update samples from Release-84 as a part of SDK release

update samples from Release-83 as a part of  SDK release

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).
- * ...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).
- * ...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 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).
+ * ...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/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 [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: [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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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",
-        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "\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",
-        "                                                    Y, \n",
-        "                                                    A,\n",
-        "                                                    test_size = 0.2,\n",
-        "                                                    random_state=0,\n",
-        "                                                    stratify=Y)\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.compose import make_column_selector as selector\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "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",
-        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
-        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+        "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
+      ]
+    },
+    {
+      "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",
-        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
-        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
-        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
-        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
+        "        (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
+        "    ]\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",
-        "          sensitive_features=A_train.Sex)\n",
+        "sweep.fit(X_train, y_train,\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

@@ -5,4 +5,3 @@ dependencies:
   - azureml-contrib-fairness
   - fairlearn==0.4.6
   - joblib
-  - shap

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",
-        "import shap"
+        "from sklearn.model_selection import train_test_split\n",
+        "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",
-        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
-        "X = pd.get_dummies(X)"
+        "A = X_raw[['sex','race']]\n",
+        "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
       ]
     },
     {
       "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_scaled = sc.fit_transform(X)\n",
-        "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
+        "(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",
-        "le = LabelEncoder()\n",
-        "Y = le.fit_transform(Y)"
-      ]
-    },
-    {
-      "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",
+        "# 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",
-        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
-        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+        "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
+      ]
+    },
+    {
+      "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",
-        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
-        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
-        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
-        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
+        "        (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
+        "    ]\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

@@ -5,4 +5,3 @@ dependencies:
   - azureml-contrib-fairness
   - fairlearn==0.4.6
   - joblib
-  - shap

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).
-- 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.
+- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl).
+- 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.
 - 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)
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
-    - Uses azure compute for training
+## Classification
+- **Classify Credit Card Fraud**
+    - Dataset: [Kaggle's credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+      - **[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
-    - Uses azure compute for training
+        - **[Jupyter Notebook](regression/auto-ml-regression.ipynb)**
+            - 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)
-    - Dataset: Hardware Performance Dataset
-    - Shows featurization and excplanation
-    - Uses azure compute for training
-
-- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
-    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
-    - Example of using automated ML for training a forecasting model
-
-- [auto-ml-classification-credit-card-fraud-local.ipynb](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
-    - Uses local compute for training
-
-- [auto-ml-classification-bank-marketing-all-features.ipynb](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)
-    - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
-    - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
-    - Uses azure compute for training
-
-- [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
-    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [auto-ml-forecasting-bike-share.ipynb](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
-    - Dataset: forecasting for a bike-sharing
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [auto-ml-forecasting-function.ipynb](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
-    - Example of training an automated ML forecasting model on multiple time-series
-    - Beer Production Forecasting
-
-- [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
-    - Continuous retraining using Pipelines and Time-Series TabularDataset
+## Time Series Forecasting
+- **Forecast Energy Demand**
+    - Dataset: [NYC energy demand data](http://mis.nyiso.com/public/P-58Blist.htm)
+        - **[Jupyter Notebook](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)**
+          - run experiment remotely on AML Compute cluster
+          - use lags and rolling window features
+          - view the featurization steps that were applied during training
+          - 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)**
+    - Dataset: [Dominick's grocery sales of orange juice](forecasting-orange-juice-sales/dominicks_OJ.csv)
+        - **[Jupyter Notebook](forecasting-orange-juice-sales/dominicks_OJ.csv)**
+          - run experiment remotely on AML Compute cluster
+          - customize time-series featurization, change column purpose and override transformer hyper parameters
+          - evaluate locally the performance of the generated best model
+          - deploy the best model as a webservice on Azure Container Instance (ACI)
+          - get online predictions from the deployed model
+- **Forecast Demand of a Bike-Sharing Service**
+    - Dataset: [Bike demand data](forecasting-bike-share/bike-no.csv)
+        - **[Jupyter Notebook](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)**
+          - run experiment remotely on AML Compute cluster
+          - integrate holiday features
+          - run rolling forecast for test set that is longer than the forecast horizon
+          - compute metrics on the predictions from the remote forecast
+- **The Forecast Function Interface**
+    - Dataset: Generated for sample purposes
+        - **[Jupyter Notebook](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)**
+          - train a forecaster using a remote AML Compute cluster
+          - capabilities of forecast function (e.g. forecast farther into the horizon)
+          - generate confidence intervals
+- **Forecast Beverage Production**
+    - Dataset: [Monthly beer production data](forecasting-beer-remote/Beer_no_valid_split_train.csv)
+        - **[Jupyter Notebook](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)**
+          - train using a remote AML Compute cluster
+          - enable the DNN learning model
+          - forecast on a remote compute cluster and compare different model performance
+- **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
-- python>=3.5.2,<3.6.8
+- pip==20.2.4
+- 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 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets
+  - azureml-widgets~=1.21.0
   - 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/1.15.0/validated_win32_requirements.txt [--no-deps]
-
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_win32_requirements.txt [--no-deps]

