workspace_name))\n",
-        "    print('db_access_token {}'.format(db_access_token))\n",
-        " \n",
-        "    config = DatabricksCompute.attach_configuration(\n",
-        "        resource_group = db_resource_group,\n",
-        "        workspace_name = db_workspace_name,\n",
-        "        access_token= db_access_token)\n",
-        "    databricks_compute=ComputeTarget.attach(ws, db_compute_name, config)\n",
-        "    databricks_compute.wait_for_completion(True)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Data Connections with Inputs and Outputs\n",
-        "The DatabricksStep supports Azure Bloband ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
-        "\n",
-        "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
-        "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",
-        "\n",
-        "### Type of Data Access\n",
-        "Databricks allows to interact with Azure Blob and ADLS in two ways.\n",
-        "- **Direct Access**: Databricks allows you to interact with Azure Blob or ADLS URIs directly. The input or output URIs will be mapped to a Databricks widget param in the Databricks notebook.\n",
-        "- **Mounting**: You will be supplied with additional parameters and secrets that will enable you to mount your ADLS or Azure Blob input or output location in your Databricks notebook."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Direct Access: Python sample code\n",
-        "If you have a data reference named \"input\" it will represent the URI of the input and you can access it directly in the Databricks python notebook like so:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```python\n",
-        "dbutils.widgets.get(\"input\")\n",
-        "y = getArgument(\"input\")\n",
-        "df = spark.read.csv(y)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Mounting: Python sample code for Azure Blob\n",
-        "Given an Azure Blob data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```python\n",
-        "# This contains the input URI\n",
-        "dbutils.widgets.get(\"input\")\n",
-        "myinput_uri = getArgument(\"input\")\n",
-        "\n",
-        "# How to get the input datastore name inside ADB notebook\n",
-        "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
-        "# that contians an access key or sas for the Azure Blob input (this name is obtained by appending \n",
-        "# the name of the input with \"_blob_secretname\". \n",
-        "dbutils.widgets.get(\"input_blob_secretname\") \n",
-        "myinput_blob_secretname = getArgument(\"input_blob_secretname\")\n",
-        "\n",
-        "# This contains the required configuration for mounting\n",
-        "dbutils.widgets.get(\"input_blob_config\")\n",
-        "myinput_blob_config = getArgument(\"input_blob_config\")\n",
-        "\n",
-        "# Usage\n",
-        "dbutils.fs.mount(\n",
-        "  source = myinput_uri,\n",
-        "  mount_point = \"/mnt/input\",\n",
-        "  extra_configs = {myinput_blob_config:dbutils.secrets.get(scope = \"amlscope\", key = myinput_blob_secretname)})\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Mounting: Python sample code for ADLS\n",
-        "Given an ADLS data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```python\n",
-        "# This contains the input URI\n",
-        "dbutils.widgets.get(\"input\") \n",
-        "myinput_uri = getArgument(\"input\")\n",
-        "\n",
-        "# This contains the client id for the service principal \n",
-        "# that has access to the adls input\n",
-        "dbutils.widgets.get(\"input_adls_clientid\") \n",
-        "myinput_adls_clientid = getArgument(\"input_adls_clientid\")\n",
-        "\n",
-        "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
-        "# that contains the secret for the above mentioned service principal\n",
-        "dbutils.widgets.get(\"input_adls_secretname\") \n",
-        "myinput_adls_secretname = getArgument(\"input_adls_secretname\")\n",
-        "\n",
-        "# This contains the refresh url for the mounting configs\n",
-        "dbutils.widgets.get(\"input_adls_refresh_url\") \n",
-        "myinput_adls_refresh_url = getArgument(\"input_adls_refresh_url\")\n",
-        "\n",
-        "# Usage \n",
-        "configs = {\"dfs.adls.oauth2.access.token.provider.type\": \"ClientCredential\",\n",
-        "           \"dfs.adls.oauth2.client.id\": myinput_adls_clientid,\n",
-        "           \"dfs.adls.oauth2.credential\": dbutils.secrets.get(scope = \"amlscope\", key =myinput_adls_secretname),\n",
-        "           \"dfs.adls.oauth2.refresh.url\": myinput_adls_refresh_url}\n",
-        "\n",
-        "dbutils.fs.mount(\n",
-        "  source = myinput_uri,\n",
-        "  mount_point = \"/mnt/output\",\n",
-        "  extra_configs = configs)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use Databricks from Azure Machine Learning Pipeline\n",
-        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference) and intermediate data (via PipelineData) to be used in DatabricksStep."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Use the default blob storage\n",
-        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
-        "print('Datastore {} will be used'.format(def_blob_store.name))\n",
-        "\n",
-        "# We are uploading a sample file in the local directory to be used as a datasource\n",
-        "def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
-        "\n",
-        "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