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
-- python>=3.5.2,<3.6.8
+- pip==20.2.4
+- 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~=1.21.0
   - 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/1.15.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_linux_requirements.txt [--no-deps]
 

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 +22,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets
+  - azureml-widgets~=1.21.0
   - 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/1.15.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_darwin_requirements.txt [--no-deps]

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

@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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",
-        "\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",
-        "\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",
-        "\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",
-        "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\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",
-        "\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",
+        "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",
+        "\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",
+        "\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",
+        "\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 1.21.0 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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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."
       ]
     },
     {

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/automl_thin_client_env.yml

@@ -18,3 +18,4 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - azureml-explain-model
+  - PyJWT < 2.0.0

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

@@ -19,3 +19,4 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - azureml-explain-model
+  - PyJWT < 2.0.0

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

@@ -68,6 +68,7 @@
         "import logging\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
+        "import json\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         " \n",
@@ -92,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -138,7 +139,8 @@
         "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 +199,7 @@
         "|**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 +228,7 @@
         "                             compute_target = compute_target,\n",
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
+        "                             scenario='Latest',\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -321,6 +325,24 @@
         "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": {},
@@ -451,7 +473,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "rakellam"
+        "name": "sekrupa"
       }
     ],
     "categories": [

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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",
-        "\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)."
+        "|**enable_dnn**|Enable Forecasting DNNs|\n"
       ]
     },
     {
@@ -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']
@@ -59,7 +59,9 @@ 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'])]
@@ -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',
@@ -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 = (
-                X_test_expand[time_column_name] < origin_time)
+            context_expand_wind = (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_tindex < horizon_time)
+        trans_roll_wind = (trans_tindex >= origin_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 = (
-                X_test_expand[time_column_name] < origin_time)
+            context_expand_wind = (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_tindex < horizon_time)
-        test_roll_wind = expand_wind & (
-            X_test[time_column_name] >= origin_time)
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_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)
@@ -274,9 +279,20 @@ 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])
 
+_, 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()
     df_all = do_rolling_forecast_with_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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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."
       ]
     },
     {
@@ -251,7 +251,8 @@
         "|**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|"
+        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\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."
       ]
     },
     {
@@ -548,6 +549,9 @@
       "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 +598,7 @@
   "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 Run
+from azureml.core import Dataset, 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
-test_dataset = run.input_datasets['test_data']
+ws = run.experiment.workspace
 
-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

@@ -1,29 +1,32 @@
-from azureml.train.estimator import Estimator
+from azureml.core import ScriptRunConfig
 
 
-def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
-                         target_column_name, inference_folder='./forecast'):
+def run_rolling_forecast(test_experiment, compute_target, train_run,
+                         test_dataset, target_column_name,
+                         inference_folder='./forecast'):
     train_run.download_file('outputs/model.pkl',
                             inference_folder + '/model.pkl')
 
     inference_env = train_run.get_environment()
 
-    est = Estimator(source_directory=inference_folder,
-                    entry_script='forecasting_script.py',
-                    script_params={
-                        '--target_column_name': target_column_name
-                    },
-                    inputs=[test_dataset.as_named_input('test_data')],
+    config = ScriptRunConfig(source_directory=inference_folder,
+                             script='forecasting_script.py',
+                             arguments=['--target_column_name',
+                                        target_column_name,
+                                        '--test_dataset',
+                                        test_dataset.as_named_input(test_dataset.name)],
                              compute_target=compute_target,
-                    environment_definition=inference_env)
+                             environment=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 = test_experiment.submit(config,
+                                 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

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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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."
       ]
     },
     {
@@ -325,12 +325,11 @@
       "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",
-        "\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)"
+        "3. Drop columns: Columns to drop from being featurized. These usually are the columns which are leaky or the columns contain no useful data."
       ]
     },
     {
@@ -367,7 +366,8 @@
         "|-|-|\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 +383,7 @@
         "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 +572,7 @@
       "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 +764,7 @@
   "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

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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": {},
@@ -590,9 +632,12 @@
         "                                                                 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",
+        "    # 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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 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",
@@ -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 1.15.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -462,7 +462,7 @@
   "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/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/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-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.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.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. 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. Submit an AmlCompute run\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",
@@ -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": {},

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/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.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

@@ -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",
-        "                                pin_sdk_version=False)\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\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",
-        "                                 pin_sdk_version=False)\n",
+        "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-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/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-estimatorstep.ipynb

@@ -168,7 +168,7 @@
         "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
         "\n",
         "#upload input data to workspaceblobstore\n",
-        "def_blob_store.upload_files(files=['20news.pkl'], target_path='20newsgroups')"
+        "def_blob_store.upload_files(files=['20news.pkl'], target_path='20newsgroups', overwrite=True)"
       ]
     },
     {

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

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

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

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb

@@ -44,9 +44,11 @@
         "import azureml.core\n",
         "from azureml.core import Workspace, Experiment, Datastore, Dataset\n",
         "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.exceptions import ComputeTargetException\n",
-        "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun\n",
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
+        "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun, PythonScriptStep\n",
+        "from azureml.pipeline.core import Pipeline, PipelineData, TrainingOutput\n",
         "from azureml.train.dnn import TensorFlow\n",
         "# from azureml.train.hyperdrive import *\n",
         "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
@@ -232,7 +234,22 @@
         "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
         "compute_target.wait_for_completion(show_output=True, timeout_in_minutes=20)\n",
         "\n",
-        "print(\"Azure Machine Learning Compute attached\")"
+        "print(\"Azure Machine Learning Compute attached\")\n",
+        "\n",
+        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print(\"Found existing cpu-cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new cpu-cluster\")\n",
+        "    \n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
+        "                                                           min_nodes=0,\n",
+        "                                                           max_nodes=4)\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "    \n",
+        "cpu_cluster.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -401,7 +418,15 @@
         "metrics_output_name = 'metrics_output'\n",
         "metrics_data = PipelineData(name='metrics_data',\n",
         "                            datastore=datastore,\n",
-        "                             pipeline_output_name=metrics_output_name)\n",
+        "                            pipeline_output_name=metrics_output_name,\n",
+        "                            training_output=TrainingOutput(\"Metrics\"))\n",
+        "\n",
+        "model_output_name = 'model_output'\n",
+        "saved_model = PipelineData(name='saved_model',\n",
+        "                            datastore=datastore,\n",
+        "                            pipeline_output_name=model_output_name,\n",
+        "                            training_output=TrainingOutput(\"Model\",\n",
+        "                                                           model_file=\"outputs/model/saved_model.pb\"))\n",
         "\n",
         "hd_step_name='hd_step01'\n",
         "hd_step = HyperDriveStep(\n",
@@ -409,7 +434,39 @@
         "    hyperdrive_config=hd_config,\n",
         "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
         "    inputs=[data_folder],\n",
-        "    metrics_output=metrics_data)"
+        "    outputs=[metrics_data, saved_model])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Find and register best model\n",
+        "When all the jobs finish, we can choose to register the model that has the highest accuracy through an additional PythonScriptStep.\n",
+        "\n",
+        "Through this additional register_model_step, we register the chosen files as a model named `tf-dnn-mnist` under the workspace for deployment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "conda_dep = CondaDependencies()\n",
+        "conda_dep.add_pip_package(\"azureml-sdk\")\n",
+        "\n",
+        "rcfg = RunConfiguration(conda_dependencies=conda_dep)\n",
+        "\n",
+        "register_model_step = PythonScriptStep(script_name='register_model.py',\n",
+        "                                       name=\"register_model_step01\",\n",
+        "                                       inputs=[saved_model],\n",
+        "                                       compute_target=cpu_cluster,\n",
+        "                                       arguments=[\"--saved-model\", saved_model],\n",
+        "                                       allow_reuse=True,\n",
+        "                                       runconfig=rcfg)\n",
+        "\n",
+        "register_model_step.run_after(hd_step)"
       ]
     },
     {
@@ -425,7 +482,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "pipeline = Pipeline(workspace=ws, steps=[hd_step])\n",
+        "pipeline = Pipeline(workspace=ws, steps=[hd_step, register_model_step])\n",
         "pipeline_run = exp.submit(pipeline)"
       ]
     },
@@ -500,58 +557,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Find and register best model\n",
-        "When all the jobs finish, we can find out the one that has the highest accuracy."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "hd_step_run = HyperDriveStepRun(step_run=pipeline_run.find_step_run(hd_step_name)[0])\n",
-        "best_run = hd_step_run.get_best_run_by_primary_metric()\n",
-        "best_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now let's list the model files uploaded during the run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(best_run.get_file_names())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can then register the folder (and all files in it) as a model named `tf-dnn-mnist` under the workspace for deployment."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For model deployment, please refer to [Training, hyperparameter tune, and deploy with TensorFlow](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb)."
+        "For model deployment, please refer to [Training, hyperparameter tune, and deploy with TensorFlow](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb)."
       ]
     }
   ],