-        "                                     data_reference_name=\"input\")\n",
-        "\n",
-        "step_1_output = PipelineData(\"output\", datastore=def_blob_store)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Add a DatabricksStep\n",
-        "Adds a Databricks notebook as a step in a Pipeline.\n",
-        "- ***name:** Name of the Module\n",
-        "- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n",
-        "- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n",
-        "- **existing_cluster_id:** Cluster ID of an existing Interactive cluster on the Databricks workspace. If you are providing this, do not provide any of the parameters below that are used to create a new cluster such as spark_version, node_type, etc.\n",
-        "- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n",
-        "- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n",
-        "- **num_workers:** Specifies a static number of workers for the databricks run cluster\n",
-        "- **min_workers:** Specifies a min number of workers to use for auto-scaling the databricks run cluster\n",
-        "- **max_workers:** Specifies a max number of workers to use for auto-scaling the databricks run cluster\n",
-        "- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n",
-        "- **notebook_path:** Path to the notebook in the databricks instance. If you are providing this, do not provide python script related paramaters or JAR related parameters.\n",
-        "- **notebook_params:** Parameters  for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n",
-        "- **python_script_path:** The path to the python script in the DBFS or S3. If you are providing this, do not provide python_script_name which is used for uploading script from local machine.\n",
-        "- **python_script_params:** Parameters for the python script (list of str)\n",
-        "- **main_class_name:** The name of the entry point in a JAR module. If you are providing this, do not provide any python script or notebook related parameters.\n",
-        "- **jar_params:** Parameters for the JAR module (list of str)\n",
-        "- **python_script_name:** name of a python script on your local machine (relative to source_directory). If you are providing this do not provide python_script_path which is used to execute a remote python script; or any of the JAR or notebook related parameters.\n",
-        "- **source_directory:** folder that contains the script and other files\n",
-        "- **hash_paths:** list of paths to hash to detect a change in source_directory (script file is always hashed)\n",
-        "- **run_name:** Name in databricks for this run\n",
-        "- **timeout_seconds:** Timeout for the databricks run\n",
-        "- **runconfig:** Runconfig to use. Either pass runconfig or each library type as a separate parameter but do not mix the two\n",
-        "- **maven_libraries:** maven libraries for the databricks run\n",
-        "- **pypi_libraries:** pypi libraries for the databricks run\n",
-        "- **egg_libraries:** egg libraries for the databricks run\n",
-        "- **jar_libraries:** jar libraries for the databricks run\n",
-        "- **rcran_libraries:** rcran libraries for the databricks run\n",
-        "- **compute_target:** Azure Databricks compute\n",
-        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs\n",
-        "- **version:** Optional version tag to denote a change in functionality for the step\n",
-        "\n",
-        "\\* *denotes required fields*  \n",
-        "*You must provide exactly one of num_workers or min_workers and max_workers paramaters*  \n",
-        "*You must provide exactly one of databricks_compute or databricks_compute_name parameters*\n",
-        "\n",
-        "## Use runconfig to specify library dependencies\n",
-        "You can use a runconfig to specify the library dependencies for your cluster in Databricks. The runconfig will contain a databricks section as follows:\n",
-        "\n",
-        "```yaml\n",
-        "environment:\n",
-        "# Databricks details\n",
-        "  databricks:\n",
-        "# List of maven libraries.\n",
-        "    mavenLibraries:\n",
-        "    - coordinates: org.jsoup:jsoup:1.7.1\n",
-        "      repo: ''\n",
-        "      exclusions:\n",
-        "      - slf4j:slf4j\n",
-        "      - '*:hadoop-client'\n",
-        "# List of PyPi libraries\n",
-        "    pypiLibraries:\n",
-        "    - package: beautifulsoup4\n",
-        "      repo: ''\n",
-        "# List of RCran libraries\n",
-        "    rcranLibraries:\n",
-        "    -\n",
-        "# Coordinates.\n",
-        "      package: ada\n",
-        "# Repo\n",
-        "      repo: http://cran.us.r-project.org\n",
-        "# List of JAR libraries\n",
-        "    jarLibraries:\n",
-        "    -\n",
-        "# Coordinates.\n",
-        "      library: dbfs:/mnt/libraries/library.jar\n",
-        "# List of Egg libraries\n",
-        "    eggLibraries:\n",
-        "    -\n",
-        "# Coordinates.\n",
-        "      library: dbfs:/mnt/libraries/library.egg\n",
-        "```\n",
-        "\n",
-        "You can then create a RunConfiguration object using this file and pass it as the runconfig parameter to DatabricksStep.\n",
-        "```python\n",
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "\n",
-        "runconfig = RunConfiguration()\n",
-        "runconfig.load(path='<directory_where_runconfig_is_stored>', name='<runconfig_file_name>')\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1. Running the demo notebook already added to the Databricks workspace\n",