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.yml

@@ -1,9 +0,0 @@
-name: aml-pipelines-parameter-tuning-with-hyperdrive
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - matplotlib
-  - numpy
-  - pandas_ml
-  - azureml-dataset-runtime[pandas,fuse]

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.yml

@@ -1,6 +0,0 @@
-name: aml-pipelines-publish-and-run-using-rest-endpoint
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - requests

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb

@@ -19,8 +19,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# How to Setup a Schedule for a Published Pipeline\n",
-        "In this notebook, we will show you how you can run an already published pipeline on a schedule."
+        "# How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint\n",
+        "In this notebook, we will show you how you can run an already published pipeline or a pipeline endpoint on a schedule."
       ]
     },
     {
@@ -159,6 +159,43 @@
         "print(\"Newly published pipeline id: {}\".format(published_pipeline1.id))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Create a Pipeline Endpoint\n",
+        "Alternatively, you can create a schedule to run a pipeline endpoint instead of a published pipeline. You will need this to create a schedule against a pipeline endpoint in the last section of this notebook. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineEndpoint\n",
+        "\n",
+        "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"ScheduledPipelineEndpoint\",\n",
+        "                                            pipeline=pipeline1, description=\"Publish pipeline endpoint for schedule test\")\n",
+        "pipeline_endpoint"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -196,14 +233,24 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create a schedule for the pipeline using a recurrence\n",
+        "### Create a schedule for the published pipeline using a recurrence\n",
         "This schedule will run on a specified recurrence interval."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
       "outputs": [],
       "source": [
         "from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule\n",
@@ -308,7 +355,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1606157800044
+        }
+      },
       "outputs": [],
       "source": [
         "# Set the wait_for_provisioning flag to False if you do not want to wait  \n",
@@ -410,7 +461,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1606157862620
+        }
+      },
       "outputs": [],
       "source": [
         "# Set the wait_for_provisioning flag to False if you do not want to wait  \n",
@@ -419,14 +474,151 @@
         "schedule = Schedule.get(ws, schedule_id)\n",
         "print(\"Disabled schedule {}. New status is: {}\".format(schedule.id, schedule.status))"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Create a schedule for a pipeline endpoint\n",
+        "Alternative to creating schedules for a published pipeline, you can also create schedules to run pipeline endpoints.\n",
+        "Retrieve the pipeline endpoint id to create a schedule. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1606157888851
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"ScheduledPipelineEndpoint\")\n",
+        "published_pipeline_endpoint_id = pipeline_endpoint_by_name.id\n",
+        "\n",
+        "recurrence = ScheduleRecurrence(frequency=\"Day\", interval=2, hours=[22], minutes=[30]) # Runs every other day at 10:30pm\n",
+        "\n",
+        "schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=\"My_Endpoint_Schedule\",\n",
+        "                                                 pipeline_endpoint_id=published_pipeline_endpoint_id,\n",
+        "                                                 experiment_name='Schedule_Run',\n",
+        "                                                 recurrence=recurrence, description=\"Schedule_Run\",\n",
+        "                                                 wait_for_provisioning=True)\n",
+        "\n",
+        "# You may want to make sure that the schedule is provisioned properly\n",
+        "# before making any further changes to the schedule\n",
+        "\n",
+        "print(\"Created schedule with id: {}\".format(schedule.id))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Get all schedules for a given pipeline endpoint\n",
+        "Once you have the pipeline endpoint ID, then you can get all schedules for that pipeline endopint."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "schedules_for_pipeline_endpoints = Schedule.\\\n",
+        "    get_schedules_for_pipeline_endpoint_id(ws,\n",
+        "                                           pipeline_endpoint_id=published_pipeline_endpoint_id)\n",
+        "print('Got all schedules for pipeline endpoint:', published_pipeline_endpoint_id, 'Count:',\n",
+        "      len(schedules_for_pipeline_endpoints))\n",
+        "\n",
+        "print('done')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Disable the schedule created for running the pipeline endpont\n",
+        "Recall the best practice of disabling schedules when not in use.\n",
+        "The number of schedule triggers allowed per month per region per subscription is 100,000.\n",
+        "This is calculated using the project trigger counts for all active schedules."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "fetched_schedule = Schedule.get(ws, schedule_id)\n",
+        "print(\"Using schedule with id: {}\".format(fetched_schedule.id))\n",
+        "\n",
+        "# Set the wait_for_provisioning flag to False if you do not want to wait  \n",
+        "# for the call to provision the schedule in the backend.\n",
+        "fetched_schedule.disable(wait_for_provisioning=True)\n",
+        "fetched_schedule = Schedule.get(ws, schedule_id)\n",
+        "print(\"Disabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))"
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "shbijlan"
       }
     ],
+    "categories": [
+      "how-to-use-azureml",
+      "machine-learning-pipelines",
+      "intro-to-pipelines"
+    ],
     "category": "tutorial",
     "compute": [
       "AML Compute"
@@ -441,7 +633,7 @@
     "framework": [
       "Azure ML"
     ],
-    "friendly_name": "How to Setup a Schedule for a Published Pipeline",
+    "friendly_name": "How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -459,6 +651,9 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.7"
     },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    },
     "order_index": 10,
     "star_tag": [
       "featured"
@@ -466,7 +661,7 @@
     "tags": [
       "None"
     ],
-    "task": "Demonstrates the use of Schedules for Published Pipelines"
+    "task": "Demonstrates the use of Schedules for Published Pipelines and Pipeline endpoints"
   },
   "nbformat": 4,
   "nbformat_minor": 2

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.yml

@@ -1,5 +0,0 @@
-name: aml-pipelines-setup-schedule-for-a-published-pipeline
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.yml

@@ -1,6 +0,0 @@
-name: aml-pipelines-setup-versioned-pipeline-endpoints
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - requests

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.yml

@@ -1,5 +0,0 @@
-name: aml-pipelines-showcasing-datapath-and-pipelineparameter
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.yml

@@ -1,5 +0,0 @@
-name: aml-pipelines-showcasing-dataset-and-pipelineparameter
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb

@@ -0,0 +1,274 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Azure Machine Learning Pipeline with KustoStep\n",
+        "To use Kusto as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a KustoStep is used. A KustoStep enables the functionality of running Kusto queries on a target Kusto cluster in Azure ML Pipelines. Each KustoStep can target one Kusto cluster and perform multiple queries on them. This notebook demonstrates the use of KustoStep in Azure Machine Learning (AML) Pipeline.\n",
+        "\n",
+        "## Before you begin:\n",
+        "\n",
+        "1. **Have an Azure Machine Learning workspace**: You will need details of this workspace later on to define KustoStep.\n",
+        "2. **Have a Service Principal**: You will need a service principal and use its credentials to access your cluster. See [this](https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-create-service-principal-portal) for more information.\n",
+        "3. **Have a Blob storage**: You will need a Azure Blob storage for uploading the output of your Kusto query."
+      ]
+    },
+    {
+      "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, KustoCompute\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 KustoStep\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. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't."
+      ]
+    },
+    {
+      "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 Kusto compute target\n",
+        "Next, you need to create a Kusto compute target and give it a name. You will use this name to refer to your Kusto compute target inside Azure Machine Learning. Your workspace will be associated to this Kusto compute target. You will also need to provide some credentials that will be used to enable access to your target Kusto cluster and database.\n",
+        "\n",
+        "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n",
+        "- **Workspace Name** - The workspace name of your Azure Machine Learning workspace\n",
+        "- **Resource ID** - The resource ID of your Kusto cluster\n",
+        "- **Tenant ID** - The tenant ID associated to your Kusto cluster\n",
+        "- **Application ID** - The Application ID associated to your Kusto cluster\n",
+        "- **Application Key** - The Application key associated to your Kusto cluster\n",
+        "- **Kusto Connection String** - The connection string of your Kusto cluster\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "sample-databrickscompute-attach"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "compute_name = \"<compute_name>\" # Name to associate with new compute in workspace\n",
+        "\n",
+        "# Account details associated to the target Kusto cluster\n",
+        "resource_id = \"<resource_id>\" # Resource ID of the Kusto cluster\n",
+        "kusto_connection_string = \"<kusto_connection_string>\" # Connection string of the Kusto cluster\n",
+        "application_id = \"<application_id>\" # Application ID associated to the Kusto cluster\n",
+        "application_key = \"<application_key>\" # Application Key associated to the Kusto cluster\n",
+        "tenant_id = \"<tenant_id>\" # Tenant ID associated to the Kusto cluster\n",
+        "\n",
+        "try:\n",
+        "    kusto_compute = KustoCompute(workspace=ws, name=compute_name)\n",
+        "    print('Compute target {} already exists'.format(compute_name))\n",
+        "except ComputeTargetException:\n",
+        "    print('Compute not found, will use provided parameters to attach new one')\n",
+        "    config = KustoCompute.attach_configuration(resource_group=ws.resource_group, workspace_name=ws.name, \n",
+        "                                               resource_id=resource_id, tenant_id=tenant_id, \n",
+        "                                               kusto_connection_string=kusto_connection_string, \n",