-        "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "databricksstep-remarks-sample"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"<my-databricks-notebook-path>\") # Databricks notebook path\n",
-        "\n",
-        "dbNbStep = DatabricksStep(\n",
-        "    name=\"DBNotebookInWS\",\n",
-        "    inputs=[step_1_input],\n",
-        "    outputs=[step_1_output],\n",
-        "    num_workers=1,\n",
-        "    notebook_path=notebook_path,\n",
-        "    notebook_params={'myparam': 'testparam'},\n",
-        "    run_name='DB_Notebook_demo',\n",
-        "    compute_target=databricks_compute,\n",
-        "    allow_reuse=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Build and submit the Experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "steps = [dbNbStep]\n",
-        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
-        "pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View Run Details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2. Running a Python script from DBFS\n",
-        "This shows how to run a Python script in DBFS. \n",
-        "\n",
-        "To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html). The CLI command is given below:\n",
-        "\n",
-        "```\n",
-        "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n",
-        "```\n",
-        "\n",
-        "The code in the below cell assumes that you have completed the previous step of uploading the script `train-db-dbfs.py` to the root folder in DBFS."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "python_script_path = os.getenv(\"DATABRICKS_PYTHON_SCRIPT_PATH\", \"<my-databricks-python-script-path>\") # Databricks python script path\n",
-        "\n",
-        "dbPythonInDbfsStep = DatabricksStep(\n",
-        "    name=\"DBPythonInDBFS\",\n",
-        "    inputs=[step_1_input],\n",
-        "    num_workers=1,\n",
-        "    python_script_path=python_script_path,\n",
-        "    python_script_params={'--input_data'},\n",
-        "    run_name='DB_Python_demo',\n",
-        "    compute_target=databricks_compute,\n",
-        "    allow_reuse=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Build and submit the Experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "steps = [dbPythonInDbfsStep]\n",
-        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
-        "pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View Run Details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3. Running a Python script in Databricks that currenlty is in local computer\n",
-        "To run a Python script that is currently in your local computer, follow the instructions below. \n",
-        "\n",
-        "The commented out code below code assumes that you have `train-db-local.py` in the `scripts` subdirectory under the current working directory.\n",
-        "\n",
-        "In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "python_script_name = \"train-db-local.py\"\n",
-        "source_directory = \".\"\n",
-        "\n",
-        "dbPythonInLocalMachineStep = DatabricksStep(\n",
-        "    name=\"DBPythonInLocalMachine\",\n",
-        "    inputs=[step_1_input],\n",
-        "    num_workers=1,\n",
-        "    python_script_name=python_script_name,\n",
-        "    source_directory=source_directory,\n",
-        "    run_name='DB_Python_Local_demo',\n",
-        "    compute_target=databricks_compute,\n",
-        "    allow_reuse=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Build and submit the Experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "steps = [dbPythonInLocalMachineStep]\n",
-        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
-        "pipeline_run = Experiment(ws, 'DB_Python_Local_demo').submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View Run Details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 4. Running a JAR job that is alreay added in DBFS\n",
-        "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
-        "\n",
-        "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n",
-        "\n",
-        "```\n",
-        "dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "main_jar_class_name = \"com.microsoft.aeva.Main\"\n",
-        "jar_library_dbfs_path = os.getenv(\"DATABRICKS_JAR_LIB_PATH\", \"<my-databricks-jar-lib-path>\") # Databricks jar library path\n",
-        "\n",
-        "dbJarInDbfsStep = DatabricksStep(\n",
-        "    name=\"DBJarInDBFS\",\n",
-        "    inputs=[step_1_input],\n",
-        "    num_workers=1,\n",
-        "    main_class_name=main_jar_class_name,\n",
-        "    jar_params={'arg1', 'arg2'},\n",
-        "    run_name='DB_JAR_demo',\n",
-        "    jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n",
-        "    compute_target=databricks_compute,\n",
-        "    allow_reuse=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Build and submit the Experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "steps = [dbJarInDbfsStep]\n",
-        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
-        "pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View Run Details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Next: ADLA as a Compute Target\n",
-        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "diray"
-      }
-    ],
-    "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": "3.6.2"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/testdata.txt