+        "                                               application_id=application_id, application_key=application_key)\n",
+        "    kusto_compute=ComputeTarget.attach(ws, compute_name, config)\n",
+        "    kusto_compute.wait_for_completion(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup output\n",
+        "To use Kusto as a compute target for Azure Machine Learning Pipeline, a KustoStep is used. Currently KustoStep only supports uploading results to Azure Blob store. Let's define an output datastore via PipelineData to be used in KustoStep."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineParameter\n",
+        "\n",
+        "# Use the default blob storage\n",
+        "def_blob_store = Datastore.get(ws, \"workspaceblobstore\")\n",
+        "print('Datastore {} will be used'.format(def_blob_store.name))\n",
+        "\n",
+        "step_1_output = PipelineData(\"output\", datastore=def_blob_store)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Add a KustoStep to Pipeline\n",
+        "Adds a Kusto query as a step in a Pipeline.\n",
+        "- **name:** Name of the Module\n",
+        "- **compute_target:** Name of Kusto compute target\n",
+        "- **database_name:** Name of the database to perform Kusto query on\n",
+        "- **query_directory:** Path to folder that contains only a text file with Kusto queries (see [here](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/) for more details on Kusto queries). \n",
+        "    - If the query is parameterized, then the text file must also include any declaration of query parameters (see [here](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/queryparametersstatement?pivots=azuredataexplorer) for more details on query parameters declaration statements). \n",
+        "    - An example of the query text file could just contain the query \"StormEvents | count | as HowManyRecords;\", where StormEvents is the table name. \n",
+        "    - Note. the text file should just contain the declarations and queries without quotation marks around them.\n",
+        "- **outputs:** Output binding to an Azure Blob Store.\n",
+        "- **parameter_dict (optional):** Dictionary that contains the values of parameters declared in the query text file in the **query_directory** mentioned above.\n",
+        "    - Dictionary key is the parameter name, and dictionary value is the parameter value.\n",
+        "    - For example, parameter_dict = {\"paramName1\": \"paramValue1\", \"paramName2\": \"paramValue2\"}\n",
+        "- **allow_reuse (optional):** Whether the step should reuse previous results when run with the same settings/inputs (default to False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "database_name = \"<database_name>\" # Name of the database to perform Kusto queries on\n",
+        "query_directory = \"<query_directory>\" # Path to folder that contains a text file with Kusto queries\n",
+        "\n",
+        "kustoStep = KustoStep(\n",
+        "    name='KustoNotebook',\n",
+        "    compute_target=compute_name,\n",
+        "    database_name=database_name,\n",
+        "    query_directory=query_directory,\n",
+        "    output=step_1_output,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Build and submit the Experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "steps = [kustoStep]\n",
+        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+        "pipeline_run = Experiment(ws, '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()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "t-kachia"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "Kusto"
+    ],
+    "datasets": [
+      "Custom"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML, Kusto"
+    ],
+    "friendly_name": "How to use KustoStep with AML Pipelines",
+    "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": "3.7.6"
+    },
+    "order_index": 5,
+    "star_tag": [
+      "featured"
+    ],
+    "tags": [
+      "None"
+    ],
+    "task": "Demonstrates the use of KustoStep"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -30,7 +30,7 @@
         "## Introduction\n",
         "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook, please also take a look at the [Automated ML setup-using-a-local-conda-environment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning#setup-using-a-local-conda-environment) section to setup the environment.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -477,7 +477,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "anshirga"
       }
     ],
     "category": "tutorial",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.yml

@@ -1,8 +0,0 @@
-name: aml-pipelines-with-automated-machine-learning-step
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.yml

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

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.yml

@@ -1,6 +0,0 @@
-name: aml-pipelines-with-notebook-runner-step
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - azureml-contrib-notebook

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/register_model.py

@@ -0,0 +1,21 @@
+import argparse
+import json
+import os
+import azureml.core
+from azureml.core import Workspace, Experiment, Model
+from azureml.core import Run
+from azureml.train.hyperdrive import HyperDriveRun
+from shutil import copy2
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--saved-model', type=str, dest='saved_model', help='path to saved model file')
+args = parser.parse_args()
+
+model_output_dir = './model/'
+
+os.makedirs(model_output_dir, exist_ok=True)
+copy2(args.saved_model, model_output_dir)
+
+ws = Run.get_context().experiment.workspace
+
+model = Model.register(workspace=ws, model_name='tf-dnn-mnist', model_path=model_output_dir)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb

@@ -284,7 +284,7 @@
         "# Specify CondaDependencies obj, add necessary packages\n",
         "aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
         "    conda_packages=['pandas','scikit-learn'], \n",
-        "    pip_packages=['azureml-sdk[automl,explain]', 'pyarrow'])\n",
+        "    pip_packages=['azureml-sdk[automl]', 'pyarrow'])\n",
         "\n",
         "print (\"Run configuration created.\")"
       ]
@@ -460,8 +460,8 @@
         "    name=\"Merge Taxi Data\",\n",
         "    script_name=\"merge.py\", \n",
         "    arguments=[\"--output_merge\", merged_data],\n",
-        "    inputs=[cleansed_green_data.parse_parquet_files(file_extension=None),\n",
-        "            cleansed_yellow_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[cleansed_green_data.parse_parquet_files(),\n",
+        "            cleansed_yellow_data.parse_parquet_files()],\n",
         "    outputs=[merged_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig=aml_run_config,\n",
@@ -497,7 +497,7 @@
         "    name=\"Filter Taxi Data\",\n",
         "    script_name=\"filter.py\", \n",
         "    arguments=[\"--output_filter\", filtered_data],\n",
-        "    inputs=[merged_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[merged_data.parse_parquet_files()],\n",
         "    outputs=[filtered_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -533,7 +533,7 @@
         "    name=\"Normalize Taxi Data\",\n",
         "    script_name=\"normalize.py\", \n",
         "    arguments=[\"--output_normalize\", normalized_data],\n",
-        "    inputs=[filtered_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[filtered_data.parse_parquet_files()],\n",
         "    outputs=[normalized_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -574,7 +574,7 @@
         "    name=\"Transform Taxi Data\",\n",
         "    script_name=\"transform.py\", \n",
         "    arguments=[\"--output_transform\", transformed_data],\n",
-        "    inputs=[normalized_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[normalized_data.parse_parquet_files()],\n",
         "    outputs=[transformed_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -614,7 +614,7 @@
         "    script_name=\"train_test_split.py\", \n",
         "    arguments=[\"--output_split_train\", output_split_train,\n",
         "               \"--output_split_test\", output_split_test],\n",
-        "    inputs=[transformed_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[transformed_data.parse_parquet_files()],\n",
         "    outputs=[output_split_train, output_split_test],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -690,7 +690,7 @@
         "    \"n_cross_validations\": 5\n",
         "}\n",
         "\n",
-        "training_dataset = output_split_train.parse_parquet_files(file_extension=None).keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor', 'cost'])\n",
+        "training_dataset = output_split_train.parse_parquet_files().keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor', 'cost'])\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             debug_log = 'automated_ml_errors.log',\n",
@@ -774,7 +774,7 @@
       "outputs": [],
       "source": [
         "# Before we proceed we need to wait for the run to complete.\n",
-        "pipeline_run.wait_for_completion()\n",
+        "pipeline_run.wait_for_completion(show_output=False)\n",
         "\n",
         "# functions to download output to local and fetch as dataframe\n",
         "def get_download_path(download_path, output_name):\n",

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.yml

@@ -1,10 +0,0 @@
-name: nyc-taxi-data-regression-model-building
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - azureml-opendatasets
-  - azureml-train-automl
-  - matplotlib
-  - pandas
-  - pyarrow

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb

@@ -180,7 +180,9 @@
       "metadata": {},
       "source": [
         "### Create a FileDataset\n",
-        "A [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred."
+        "A [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.",
+        "\n",
+        "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.yml

@@ -1,7 +0,0 @@
-name: file-dataset-image-inference-mnist
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-pipeline-steps
-  - azureml-widgets
-  - pandas

how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb

@@ -160,7 +160,8 @@
       "metadata": {},
       "source": [
         "### Create a TabularDataset\n",
-        "A [TabularDataSet](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred."
+        "A [TabularDataSet](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n",
+        "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later."
       ]
     },
     {
@@ -175,8 +176,7 @@
         "\n",
         "path_on_datastore = iris_data.path('iris/')\n",
         "input_iris_ds = Dataset.Tabular.from_delimited_files(path=path_on_datastore, validate=False)\n",
-        "registered_iris_ds = input_iris_ds.register(ws, iris_ds_name, create_new_version=True)\n",
-        "named_iris_ds = registered_iris_ds.as_named_input(iris_ds_name)"
+        "named_iris_ds = input_iris_ds.as_named_input(iris_ds_name)"
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.yml

@@ -1,7 +0,0 @@
-name: tabular-dataset-inference-iris
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-pipeline-steps
-  - azureml-widgets
-  - pandas

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/mpi_scripts/neural_style.py