@@ -1 +0,0 @@
-Test1

how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/train-db-dbfs.py

@@ -1,5 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-print("In train.py")
-print("As a data scientist, this is where I use my training code.")

how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/train-db-local.py

@@ -1,5 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-print("In train.py")
-print("As a data scientist, this is where I use my training code.")

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.yml

@@ -1,6 +0,0 @@
-name: multi-model-register-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - numpy
-  - scikit-learn

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml

@@ -1,6 +0,0 @@
-name: model-register-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - numpy
-  - scikit-learn

how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb

@@ -77,7 +77,7 @@
       "source": [
         "## Create trained model\n",
         "\n",
-        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
       ]
     },
     {
@@ -382,13 +382,111 @@
       "source": [
         "## Update Service\n",
         "\n",
-        "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
+        "If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n"
-        "\n",
+      ]
-        "```python\n",
+    },
-        "local_service.update(models=[SomeOtherModelObject],\n",
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_service.update(models=[model],\n",
         "                     inference_config=inference_config,\n",
-        "                     deployment_config=local_config)\n",
+        "                     deployment_config=deployment_config)\n"
-        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy model to AKS cluster based on the LocalWebservice's configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
+        "from azureml.core.compute import AksCompute, ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your AKS cluster\n",
+        "aks_name = 'my-aks-9' \n",
+        "\n",
+        "# Verify the cluster does not exist already\n",
+        "try:\n",
+        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    # Use the default configuration (can also provide parameters to customize)\n",
+        "    prov_config = AksCompute.provisioning_configuration()\n",
+        "\n",
+        "    # Create the cluster\n",
+        "    aks_target = ComputeTarget.create(workspace = ws, \n",
+        "                                      name = aks_name, \n",
+        "                                      provisioning_configuration = prov_config)\n",
+        "\n",
+        "if aks_target.get_status() != \"Succeeded\":\n",
+        "    aks_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AksWebservice\n",
+        "# Set the web service configuration (using default here)\n",
+        "aks_config = AksWebservice.deploy_configuration()\n",
+        "\n",
+        "# # Enable token auth and disable (key) auth on the webservice\n",
+        "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service_name ='aks-service-1'\n",
+        "\n",
+        "aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n",
+        "                                            deployment_config=aks_config,\n",
+        "                                            deployment_target=aks_target)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Test aks service\n",
+        "\n",
+        "sample_input = json.dumps({\n",
+        "    'data': dataset_x[0:2].tolist()\n",
+        "})\n",
+        "\n",
+        "aks_service.run(sample_input)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Delete the service if not needed.\n",
+        "aks_service.delete()"
       ]
     },
     {

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

@@ -1,4 +0,0 @@
-name: deploy-aks-with-controlled-rollout
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb

@@ -276,21 +276,24 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.exceptions import ComputeTargetException\n",
+        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "aks_name = \"my-aks\"\n",
+        "aks_name = \"my-aks-insights\"\n",
         "\n",
+        "creating_compute = False\n",
         "try:\n",
         "    aks_target = ComputeTarget(ws, aks_name)\n",
-        "    print(\"Using existing AKS cluster {}.\".format(aks_name))\n",
+        "    print(\"Using existing AKS compute target {}.\".format(aks_name))\n",
         "except ComputeTargetException:\n",
-        "    print(\"Creating a new AKS cluster {}.\".format(aks_name))\n",
+        "    print(\"Creating a new AKS compute target {}.\".format(aks_name))\n",
         "\n",
         "    # Use the default configuration (can also provide parameters to customize).\n",
         "    prov_config = AksCompute.provisioning_configuration()\n",
         "    aks_target = ComputeTarget.create(workspace=ws,\n",
         "                                      name=aks_name,\n",
-        "                                      provisioning_configuration=prov_config)"
+        "                                      provisioning_configuration=prov_config)\n",
+        "    creating_compute = True"
       ]
     },
     {
@@ -300,7 +303,7 @@
       "outputs": [],
       "source": [
         "%%time\n",
-        "if aks_target.provisioning_state != \"Succeeded\":\n",
+        "if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n",
         "    aks_target.wait_for_completion(show_output=True)"
       ]
     },
@@ -380,7 +383,7 @@
         "    aks_service.wait_for_deployment(show_output=True)\n",
         "    print(aks_service.state)\n",
         "else:\n",
-        "    raise ValueError(\"AKS provisioning failed. Error: \", aks_service.error)"
+        "    raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)"
       ]
     },
     {
@@ -458,7 +461,9 @@
         "%%time\n",
         "aks_service.delete()\n",
         "aci_service.delete()\n",
-        "model.delete()"
+        "model.delete()\n",
+        "if creating_compute:\n",
+        "    aks_target.delete()"
       ]
     }
   ],

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.yml

@@ -1,4 +0,0 @@
-name: enable-app-insights-in-production-service
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml

@@ -1,8 +0,0 @@
-name: onnx-convert-aml-deploy-tinyyolo
-dependencies:
-- pip:
-  - azureml-sdk
-  - numpy
-  - git+https://github.com/apple/coremltools@v2.1
-  - onnx<1.7.0
-  - onnxmltools

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

@@ -70,16 +70,16 @@
         "\n",
         "import urllib.request\n",
         "\n",
-        "onnx_model_url = \"https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz\"\n",
+        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
         "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion_ferplus.tar.gz\")\n",
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\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.tar.gz"
+        "!tar xvzf emotion-ferplus-7.tar.gz"
       ]
     },
     {
@@ -570,7 +570,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "plt.figure(figsize = (16, 6), frameon=False)\n",
+        "plt.figure(figsize = (16, 6))\n",
         "plt.subplot(1, 8, 1)\n",
         "\n",
         "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",

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

@@ -1,9 +0,0 @@
-name: onnx-inference-facial-expression-recognition-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - matplotlib
-  - numpy
-  - onnx<1.7.0
-  - opencv-python-headless

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

@@ -70,9 +70,9 @@
         "\n",
         "import urllib.request\n",
         "\n",
-        "onnx_model_url = \"https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz\"\n",
+        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
         "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist.tar.gz\")"
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
       ]
     },
     {
@@ -86,7 +86,7 @@
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf mnist.tar.gz"
+        "!tar xvzf mnist-7.tar.gz"
       ]
     },
     {
@@ -521,7 +521,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "plt.figure(figsize = (16, 6), frameon=False)\n",
+        "plt.figure(figsize = (16, 6))\n",
         "plt.subplot(1, 8, 1)\n",
         "\n",
         "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
@@ -684,18 +684,7 @@
         "\n",
         "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and  bias ($b$).\n",
         "\n",
-        "![](https://www.cntk.ai/jup/cntk103d_filterset_v2.png)\n",
+        "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function."
-        "\n",
-        "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
       ]
     },
     {
@@ -707,24 +696,6 @@
         "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Max-Pooling for Convolutional Neural Nets\n",
-        "\n",
-        "![](http://www.cntk.ai/jup/c103d_max_pooling.gif)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Pre-Trained Model Architecture\n",
-        "\n",
-        "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,

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

@@ -1,9 +0,0 @@
-name: onnx-inference-mnist-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - matplotlib
-  - numpy
-  - onnx<1.7.0
-  - opencv-python-headless

how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.yml

@@ -1,4 +0,0 @@
-name: onnx-model-register-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.yml

@@ -1,4 +0,0 @@
-name: onnx-modelzoo-aml-deploy-resnet50
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -1,5 +0,0 @@
-name: production-deploy-to-aks-gpu
-dependencies:
-- pip:
-  - azureml-sdk
-  - tensorflow

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

@@ -226,7 +226,7 @@
         "# Leaf domain label generates a name using the formula\n",
         "#  \"<leaf-domain-label>######.<azure-region>.cloudapp.azure.net\"\n",
         "#  where \"######\" is a random series of characters\n",
-        "provisioning_config.enable_ssl(leaf_domain_label = \"contoso\")\n",
+        "provisioning_config.enable_ssl(leaf_domain_label = \"contoso\", overwrite_existing_domain = True)\n",
         "\n",
         "aks_name = 'my-aks-ssl-1' \n",
         "# Create the cluster\n",

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.yml

@@ -1,8 +0,0 @@
-name: production-deploy-to-aks-ssl
-dependencies:
-- pip:
-  - azureml-sdk
-  - matplotlib
-  - tqdm
-  - scipy
-  - sklearn

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.yml

@@ -1,8 +0,0 @@
-name: production-deploy-to-aks
-dependencies:
-- pip:
-  - azureml-sdk
-  - matplotlib
-  - tqdm
-  - scipy
-  - sklearn

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.yml

@@ -1,4 +0,0 @@
-name: model-register-and-deploy-spark
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -23,7 +23,7 @@
         "# Train and explain models remotely via Azure Machine Learning Compute\n",
         "\n",
         "\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Leanrning Compute Target (AMLCompute).**_\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Learning Compute Target (AMLCompute).**_\n",
         "\n",
         "\n",
         "\n",
@@ -35,10 +35,7 @@
         "    1. Initialize a Workspace\n",
         "    1. Create an Experiment\n",
         "    1. Introduction to AmlCompute\n",
-        "    1. Submit an AmlCompute run in a few different ways\n",
+        "    1. Submit an AmlCompute run\n",
-        "        1. Option 1: Provision as a run based compute target \n",
-        "        1. Option 2: Provision as a persistent compute target (Basic)\n",
-        "        1. Option 3: Provision as a persistent compute target (Advanced)\n",
         "1. Additional operations to perform on AmlCompute\n",
         "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
         "1. [Visualize explanations](#Visualize)\n",
@@ -158,7 +155,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Submit an AmlCompute run in a few different ways\n",
+        "## Submit an AmlCompute run\n",
         "\n",
         "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
         "\n",
@@ -204,7 +201,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Option 1: Provision a compute target (Basic)\n",
+        "### Provision a compute target\n",
         "\n",
         "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
         "\n",
@@ -262,7 +259,7 @@
         "run_config.environment.docker.enabled = True\n",
         "\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
+        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",
         "\n",
         "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
@@ -327,183 +324,6 @@
         "run.get_metrics()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 2: Provision a compute target (Advanced)\n",
-        "\n",
-        "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
-        "\n",
-        "In addition to `vm_size` and `max_nodes`, you can specify:\n",
-        "* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n",
-        "* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n",
-        "* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n",
-        "* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n",
-        "* `vnet_name`: Name of VNet\n",
-        "* `subnet_name`: Name of SubNet within the VNet"
-      ]
-    },
-    {
-      "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",
-        "    cpu_cluster = 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_D2_V2',\n",
-        "                                                           vm_priority='lowpriority',\n",
-        "                                                           min_nodes=2,\n",
-        "                                                           max_nodes=4,\n",
-        "                                                           idle_seconds_before_scaledown='300',\n",
-        "                                                           vnet_resourcegroup_name='<my-resource-group>',\n",
-        "                                                           vnet_name='<my-vnet-name>',\n",
-        "                                                           subnet_name='<my-subnet-name>')\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "\n",
-        "cpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure & Run"
-      ]
-    },
-    {
-      "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 RunConfig object\n",
-        "run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute target created in previous step\n",
-        "run_config.target = cpu_cluster.name\n",
-        "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
-        "]\n",
-        "\n",
-        "\n",
-        "\n",
-        "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
-        "# In production scenario user would choose their dependencies\n",
-        "import pkg_resources\n",
-        "available_packages = pkg_resources.working_set\n",
-        "sklearn_ver = None\n",
-        "pandas_ver = None\n",
-        "for dist in available_packages:\n",
-        "    if dist.key == 'scikit-learn':\n",
-        "        sklearn_ver = dist.version\n",
-        "    elif dist.key == 'pandas':\n",
-        "        pandas_ver = dist.version\n",
-        "sklearn_dep = 'scikit-learn'\n",
-        "pandas_dep = 'pandas'\n",
-        "if sklearn_ver:\n",
-        "    sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
-        "if pandas_ver:\n",
-        "    pandas_dep = 'pandas=={}'.format(pandas_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",
-        "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
-        "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
-        "\n",
-        "from azureml.core import Run\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='train_explain.py', \n",
-        "                      run_config=run_config) \n",
-        "run = experiment.submit(config=src)\n",
-        "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": [
-        "## Additional operations to perform on AmlCompute\n",
-        "\n",
-        "You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get_status () gets the latest status of the AmlCompute target\n",
-        "cpu_cluster.get_status().serialize()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n",
-        "# cpu_cluster.update(min_nodes=1)\n",
-        "# cpu_cluster.update(max_nodes=10)\n",
-        "cpu_cluster.update(idle_seconds_before_scaledown=300)\n",
-        "# cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
-        "# 'cpu-cluster' in this case but use a different VM family for instance.\n",
-        "\n",
-        "# cpu_cluster.delete()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -518,7 +338,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
+        "from azureml.interpret import ExplanationClient\n",
         "\n",
         "# Get model explanation data\n",
         "client = ExplanationClient.from_run(run)\n",