@@ -1,185 +0,0 @@
-# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
-import argparse
-import os
-import sys
-import re
-
-from PIL import Image
-import torch
-from torchvision import transforms
-
-
-def load_image(filename, size=None, scale=None):
-    img = Image.open(filename)
-    if size is not None:
-        img = img.resize((size, size), Image.ANTIALIAS)
-    elif scale is not None:
-        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
-    return img
-
-
-def save_image(filename, data):
-    img = data.clone().clamp(0, 255).numpy()
-    img = img.transpose(1, 2, 0).astype("uint8")
-    img = Image.fromarray(img)
-    img.save(filename)
-
-
-class TransformerNet(torch.nn.Module):
-    def __init__(self):
-        super(TransformerNet, self).__init__()
-        # Initial convolution layers
-        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
-        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
-        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
-        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
-        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
-        self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
-        # Residual layers
-        self.res1 = ResidualBlock(128)
-        self.res2 = ResidualBlock(128)
-        self.res3 = ResidualBlock(128)
-        self.res4 = ResidualBlock(128)
-        self.res5 = ResidualBlock(128)
-        # Upsampling Layers
-        self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
-        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
-        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
-        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
-        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
-        # Non-linearities
-        self.relu = torch.nn.ReLU()
-
-    def forward(self, X):
-        y = self.relu(self.in1(self.conv1(X)))
-        y = self.relu(self.in2(self.conv2(y)))
-        y = self.relu(self.in3(self.conv3(y)))
-        y = self.res1(y)
-        y = self.res2(y)
-        y = self.res3(y)
-        y = self.res4(y)
-        y = self.res5(y)
-        y = self.relu(self.in4(self.deconv1(y)))
-        y = self.relu(self.in5(self.deconv2(y)))
-        y = self.deconv3(y)
-        return y
-
-
-class ConvLayer(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride):
-        super(ConvLayer, self).__init__()
-        reflection_padding = kernel_size // 2
-        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
-        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
-
-    def forward(self, x):
-        out = self.reflection_pad(x)
-        out = self.conv2d(out)
-        return out
-
-
-class ResidualBlock(torch.nn.Module):
-    """ResidualBlock
-    introduced in: https://arxiv.org/abs/1512.03385
-    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
-    """
-
-    def __init__(self, channels):
-        super(ResidualBlock, self).__init__()
-        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
-        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
-        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
-        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
-        self.relu = torch.nn.ReLU()
-
-    def forward(self, x):
-        residual = x
-        out = self.relu(self.in1(self.conv1(x)))
-        out = self.in2(self.conv2(out))
-        out = out + residual
-        return out
-
-
-class UpsampleConvLayer(torch.nn.Module):
-    """UpsampleConvLayer
-    Upsamples the input and then does a convolution. This method gives better results
-    compared to ConvTranspose2d.
-    ref: http://distill.pub/2016/deconv-checkerboard/
-    """
-
-    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
-        super(UpsampleConvLayer, self).__init__()
-        self.upsample = upsample
-        if upsample:
-            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
-        reflection_padding = kernel_size // 2
-        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
-        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
-
-    def forward(self, x):
-        x_in = x
-        if self.upsample:
-            x_in = self.upsample_layer(x_in)
-        out = self.reflection_pad(x_in)
-        out = self.conv2d(out)
-        return out
-
-
-def stylize(args):
-    device = torch.device("cuda" if args.cuda else "cpu")
-    with torch.no_grad():
-        style_model = TransformerNet()
-        state_dict = torch.load(os.path.join(args.model_dir, args.style + ".pth"))
-        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
-        for k in list(state_dict.keys()):
-            if re.search(r'in\d+\.running_(mean|var)$', k):
-                del state_dict[k]
-        style_model.load_state_dict(state_dict)
-        style_model.to(device)
-
-        filenames = os.listdir(args.content_dir)
-
-        for filename in filenames:
-            print("Processing {}".format(filename))
-            full_path = os.path.join(args.content_dir, filename)
-            content_image = load_image(full_path, scale=args.content_scale)
-            content_transform = transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Lambda(lambda x: x.mul(255))
-            ])
-            content_image = content_transform(content_image)
-            content_image = content_image.unsqueeze(0).to(device)
-
-            output = style_model(content_image).cpu()
-
-            output_path = os.path.join(args.output_dir, filename)
-            save_image(output_path, output[0])
-
-
-def main():
-    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
-
-    arg_parser.add_argument("--content-scale", type=float, default=None,
-                            help="factor for scaling down the content image")
-    arg_parser.add_argument("--model-dir", type=str, required=True,
-                            help="saved model to be used for stylizing the image.")
-    arg_parser.add_argument("--cuda", type=int, required=True,
-                            help="set it to 1 for running on GPU, 0 for CPU")
-    arg_parser.add_argument("--style", type=str,
-                            help="style name")
-
-    arg_parser.add_argument("--content-dir", type=str, required=True,
-                            help="directory holding the images")
-    arg_parser.add_argument("--output-dir", type=str, required=True,
-                            help="directory holding the output images")
-    args = arg_parser.parse_args()
-
-    if args.cuda and not torch.cuda.is_available():
-        print("ERROR: cuda is not available, try running on CPU")
-        sys.exit(1)
-    os.makedirs(args.output_dir, exist_ok=True)
-    stylize(args)
-
-
-if __name__ == "__main__":
-    main()