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

@@ -1,11 +0,0 @@
-name: explain-model-on-amlcompute
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-interpret
-  - interpret-community[visualization]
-  - matplotlib
-  - azureml-contrib-interpret
-  - sklearn-pandas<2.0.0
-  - azureml-dataset-runtime
-  - ipywidgets

how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py

@@ -4,7 +4,7 @@
 from sklearn import datasets
 from sklearn.linear_model import Ridge
 from interpret.ext.blackbox import TabularExplainer
-from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient
+from azureml.interpret import ExplanationClient
 from sklearn.model_selection import train_test_split
 from azureml.core.run import Run
 import joblib

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

@@ -57,7 +57,7 @@
         "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
         "\n",
         "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data\n",
+        "2. Run 'explain-model-sample' with AML Run History, which leverages run history service to store and manage the explanation data\n",
         "---\n",
         "\n",
         "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
@@ -451,7 +451,7 @@
       "source": [
         "import azureml.core\n",
         "from azureml.core import Workspace, Experiment\n",
-        "from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
+        "from azureml.interpret import ExplanationClient\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"
       ]
@@ -475,7 +475,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "experiment_name = 'explain_model'\n",
+        "experiment_name = 'explain-model-sample'\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "run = experiment.start_logging()\n",
         "client = ExplanationClient.from_run(run)"

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

@@ -1,9 +0,0 @@
-name: save-retrieve-explanations-run-history
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-interpret
-  - interpret-community[visualization]
-  - matplotlib
-  - azureml-contrib-interpret
-  - ipywidgets

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

@@ -323,7 +323,7 @@
         "\n",
         "# azureml-defaults is required to host the model as a web service.\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-interpret'\n",
         "]\n",
         " \n",

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

@@ -1,10 +0,0 @@
-name: train-explain-model-locally-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-interpret
-  - interpret-community[visualization]
-  - matplotlib
-  - azureml-contrib-interpret
-  - sklearn-pandas<2.0.0
-  - ipywidgets

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

@@ -267,7 +267,7 @@
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
+        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",
         " \n",
         "\n",
@@ -295,8 +295,7 @@
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
         "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
-        "                                pin_sdk_version=False)\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
         "\n",
@@ -368,7 +367,7 @@
       "outputs": [],
       "source": [
         "# Retrieve global explanation for visualization\n",
-        "from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
+        "from azureml.interpret import ExplanationClient\n",
         "\n",
         "# get model explanation data\n",
         "client = ExplanationClient.from_run(run)\n",
@@ -432,7 +431,7 @@
         "\n",
         "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-interpret'\n",
         "]\n",
         " \n",
@@ -460,8 +459,7 @@
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
         "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
-        "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
+        "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
-        "                                 pin_sdk_version=False)\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-on-amlcompute-and-deploy.yml

@@ -1,12 +0,0 @@
-name: train-explain-model-on-amlcompute-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-interpret
-  - interpret-community[visualization]
-  - matplotlib
-  - azureml-contrib-interpret
-  - sklearn-pandas<2.0.0
-  - azureml-dataset-runtime
-  - azureml-core
-  - ipywidgets

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

@@ -15,7 +15,7 @@ from sklearn_pandas import DataFrameMapper
 
 from azureml.core.run import Run
 from interpret.ext.blackbox import TabularExplainer
-from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient
+from azureml.interpret import ExplanationClient
 from azureml.interpret.scoring.scoring_explainer import LinearScoringExplainer, save
 
 OUTPUT_DIR = './outputs/'

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md

@@ -9,7 +9,7 @@ These notebooks below are designed to go in sequence.
 4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
 5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
 6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
-7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
+7. [aml-pipelines-with-commandstep.ipynb](aml-pipelines-with-commandstep.ipynb): This notebook shows how to use the CommandStep.
 8. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
 9. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
 10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore.
@@ -19,5 +19,6 @@ These notebooks below are designed to go in sequence.
 14. [aml-pipelines-how-to-use-pipeline-drafts.ipynb](http://aka.ms/pl-pl-draft): This notebook shows how to use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines.
 15. [aml-pipelines-hot-to-use-modulestep.ipynb](https://aka.ms/pl-modulestep): This notebook shows how to define Module, ModuleVersion and how to use them in an AML Pipeline using ModuleStep.
 16. [aml-pipelines-with-notebook-runner-step.ipynb](https://aka.ms/pl-nbrstep): This notebook shows how you can run another notebook as a step in Azure Machine Learning Pipeline.
+17. [aml-pipelines-with-commandstep-r.ipynb](aml-pipelines-with-commandstep-r.ipynb): This notebook shows how to use CommandStep to run R scripts.
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.png)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb

@@ -22,6 +22,8 @@
         "# Azure Machine Learning Pipeline with DataTransferStep\n",
         "This notebook is used to demonstrate the use of DataTransferStep in an Azure Machine Learning Pipeline.\n",
         "\n",
+        "> **Note:** In Azure Machine Learning, you can write output data directly to Azure Blob Storage, Azure Data Lake Storage Gen 1, Azure Data Lake Storage Gen 2, Azure FileShare without going through extra DataTransferStep. Learn how to use [OutputFileDatasetConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.data.output_dataset_config.outputfiledatasetconfig?view=azure-ml-py) to achieve that with sample notebooks [here](https://aka.ms/pipeline-with-dataset).**\n",
+        "\n",
         "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Azure SQL Database and you may want to move it to Azure Data Lake storage. Or, your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
         "\n",
         "The below examples show how to move data between different storage types supported in Azure Machine Learning.\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.yml

@@ -1,5 +0,0 @@
-name: aml-pipelines-data-transfer
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets

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

@@ -1,5 +0,0 @@
-name: aml-pipelines-getting-started
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb

@@ -341,7 +341,7 @@
       "outputs": [],
       "source": [
         "pipeline = Pipeline(workspace=ws, steps=[step])\n",
-        "pipeline_run = Experiment(ws, 'azurebatch_experiment').submit(pipeline)"
+        "pipeline_run = Experiment(ws, 'azurebatch_sample').submit(pipeline)"
       ]
     },
     {

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

@@ -1,5 +0,0 @@
-name: aml-pipelines-how-to-use-modulestep
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets