update samples from Release-68 as a part of SDK release

update samples from Release-67 as a part of  SDK release

README.md

@@ -65,7 +65,7 @@ Visit following repos to see projects contributed by Azure ML users:
  - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
  
 ## Data/Telemetry 
-This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
+This repository collects usage data and sends it to Microsoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
 
 To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
 

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/README.md

@@ -4,7 +4,7 @@ Learn how to use Azure Machine Learning services for experimentation and model m
 
 As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 
-* [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
+* [train-within-notebook](./training/train-within-notebook): Train a model while tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.

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

@@ -154,12 +154,6 @@ jupyter notebook
 - [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
     - Continuous retraining using Pipelines and Time-Series TabularDataset
 
-- [auto-ml-classification-text-dnn.ipynb](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)
-    - Classification with text data using deep learning in AutoML
-    - 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.
-    - Bidirectional Long-Short Term neural network (BiLSTM) when a CPU compute is used, thereby optimizing the choice of DNN for the uesr's setup.
-
 <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.
 

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

@@ -6,12 +6,12 @@ dependencies:
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
-- numpy~=1.16.0
+- numpy~=1.18.0
 - cython
 - urllib3<1.24
 - scipy==1.4.1
-- scikit-learn>=0.19.0,<=0.20.3
+- scikit-learn==0.22.1
-- pandas>=0.22.0,<=0.23.4
+- pandas==0.25.1
 - py-xgboost<=0.90
 - conda-forge::fbprophet==0.5
 - holidays==0.9.11
@@ -20,12 +20,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
-  - azureml-train-automl
-  - azureml-train
   - azureml-widgets
-  - azureml-pipeline
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.14.0/validated_win32_requirements.txt [--no-deps]
 

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

@@ -0,0 +1,28 @@
+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
+- nb_conda
+- matplotlib==2.1.0
+- numpy~=1.18.0
+- cython
+- urllib3<1.24
+- scipy==1.4.1
+- scikit-learn==0.22.1
+- pandas==0.25.1
+- py-xgboost<=0.90
+- conda-forge::fbprophet==0.5
+- holidays==0.9.11
+- pytorch::pytorch=1.4.0
+- cudatoolkit=10.1.243
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-widgets
+  - pytorch-transformers==1.0.0
+  - spacy==2.1.8
+  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.14.0/validated_linux_requirements.txt [--no-deps]
+

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

@@ -7,12 +7,12 @@ dependencies:
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
-- numpy~=1.16.0
+- numpy~=1.18.0
 - cython
 - urllib3<1.24
 - scipy==1.4.1
-- scikit-learn>=0.19.0,<=0.20.3
+- scikit-learn==0.22.1
-- pandas>=0.22.0,<=0.23.4
+- pandas==0.25.1
 - py-xgboost<=0.90
 - conda-forge::fbprophet==0.5
 - holidays==0.9.11
@@ -21,11 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
-  - azureml-train-automl
-  - azureml-train
   - azureml-widgets
-  - azureml-pipeline
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.14.0/validated_darwin_requirements.txt [--no-deps]

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

@@ -12,7 +12,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_env.yml"
+  AUTOML_ENV_FILE="automl_env_linux.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then

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

@@ -89,7 +89,7 @@
         "from azureml.automl.core.featurization import FeaturizationConfig\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret._internal.explanation_client import ExplanationClient"
       ]
     },
     {
@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -500,11 +500,10 @@
       "source": [
         "# Wait for the best model explanation run to complete\n",
         "from azureml.core.run import Run\n",
-        "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
+        "model_explainability_run_id = remote_run.id + \"_\" + \"ModelExplain\"\n",
         "print(model_explainability_run_id)\n",
-        "if model_explainability_run_id is not None:\n",
+        "model_explainability_run = Run(experiment=experiment, run_id=model_explainability_run_id)\n",
-        "    model_explainability_run = Run(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "model_explainability_run.wait_for_completion()\n",
-        "    model_explainability_run.wait_for_completion()\n",
         "\n",
         "# Get the best run object\n",
         "best_run, fitted_model = remote_run.get_output()"
@@ -733,24 +732,6 @@
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete a Web Service\n",
-        "\n",
-        "Deletes the specified web service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.delete()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -775,7 +756,9 @@
       "source": [
         "## Test\n",
         "\n",
-        "Now that the model is trained, run the test data through the trained model to get the predicted values."
+        "Now that the model is trained, run the test data through the trained model to get the predicted values.  This calls the ACI web service to do the prediction.\n",
+        "\n",
+        "Note that the JSON passed to the ACI web service is an array of rows of data.  Each row should either be an array of values in the same order that was used for training or a dictionary where the keys are the same as the column names used for training.  The example below uses dictionary rows."
       ]
     },
     {
@@ -815,10 +798,27 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_pred  = fitted_model.predict(X_test)\n",
+        "import json\n",
+        "import requests\n",
+        "\n",
+        "X_test_json = X_test.to_json(orient='records')\n",
+        "data = \"{\\\"data\\\": \" + X_test_json +\"}\"\n",
+        "headers = {'Content-Type': 'application/json'}\n",
+        "\n",
+        "resp = requests.post(aci_service.scoring_uri, data, headers=headers)\n",
+        "\n",
+        "y_pred = json.loads(json.loads(resp.text))['result']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "actual = array(y_test)\n",
         "actual = actual[:,0]\n",
-        "print(y_pred.shape, \" \", actual.shape)"
+        "print(len(y_pred), \" \", len(actual))"
       ]
     },
     {
@@ -827,8 +827,7 @@
       "source": [
         "### Calculate metrics for the prediction\n",
         "\n",
-        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
+        "Now visualize the data as a confusion matrix that compared the predicted values against the actual values.\n"
-        "from the trained model that was returned."
       ]
     },
     {
@@ -838,12 +837,45 @@
       "outputs": [],
       "source": [
         "%matplotlib notebook\n",
-        "test_pred = plt.scatter(actual, y_pred, color='b')\n",
+        "from sklearn.metrics import confusion_matrix\n",
-        "test_test = plt.scatter(actual, actual, color='g')\n",
+        "import numpy as np\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "import itertools\n",
+        "\n",
+        "cf =confusion_matrix(actual,y_pred)\n",
+        "plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
+        "plt.colorbar()\n",
+        "plt.title('Confusion Matrix')\n",
+        "plt.xlabel('Predicted')\n",
+        "plt.ylabel('Actual')\n",
+        "class_labels = ['no','yes']\n",
+        "tick_marks = np.arange(len(class_labels))\n",
+        "plt.xticks(tick_marks,class_labels)\n",
+        "plt.yticks([-0.5,0,1,1.5],['','no','yes',''])\n",
+        "# plotting text value inside cells\n",
+        "thresh = cf.max() / 2.\n",
+        "for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
+        "    plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
         "plt.show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete a Web Service\n",
+        "\n",
+        "Deletes the specified web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aci_service.delete()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -1,587 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/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",
-        "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",
-        "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 1.11.0 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",
-        "# 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 = 1)\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 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)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\": 20,\n",
-        "    \"primary_metric\": 'accuracy',\n",
-        "    \"max_concurrent_iterations\": 4, \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",
-        "                             **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/auto-ml-classification-text-dnn.yml

@@ -1,4 +0,0 @@
-name: auto-ml-classification-text-dnn
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -1,63 +0,0 @@
-import pandas as pd
-from azureml.core import Environment
-from azureml.core.conda_dependencies import CondaDependencies
-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):
-
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml',
-                            'inference/condafile.yml')
-
-    inference_env = Environment("myenv")
-    inference_env.docker.enabled = True
-    inference_env.python.conda_dependencies = CondaDependencies(
-        conda_dependencies_file_path='inference/condafile.yml')
-
-    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

@@ -1,60 +0,0 @@
-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

@@ -88,7 +88,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -204,7 +204,6 @@
         "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n",
         "                              conda_packages=['numpy==1.16.2'], \n",
         "                              pin_sdk_version=False)\n",
-        "#cd.add_pip_package('azureml-explain-model')\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"

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

@@ -0,0 +1,92 @@
+# Experimental Notebooks for Automated ML
+Notebooks listed in this folder are leveraging experimental features. Namespaces or function signitures may change in future SDK releases. The notebooks published here will reflect the latest supported APIs. All of these notebooks can run on a client-only installation of the Automated ML SDK.
+The client only installation doesn't contain any of the machine learning libraries, such as scikit-learn, xgboost, or tensorflow, making it much faster to install and is less likely to conflict with any packages in an existing environment. However, since the ML libraries are not available locally, models cannot be downloaded and loaded directly in the client. To replace the functionality of having models locally, these notebooks also demonstrate the ModelProxy feature which will allow you to submit a predict/forecast to the training environment. 
+
+<a name="localconda"></a>
+## Setup using a Local Conda environment
+
+To run these notebook on your own notebook server, use these installation instructions.
+The instructions below will install everything you need and then start a Jupyter notebook.
+If you would like to use a lighter-weight version of the client that does not install all of the machine learning libraries locally, you can leverage the [experimental notebooks.](experimental/README.md)
+
+### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
+- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
+There's no need to install mini-conda specifically.
+
+### 2. Downloading the sample notebooks
+- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
+
+### 3. Setup a new conda environment
+The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
+
+Packages installed by the **automl_setup** script:
+    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>pandas</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
+
+For more details refer to the [automl_env.yml](./automl_env.yml)
+## Windows
+Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
+```
+automl_setup
+```
+## Mac
+Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
+
+Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_mac.sh
+```
+
+## Linux
+cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_linux.sh
+```
+
+### 4. Running configuration.ipynb
+- Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
+- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
+
+### 5. Running Samples
+- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
+- Follow the instructions in the individual notebooks to explore various features in automated ML.
+
+### 6. Starting jupyter notebook manually
+To start your Jupyter notebook manually, use:
+
+```
+conda activate azure_automl
+jupyter notebook
+```
+
+or on Mac or Linux:
+
+```
+source activate azure_automl
+jupyter notebook
+```
+
+
+<a name="samples"></a>
+# Automated ML SDK Sample Notebooks
+
+- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
+    - Dataset: Hardware Performance Dataset
+    - Simple example of using automated ML for regression
+    - Uses azure compute for training
+    - Uses ModelProxy for submitting prediction to training environment on azure compute
+
+<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.
+
+<a name="pythoncommand"></a>
+# Running using python command
+Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
+You can then run this file using the python command.
+However, on Windows the file needs to be modified before it can be run.
+The following condition must be added to the main code in the file:
+
+    if __name__ == "__main__":
+
+The main code of the file must be indented so that it is under this condition.

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

@@ -0,0 +1,20 @@
+name: azure_automl_experimental
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- pip<=19.3.1
+- python>=3.5.2,<3.8
+- nb_conda
+- matplotlib==2.1.0
+- numpy~=1.18.0
+- cython
+- urllib3<1.24
+- scikit-learn==0.22.1
+- pandas==0.25.1
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-defaults
+  - azureml-sdk
+  - azureml-widgets
+  - azureml-explain-model

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

@@ -0,0 +1,21 @@
+name: azure_automl_experimental
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- pip<=19.3.1
+- nomkl
+- python>=3.5.2,<3.8
+- nb_conda
+- matplotlib==2.1.0
+- numpy~=1.18.0
+- cython
+- urllib3<1.24
+- scikit-learn==0.22.1
+- pandas==0.25.1
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-defaults
+  - azureml-sdk
+  - azureml-widgets
+  - azureml-explain-model

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

@@ -0,0 +1,63 @@
+@echo off
+set conda_env_name=%1
+set automl_env_file=%2
+set options=%3
+set PIP_NO_WARN_SCRIPT_LOCATION=0
+
+IF "%conda_env_name%"=="" SET conda_env_name="azure_automl_experimental"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+
+IF NOT EXIST %automl_env_file% GOTO YmlMissing
+
+IF "%CONDA_EXE%"=="" GOTO CondaMissing
+
+call conda activate %conda_env_name% 2>nul:
+
+if not errorlevel 1 (
+  echo Upgrading existing conda environment %conda_env_name%
+  call pip uninstall azureml-train-automl -y -q
+  call conda env update --name %conda_env_name% --file %automl_env_file%
+  if errorlevel 1 goto ErrorExit
+) else (
+  call conda env create -f %automl_env_file% -n %conda_env_name%
+)
+
+call conda activate %conda_env_name% 2>nul:
+if errorlevel 1 goto ErrorExit
+
+call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
+
+REM azureml.widgets is now installed as part of the pip install under the conda env.
+REM Removing the old user install so that the notebooks will use the latest widget.
+call jupyter nbextension uninstall --user --py azureml.widgets
+
+echo.
+echo.
+echo ***************************************
+echo * AutoML setup completed successfully *
+echo ***************************************
+IF NOT "%options%"=="nolaunch" (
+  echo.
+  echo Starting jupyter notebook - please run the configuration notebook 
+  echo.
+  jupyter notebook --log-level=50 --notebook-dir='..\..'
+)
+
+goto End
+
+:CondaMissing
+echo Please run this script from an Anaconda Prompt window.
+echo You can start an Anaconda Prompt window by
+echo typing Anaconda Prompt on the Start menu.
+echo If you don't see the Anaconda Prompt app, install Miniconda.
+echo If you are running an older version of Miniconda or Anaconda,
+echo you can upgrade using the command: conda update conda
+goto End
+
+:YmlMissing
+echo File %automl_env_file% not found.
+
+:ErrorExit
+echo Install failed
+
+:End

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

@@ -0,0 +1,53 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+OPTIONS=$3
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl_experimental"
+fi
+
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_env.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
+   pip uninstall azureml-train-automl -y -q
+   conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
+   jupyter nbextension uninstall --user --py azureml.widgets
+else
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+

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

@@ -0,0 +1,55 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+OPTIONS=$3
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl_experimental"
+fi
+
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_env.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
+   pip uninstall azureml-train-automl -y -q
+   conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
+   jupyter nbextension uninstall --user --py azureml.widgets
+else
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   conda install lightgbm -c conda-forge -y &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+
+

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

@@ -0,0 +1,481 @@
+{
+  "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/experimental/regression/auto-ml-regression.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Regression with Aml Compute**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "3. Train the model using remote compute.\n",
+        "4. Explore the results.\n",
+        "5. Test the best fitted model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        " \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.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose a name for the experiment.\n",
+        "experiment_name = 'automl-regression-model-proxy'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Run History Name'] = experiment_name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "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 = \"reg-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "compute_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Data\n",
+        "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "\n",
+        "# Split the dataset into train and test datasets\n",
+        "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n",
+        "\n",
+        "label = \"ERP\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification, regression or forecasting|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
+        "|**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",
+        "\n",
+        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "automlconfig-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"primary_metric\": 'r2_score',\n",
+        "    \"enable_early_stopping\": True, \n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
+        "    \"max_concurrent_iterations\": 4,\n",
+        "    \"max_cores_per_iteration\": -1,\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'regression',\n",
+        "                             compute_target = compute_target,\n",
+        "                             training_data = train_data,\n",
+        "                             label_column_name = label,\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# If you need to retrieve a run that already started, use the following code\n",
+        "#from azureml.train.automl.run import AutoMLRun\n",
+        "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Widget for Monitoring Runs\n",
+        "\n",
+        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+        "\n",
+        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(remote_run).show() "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Child Run\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_best_child` method returns the best run. Overloads on `get_best_child` allow you to retrieve the best run for *any* logged metric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = remote_run.get_best_child()\n",
+        "print(best_run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Best Child Run Based on Any Other Metric\n",
+        "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lookup_metric = \"root_mean_squared_error\"\n",
+        "best_run = remote_run.get_best_child(metric = lookup_metric)\n",
+        "print(best_run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# preview the first 3 rows of the dataset\n",
+        "\n",
+        "test_data = test_data.to_pandas_dataframe()\n",
+        "y_test = test_data['ERP'].fillna(0)\n",
+        "test_data = test_data.drop('ERP', 1)\n",
+        "test_data = test_data.fillna(0)\n",
+        "\n",
+        "\n",
+        "train_data = train_data.to_pandas_dataframe()\n",
+        "y_train = train_data['ERP'].fillna(0)\n",
+        "train_data = train_data.drop('ERP', 1)\n",
+        "train_data = train_data.fillna(0)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Creating ModelProxy for submitting prediction runs to the training environment.\n",
+        "We will create a ModelProxy for the best child run, which will allow us to submit a run that does the prediction in the training environment. Unlike the local client, which can have different versions of some libraries, the training environment will have all the compatible libraries for the model already."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.model_proxy import ModelProxy\n",
+        "best_model_proxy = ModelProxy(best_run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred_train = best_model_proxy.predict(train_data).to_pandas_dataframe()\n",
+        "y_residual_train = y_train - y_pred_train\n",
+        "\n",
+        "y_pred_test = best_model_proxy.predict(test_data).to_pandas_dataframe()\n",
+        "y_residual_test = y_test - y_pred_test"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "# Set up a multi-plot chart.\n",
+        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
+        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
+        "f.set_figheight(6)\n",
+        "f.set_figwidth(16)\n",
+        "\n",
+        "# Plot residual values of training set.\n",
+        "a0.axis([0, 360, -100, 100])\n",
+        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
+        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
+        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
+        "a0.set_xlabel('Training samples', fontsize = 12)\n",
+        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
+        "\n",
+        "# Plot residual values of test set.\n",
+        "a1.axis([0, 90, -100, 100])\n",
+        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
+        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
+        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
+        "a1.set_xlabel('Test samples', fontsize = 12)\n",
+        "a1.set_yticklabels([])\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
+        "test_test = plt.scatter(y_test, y_test, color='g')\n",
+        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "rakellam"
+      }
+    ],
+    "categories": [
+      "how-to-use-azureml",
+      "automated-machine-learning"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,4 @@
+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

@@ -114,7 +114,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -217,7 +217,7 @@
         "\n",
         "**Time column** is the time axis along which to predict.\n",
         "\n",
-        "**Grain** is another word for an individual time series in your dataset. Grains are identified by values of the columns listed `grain_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",
+        "**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",
         "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."
       ]
@@ -269,7 +269,7 @@
       "source": [
         "target_column_name = 'BeerProduction'\n",
         "time_column_name = 'DATE'\n",
-        "grain_column_names = []\n",
+        "time_series_id_column_names = []\n",
         "freq = 'M' #Monthly data"
       ]
     },
@@ -329,7 +329,7 @@
       },
       "outputs": [],
       "source": [
-        "max_horizon = 12"
+        "forecast_horizon = 12"
       ]
     },
     {
@@ -364,11 +364,10 @@
       },
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'max_horizon': max_horizon,\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
-        "    'enable_dnn' : True,\n",
+        ")\n",
-        "}\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
@@ -380,7 +379,8 @@
         "                             compute_target=compute_target,\n",
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
-        "                            **automl_settings)"
+        "                             enable_dnn=True,\n",
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -581,7 +581,7 @@
       "source": [
         "from helper import run_inference\n",
         "\n",
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset, valid_dataset, max_horizon,\n",
+        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset, valid_dataset, forecast_horizon,\n",
         "                 target_column_name, time_column_name, freq)"
       ]
     },
@@ -603,7 +603,7 @@
         "from helper import run_multiple_inferences\n",
         "\n",
         "summary_df = run_multiple_inferences(summary_df, experiment, test_experiment, compute_target, script_folder, test_dataset, \n",
-        "                  valid_dataset, max_horizon, target_column_name, time_column_name, freq)"
+        "                  valid_dataset, forecast_horizon, target_column_name, time_column_name, freq)"
       ]
     },
     {

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 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -238,6 +238,22 @@
         "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**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|"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -257,11 +273,7 @@
         "|**compute_target**|The remote compute for training.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
-        "|**time_column_name**|Name of the datetime column in the input data|\n",
+        "|**forecasting_parameters**|A class that holds all the forecasting related parameters.|\n",
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
-        "|**country_or_region**|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|\n",
         "\n",
         "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. 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."
       ]
@@ -281,7 +293,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "max_horizon = 14"
+        "forecast_horizon = 14"
       ]
     },
     {
@@ -297,13 +309,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'max_horizon': max_horizon,    \n",
+        "    time_column_name=time_column_name,\n",
-        "    'country_or_region': 'US', # set country_or_region will trigger holiday featurizer\n",
+        "    forecast_horizon=forecast_horizon,\n",
-        "    'target_lags': 'auto', # use heuristic based lag setting    \n",
+        "    country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
-        "    'drop_column_names': ['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
+        "    target_lags='auto', # use heuristic based lag setting    \n",
-        "}\n",
+        "    drop_column_names=['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
+        ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
@@ -317,7 +330,7 @@
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
         "                             verbosity=logging.INFO,\n",
-        "                            **time_series_settings)"
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -422,7 +435,7 @@
       "source": [
         "We now use the best fitted model from the AutoML Run to make forecasts for the test set. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
         "\n",
-        "The scoring will run on a remote compute. In this example, it will reuse the training compute.|"
+        "The scoring will run on a remote compute. In this example, it will reuse the training compute."
       ]
     },
     {
@@ -439,7 +452,7 @@
       "metadata": {},
       "source": [
         "### Retrieving forecasts from the model\n",
-        "To run the forecast on the remote compute we will use two helper scripts: forecasting_script and forecasting_helper. These scripts contain the utility methods which will be used by the remote estimator. We copy these scripts to the project folder to upload them to remote compute."
+        "To run the forecast on the remote compute we will use a helper script: forecasting_script. This script contains the utility methods which will be used by the remote estimator. We copy the script to the project folder to upload it to remote compute."
       ]
     },
     {
@@ -453,15 +466,14 @@
         "\n",
         "script_folder = os.path.join(os.getcwd(), 'forecast')\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy('forecasting_script.py', script_folder)\n",
+        "shutil.copy('forecasting_script.py', script_folder)"
-        "shutil.copy('forecasting_helper.py', script_folder)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "For brevity we have created the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
+        "For brevity, we have created a function called run_forecast that submits the test data to the best model determined during the training run and retrieves forecasts. The test set is longer than the forecast horizon specified at train time, so the forecasting script uses a so-called rolling evaluation to generate predictions over the whole test set. A rolling evaluation iterates the forecaster over the test set, using the actuals in the test set to make lag features as needed. "
       ]
     },
     {
@@ -472,8 +484,7 @@
       "source": [
         "from run_forecast import run_rolling_forecast\n",
         "\n",
-        "remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, max_horizon,\n",
+        "remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, target_column_name)\n",
-        "                 target_column_name, time_column_name)\n",
         "remote_run"
       ]
     },
@@ -537,7 +548,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The MAPE seems high; it is being skewed by an actual with a small absolute value. For a more informative evaluation, we can calculate the metrics by forecast horizon:"
+        "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:"
       ]
     },
     {
@@ -557,7 +568,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "It's also interesting to see the distributions of APE (absolute percentage error) by horizon. On a log scale, the outlying APE in the horizon-3 group is clear."
+        "To drill down more, we can look at the distributions of APE (absolute percentage error) by horizon. From the chart, it is clear that the overall MAPE is being skewed by one particular point where the actual value is of small absolute value."
       ]
     },
     {
@@ -567,7 +578,7 @@
       "outputs": [],
       "source": [
         "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
-        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
+        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, forecast_horizon + 1)]\n",
         "\n",
         "%matplotlib inline\n",
         "plt.boxplot(APEs)\n",
@@ -631,5 +642,5 @@
     "version": 3
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 4
 }

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

@@ -1,99 +0,0 @@
-import pandas as pd
-import numpy as np
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
-def do_rolling_forecast(fitted_model, X_test, y_test, target_column_name,
-                        time_column_name, max_horizon, freq='D'):
-    """
-    Produce forecasts on a rolling origin over the given test set.
-
-    Each iteration makes a forecast for the next 'max_horizon' periods
-    with respect to the current origin, then advances the origin by the
-    horizon time duration. The prediction context for each forecast is set so
-    that the forecaster uses the actual target values prior to the current
-    origin time for constructing lag features.
-
-    This function returns a concatenated DataFrame of rolling forecasts.
-     """
-    df_list = []
-    origin_time = X_test[time_column_name].min()
-    while origin_time <= X_test[time_column_name].max():
-        # Set the horizon time - end date of the forecast
-        horizon_time = origin_time + max_horizon * to_offset(freq)
-
-        # Extract test data from an expanding window up-to the horizon
-        expand_wind = (X_test[time_column_name] < horizon_time)
-        X_test_expand = X_test[expand_wind]
-        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
-        y_query_expand.fill(np.NaN)
-
-        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)
-            y_query_expand[context_expand_wind] = y_test[
-                test_context_expand_wind]
-
-        # Make a forecast out to the maximum horizon
-        y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)
-
-        # 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)
-        df_list.append(align_outputs(y_fcst[trans_roll_wind],
-                                     X_trans[trans_roll_wind],
-                                     X_test[test_roll_wind],
-                                     y_test[test_roll_wind],
-                                     target_column_name))
-
-        # Advance the origin time
-        origin_time = horizon_time
-
-    return pd.concat(df_list, ignore_index=True)

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

@@ -1,37 +1,21 @@
 import argparse
 import azureml.train.automl
-from azureml.automl.runtime.shared import forecasting_models
 from azureml.core import Run
 from sklearn.externals import joblib
-import forecasting_helper
 
 
 parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--max_horizon', type=int, dest='max_horizon',
-    default=10, help='Max Horizon for forecasting')
 parser.add_argument(
     '--target_column_name', type=str, dest='target_column_name',
     help='Target Column Name')
-parser.add_argument(
-    '--time_column_name', type=str, dest='time_column_name',
-    help='Time Column Name')
-parser.add_argument(
-    '--frequency', type=str, dest='freq',
-    help='Frequency of prediction')
 
 args = parser.parse_args()
-max_horizon = args.max_horizon
 target_column_name = args.target_column_name
-time_column_name = args.time_column_name
-freq = args.freq
 
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
 
-grain_column_names = []
-
 df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
@@ -39,14 +23,12 @@ y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[targ
 
 fitted_model = joblib.load('model.pkl')
 
-df_all = forecasting_helper.do_rolling_forecast(
+y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
-    fitted_model,
+
-    X_test_df,
+# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
-    y_test_df.values.T[0],
+assign_dict = {'horizon_origin': X_trans['horizon_origin'].values, 'predicted': y_pred,
-    target_column_name,
+               target_column_name: y_test_df[target_column_name].values}
-    time_column_name,
+df_all = X_test_df.assign(**assign_dict)
-    max_horizon,
-    freq)
 
 file_name = 'outputs/predictions.csv'
 export_csv = df_all.to_csv(file_name, header=True)

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

@@ -5,8 +5,7 @@ from azureml.core.run import Run
 
 
 def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
-                         max_horizon, target_column_name, time_column_name,
+                         target_column_name, inference_folder='./forecast'):
-                         freq='D', inference_folder='./forecast'):
     condafile = inference_folder + '/condafile.yml'
     train_run.download_file('outputs/model.pkl',
                             inference_folder + '/model.pkl')
@@ -20,10 +19,7 @@ def run_rolling_forecast(test_experiment, compute_target, train_run, test_datase
     est = Estimator(source_directory=inference_folder,
                     entry_script='forecasting_script.py',
                     script_params={
-                        '--max_horizon': max_horizon,
+                        '--target_column_name': target_column_name
-                        '--target_column_name': target_column_name,
-                        '--time_column_name': time_column_name,
-                        '--frequency': freq
                     },
                     inputs=[test_dataset.as_named_input('test_data')],
                     compute_target=compute_target,

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 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -288,7 +288,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "max_horizon = 48"
+        "forecast_horizon = 48"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "|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).|"
       ]
     },
     {
@@ -297,7 +310,7 @@
       "source": [
         "## Train\n",
         "\n",
-        "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the name of the time column and the maximum forecast horizon.\n",
+        "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",
@@ -310,8 +323,7 @@
         "|**compute_target**|The remote compute for training.|\n",
         "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
         "|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\n",
-        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecasting_parameters**|A class holds all the forecasting related parameters.|\n"
-        "|**max_horizon**|The number of periods out you would like to predict past your training data. Periods are inferred from your data.|\n"
       ]
     },
     {
@@ -327,10 +339,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'max_horizon': max_horizon,\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
-        "}\n",
+        ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
@@ -342,7 +354,7 @@
         "                             enable_early_stopping=True,\n",
         "                             n_cross_validations=3,                             \n",
         "                             verbosity=logging.INFO,\n",
-        "                            **automl_settings)"
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -550,7 +562,7 @@
       "metadata": {},
       "source": [
         "## Advanced Training <a id=\"advanced_training\"></a>\n",
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
+        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
     {
@@ -558,7 +570,7 @@
       "metadata": {},
       "source": [
         "### Using lags and rolling window features\n",
-        "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
+        "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `forecast_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
         "\n",
         "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blocked_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
@@ -569,12 +581,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_advanced_settings = {\n",
+        "advanced_forecasting_parameters = ForecastingParameters(\n",
-        "    'time_column_name': time_column_name,\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon,\n",
-        "    'max_horizon': max_horizon,\n",
+        "    target_lags=12, target_rolling_window_size=4\n",
-        "    'target_lags': 12,\n",
+        ")\n",
-        "    'target_rolling_window_size': 4,\n",
-        "}\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
@@ -586,7 +596,7 @@
         "                             enable_early_stopping = True,\n",
         "                             n_cross_validations=3,                             \n",
         "                             verbosity=logging.INFO,\n",
-        "                            **automl_advanced_settings)"
+        "                             forecasting_parameters=advanced_forecasting_parameters)"
       ]
     },
     {
@@ -635,7 +645,7 @@
       "metadata": {},
       "source": [
         "## Advanced Results<a id=\"advanced_results\"></a>\n",
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
+        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
     {

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

@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -311,14 +311,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "lags = [1,2,3]\n",
         "forecast_horizon = n_test_periods\n",
-        "time_series_settings = {    \n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'time_column_name': TIME_COLUMN_NAME,\n",
+        "    time_column_name=TIME_COLUMN_NAME,\n",
-        "    'time_series_id_column_names': [ TIME_SERIES_ID_COLUMN_NAME ],\n",
+        "    forecast_horizon=forecast_horizon,\n",
-        "    'forecast_horizon': forecast_horizon ,\n",
+        "    time_series_id_column_names=[ TIME_SERIES_ID_COLUMN_NAME ],\n",
-        "    'target_lags': lags\n",
+        "    target_lags=lags\n",
-        "}"
+        ")"
       ]
     },
     {
@@ -351,7 +352,7 @@
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
         "                             label_column_name=target_label,\n",
-        "                             **time_series_settings)\n",
+        "                             forecasting_parameters=forecasting_parameters)\n",
         "\n",
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]

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 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -178,7 +178,7 @@
       "source": [
         "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
         "\n",
-        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
+        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: "
       ]
     },
     {
@@ -187,8 +187,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "grain_column_names = ['Store', 'Brand']\n",
+        "time_series_id_column_names = ['Store', 'Brand']\n",
-        "nseries = data.groupby(grain_column_names).ngroups\n",
+        "nseries = data.groupby(time_series_id_column_names).ngroups\n",
         "print('Data contains {0} individual time-series.'.format(nseries))"
       ]
     },
@@ -207,7 +207,7 @@
       "source": [
         "use_stores = [2, 5, 8]\n",
         "data_subset = data[data.Store.isin(use_stores)]\n",
-        "nseries = data_subset.groupby(grain_column_names).ngroups\n",
+        "nseries = data_subset.groupby(time_series_id_column_names).ngroups\n",
         "print('Data subset contains {0} individual time-series.'.format(nseries))"
       ]
     },
@@ -216,7 +216,7 @@
       "metadata": {},
       "source": [
         "### Data Splitting\n",
-        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the grain columns."
+        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns."
       ]
     },
     {
@@ -227,15 +227,15 @@
       "source": [
         "n_test_periods = 20\n",
         "\n",
-        "def split_last_n_by_grain(df, n):\n",
+        "def split_last_n_by_series_id(df, n):\n",
-        "    \"\"\"Group df by grain and split on last n rows for each group.\"\"\"\n",
+        "    \"\"\"Group df by series identifiers and split on last n rows for each group.\"\"\"\n",
         "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
-        "                  .groupby(grain_column_names, group_keys=False))\n",
+        "                  .groupby(time_series_id_column_names, group_keys=False))\n",
         "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
         "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
         "    return df_head, df_tail\n",
         "\n",
-        "train, test = split_last_n_by_grain(data_subset, n_test_periods)"
+        "train, test = split_last_n_by_series_id(data_subset, n_test_periods)"
       ]
     },
     {
@@ -301,11 +301,11 @@
         "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
         "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
         "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
-        "* Create grain-based features to enable fixed effects across different series\n",
+        "* Create features based on time series identifiers to enable fixed effects across different series\n",
         "* Create time-based features to assist in learning seasonal patterns\n",
         "* Encode categorical variables to numeric quantities\n",
         "\n",
-        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please check out the forecasting grouping notebook. \n",
+        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n",
         "\n",
         "You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: "
       ]
@@ -327,7 +327,7 @@
         "\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",
         "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 methods are constant for target data and mean, median, most frequent and constant for training data. 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",
+        "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)"
@@ -350,7 +350,24 @@
         "# Fill missing values in the target column, Quantity, with zeros.\n",
         "featurization_config.add_transformer_params('Imputer', ['Quantity'], {\"strategy\": \"constant\", \"fill_value\": 0})\n",
         "# Fill missing values in the INCOME column with median value.\n",
-        "featurization_config.add_transformer_params('Imputer', ['INCOME'], {\"strategy\": \"median\"})"
+        "featurization_config.add_transformer_params('Imputer', ['INCOME'], {\"strategy\": \"median\"})\n",
+        "# Fill missing values in the Price column with forward fill (last value carried forward).\n",
+        "featurization_config.add_transformer_params('Imputer', ['Price'], {\"strategy\": \"ffill\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|"
       ]
     },
     {
@@ -361,9 +378,9 @@
         "\n",
         "The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters.\n",
         "\n",
-        "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If grain columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
+        "For forecasting tasks, there are some additional parameters that can be set in the `ForecastingParameters` class: the name of the column holding the date/time, the timeseries id column names, and the maximum forecast horizon. A time column is required for forecasting, while the time_series_id is optional. If time_series_id columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
         "\n",
-        "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 maximum 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",
+        "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",
@@ -389,11 +406,8 @@
         "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models|\n",
         "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models|\n",
         "|**debug_log**|Log file path for writing debugging information|\n",
-        "|**time_column_name**|Name of the datetime column in the input data|\n",
-        "|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
         "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
-        "|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used.|"
+        "|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used"
       ]
     },
     {
@@ -402,11 +416,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'grain_column_names': grain_column_names,\n",
+        "    time_column_name=time_column_name,\n",
-        "    'max_horizon': n_test_periods\n",
+        "    forecast_horizon=n_test_periods,\n",
-        "}\n",
+        "    time_series_id_column_names=time_series_id_column_names\n",
+        ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_oj_sales_errors.log',\n",
@@ -420,7 +435,7 @@
         "                             n_cross_validations=3,\n",
         "                             verbosity=logging.INFO,\n",
         "                             max_cores_per_iteration=-1,\n",
-        "                             **time_series_settings)"
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -537,9 +552,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The featurized data, aligned to y, will also be returned.\n",
+        "# forecast returns the predictions and the featurized data, aligned to X_test.\n",
         "# This contains the assumptions that were made in the forecast\n",
-        "# and helps align the forecast to the original data\n",
         "y_predictions, X_trans = fitted_model.forecast(X_test)"
       ]
     },
@@ -560,7 +574,7 @@
         "\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",
         "\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."
+        "We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics."
       ]
     },
     {
@@ -569,9 +583,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from forecasting_helper import align_outputs\n",
+        "assign_dict = {'predicted': y_predictions, target_column_name: y_test}\n",
-        "\n",
+        "df_all = X_test.assign(**assign_dict)"
-        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
       ]
     },
     {
@@ -794,5 +807,5 @@
     "task": "Forecasting"
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 4
 }

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/forecasting_helper.py

@@ -1,98 +0,0 @@
-import pandas as pd
-import numpy as np
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
-def do_rolling_forecast(fitted_model, X_test, y_test, target_column_name, time_column_name, max_horizon, freq='D'):
-    """
-    Produce forecasts on a rolling origin over the given test set.
-
-    Each iteration makes a forecast for the next 'max_horizon' periods
-    with respect to the current origin, then advances the origin by the
-    horizon time duration. The prediction context for each forecast is set so
-    that the forecaster uses the actual target values prior to the current
-    origin time for constructing lag features.
-
-    This function returns a concatenated DataFrame of rolling forecasts.
-     """
-    df_list = []
-    origin_time = X_test[time_column_name].min()
-    while origin_time <= X_test[time_column_name].max():
-        # Set the horizon time - end date of the forecast
-        horizon_time = origin_time + max_horizon * to_offset(freq)
-
-        # Extract test data from an expanding window up-to the horizon
-        expand_wind = (X_test[time_column_name] < horizon_time)
-        X_test_expand = X_test[expand_wind]
-        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
-        y_query_expand.fill(np.NaN)
-
-        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)
-            y_query_expand[context_expand_wind] = y_test[
-                test_context_expand_wind]
-
-        # Make a forecast out to the maximum horizon
-        y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)
-
-        # 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)
-        df_list.append(align_outputs(y_fcst[trans_roll_wind],
-                                     X_trans[trans_roll_wind],
-                                     X_test[test_roll_wind],
-                                     y_test[test_roll_wind],
-                                     target_column_name))
-
-        # Advance the origin time
-        origin_time = horizon_time
-
-    return pd.concat(df_list, ignore_index=True)

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/metrics_helper.py

@@ -1,22 +0,0 @@
-import pandas as pd
-import numpy as np
-
-
-def APE(actual, pred):
-    """
-    Calculate absolute percentage error.
-    Returns a vector of APE values with same length as actual/pred.
-    """
-    return 100 * np.abs((actual - pred) / actual)
-
-
-def MAPE(actual, pred):
-    """
-    Calculate mean absolute percentage error.
-    Remove NA and values where actual is close to zero
-    """
-    not_na = ~(np.isnan(actual) | np.isnan(pred))
-    not_zero = ~np.isclose(actual, 0.0)
-    actual_safe = actual[not_na & not_zero]
-    pred_safe = pred[not_na & not_zero]
-    return np.mean(APE(actual_safe, pred_safe))

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

@@ -80,7 +80,7 @@
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret._internal.explanation_client import ExplanationClient"
       ]
     },
     {
@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -354,7 +354,7 @@
       "metadata": {},
       "source": [
         "## Explanation\n",
-        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. We will also show how to run the automl model and the explainer model through deploying an AKS web service.\n",
+        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-interpret package. We will also show how to run the automl model and the explainer model through deploying an AKS web service.\n",
         "\n",
         "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",
@@ -434,7 +434,7 @@
       "metadata": {},
       "source": [
         "#### Initialize the Mimic Explainer for feature importance\n",
-        "For explaining the AutoML models, use the MimicWrapper from azureml.explain.model package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
+        "For explaining the AutoML models, use the MimicWrapper from azureml-interpret package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
       ]
     },
     {
@@ -443,7 +443,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
+        "from interpret.ext.glassbox import LGBMExplainableModel\n",
+        "from azureml.interpret.mimic_wrapper import MimicWrapper\n",
         "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator,\n",
         "                         explainable_model=automl_explainer_setup_obj.surrogate_model, \n",
         "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
@@ -486,7 +487,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer\n",
+        "from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer\n",
         "import joblib\n",
         "\n",
         "# Initialize the ScoringExplainer\n",
@@ -507,7 +508,7 @@
       "source": [
         "### Deploying the scoring and explainer models to a web service to Azure Kubernetes Service (AKS)\n",
         "\n",
-        "We use the TreeScoringExplainer from azureml.explain.model package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
+        "We use the TreeScoringExplainer from azureml.interpret package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
       ]
     },
     {
@@ -529,7 +530,7 @@
       "source": [
         "#### Create the conda dependencies for setting up the service\n",
         "\n",
-        "We need to create the conda dependencies comprising of the azureml-explain-model, azureml-train-automl and azureml-defaults packages."
+        "We need to download the conda dependencies using the automl_run object."
       ]
     },
     {
@@ -561,16 +562,10 @@
       "outputs": [],
       "source": [
         "%%writefile score.py\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import os\n",
-        "import pickle\n",
-        "import azureml.train.automl\n",
-        "import azureml.explain.model\n",
-        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \\\n",
-        "    automl_setup_model_explanations\n",
         "import joblib\n",
+        "import pandas as pd\n",
         "from azureml.core.model import Model\n",
+        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
         "\n",
         "\n",
         "def init():\n",

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

@@ -98,7 +98,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -625,7 +625,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
+        "from azureml.interpret._internal.explanation_client import ExplanationClient\n",
         "client = ExplanationClient.from_run(automl_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
@@ -659,7 +659,7 @@
         "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",
-        "We use the *TreeScoringExplainer* from *azureml.explain.model* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n",
+        "We use the *TreeScoringExplainer* from *azureml-interpret* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n",
         "In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
       ]
     },
@@ -681,7 +681,7 @@
       "metadata": {},
       "source": [
         "### Create the conda dependencies for setting up the service\n",
-        "We need to create the conda dependencies comprising of the *azureml-explain-model*, *azureml-train-automl* and *azureml-defaults* packages. "
+        "We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*."
       ]
     },
     {

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

@@ -1,14 +1,7 @@
-import json
-import numpy as np
 import pandas as pd
-import os
-import pickle
-import azureml.train.automl
-import azureml.explain.model
-from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
-    automl_setup_model_explanations
 import joblib
 from azureml.core.model import Model
+from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations
 
 
 def init():

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

@@ -1,17 +1,18 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import os
+import joblib
 
-from azureml.core.run import Run
+from interpret.ext.glassbox import LGBMExplainableModel
+from automl.client.core.common.constants import MODEL_PATH
 from azureml.core.experiment import Experiment
 from azureml.core.dataset import Dataset
-from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
+from azureml.core.run import Run
-    automl_setup_model_explanations, automl_check_model_if_explainable
+from azureml.interpret.mimic_wrapper import MimicWrapper
-from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
+from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer
-from azureml.explain.model.mimic_wrapper import MimicWrapper
+from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations, \
-from azureml.automl.core.shared.constants import MODEL_PATH
+    automl_check_model_if_explainable
-from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer
+
-import joblib
 
 OUTPUT_DIR = './outputs/'
 os.makedirs(OUTPUT_DIR, exist_ok=True)

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 1.11.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.14.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -0,0 +1,56 @@
+# Adding an init script to an Azure Databricks cluster
+
+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
+
+1. Create the base directory you want to store the init script in if it does not exist.
+    ```
+    dbutils.fs.mkdirs("dbfs:/databricks/init/")
+    ```
+
+2. Create the script azureml-cluster-init.sh
+    ```
+    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
+    """, True)
+    ```
+
+3. Check that the script exists.
+    ```
+    display(dbutils.fs.ls("dbfs:/databricks/init/azureml-cluster-init.sh"))
+    ```
+
+1. Configure the cluster to run the script.
+    * Using the cluster configuration page
+        1. On the cluster configuration page, click the Advanced Options toggle.
+        1. At the bottom of the page, click the Init Scripts tab.
+        1. In the Destination drop-down, select a destination type. Example: 'DBFS'
+        1. Specify a path to the init script.
+            ```
+            dbfs:/databricks/init/azureml-cluster-init.sh
+            ```
+        1. Click Add
+
+    * Using the API.
+        ```
+        curl -n -X POST -H 'Content-Type: application/json' -d '{
+        "cluster_id": "<cluster_id>",
+        "num_workers": <num_workers>,
+        "spark_version": "<spark_version>",
+        "node_type_id": "<node_type_id>",
+        "cluster_log_conf": {
+            "dbfs" : {
+            "destination": "dbfs:/cluster-logs"
+            }
+        },
+        "init_scripts": [ {
+            "dbfs": {
+            "destination": "dbfs:/databricks/init/azureml-cluster-init.sh"
+            }
+        } ]
+        }' https://<databricks-instance>/api/2.0/clusters/edit
+        ```

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

@@ -13,12 +13,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
+        "## AutoML Installation\n",
         "\n",
-        "**install azureml-sdk with Automated ML**\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",
-        "* Source: Upload Python Egg or PyPi\n",
+        "\n",
-        "* PyPi Name: `azureml-sdk[automl]`\n",
+        "%pip install -r https://aka.ms/automl_linux_requirements.txt\n",
-        "* Select Install Library"
+        "\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"
       ]
     },
     {

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

@@ -13,12 +13,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
+        "## AutoML Installation\n",
         "\n",
-        "**install azureml-sdk with Automated ML**\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",
-        "* Source: Upload Python Egg or PyPi\n",
+        "\n",
-        "* PyPi Name: `azureml-sdk[automl]`\n",
+        "%pip install -r https://aka.ms/automl_linux_requirements.txt\n",
-        "* Select Install Library"
+        "\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.**"
       ]
     },
     {

how-to-use-azureml/azureml-sdk-for-r/README.md

@@ -1,36 +0,0 @@
-## Examples to get started with Azure Machine Learning SDK for R
-
-Learn how to use Azure Machine Learning SDK for R for experimentation and model management.
-
-As a pre-requisite, go through the [Installation](vignettes/installation.Rmd) and [Configuration](vignettes/configuration.Rmd) vignettes to first install the package and set up your Azure Machine Learning Workspace unless you are running these examples on an Azure Machine Learning compute instance. Azure Machine Learning compute instances have the Azure Machine Learning SDK pre-installed and your workspace details pre-configured.
-
-
-Samples
-* Deployment
-	* [deploy-to-aci](./samples/deployment/deploy-to-aci): Deploy a model as a web service to Azure Container Instances (ACI).
-	* [deploy-to-local](./samples/deployment/deploy-to-local): Deploy a model as a web service locally.
-* Training
-	* [train-on-amlcompute](./samples/training/train-on-amlcompute): Train a model on a remote AmlCompute cluster.
-	* [train-on-local](./samples/training/train-on-local): Train a model locally with Docker.
-
-Vignettes
-* [deploy-to-aks](./vignettes/deploy-to-aks): Production deploy a model as a web service to Azure Kubernetes Service (AKS).
-* [hyperparameter-tune-with-keras](./vignettes/hyperparameter-tune-with-keras): Hyperparameter tune a Keras model using HyperDrive, Azure ML's hyperparameter tuning functionality.
-* [train-and-deploy-to-aci](./vignettes/train-and-deploy-to-aci): Train a caret model and deploy as a web service to Azure Container Instances (ACI).
-* [train-with-tensorflow](./vignettes/train-with-tensorflow): Train a deep learning TensorFlow model with Azure ML.
-
-Find more information on the [official documentation site for Azure Machine Learning SDK for R](https://azure.github.io/azureml-sdk-for-r/).
-
-
-### Troubleshooting
-
-- If the following error occurs when submitting an experiment using RStudio:
-   ```R
-    Error in py_call_impl(callable, dots$args, dots$keywords) : 
-     PermissionError: [Errno 13] Permission denied
-   ```
-  Move the files for your project into a subdirectory and reset the working directory to that directory before re-submitting.
-  
-  In order to submit an experiment, the Azure ML SDK must create a .zip file of the project directory to send to the service. However,
-  the SDK does not have permission to write into the .Rproj.user subdirectory that is automatically created during an RStudio
-  session. For this reason, the recommended best practice is to isolate project files into their own directory.

how-to-use-azureml/azureml-sdk-for-r/samples/README.md

@@ -1,11 +0,0 @@
-## Azure Machine Learning samples
-These samples are short code examples for using Azure Machine Learning SDK for R. If you are new to the R SDK, we recommend that you first take a look at the more detailed end-to-end [vignettes](../vignettes).
-
-Before running a sample in RStudio, set the working directory to the folder that contains the sample script in RStudio using `setwd(dirname)` or Session -> Set Working Directory -> To Source File Location. Each vignette assumes that the data and scripts are in the current working directory.
-
-1. [train-on-amlcompute](training/train-on-amlcompute): Train a model on a remote AmlCompute cluster.
-2. [train-on-local](training/train-on-local): Train a model locally with Docker.
-2. [deploy-to-aci](deployment/deploy-to-aci): Deploy a model as a web service to Azure Container Instances (ACI).
-3. [deploy-to-local](deployment/deploy-to-local): Deploy a model as a web service locally.
-
-> Before you run these samples, make sure you have an Azure Machine Learning workspace. You can follow the [configuration vignette](../vignettes/configuration.Rmd) to set up a workspace. (You do not need to do this if you are running these examples on an Azure Machine Learning compute instance).

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-aci/deploy-to-aci.R

@@ -1,59 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-library(azuremlsdk)
-library(jsonlite)
-
-ws <- load_workspace_from_config()
-
-# Register the model
-model <- register_model(ws, model_path = "project_files/model.rds",
-                        model_name = "model.rds")
-
-# Create environment
-r_env <- r_environment(name = "r_env")
-
-# Create inference config
-inference_config <- inference_config(
-  entry_script = "score.R",
-  source_directory = "project_files",
-  environment = r_env)
-
-# Create ACI deployment config
-deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,
-                                                      memory_gb = 1)
-
-# Deploy the web service
-service <- deploy_model(ws,
-                        'rservice',
-                        list(model),
-                        inference_config,
-                        deployment_config)
-wait_for_deployment(service, show_output = TRUE)
-
-# If you encounter any issue in deploying the webservice, please visit
-# https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment
-
-# Inferencing
-# versicolor
-plant <- data.frame(Sepal.Length = 6.4,
-                    Sepal.Width = 2.8,
-                    Petal.Length = 4.6,
-                    Petal.Width = 1.8)
-# setosa
-plant <- data.frame(Sepal.Length = 5.1,
-                    Sepal.Width = 3.5,
-                    Petal.Length = 1.4,
-                    Petal.Width = 0.2)
-# virginica
-plant <- data.frame(Sepal.Length = 6.7,
-                    Sepal.Width = 3.3,
-                    Petal.Length = 5.2,
-                    Petal.Width = 2.3)
-
-# Test the web service
-predicted_val <- invoke_webservice(service, toJSON(plant))
-predicted_val
-
-# Delete the web service
-delete_webservice(service)

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-aci/project_files/score.R

@@ -1,17 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-library(jsonlite)
-
-init <- function() {
-    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
-    model <- readRDS(file.path(model_path, "model.rds"))
-    message("model is loaded")
-
-    function(data) {
-        plant <- as.data.frame(fromJSON(data))
-        prediction <- predict(model, plant)
-        result <- as.character(prediction)
-        toJSON(result)
-    }
-}

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/deploy-to-local.R

@@ -1,112 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-# Register model and deploy locally
-# This example shows how to deploy a web service in step-by-step fashion:
-#   
-# 1) Register model
-# 2) Deploy the model as a web service in a local Docker container.
-# 3) Invoke web service with SDK or call web service with raw HTTP call.
-# 4) Quickly test changes to your entry script by reloading the local service.
-# 5) Optionally, you can also make changes to model and update the local service.
-
-library(azuremlsdk)
-library(jsonlite)
-
-ws <- load_workspace_from_config()
-
-# Register the model
-model <- register_model(ws, model_path = "project_files/model.rds",
-                        model_name = "model.rds")
-
-# Create environment
-r_env <- r_environment(name = "r_env")
-
-# Create inference config
-inference_config <- inference_config(
-  entry_script = "score.R",
-  source_directory = "project_files",
-  environment = r_env)
-
-# Create local deployment config
-local_deployment_config <- local_webservice_deployment_config()
-
-# Deploy the web service
-# NOTE:
-# The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:
-# # PowerShell command to switch to Linux engine
-# & 'C:\Program Files\Docker\Docker\DockerCli.exe' -SwitchLinuxEngine
-service <- deploy_model(ws,
-                        'rservice-local',
-                        list(model),
-                        inference_config,
-                        local_deployment_config)
-# Wait for deployment
-wait_for_deployment(service, show_output = TRUE)
-
-# Show the port of local service
-message(service$port)
-
-# If you encounter any issue in deploying the webservice, please visit
-# https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment
-
-# Inferencing
-# versicolor
-# plant <- data.frame(Sepal.Length = 6.4,
-#                     Sepal.Width = 2.8,
-#                     Petal.Length = 4.6,
-#                     Petal.Width = 1.8)
-# setosa
-plant <- data.frame(Sepal.Length = 5.1,
-                    Sepal.Width = 3.5,
-                    Petal.Length = 1.4,
-                    Petal.Width = 0.2)
-# # virginica
-# plant <- data.frame(Sepal.Length = 6.7,
-#                     Sepal.Width = 3.3,
-#                     Petal.Length = 5.2,
-#                     Petal.Width = 2.3)
-
-#Test the web service
-invoke_webservice(service, toJSON(plant))
-
-## The last few lines of the logs should have the correct prediction and should display -> R[write to console]: "setosa" 
-cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
-
-## Test the web service with a HTTP Raw request
-# 
-# NOTE:
-# To test the service locally use the https://localhost:<local_service$port> URL
-
-# Import the request library
-library(httr)
-# Get the service scoring URL from the service object, its URL is for testing locally
-local_service_url <- service$scoring_uri #Same as https://localhost:<local_service$port>
-
-#POST request to web service
-resp <- POST(local_service_url, body = plant, encode = "json", verbose())
-
-## The last few lines of the logs should have the correct prediction and should display -> R[write to console]: "setosa" 
-cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
-
-
-# Optional, use a new scoring script
-inference_config <- inference_config(
-  entry_script = "score_new.R",
-  source_directory = "project_files",
-  environment = r_env)
-
-## Then reload the service to see the changes made
-reload_local_webservice_assets(service)
-
-## Check reloaded service, you will see the last line will say "this is a new scoring script! I was reloaded"
-invoke_webservice(service, toJSON(plant))
-cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
-
-# Update service
-# If you want to change your model(s), environment, or deployment configuration, call update() to rebuild the Docker image.
-
-# update_local_webservice(service, models = [NewModelObject], deployment_config = deployment_config, wait = FALSE, inference_config = inference_config)
-
-# Delete service
-delete_local_webservice(service)

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/project_files/score.R

@@ -1,18 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-library(jsonlite)
-
-init <- function() {
-    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
-    model <- readRDS(file.path(model_path, "model.rds"))
-    message("model is loaded")
-
-    function(data) {
-        plant <- as.data.frame(fromJSON(data))
-        prediction <- predict(model, plant)
-        result <- as.character(prediction)
-        message(result)
-        toJSON(result)
-    }
-}

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/project_files/score_new.R

@@ -1,19 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-library(jsonlite)
-
-init <- function() {
-    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
-    model <- readRDS(file.path(model_path, "model.rds"))
-    message("model is loaded")
-
-    function(data) {
-        plant <- as.data.frame(fromJSON(data))
-        prediction <- predict(model, plant)
-        result <- as.character(prediction)
-        message(result)
-        message("this is a new scoring script! I was reloaded")
-        toJSON(result)
-    }
-}

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-amlcompute/scripts/train.R

@@ -1,34 +0,0 @@
-# This script loads a dataset of which the last column is supposed to be the
-# class and logs the accuracy
-
-library(azuremlsdk)
-library(caret)
-library(optparse)
-library(datasets)
-
-
-iris_data <- data(iris)
-summary(iris_data)
-
-in_train <- createDataPartition(y = iris_data$Species, p = .8, list = FALSE)
-train_data <- iris_data[in_train,]
-test_data <- iris_data[-in_train,]
-
-# Run algorithms using 10-fold cross validation
-control <- trainControl(method = "cv", number = 10)
-metric <- "Accuracy"
-
-set.seed(7)
-model <- train(Species ~ .,
-               data = train_data,
-               method = "lda",
-               metric = metric,
-               trControl = control)
-predictions <- predict(model, test_data)
-conf_matrix <- confusionMatrix(predictions, test_data$Species)
-message(conf_matrix)
-
-log_metric_to_run(metric, conf_matrix$overall["Accuracy"])
-
-saveRDS(model, file = "./outputs/model.rds")
-message("Model saved")

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-amlcompute/train-on-amlcompute.R

@@ -1,41 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-# Reminder: set working directory to current file location prior to running this script
-
-library(azuremlsdk)
-
-ws <- load_workspace_from_config()
-
-# Create AmlCompute cluster
-cluster_name <- "r-cluster"
-compute_target <- get_compute(ws, cluster_name = cluster_name)
-if (is.null(compute_target)) {
-    vm_size <- "STANDARD_D2_V2"
-    compute_target <- create_aml_compute(workspace = ws,
-                                       cluster_name = cluster_name,
-                                       vm_size = vm_size,
-                                       max_nodes = 1)
-
-    wait_for_provisioning_completion(compute_target, show_output = TRUE)
-}
-
-# Define estimator
-est <- estimator(source_directory = "scripts",
-                 entry_script = "train.R",
-                 compute_target = compute_target)
-
-experiment_name <- "train-r-script-on-amlcompute"
-exp <- experiment(ws, experiment_name)
-
-# Submit job and display the run details
-run <- submit_experiment(exp, est)
-view_run_details(run)
-wait_for_run_completion(run, show_output = TRUE)
-
-# Get the run metrics
-metrics <- get_run_metrics(run)
-metrics
-
-# Delete cluster
-delete_compute(compute_target)

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-local/scripts/train.R

@@ -1,28 +0,0 @@
-# This script loads a dataset of which the last column is supposed to be the
-# class and logs the accuracy
-
-library(azuremlsdk)
-library(caret)
-library(datasets)
-
-iris_data <- data(iris)
-summary(iris_data)
-
-in_train <- createDataPartition(y = iris_data$Species, p = .8, list = FALSE)
-train_data <- iris_data[in_train,]
-test_data <- iris_data[-in_train,]
-# Run algorithms using 10-fold cross validation
-control <- trainControl(method = "cv", number = 10)
-metric <- "Accuracy"
-
-set.seed(7)
-model <- train(Species ~ .,
-               data = train_data,
-               method = "lda",
-               metric = metric,
-               trControl = control)
-predictions <- predict(model, test_data)
-conf_matrix <- confusionMatrix(predictions, test_data$Species)
-message(conf_matrix)
-
-log_metric_to_run(metric, conf_matrix$overall["Accuracy"])

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-local/train-on-local.R

@@ -1,26 +0,0 @@
-# Copyright(c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-# Reminder: set working directory to current file location prior to running this script
-
-library(azuremlsdk)
-
-ws <- load_workspace_from_config()
-
-# Define estimator
-est <- estimator(source_directory = "scripts",
-                 entry_script = "train.R",
-                 compute_target = "local")
-
-# Initialize experiment
-experiment_name <- "train-r-script-on-local"
-exp <- experiment(ws, experiment_name)
-
-# Submit job and display the run details
-run <- submit_experiment(exp, est)
-view_run_details(run)
-wait_for_run_completion(run, show_output = TRUE)
-
-# Get the run metrics
-metrics <- get_run_metrics(run)
-metrics

how-to-use-azureml/azureml-sdk-for-r/vignettes/README.md

@@ -1,17 +0,0 @@
-## Azure Machine Learning vignettes
-
-These vignettes are end-to-end tutorials for using Azure Machine Learning SDK for R.
-
-Before running a vignette in RStudio, set the working directory to the folder that contains the vignette file (.Rmd file) in RStudio using `setwd(dirname)` or Session -> Set Working Directory -> To Source File Location. Each vignette assumes that the data and scripts are in the current working directory.
-
-The following vignettes are included:
-1. [installation](installation.Rmd): Install the Azure ML SDK for R.
-2. [configuration](configuration.Rmd): Set up an Azure ML workspace.
-3. [train-and-deploy-to-aci](train-and-deploy-to-aci): Train a caret model and deploy as a web service to Azure Container Instances (ACI).
-4. [train-with-tensorflow](train-with-tensorflow/): Train a deep learning TensorFlow model with Azure ML.
-5. [hyperparameter-tune-with-keras](hyperparameter-tune-with-keras/): Hyperparameter tune a Keras model using HyperDrive, Azure ML's hyperparameter tuning functionality.
-6. [deploy-to-aks](deploy-to-aks/): Production deploy a model as a web service to Azure Kubernetes Service (AKS).
-
-> Before you run these samples, make sure you have an Azure Machine Learning workspace. You can follow the [configuration vignette](../vignettes/configuration.Rmd) to set up a workspace. (You do not need to do this if you are running these examples on an Azure Machine Learning compute instance).
-
-For additional examples on using the R SDK, see the [samples](../samples) folder.

how-to-use-azureml/azureml-sdk-for-r/vignettes/configuration.Rmd

@@ -1,108 +0,0 @@
----
-title: "Set up an Azure ML workspace"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Set up an Azure ML workspace}
-  %\VignetteEngine{knitr::rmarkdown}
-  \use_package{UTF-8}
----
-
-This tutorial gets you started with the Azure Machine Learning service by walking through the requirements and instructions for setting up a workspace, the top-level resource for Azure ML.
-
-You do not need run this if you are working on an Azure Machine Learning Compute Instance, as the compute instance is already associated with an existing workspace.
-
-## What is an Azure ML workspace?
-The workspace is the top-level resource for Azure ML, providing a centralized place to work with all the artifacts you create when you use Azure ML. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts.
-
-When you create a new workspace, it automatically creates several Azure resources that are used by the workspace:
-
-* Azure Container Registry: Registers docker containers that you use during training and when you deploy a model. To minimize costs, ACR is lazy-loaded until deployment images are created.
-* Azure Storage account: Used as the default datastore for the workspace.
-* Azure Application Insights: Stores monitoring information about your models.
-* Azure Key Vault: Stores secrets that are used by compute targets and other sensitive information that's needed by the workspace.
-
-## Setup
-This section describes the steps required before you can access any Azure ML service functionality.
-
-### Azure subscription
-In order to create an Azure ML workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com/). Later in this tutorial you will need information such as your subscription ID in order to create and access workspaces.
-
-### Azure ML SDK installation
-Follow the [installation guide](https://azure.github.io/azureml-sdk-for-r/articles/installation.html) to install **azuremlsdk** on your machine.
-
-## Configure your workspace
-### Workspace parameters
-To use an Azure ML workspace, you will need to supply the following information:
-
-* Your subscription ID
-* A resource group name
-* (Optional) The region that will host your workspace
-* A name for your workspace
-
-You can get your subscription ID from the [Azure portal](https://portal.azure.com/).
-
-You will also need access to a [resource group](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the Azure portal. If you don't have a resource group, the `create_workspace()` method will create one for you using the name you provide.
-
-The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service). You should pick a region that is close to your location or that contains your data.
-
-The name for your workspace is unique within the subscription and should be descriptive enough to discern among other workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.
-
-The following code chunk allows you to specify your workspace parameters. It uses `Sys.getenv` to read values from environment variables, which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values. Replace the default values in the code below with your default parameter values.
-
-``` {r configure_parameters, eval=FALSE}
-subscription_id <- Sys.getenv("SUBSCRIPTION_ID", unset = "<my-subscription-id>")
-resource_group <- Sys.getenv("RESOURCE_GROUP", default="<my-resource-group>")
-workspace_name <- Sys.getenv("WORKSPACE_NAME", default="<my-workspace-name>")
-workspace_region <- Sys.getenv("WORKSPACE_REGION", default="eastus2")
-``` 
-
-### Create a new workspace
-If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, `create_workspace()` will create one for you using the name you provide. If you don't want it to do so, set the `create_resource_group = FALSE` parameter.
-
-Note: As with other Azure services, there are limits on certain resources (e.g. AmlCompute quota) associated with the Azure ML 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.
-
-This cell will create an Azure ML workspace for you in a subscription, provided you have the correct permissions.
-
-This will fail if:
-
-* You do not have permission to create a workspace in the resource group.
-* You do not have permission to create a resource group if it does not exist.
-* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.
-
-If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.
-
-There are additional parameters that are not shown below that can be configured when creating a workspace. Please see [`create_workspace()`](https://azure.github.io/azureml-sdk-for-r/reference/create_workspace.html) for more details.
-
-``` {r create_workspace, eval=FALSE}
-library(azuremlsdk)
-
-ws <- create_workspace(name = workspace_name,
-                       subscription_id = subscription_id,
-                       resource_group = resource_group,
-                       location = workspace_region,
-                       exist_ok = TRUE)
-```
-
-You can out write out the workspace ARM properties to a config file with [`write_workspace_config()`](https://azure.github.io/azureml-sdk-for-r/reference/write_workspace_config.html). The method provides a simple way of reusing the same workspace across multiple files or projects. Users can save the workspace details with `write_workspace_config()`, and use [`load_workspace_from_config()`](https://azure.github.io/azureml-sdk-for-r/reference/load_workspace_from_config.html) to load the same workspace in different files or projects without retyping the workspace ARM properties. The method defaults to writing out the config file to the current working directory with "config.json" as the file name. To specify a different path or file name, set the `path` and `file_name` parameters.
-
-``` {r write_config, eval=FALSE}
-write_workspace_config(ws)
-```
-
-### Access an existing workspace
-You can access an existing workspace in a couple of ways. If your workspace properties were previously saved to a config file, you can load the workspace as follows:
-
-``` {r load_config, eval=FALSE}
-ws <- load_workspace_from_config()
-```
-
-If Azure ML cannot find the config file, specify the path to the config file with the `path` parameter. The method defaults to starting the search in the current directory.
-
-You can also initialize a workspace using the [`get_workspace()`](https://azure.github.io/azureml-sdk-for-r/reference/get_workspace.html) method.
-
-``` {r get_workspace, eval=FALSE}
-ws <- get_workspace(name = workspace_name,
-                    subscription_id = subscription_id,
-                    resource_group = resource_group)
-```

how-to-use-azureml/azureml-sdk-for-r/vignettes/deploy-to-aks/deploy-to-aks.Rmd

@@ -1,188 +0,0 @@
----
-title: "Deploy a web service to Azure Kubernetes Service"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Deploy a web service to Azure Kubernetes Service}
-  %\VignetteEngine{knitr::rmarkdown}
-  \use_package{UTF-8}
----
-
-This tutorial demonstrates how to deploy a model as a web service on [Azure Kubernetes Service](https://azure.microsoft.com/en-us/services/kubernetes-service/) (AKS). AKS is good for high-scale production deployments; use it if you need one or more of the following capabilities:
-
-* Fast response time
-* Autoscaling of the deployed service
-* Hardware acceleration options such as GPU
-
-You will learn to:
-
-* Set up your testing environment
-* Register a model
-* Provision an AKS cluster
-* Deploy the model to AKS
-* Test the deployed service
-
-## Prerequisites
-If you don’t have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
-
-## Set up your testing environment
-Start by setting up your environment. This includes importing the **azuremlsdk** package and connecting to your workspace.
-
-### Import package
-```{r import_package, eval=FALSE}
-library(azuremlsdk)
-```
-
-### Load your workspace
-Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a **config.json** file if you previously wrote one out with `write_workspace_config()`. 
-```{r load_workspace, eval=FALSE}
-ws <- load_workspace_from_config()
-```
-
-Or, you can retrieve a workspace by directly specifying your workspace details:
-```{r get_workspace, eval=FALSE}
-ws <- get_workspace("<your workspace name>", "<your subscription ID>", "<your resource group>")
-```
-
-## Register the model
-In this tutorial we will deploy a model that was trained in one of the [samples](https://github.com/Azure/azureml-sdk-for-r/blob/master/samples/training/train-on-amlcompute/train-on-amlcompute.R). The model was trained with the Iris dataset and can be used to determine if a flower is one of three Iris flower species (setosa, versicolor, virginica). We have provided the model file (`model.rds`) for the tutorial; it is located in the "project_files" directory of this vignette.
-
-First, register the model to your workspace with [`register_model()`](https://azure.github.io/azureml-sdk-for-r/reference/register_model.html). A registered model can be any collection of files, but in this case the R model file is sufficient. Azure ML will use the registered model for deployment.
-
-```{r register_model, eval=FALSE}
-model <- register_model(ws, 
-                        model_path = "project_files/model.rds", 
-                        model_name = "iris_model",
-                        description = "Predict an Iris flower type")
-```
-
-## Provision an AKS cluster
-When deploying a web service to AKS, you deploy to an AKS cluster that is connected to your workspace. There are two ways to connect an AKS cluster to your workspace:
-
-* Create the AKS cluster. The process automatically connects the cluster to the workspace. 
-* Attach an existing AKS cluster to your workspace. You can attach a cluster with the [`attach_aks_compute()`](https://azure.github.io/azureml-sdk-for-r/reference/attach_aks_compute.html) method.
-
-Creating or attaching an AKS cluster is a one-time process for your workspace. You can reuse this cluster for multiple deployments. If you delete the cluster or the resource group that contains it, you must create a new cluster the next time you need to deploy.
-
-In this tutorial, we will go with the first method of provisioning a new cluster. See the [`create_aks_compute()`](https://azure.github.io/azureml-sdk-for-r/reference/create_aks_compute.html) reference for the full set of configurable parameters. If you pick custom values for the `agent_count` and `vm_size` parameters, you need to make sure `agent_count` multiplied by `vm_size` is greater than or equal to `12` virtual CPUs.
-
-``` {r provision_cluster, eval=FALSE}
-aks_target <- create_aks_compute(ws, cluster_name = 'myakscluster')
-
-wait_for_provisioning_completion(aks_target, show_output = TRUE)
-```
-
-The Azure ML SDK does not provide support for scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure portal. You can only change the node count, not the VM size of the cluster.
-
-## Deploy as a web service
-### Define the inference dependencies
-To deploy a model, you need an **inference configuration**, which describes the environment needed to host the model and web service. To create an inference config, you will first need a scoring script and an Azure ML environment.
-
-The scoring script (`entry_script`) is an R script that will take as input variable values (in JSON format) and output a prediction from your model. For this tutorial, use the provided scoring file `score.R`. The scoring script must contain an `init()` method that loads your model and returns a function that uses the model to make a prediction based on the input data. See the [documentation](https://azure.github.io/azureml-sdk-for-r/reference/inference_config.html#details) for more details.
-
-Next, define an Azure ML **environment** for your script’s package dependencies. With an environment, you specify R packages (from CRAN or elsewhere) that are needed for your script to run. You can also provide the values of environment variables that your script can reference to modify its behavior. 
-
-By default Azure ML will build a default Docker image that includes R, the Azure ML SDK, and additional required dependencies for deployment. See the documentation here for the full list of dependencies that will be installed in the default container. You can also specify additional packages to be installed at runtime, or even a custom Docker image to be used instead of the base image that will be built, using the other available parameters to [`r_environment()`](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html).
-
-```{r create_env, eval=FALSE}
-r_env <- r_environment(name = "deploy_env")
-```
-
-Now you have everything you need to create an inference config for encapsulating your scoring script and environment dependencies.
-
-``` {r create_inference_config, eval=FALSE}
-inference_config <- inference_config(
-  entry_script = "score.R",
-  source_directory = "project_files",
-  environment = r_env)
-```
-
-### Deploy to AKS
-Now, define the deployment configuration that describes the compute resources needed, for example, the number of cores and memory. See the [`aks_webservice_deployment_config()`](https://azure.github.io/azureml-sdk-for-r/reference/aks_webservice_deployment_config.html) for the full set of configurable parameters.
-
-``` {r deploy_config, eval=FALSE}
-aks_config <- aks_webservice_deployment_config(cpu_cores = 1, memory_gb = 1)
-```
-
-Now, deploy your model as a web service to the AKS cluster you created earlier.
-
-```{r deploy_service, eval=FALSE}
-aks_service <- deploy_model(ws, 
-                            'my-new-aksservice', 
-                            models = list(model), 
-                            inference_config = inference_config, 
-                            deployment_config = aks_config,
-                            deployment_target = aks_target)
-
-wait_for_deployment(aks_service, show_output = TRUE)
-```
-
-To inspect the logs from the deployment:
-```{r get_logs, eval=FALSE}
-get_webservice_logs(aks_service)
-```
-
-If you encounter any issue in deploying the web service, please visit the [troubleshooting guide](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment).
-
-## Test the deployed service
-Now that your model is deployed as a service, you can test the service from R using [`invoke_webservice()`](https://azure.github.io/azureml-sdk-for-r/reference/invoke_webservice.html). Provide a new set of data to predict from, convert it to JSON, and send it to the service.
-
-``` {r test_service, eval=FALSE}
-library(jsonlite)
-# versicolor
-plant <- data.frame(Sepal.Length = 6.4,
-                    Sepal.Width = 2.8,
-                    Petal.Length = 4.6,
-                    Petal.Width = 1.8)
-
-# setosa
-# plant <- data.frame(Sepal.Length = 5.1,
-#                    Sepal.Width = 3.5,
-#                    Petal.Length = 1.4,
-#                    Petal.Width = 0.2)
-
-# virginica
-# plant <- data.frame(Sepal.Length = 6.7,
-#                    Sepal.Width = 3.3,
-#                    Petal.Length = 5.2,
-#                    Petal.Width = 2.3)
-
-predicted_val <- invoke_webservice(aks_service, toJSON(plant))
-message(predicted_val)
-```
-
-You can also get the web service’s HTTP endpoint, which accepts REST client calls. You can share this endpoint with anyone who wants to test the web service or integrate it into an application.
-
-``` {r eval=FALSE}
-aks_service$scoring_uri
-```
-
-## Web service authentication
-When deploying to AKS, key-based authentication is enabled by default. You can also enable token-based authentication. Token-based authentication requires clients to use an Azure Active Directory account to request an authentication token, which is used to make requests to the deployed service.
-
-To disable key-based auth, set the `auth_enabled = FALSE` parameter when creating the deployment configuration with [`aks_webservice_deployment_config()`](https://azure.github.io/azureml-sdk-for-r/reference/aks_webservice_deployment_config.html). 
-To enable token-based auth, set `token_auth_enabled = TRUE` when creating the deployment config.
-
-### Key-based authentication
-If key authentication is enabled, you can use the [`get_webservice_keys()`](https://azure.github.io/azureml-sdk-for-r/reference/get_webservice_keys.html) method to retrieve a primary and secondary authentication key. To generate a new key, use [`generate_new_webservice_key()`](https://azure.github.io/azureml-sdk-for-r/reference/generate_new_webservice_key.html).
-
-### Token-based authentication
-If token authentication is enabled, you can use the [`get_webservice_token()`](https://azure.github.io/azureml-sdk-for-r/reference/get_webservice_token.html) method to retrieve a JWT token and that token's expiration time. Make sure to request a new token after the token's expiration time.
-
-## Clean up resources
-Delete the resources once you no longer need them. Do not delete any resource you plan on still using.
-
-Delete the web service:
-```{r delete_service, eval=FALSE}
-delete_webservice(aks_service)
-```
-
-Delete the registered model:
-```{r delete_model, eval=FALSE}
-delete_model(model)
-```
-
-Delete the AKS cluster:
-```{r delete_cluster, eval=FALSE}
-delete_compute(aks_target)
-```

how-to-use-azureml/azureml-sdk-for-r/vignettes/deploy-to-aks/project_files/score.R

@@ -1,17 +0,0 @@
-#' Copyright(c) Microsoft Corporation.
-#' Licensed under the MIT license.
-
-library(jsonlite)
-
-init <- function() {
-    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
-    model <- readRDS(file.path(model_path, "model.rds"))
-    message("model is loaded")
-
-    function(data) {
-        plant <- as.data.frame(fromJSON(data))
-        prediction <- predict(model, plant)
-        result <- as.character(prediction)
-        toJSON(result)
-    }
-}

how-to-use-azureml/azureml-sdk-for-r/vignettes/hyperparameter-tune-with-keras/hyperparameter-tune-with-keras.Rmd

@@ -1,242 +0,0 @@
----
-title: "Hyperparameter tune a Keras model"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Hyperparameter tune a Keras model}
-  %\VignetteEngine{knitr::rmarkdown}
-  \use_package{UTF-8}
----
-
-This tutorial demonstrates how you can efficiently tune hyperparameters for a model using HyperDrive, Azure ML's hyperparameter tuning functionality. You will train a Keras model on the CIFAR10 dataset, automate hyperparameter exploration, launch parallel jobs, log your results, and find the best run.
-
-### What are hyperparameters?
-
-Hyperparameters are variable parameters chosen to train a model. Learning rate, number of epochs, and batch size are all examples of hyperparameters.
-
-Using brute-force methods to find the optimal values for parameters can be time-consuming, and poor-performing runs can result in wasted money. To avoid this, HyperDrive automates hyperparameter exploration in a time-saving and cost-effective manner by launching several parallel runs with different configurations and finding the configuration that results in best performance on your primary metric.
-
-Let's get started with the example to see how it works!
-
-## Prerequisites
-
-If you don’t have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
-
-## Set up development environment
-The setup for your development work in this tutorial includes the following actions:
-
-* Import required packages
-* Connect to a workspace
-* Create an experiment to track your runs
-* Create a remote compute target to use for training
-
-### Import **azuremlsdk** package
-```{r eval=FALSE}
-library(azuremlsdk)
-```
-
-### Load your workspace
-Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a **config.json** file if you previously wrote one out with [`write_workspace_config()`](https://azure.github.io/azureml-sdk-for-r/reference/write_workspace_config.html).
-```{r load_workpace, eval=FALSE}
-ws <- load_workspace_from_config()
-```
-
-Or, you can retrieve a workspace by directly specifying your workspace details:
-```{r get_workpace, eval=FALSE}
-ws <- get_workspace("<your workspace name>", "<your subscription ID>", "<your resource group>")
-```
-
-### Create an experiment
-An Azure ML **experiment** tracks a grouping of runs, typically from the same training script. Create an experiment to track hyperparameter tuning runs for the Keras model.
-
-```{r create_experiment, eval=FALSE}
-exp <- experiment(workspace = ws, name = 'hyperdrive-cifar10')
-```
-
-If you would like to track your runs in an existing experiment, simply specify that experiment's name to the `name` parameter of `experiment()`.
-
-### Create a compute target
-By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. In this tutorial, you create a GPU-enabled cluster as your training environment. The code below creates the compute cluster for you if it doesn't already exist in your workspace.
-
-You may need to wait a few minutes for your compute cluster to be provisioned if it doesn't already exist.
-
-```{r create_cluster, eval=FALSE}
-cluster_name <- "gpucluster"
-
-compute_target <- get_compute(ws, cluster_name = cluster_name)
-if (is.null(compute_target))
-{
-  vm_size <- "STANDARD_NC6"
-  compute_target <- create_aml_compute(workspace = ws, 
-                                       cluster_name = cluster_name,
-                                       vm_size = vm_size, 
-                                       max_nodes = 4)
-  
-  wait_for_provisioning_completion(compute_target, show_output = TRUE)
-}
-```
-
-## Prepare the training script
-A training script called `cifar10_cnn.R` has been provided for you in the "project_files" directory of this tutorial.
-
-In order to leverage HyperDrive, the training script for your model must log the relevant metrics during model training. When you configure the hyperparameter tuning run, you specify the primary metric to use for evaluating run performance. You must log this metric so it is available to the hyperparameter tuning process.
-
-In order to log the required metrics, you need to do the following **inside the training script**:
-
-* Import the **azuremlsdk** package
-```
-library(azuremlsdk)
-```
-
-* Take the hyperparameters as command-line arguments to the script. This is necessary so that when HyperDrive carries out the hyperparameter sweep, it can run the training script with different values to the hyperparameters as defined by the search space.
-
-* Use the [`log_metric_to_run()`](https://azure.github.io/azureml-sdk-for-r/reference/log_metric_to_run.html) function to log the hyperparameters and the primary metric.
-```
-log_metric_to_run("batch_size", batch_size)
-...
-log_metric_to_run("epochs", epochs)
-...
-log_metric_to_run("lr", lr)
-...
-log_metric_to_run("decay", decay)
-...
-log_metric_to_run("Loss", results[[1]])
-```
-
-## Create an estimator
-
-An Azure ML **estimator** encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target. By default, the Docker image built for your training job will include R, the Azure ML SDK, and a set of commonly used R packages. See the full list of default packages included [here](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html). The estimator is used to define the configuration for each of the child runs that the parent HyperDrive run will kick off.
-
-To create the estimator, define the following:
-
-* The directory that contains your scripts needed for training (`source_directory`). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
-* The training script that will be executed (`entry_script`).
-* The compute target (`compute_target`), in this case the AmlCompute cluster you created earlier.
-* Any environment dependencies required for training. Since the training script requires the Keras package, which is not included in the image by default, pass the package name to the `cran_packages` parameter to have it installed in the Docker container where the job will run. See the [`estimator()`](https://azure.github.io/azureml-sdk-for-r/reference/estimator.html) reference for the full set of configurable options.
-* Set the `use_gpu = TRUE` flag so the default base GPU Docker image will be built, since the job will be run on a GPU cluster.
-
-```{r create_estimator, eval=FALSE}
-est <- estimator(source_directory = "project_files",
-                 entry_script = "cifar10_cnn.R",
-                 compute_target = compute_target,
-                 cran_packages = c("keras"),
-                 use_gpu = TRUE)
-```
-
-## Configure the HyperDrive run
-To kick off hyperparameter tuning in Azure ML, you will need to configure a HyperDrive run, which will in turn launch individual children runs of the training scripts with the corresponding hyperparameter values.
-
-### Define search space
-
-In this experiment, we will use four hyperparameters: batch size, number of epochs, learning rate, and decay. In order to begin tuning, we must define the range of values we would like to explore from and how they will be distributed. This is called a parameter space definition and can be created with discrete or continuous ranges.
-
-__Discrete hyperparameters__ are specified as a choice among discrete values represented as a list.
-
-Advanced discrete hyperparameters can also be specified using a distribution. The following distributions are supported:
-
- * `quniform(low, high, q)`
- * `qloguniform(low, high, q)`
- * `qnormal(mu, sigma, q)`
- * `qlognormal(mu, sigma, q)`
-
-__Continuous hyperparameters__ are specified as a distribution over a continuous range of values. The following distributions are supported:
-
- * `uniform(low, high)`
- * `loguniform(low, high)`
- * `normal(mu, sigma)`
- * `lognormal(mu, sigma)`
-
-Here, we will use the [`random_parameter_sampling()`](https://azure.github.io/azureml-sdk-for-r/reference/random_parameter_sampling.html) function to define the search space for each hyperparameter. `batch_size` and `epochs` will be chosen from discrete sets while `lr` and `decay` will be drawn from continuous distributions.
-
-Other available sampling function options are:
-
- * [`grid_parameter_sampling()`](https://azure.github.io/azureml-sdk-for-r/reference/grid_parameter_sampling.html)
- * [`bayesian_parameter_sampling()`](https://azure.github.io/azureml-sdk-for-r/reference/bayesian_parameter_sampling.html)
-
-```{r search_space, eval=FALSE}
-sampling <- random_parameter_sampling(list(batch_size = choice(c(16, 32, 64)),
-                                           epochs = choice(c(200, 350, 500)),
-                                           lr = normal(0.0001, 0.005),
-                                           decay = uniform(1e-6, 3e-6)))
-```
-
-### Define termination policy
-
-To prevent resource waste, Azure ML can detect and terminate poorly performing runs. HyperDrive will do this automatically if you specify an early termination policy.
-
-Here, you will use the [`bandit_policy()`](https://azure.github.io/azureml-sdk-for-r/reference/bandit_policy.html), which terminates any runs where the primary metric is not within the specified slack factor with respect to the best performing training run.
-
-```{r termination_policy, eval=FALSE}
-policy <- bandit_policy(slack_factor = 0.15)
-```
-
-Other termination policy options are:
-
- * [`median_stopping_policy()`](https://azure.github.io/azureml-sdk-for-r/reference/median_stopping_policy.html)
- * [`truncation_selection_policy()`](https://azure.github.io/azureml-sdk-for-r/reference/truncation_selection_policy.html)
- 
-If no policy is provided, all runs will continue to completion regardless of performance.
-
-### Finalize configuration
-
-Now, you can create a `HyperDriveConfig` object to define your HyperDrive run. Along with the sampling and policy definitions, you need to specify the name of the primary metric that you want to track and whether we want to maximize it or minimize it. The `primary_metric_name` must correspond with the name of the primary metric you logged in your training script. `max_total_runs` specifies the total number of child runs to launch. See the [hyperdrive_config()](https://azure.github.io/azureml-sdk-for-r/reference/hyperdrive_config.html) reference for the full set of configurable parameters.
-
-```{r create_config, eval=FALSE}
-hyperdrive_config <- hyperdrive_config(hyperparameter_sampling = sampling,
-                                       primary_metric_goal("MINIMIZE"),
-                                       primary_metric_name = "Loss",
-                                       max_total_runs = 4,
-                                       policy = policy,
-                                       estimator = est)
-```
-
-## Submit the HyperDrive run
-
-Finally submit the experiment to run on your cluster. The parent HyperDrive run will launch the individual child runs. `submit_experiment()` will return a `HyperDriveRun` object that you will use to interface with the run. In this tutorial, since the cluster we created scales to a max of `4` nodes, all 4 child runs will be launched in parallel.
-
-```{r submit_run, eval=FALSE}
-hyperdrive_run <- submit_experiment(exp, hyperdrive_config)
-```
-
-You can view the HyperDrive run’s details as a table. Clicking the “Web View” link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
-
-```{r eval=FALSE}
-view_run_details(hyperdrive_run)
-```
-
-Wait until hyperparameter tuning is complete before you run more code.
-
-```{r eval=FALSE}
-wait_for_run_completion(hyperdrive_run, show_output = TRUE)
-```
-
-## Analyse runs by performance
-
-Finally, you can view and compare the metrics collected during all of the child runs!
-
-```{r analyse_runs, eval=FALSE}
-# Get the metrics of all the child runs
-child_run_metrics <- get_child_run_metrics(hyperdrive_run)
-child_run_metrics
-
-# Get the child run objects sorted in descending order by the best primary metric
-child_runs <- get_child_runs_sorted_by_primary_metric(hyperdrive_run)
-child_runs
-
-# Directly get the run object of the best performing run
-best_run <- get_best_run_by_primary_metric(hyperdrive_run)
-
-# Get the metrics of the best performing run
-metrics <- get_run_metrics(best_run)
-metrics
-```
-
-The `metrics` variable will include the values of the hyperparameters that resulted in the best performing run.
-
-## Clean up resources
-Delete the resources once you no longer need them. Don't delete any resource you plan to still use. 
-
-Delete the compute cluster:
-```{r delete_compute, eval=FALSE}
-delete_compute(compute_target)
-```

how-to-use-azureml/azureml-sdk-for-r/vignettes/hyperparameter-tune-with-keras/project_files/cifar10_cnn.R

@@ -1,124 +0,0 @@
-#' Modified from: "https://github.com/rstudio/keras/blob/master/vignettes/
-#' examples/cifar10_cnn.R"
-#' 
-#' Train a simple deep CNN on the CIFAR10 small images dataset.
-#'  
-#' It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50
-#' epochs, though it is still underfitting at that point.
-
-library(keras)
-install_keras()
-
-library(azuremlsdk)
-
-# Parameters --------------------------------------------------------------
-
-args <- commandArgs(trailingOnly = TRUE)
-
-batch_size <- as.numeric(args[2])
-log_metric_to_run("batch_size", batch_size)
-
-epochs <- as.numeric(args[4])
-log_metric_to_run("epochs", epochs)
-
-lr <- as.numeric(args[6])
-log_metric_to_run("lr", lr)
-
-decay <- as.numeric(args[8])
-log_metric_to_run("decay", decay)
-
-data_augmentation <- TRUE
-
-
-# Data Preparation --------------------------------------------------------
-
-# See ?dataset_cifar10 for more info
-cifar10 <- dataset_cifar10()
-
-# Feature scale RGB values in test and train inputs  
-x_train <- cifar10$train$x / 255
-x_test <- cifar10$test$x / 255
-y_train <- to_categorical(cifar10$train$y, num_classes = 10)
-y_test <- to_categorical(cifar10$test$y, num_classes = 10)
-
-
-# Defining Model ----------------------------------------------------------
-
-# Initialize sequential model
-model <- keras_model_sequential()
-
-model %>%
-
-# Start with hidden 2D convolutional layer being fed 32x32 pixel images
-layer_conv_2d(
-    filter = 32, kernel_size = c(3, 3), padding = "same",
-    input_shape = c(32, 32, 3)
-  ) %>%
-  layer_activation("relu") %>%
-
-  # Second hidden layer
-  layer_conv_2d(filter = 32, kernel_size = c(3, 3)) %>%
-  layer_activation("relu") %>%
-
-  # Use max pooling
-  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
-  layer_dropout(0.25) %>%
-
-  # 2 additional hidden 2D convolutional layers
-  layer_conv_2d(filter = 32, kernel_size = c(3, 3), padding = "same") %>%
-  layer_activation("relu") %>%
-  layer_conv_2d(filter = 32, kernel_size = c(3, 3)) %>%
-  layer_activation("relu") %>%
-
-  # Use max pooling once more
-  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
-  layer_dropout(0.25) %>%
-
-  # Flatten max filtered output into feature vector 
-  # and feed into dense layer
-  layer_flatten() %>%
-  layer_dense(512) %>%
-  layer_activation("relu") %>%
-  layer_dropout(0.5) %>%
-
-  # Outputs from dense layer are projected onto 10 unit output layer
-  layer_dense(10) %>%
-  layer_activation("softmax")
-
-opt <- optimizer_rmsprop(lr, decay)
-
-model %>%
-    compile(loss = "categorical_crossentropy",
-          optimizer = opt,
-          metrics = "accuracy"
-)
-
-
-# Training ----------------------------------------------------------------
-
-if (!data_augmentation) {
-
-    model %>%
-    fit(x_train,
-        y_train,
-        batch_size = batch_size,
-        epochs = epochs,
-        validation_data = list(x_test, y_test),
-        shuffle = TRUE
-  )
-
-} else {
-
-    datagen <- image_data_generator(rotation_range = 20,
-                                  width_shift_range = 0.2,
-                                  height_shift_range = 0.2,
-                                  horizontal_flip = TRUE
-  )
-
-    datagen %>% fit_image_data_generator(x_train)
-
-    results <- evaluate(model, x_train, y_train, batch_size)
-    log_metric_to_run("Loss", results[[1]])
-    cat("Loss: ", results[[1]], "\n")
-    cat("Accuracy: ", results[[2]], "\n")
-}

how-to-use-azureml/azureml-sdk-for-r/vignettes/installation.Rmd

@@ -1,100 +0,0 @@
----
-title: "Install the Azure ML SDK for R"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Install the Azure ML SDK for R}
-  %\VignetteEngine{knitr::rmarkdown}
-  \use_package{UTF-8}
----
-
-This article covers the step-by-step instructions for installing the Azure ML SDK for R.
-
-You do not need run this if you are working on an Azure Machine Learning Compute Instance, as the compute instance already has the Azure ML SDK preinstalled.
-
-## Install Conda
-
-If you do not have Conda already installed on your machine, you will first need to install it, since the Azure ML R SDK uses **reticulate** to bind to the Python SDK. We recommend installing [Miniconda](https://docs.conda.io/en/latest/miniconda.html), which is a smaller, lightweight version of Anaconda. Choose the 64-bit binary for Python 3.5 or later.
-
-## Install the **azuremlsdk** R package
-You will need **remotes** to install **azuremlsdk** from the GitHub repo.
-``` {r install_remotes, eval=FALSE}
-install.packages('remotes')
-``` 
-
-Then, you can use the `install_github` function to install the package.
-``` {r install_azuremlsdk, eval=FALSE}
-remotes::install_cran('azuremlsdk', repos = 'https://cloud.r-project.org/')
-```
-
-If you are using R installed from CRAN, which comes with 32-bit and 64-bit binaries, you may need to specify the parameter `INSTALL_opts=c("--no-multiarch")` to only build for the current 64-bit architecture.
-``` {r eval=FALSE}
-remotes::install_cran('azuremlsdk', repos = 'https://cloud.r-project.org/', INSTALL_opts=c("--no-multiarch"))
-```
-
-## Install the Azure ML Python SDK
-Lastly, use the **azuremlsdk** R library to install the Python SDK. By default, `azuremlsdk::install_azureml()` will install the [latest version of the Python SDK](https://pypi.org/project/azureml-sdk/) in a conda environment called `r-azureml` if reticulate < 1.14 or `r-reticulate` if reticulate ≥ 1.14.
-``` {r install_pythonsdk, eval=FALSE}
-azuremlsdk::install_azureml()
-```
-
-If you would like to override the default version, environment name, or Python version, you can pass in those arguments. If you would like to restart the R session after installation or delete the conda environment if it already exists and create a new environment, you can also do so: 
-``` {r eval=FALSE}
-azuremlsdk::install_azureml(version = NULL, 
-                            custom_envname = "<your conda environment name>",
-                            conda_python_version = "<desired python version>",
-                            restart_session = TRUE,
-                            remove_existing_env = TRUE)
-```
-
-## Test installation
-You can confirm your installation worked by loading the library and successfully retrieving a run.
-``` {r test_installation, eval=FALSE}
-library(azuremlsdk)
-get_current_run()
-```
-
-## Troubleshooting
--   In step 3 of the installation, if you get ssl errors on windows, it is due to an
-outdated openssl binary. Install the latest openssl binaries from
-[here](https://wiki.openssl.org/index.php/Binaries).
-
-- If installation fails due to this error:
-
-  ```R
-  Error in strptime(xx, f, tz = tz) : 
-    (converted from warning) unable to identify current timezone 'C':
-  please set environment variable 'TZ'
-  In R CMD INSTALL
-  Error in i.p(...) : 
-    (converted from warning) installation of package ‘C:/.../azureml_0.4.0.tar.gz’ had non-zero exit
-    status
-  ```
-  
-  You will need to set your time zone environment variable to GMT and restart the installation process.
-  
-  ```R
-  Sys.setenv(TZ='GMT')
-  ```
-
-- If the following permission error occurs while installing in RStudio,
-  change your RStudio session to administrator mode, and re-run the installation command.
-
-  ```R
-  Downloading GitHub repo Azure/azureml-sdk-for-r@master
-  Skipping 2 packages ahead of CRAN: reticulate, rlang
-  Running `R CMD build`...
-  
-  Error: (converted from warning) invalid package
-  'C:/.../file2b441bf23631'
-  In R CMD INSTALL
-  Error in i.p(...) : 
-    (converted from warning) installation of package
-    ‘C:/.../file2b441bf23631’ had non-zero exit status
-  In addition: Warning messages:
-  1: In file(con, "r") :
-    cannot open file 'C:...\file2b44144a540f': Permission denied
-  2: In file(con, "r") :
-    cannot open file 'C:...\file2b4463c21577': Permission denied
-  ```
-  

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-and-deploy-to-aci/project_files/accident_predict.R

@@ -1,16 +0,0 @@
-#' Copyright(c) Microsoft Corporation.
-#' Licensed under the MIT license.
-
-library(jsonlite)
-
-init <- function() {
-    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
-    model <- readRDS(file.path(model_path, "model.rds"))
-    message("logistic regression model loaded")
-
-    function(data) {
-        vars <- as.data.frame(fromJSON(data))
-        prediction <- as.numeric(predict(model, vars, type = "response") * 100)
-        toJSON(prediction)
-    }
-}

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-and-deploy-to-aci/project_files/accidents.R

@@ -1,33 +0,0 @@
-#' Copyright(c) Microsoft Corporation.
-#' Licensed under the MIT license.
-
-library(azuremlsdk)
-library(optparse)
-library(caret)
-
-options <- list(
-  make_option(c("-d", "--data_folder"))
-)
-
-opt_parser <- OptionParser(option_list = options)
-opt <- parse_args(opt_parser)
-
-paste(opt$data_folder)
-
-accidents <- readRDS(file.path(opt$data_folder, "accidents.Rd"))
-summary(accidents)
-
-mod <- glm(dead ~ dvcat + seatbelt + frontal + sex + ageOFocc + yearVeh + airbag + occRole, family = binomial, data = accidents)
-summary(mod)
-predictions <- factor(ifelse(predict(mod) > 0.1, "dead", "alive"))
-conf_matrix <- confusionMatrix(predictions, accidents$dead)
-message(conf_matrix)
-
-log_metric_to_run("Accuracy", conf_matrix$overall["Accuracy"])
-
-output_dir = "outputs"
-if (!dir.exists(output_dir)) {
-    dir.create(output_dir)
-}
-saveRDS(mod, file = "./outputs/model.rds")
-message("Model saved")

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-and-deploy-to-aci/train-and-deploy-to-aci.Rmd

@@ -1,326 +0,0 @@
----
-title: "Train and deploy your first model with Azure ML"
-author: "David Smith"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Train and deploy your first model with Azure ML}
-  %\VignetteEngine{knitr::rmarkdown}
-  \use_package{UTF-8}
----
-
-In this tutorial, you learn the foundational design patterns in Azure Machine Learning.  You'll train and deploy a **caret** model to predict the likelihood of a fatality in an automobile accident. After completing this tutorial, you'll have the practical knowledge of the R SDK to scale up to developing more-complex experiments and workflows.
-
-In this tutorial, you learn the following tasks:
-
-* Connect your workspace
-* Load data and prepare for training
-* Upload data to the datastore so it is available for remote training
-* Create a compute resource
-* Train a caret model to predict probability of fatality
-* Deploy a prediction endpoint
-* Test the model from R
-
-## Prerequisites
-
-If you don't have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
-
-## Set up your development environment
-The setup for your development work in this tutorial includes the following actions:
-
-* Install required packages
-* Connect to a workspace, so that your local computer can communicate with remote resources
-* Create an experiment to track your runs
-* Create a remote compute target to use for training
-
-### Install required packages
-This tutorial assumes you already have the Azure ML SDK installed. Go ahead and import the **azuremlsdk** package.
-
-```{r eval=FALSE}
-library(azuremlsdk)
-```
-
-The tutorial uses data from the [**DAAG** package](https://cran.r-project.org/package=DAAG). Install the package if you don't have it.
-
-```{r eval=FALSE}
-install.packages("DAAG")
-```
-
-The training and scoring scripts (`accidents.R` and `accident_predict.R`) have some additional dependencies. If you plan on running those scripts locally, make sure you have those required packages as well.
-
-### Load your workspace
-Instantiate a workspace object from your existing workspace. The following code will load the workspace details from the **config.json** file. You can also retrieve a workspace using [`get_workspace()`](https://azure.github.io/azureml-sdk-for-r/reference/get_workspace.html).
-
-```{r load_workpace, eval=FALSE}
-ws <- load_workspace_from_config()
-```
-
-### Create an experiment
-An Azure ML experiment tracks a grouping of runs, typically from the same training script. Create an experiment to track the runs for training the caret model on the accidents data.
-
-```{r create_experiment, eval=FALSE}
-experiment_name <- "accident-logreg"
-exp <- experiment(ws, experiment_name)
-```
-
-### Create a compute target
-By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create a single-node AmlCompute cluster as your training environment. The code below creates the compute cluster for you if it doesn't already exist in your workspace.
-
-You may need to wait a few minutes for your compute cluster to be provisioned if it doesn't already exist.
-
-```{r create_cluster, eval=FALSE}
-cluster_name <- "rcluster"
-compute_target <- get_compute(ws, cluster_name = cluster_name)
-if (is.null(compute_target)) {
-  vm_size <- "STANDARD_D2_V2" 
-  compute_target <- create_aml_compute(workspace = ws,
-                                       cluster_name = cluster_name,
-                                       vm_size = vm_size,
-                                       max_nodes = 1)
-  
-  wait_for_provisioning_completion(compute_target, show_output = TRUE)
-}
-```
-
-## Prepare data for training
-This tutorial uses data from the **DAAG** package. This dataset includes data from over 25,000 car crashes in the US, with variables you can use to predict the likelihood of a fatality. First, import the data into R and transform it into a new dataframe `accidents` for analysis, and export it to an `Rdata` file.
-
-```{r load_data, eval=FALSE}
-library(DAAG)
-data(nassCDS)
-
-accidents <- na.omit(nassCDS[,c("dead","dvcat","seatbelt","frontal","sex","ageOFocc","yearVeh","airbag","occRole")])
-accidents$frontal <- factor(accidents$frontal, labels=c("notfrontal","frontal"))
-accidents$occRole <- factor(accidents$occRole)
-
-saveRDS(accidents, file="accidents.Rd")
-```
-
-### Upload data to the datastore
-Upload data to the cloud so that it can be access by your remote training environment. Each Azure ML workspace comes with a default datastore that stores the connection information to the Azure blob container that is provisioned in the storage account attached to the workspace. The following code will upload the accidents data you created above to that datastore.
-
-```{r upload_data, eval=FALSE}
-ds <- get_default_datastore(ws)
-
-target_path <- "accidentdata"
-upload_files_to_datastore(ds,
-                          list("./project_files/accidents.Rd"),
-                          target_path = target_path,
-                          overwrite = TRUE)
-```
-
-
-## Train a model
-
-For this tutorial, fit a logistic regression model on your uploaded data using your remote compute cluster. To submit a job, you need to:
-
-* Prepare the training script
-* Create an estimator
-* Submit the job
-
-### Prepare the training script
-A training script called `accidents.R` has been provided for you in the "project_files" directory of this tutorial. Notice the following details **inside the training script** that have been done to leverage the Azure ML service for training:
-
-* The training script takes an argument `-d` to find the directory that contains the training data. When you define and submit your job later, you point to the datastore for this argument. Azure ML will mount the storage folder to the remote cluster for the training job.
-* The training script logs the final accuracy as a metric to the run record in Azure ML using `log_metric_to_run()`. The Azure ML SDK provides a set of logging APIs for logging various metrics during training runs. These metrics are recorded and persisted in the experiment run record. The metrics can then be accessed at any time or viewed in the run details page in [Azure Machine Learning studio](http://ml.azure.com). See the [reference](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-training-experimentation) for the full set of logging methods `log_*()`.
-* The training script saves your model into a directory named **outputs**. The `./outputs` folder receives special treatment by Azure ML. During training, files written to `./outputs` are automatically uploaded to your run record by Azure ML and persisted as artifacts. By saving the trained model to `./outputs`, you'll be able to access and retrieve your model file even after the run is over and you no longer have access to your remote training environment.
-
-### Create an estimator
-
-An Azure ML estimator encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target. By default, the Docker image built for your training job will include R, the Azure ML SDK, and a set of commonly used R packages. See the full list of default packages included [here](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html).
-
-To create the estimator, define:
-
-* The directory that contains your scripts needed for training (`source_directory`). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
-* The training script that will be executed (`entry_script`).
-* The compute target (`compute_target`), in this case the AmlCompute cluster you created earlier.
-* The parameters required from the training script (`script_params`). Azure ML will run your training script as a command-line script with `Rscript`. In this tutorial you specify one argument to the script, the data directory mounting point, which you can access with `ds$path(target_path)`.
-* Any environment dependencies required for training. The default Docker image built for training already contains the three packages (`caret`, `e1071`, and `optparse`) needed in the training script.  So you don't need to specify additional information. If you are using R packages that are not included by default, use the estimator's `cran_packages` parameter to add additional CRAN packages. See the [`estimator()`](https://azure.github.io/azureml-sdk-for-r/reference/estimator.html) reference for the full set of configurable options.
-
-```{r create_estimator, eval=FALSE}
-est <- estimator(source_directory = "project_files",
-                 entry_script = "accidents.R",
-                 script_params = list("--data_folder" = ds$path(target_path)),
-                 compute_target = compute_target
-                 )
-```
-
-### Submit the job on the remote cluster
-
-Finally submit the job to run on your cluster. `submit_experiment()` returns a Run object that you then use to interface with the run. In total, the first run takes **about 10 minutes**. But for later runs, the same Docker image is reused as long as the script dependencies don't change.  In this case, the image is cached and the container startup time is much faster.
-
-```{r submit_job, eval=FALSE}
-run <- submit_experiment(exp, est)
-```
-
-You can view a table of the run's details. Clicking the "Web View" link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
-
-```{r view_run, eval=FALSE}
-view_run_details(run)
-```
-
-Model training happens in the background. Wait until the model has finished training before you run more code.
-
-```{r wait_run, eval=FALSE}
-wait_for_run_completion(run, show_output = TRUE)
-```
-
-You -- and colleagues with access to the workspace -- can submit multiple experiments in parallel, and Azure ML will take of scheduling the tasks on the compute cluster. You can even configure the cluster to automatically scale up to multiple nodes, and scale back when there are no more compute tasks in the queue. This configuration is a cost-effective way for teams to share compute resources.
-
-## Retrieve training results
-Once your model has finished training, you can access the artifacts of your job that were persisted to the run record, including any metrics logged and the final trained model.
-
-### Get the logged metrics
-In the training script `accidents.R`, you logged a metric from your model: the accuracy of the predictions in the training data. You can see metrics in the [studio](https://ml.azure.com), or extract them to the local session as an R list as follows:
-
-```{r metrics, eval=FALSE}
-metrics <- get_run_metrics(run)
-metrics
-```
-
-If you've run multiple experiments (say, using differing variables, algorithms, or hyperparamers), you can use the metrics from each run to compare and choose the model you'll use in production.
-
-### Get the trained model
-You can retrieve the trained model and look at the results in your local R session. The following code will download the contents of the `./outputs` directory, which includes the model file.
-
-```{r retrieve_model, eval=FALSE}
-download_files_from_run(run, prefix="outputs/")
-accident_model <- readRDS("project_files/outputs/model.rds")
-summary(accident_model)
-```
-
-You see some factors that contribute to an increase in the estimated probability of death:
-
-* higher impact speed 
-* male driver
-* older occupant
-* passenger
-
-You see lower probabilities of death with:
-
-* presence of airbags
-* presence seatbelts
-* frontal collision 
-
-The vehicle year of manufacture does not have a significant effect.
-
-You can use this model to make new predictions:
-
-```{r manual_predict, eval=FALSE}
-newdata <- data.frame( # valid values shown below
- dvcat="10-24",        # "1-9km/h" "10-24"   "25-39"   "40-54"   "55+"  
- seatbelt="none",      # "none"   "belted"  
- frontal="frontal",    # "notfrontal" "frontal"
- sex="f",              # "f" "m"
- ageOFocc=16,          # age in years, 16-97
- yearVeh=2002,         # year of vehicle, 1955-2003
- airbag="none",        # "none"   "airbag"   
- occRole="pass"        # "driver" "pass"
- )
-
-## predicted probability of death for these variables, as a percentage
-as.numeric(predict(accident_model,newdata, type="response")*100)
-```
-
-## Deploy as a web service
-
-With your model, you can predict the danger of death from a collision. Use Azure ML to deploy your model as a prediction service. In this tutorial, you will deploy the web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI).
-
-### Register the model
-
-First, register the model you downloaded to your workspace with [`register_model()`](https://azure.github.io/azureml-sdk-for-r/reference/register_model.html). A registered model can be any collection of files, but in this case the R model object is sufficient. Azure ML will use the registered model for deployment.
-
-```{r register_model, eval=FALSE}
-model <- register_model(ws, 
-                        model_path = "project_files/outputs/model.rds", 
-                        model_name = "accidents_model",
-                        description = "Predict probablity of auto accident")
-```
-
-### Define the inference dependencies
-To create a web service for your model, you first need to create a scoring script (`entry_script`), an R script that will take as input variable values (in JSON format) and output a prediction from your model. For this tutorial, use the provided scoring file `accident_predict.R`. The scoring script must contain an `init()` method that loads your model and returns a function that uses the model to make a prediction based on the input data. See the [documentation](https://azure.github.io/azureml-sdk-for-r/reference/inference_config.html#details) for more details.
-
-Next, define an Azure ML **environment** for your script's package dependencies. With an environment, you specify R packages (from CRAN or elsewhere) that are needed for your script to run. You can also provide the values of environment variables that your script can reference to modify its behavior. By default, Azure ML will build the same default Docker image used with the estimator for training. Since the tutorial has no special requirements, create an environment with no special attributes.
-
-```{r create_environment, eval=FALSE}
-r_env <- r_environment(name = "basic_env")
-```
-
-If you want to use your own Docker image for deployment instead, specify the `custom_docker_image` parameter. See the [`r_environment()`](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html) reference for the full set of configurable options for defining an environment.
-
-Now you have everything you need to create an **inference config** for encapsulating your scoring script and environment dependencies.
-
-``` {r create_inference_config, eval=FALSE}
-inference_config <- inference_config(
-  entry_script = "accident_predict.R",
-  source_directory = "project_files",
-  environment = r_env)
-```
-
-### Deploy to ACI
-In this tutorial, you will deploy your service to ACI. This code provisions a single container to respond to inbound requests, which is suitable for testing and light loads. See [`aci_webservice_deployment_config()`](https://azure.github.io/azureml-sdk-for-r/reference/aci_webservice_deployment_config.html) for additional configurable options. (For production-scale deployments, you can also [deploy to Azure Kubernetes Service](https://azure.github.io/azureml-sdk-for-r/articles/deploy-to-aks/deploy-to-aks.html).)
-
-``` {r create_aci_config, eval=FALSE}
-aci_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 0.5)
-```
-
-Now you deploy your model as a web service. Deployment **can take several minutes**. 
-
-```{r deploy_service, eval=FALSE}
-aci_service <- deploy_model(ws, 
-                            'accident-pred', 
-                            list(model), 
-                            inference_config, 
-                            aci_config)
-
-wait_for_deployment(aci_service, show_output = TRUE)
-```
-
-If you encounter any issue in deploying the web service, please visit the [troubleshooting guide](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment).
-
-## Test the deployed service
-
-Now that your model is deployed as a service, you can test the service from R using [`invoke_webservice()`](https://azure.github.io/azureml-sdk-for-r/reference/invoke_webservice.html).  Provide a new set of data to predict from, convert it to JSON, and send it to the service.
-
-```{r test_deployment, eval=FALSE}
-library(jsonlite)
-
-newdata <- data.frame( # valid values shown below
- dvcat="10-24",        # "1-9km/h" "10-24"   "25-39"   "40-54"   "55+"  
- seatbelt="none",      # "none"   "belted"  
- frontal="frontal",    # "notfrontal" "frontal"
- sex="f",              # "f" "m"
- ageOFocc=22,          # age in years, 16-97
- yearVeh=2002,         # year of vehicle, 1955-2003
- airbag="none",        # "none"   "airbag"   
- occRole="pass"        # "driver" "pass"
- )
-
-prob <- invoke_webservice(aci_service, toJSON(newdata))
-prob
-```
-
-You can also get the web service's HTTP endpoint, which accepts REST client calls. You can share this endpoint with anyone who wants to test the web service or integrate it into an application.
-
-```{r get_endpoint, eval=FALSE}
-aci_service$scoring_uri
-```
-
-## Clean up resources
-
-Delete the resources once you no longer need them. Don't delete any resource you plan to still use. 
-
-Delete the web service:
-```{r delete_service, eval=FALSE}
-delete_webservice(aci_service)
-```
-
-Delete the registered model:
-```{r delete_model, eval=FALSE}
-delete_model(model)
-```
-
-Delete the compute cluster:
-```{r delete_compute, eval=FALSE}
-delete_compute(compute_target)
-```

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-with-tensorflow/project_files/tf_mnist.R

@@ -1,62 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-# Copyright 2016 RStudio, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-
-library(tensorflow)
-install_tensorflow(version = "1.13.2-gpu")
-
-library(azuremlsdk)
-
-# Create the model
-x <- tf$placeholder(tf$float32, shape(NULL, 784L))
-W <- tf$Variable(tf$zeros(shape(784L, 10L)))
-b <- tf$Variable(tf$zeros(shape(10L)))
-
-y <- tf$nn$softmax(tf$matmul(x, W) + b)
-
-# Define loss and optimizer
-y_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
-cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * log(y),
-                                               reduction_indices = 1L))
-train_step <- tf$train$GradientDescentOptimizer(0.5)$minimize(cross_entropy)
-
-# Create session and initialize  variables
-sess <- tf$Session()
-sess$run(tf$global_variables_initializer())
-
-# Load mnist data    )
-datasets <- tf$contrib$learn$datasets
-mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
-
-# Train
-for (i in 1:1000) {
-    batches <- mnist$train$next_batch(100L)
-    batch_xs <- batches[[1]]
-    batch_ys <- batches[[2]]
-    sess$run(train_step,
-           feed_dict = dict(x = batch_xs, y_ = batch_ys))
-}
-
-# Test trained model
-correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L))
-accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
-cat("Accuracy: ", sess$run(accuracy,
-                           feed_dict = dict(x = mnist$test$images,
-                                            y_ = mnist$test$labels)))
-
-log_metric_to_run("accuracy",
-                  sess$run(accuracy, feed_dict = dict(x = mnist$test$images,
-                                                      y_ = mnist$test$labels)))

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-with-tensorflow/train-with-tensorflow.Rmd

@@ -1,143 +0,0 @@
----
-title: "Train a TensorFlow model"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Train a TensorFlow model}
-  %\VignetteEngine{knitr::rmarkdown}
-  \use_package{UTF-8}
----
-
-This tutorial demonstrates how run a TensorFlow job at scale using Azure ML. You will train a TensorFlow model to classify handwritten digits (MNIST) using a deep neural network (DNN) and log your results to the Azure ML service.
-
-## Prerequisites
-If you don’t have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
-
-## Set up development environment
-The setup for your development work in this tutorial includes the following actions:
-
-* Import required packages
-* Connect to a workspace
-* Create an experiment to track your runs
-* Create a remote compute target to use for training
-
-### Import **azuremlsdk** package
-```{r eval=FALSE}
-library(azuremlsdk)
-```
-
-### Load your workspace
-Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a **config.json** file if you previously wrote one out with [`write_workspace_config()`](https://azure.github.io/azureml-sdk-for-r/reference/write_workspace_config.html).
-```{r load_workpace, eval=FALSE}
-ws <- load_workspace_from_config()
-```
-
-Or, you can retrieve a workspace by directly specifying your workspace details:
-```{r get_workpace, eval=FALSE}
-ws <- get_workspace("<your workspace name>", "<your subscription ID>", "<your resource group>")
-```
-
-### Create an experiment
-An Azure ML **experiment** tracks a grouping of runs, typically from the same training script. Create an experiment to track the runs for training the TensorFlow model on the MNIST data.
-
-```{r create_experiment, eval=FALSE}
-exp <- experiment(workspace = ws, name = "tf-mnist")
-```
-
-If you would like to track your runs in an existing experiment, simply specify that experiment's name to the `name` parameter of `experiment()`.
-
-### Create a compute target
-By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. In this tutorial, you create a GPU-enabled cluster as your training environment. The code below creates the compute cluster for you if it doesn't already exist in your workspace.
-
-You may need to wait a few minutes for your compute cluster to be provisioned if it doesn't already exist.
-
-```{r create_cluster, eval=FALSE}
-cluster_name <- "gpucluster"
-compute_target <- get_compute(ws, cluster_name = cluster_name)
-if (is.null(compute_target))
-{
-  vm_size <- "STANDARD_NC6"
-  compute_target <- create_aml_compute(workspace = ws, 
-                                       cluster_name = cluster_name,
-                                       vm_size = vm_size, 
-                                       max_nodes = 4)
-  
-  wait_for_provisioning_completion(compute_target, show_output = TRUE)
-}
-```
-
-## Prepare the training script
-
-A training script called `tf_mnist.R` has been provided for you in the "project_files" directory of this tutorial. The Azure ML SDK provides a set of logging APIs for logging various metrics during training runs. These metrics are recorded and persisted in the experiment run record, and can be be accessed at any time or viewed in the run details page in [Azure Machine Learning studio](http://ml.azure.com/).
-
-In order to collect and upload run metrics, you need to do the following **inside the training script**:
-
-* Import the **azuremlsdk** package
-```
-library(azuremlsdk)
-```
-
-* Add the [`log_metric_to_run()`](https://azure.github.io/azureml-sdk-for-r/reference/log_metric_to_run.html) function to track our primary metric,  "accuracy", for this experiment. If you have your own training script with several important metrics, simply create a logging call for each one within the script.
-```
-log_metric_to_run("accuracy",
-                  sess$run(accuracy,
-                  feed_dict = dict(x = mnist$test$images, y_ = mnist$test$labels)))
-```
-
-See the [reference](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-training-experimentation) for the full set of logging methods `log_*()` available from the R SDK.
-
-## Create an estimator
-
-An Azure ML **estimator** encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target. By default, the Docker image built for your training job will include R, the Azure ML SDK, and a set of commonly used R packages. See the full list of default packages included [here](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html).
-
-To create the estimator, define the following:
-
-* The directory that contains your scripts needed for training (`source_directory`). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
-* The training script that will be executed (`entry_script`).
-* The compute target (`compute_target`), in this case the AmlCompute cluster you created earlier.
-* Any environment dependencies required for training. Since the training script requires the TensorFlow package, which is not included in the image by default, pass the package name to the `cran_packages` parameter to have it installed in the Docker container where the job will run. See the [`estimator()`](https://azure.github.io/azureml-sdk-for-r/reference/estimator.html) reference for the full set of configurable options.
-* Set the `use_gpu = TRUE` flag so the default base GPU Docker image will be built, since the job will be run on a GPU cluster.
-
-```{r create_estimator, eval=FALSE}
-est <- estimator(source_directory = "project_files",
-                 entry_script = "tf_mnist.R",
-                 compute_target = compute_target,
-                 cran_packages = c("tensorflow"),
-                 use_gpu = TRUE)
-```
-
-## Submit the job
-
-Finally submit the job to run on your cluster. [`submit_experiment()`](https://azure.github.io/azureml-sdk-for-r/reference/submit_experiment.html) returns a `Run` object that you can then use to interface with the run.
-
-```{r submit_job, eval=FALSE}
-run <- submit_experiment(exp, est)
-```
-
-You can view the run’s details as a table. Clicking the “Web View” link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
-
-```{r eval=FALSE}
-view_run_details(run)
-```
-
-Model training happens in the background. Wait until the model has finished training before you run more code.
-
-```{r eval=FALSE}
-wait_for_run_completion(run, show_output = TRUE)
-```
-
-## View run metrics
-Once your job has finished, you can view the metrics collected during your TensorFlow run.
-
-```{r get_metrics, eval=FALSE}
-metrics <- get_run_metrics(run)
-metrics
-```
-
-## Clean up resources
-Delete the resources once you no longer need them. Don't delete any resource you plan to still use. 
-
-Delete the compute cluster:
-```{r delete_compute, eval=FALSE}
-delete_compute(compute_target)
-```

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

@@ -334,14 +334,27 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Use the default configuration (can also provide parameters to customize)\n",
+        "from azureml.core.compute import ComputeTarget\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
+        "# Choose a name for your AKS cluster\n",
         "aks_name = 'my-aks-9' \n",
-        "# Create the cluster\n",
+        "\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    # Use the default configuration (can also provide parameters to customize)\n",
+        "    prov_config = AksCompute.provisioning_configuration()\n",
+        "\n",
+        "    # Create the cluster\n",
+        "    aks_target = ComputeTarget.create(workspace = ws, \n",
         "                                    name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
+        "                                    provisioning_configuration = prov_config)\n",
+        "\n",
+        "if aks_target.get_status() != \"Succeeded\":\n",
+        "    aks_target.wait_for_completion(show_output=True)"
       ]
     },
     {

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

@@ -287,8 +287,8 @@
         "# 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",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
+        "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
-        "                                                                            pip_packages=azureml_pip_packages)\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",
@@ -427,8 +427,8 @@
         "# 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",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
+        "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
-        "                                                                            pip_packages=azureml_pip_packages)\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",

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

@@ -6,6 +6,6 @@ dependencies:
   - interpret-community[visualization]
   - matplotlib
   - azureml-contrib-interpret
-  - sklearn-pandas
+  - sklearn-pandas<2.0.0
   - azureml-dataset-runtime
   - ipywidgets

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

@@ -350,8 +350,7 @@
         "# 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",
-        "myenv = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
+        "myenv = CondaDependencies.create(pip_packages=['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
-        "                                 pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
         "                                 pin_sdk_version=False)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",

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

@@ -6,5 +6,5 @@ dependencies:
   - interpret-community[visualization]
   - matplotlib
   - azureml-contrib-interpret
-  - sklearn-pandas
+  - sklearn-pandas<2.0.0
   - ipywidgets

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

@@ -294,8 +294,8 @@
         "# 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",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
+        "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
-        "                                pip_packages=['sklearn_pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
         "                                pin_sdk_version=False)\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
@@ -459,8 +459,8 @@
         "# 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",
-        "myenv = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
+        "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
-        "                                 pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
         "                                 pin_sdk_version=False)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",

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

@@ -6,7 +6,7 @@ dependencies:
   - interpret-community[visualization]
   - matplotlib
   - azureml-contrib-interpret
-  - sklearn-pandas
+  - 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.ipynb

@@ -34,7 +34,7 @@
         "| Azure Data Lake Storage Gen 1 | Yes | Yes |\n",
         "| Azure Data Lake Storage Gen 2 | Yes | Yes |\n",
         "| Azure SQL Database | Yes | Yes |\n",
-        "| Azure Database for PostgreSQL | Yes | Yes |",
+        "| Azure Database for PostgreSQL | Yes | Yes |\n",
         "| Azure Database for MySQL | Yes | Yes |"
       ]
     },
@@ -558,7 +558,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "shbijlan"
       }
     ],
     "category": "tutorial",

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

@@ -100,7 +100,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
-        "cluster_name = \"cpu-cluster\"\n",
+        "cluster_name = \"amlcomp\"\n",
         "\n",
         "try:\n",
         "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
@@ -147,10 +147,8 @@
         "    'script_params' accepts a dictionary. However 'estimator_entry_script_arguments' parameter expects arguments as\n",
         "    a list.\n",
         "\n",
-        "> Estimator object initialization involves specifying a list of DataReference objects in its 'inputs' parameter.\n",
+        "> Estimator object initialization involves specifying a list of data input and output.\n",
-        "    In Pipelines, a step can take another step's output or DataReferences as input. So when creating an EstimatorStep,\n",
+        "    In Pipelines, a step can take another step's output as input. So when creating an EstimatorStep.\n",
-        "    the parameters 'inputs' and 'outputs' need to be set explicitly and that will override 'inputs' parameter\n",
-        "    specified in the Estimator object.\n",
         "   \n",
         "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
@@ -166,17 +164,27 @@
       "outputs": [],
       "source": [
         "from azureml.core import Datastore\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "from azureml.pipeline.core import PipelineData\n",
         "\n",
         "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
         "\n",
-        "input_data = DataReference(\n",
+        "#upload input data to workspaceblobstore\n",
-        "    datastore=def_blob_store,\n",
+        "def_blob_store.upload_files(files=['20news.pkl'], target_path='20newsgroups')"
-        "    data_reference_name=\"input_data\",\n",
+      ]
-        "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "from azureml.data import OutputFileDatasetConfig\n",
         "\n",
-        "output = PipelineData(\"output\", datastore=def_blob_store)\n",
+        "# create dataset to be used as the input to estimator step\n",
+        "input_data = Dataset.File.from_files(def_blob_store.path('20newsgroups/20news.pkl'))\n",
+        "\n",
+        "# OutputFileDatasetConfig by default write output to the default workspaceblobstore\n",
+        "output = OutputFileDatasetConfig()\n",
         "\n",
         "source_directory = 'estimator_train'"
       ]
@@ -204,10 +212,8 @@
         "\n",
         "- **name:** Name of the step\n",
         "- **estimator:** Estimator object\n",
-        "- **estimator_entry_script_arguments:** \n",
+        "- **estimator_entry_script_arguments:** A list of command-line arguments\n",
         "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
-        "- **inputs:** Inputs\n",
-        "- **outputs:** Output is list of PipelineData\n",
         "- **compute_target:** Compute target to use \n",
         "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
         "- **version:** Optional version tag to denote a change in functionality for the step"
@@ -227,10 +233,8 @@
         "\n",
         "est_step = EstimatorStep(name=\"Estimator_Train\", \n",
         "                         estimator=est, \n",
-        "                         estimator_entry_script_arguments=[\"--datadir\", input_data, \"--output\", output],\n",
+        "                         estimator_entry_script_arguments=[\"--datadir\", input_data.as_mount(), \"--output\", output],\n",
         "                         runconfig_pipeline_params=None, \n",
-        "                         inputs=[input_data], \n",
-        "                         outputs=[output], \n",
         "                         compute_target=cpu_cluster)"
       ]
     },

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

@@ -42,11 +42,9 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.core import Workspace, Experiment, Datastore, Dataset\n",
-        "from azureml.core.datastore import Datastore\n",
         "from azureml.core.compute import ComputeTarget, AmlCompute\n",
         "from azureml.exceptions import ComputeTargetException\n",
-        "from azureml.data.data_reference import DataReference\n",
         "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun\n",
         "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.train.dnn import TensorFlow\n",
@@ -179,8 +177,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ds = ws.get_default_datastore()\n",
+        "datastore = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)"
+        "datastore.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Azure Machine Learning datasets\n",
+        "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = Dataset.File.from_files(datastore.path('mnist'))"
       ]
     },
     {
@@ -204,7 +219,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "cluster_name = \"gpu-cluster\"\n",
+        "cluster_name = \"amlcomp\"\n",
         "\n",
         "try:\n",
         "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
@@ -264,7 +279,8 @@
         "                 compute_target=compute_target,\n",
         "                 entry_script='tf_mnist.py', \n",
         "                 use_gpu=True,\n",
-        "                 framework_version='1.13')"
+        "                 framework_version='2.0',\n",
+        "                 pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
       ]
     },
     {
@@ -344,7 +360,7 @@
         "## Add HyperDrive as a step of pipeline\n",
         "\n",
         "### Setup an input for the hypderdrive step\n",
-        "Let's setup a data reference for inputs of hyperdrive step."
+        "You can mount dataset to remote compute."
       ]
     },
     {
@@ -353,9 +369,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data_folder = DataReference(\n",
+        "data_folder = dataset.as_mount()"
-        "    datastore=ds,\n",
-        "    data_reference_name=\"mnist_data\")"
       ]
     },
     {
@@ -386,7 +400,7 @@
       "source": [
         "metrics_output_name = 'metrics_output'\n",
         "metrics_data = PipelineData(name='metrics_data',\n",
-        "                             datastore=ds,\n",
+        "                             datastore=datastore,\n",
         "                             pipeline_output_name=metrics_output_name)\n",
         "\n",
         "hd_step_name='hd_step01'\n",
@@ -577,7 +591,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
     },
     "order_index": 8,
     "star_tag": [

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

@@ -6,3 +6,4 @@ dependencies:
   - matplotlib
   - numpy
   - pandas_ml
+  - azureml-dataset-runtime[pandas,fuse]

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

@@ -87,7 +87,7 @@
       "source": [
         "## Create an Azure ML experiment\n",
         "\n",
-        "Let's create an experiment named \"automl-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure."
+        "Let's create an experiment named \"showcasing-datapath\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure."
       ]
     },
     {
@@ -479,7 +479,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "shbijlan"
       }
     ],
     "category": "tutorial",

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

@@ -4,34 +4,103 @@
 import numpy as np
 import argparse
 import os
+import re
 import tensorflow as tf
+import time
+import glob
 
 from azureml.core import Run
 from utils import load_data
+from tensorflow.keras import Model, layers
 
-print("TensorFlow version:", tf.VERSION)
+
+# Create TF Model.
+class NeuralNet(Model):
+    # Set layers.
+    def __init__(self):
+        super(NeuralNet, self).__init__()
+        # First hidden layer.
+        self.h1 = layers.Dense(n_h1, activation=tf.nn.relu)
+        # Second hidden layer.
+        self.h2 = layers.Dense(n_h2, activation=tf.nn.relu)
+        self.out = layers.Dense(n_outputs)
+
+    # Set forward pass.
+    def call(self, x, is_training=False):
+        x = self.h1(x)
+        x = self.h2(x)
+        x = self.out(x)
+        if not is_training:
+            # Apply softmax when not training.
+            x = tf.nn.softmax(x)
+        return x
+
+
+def cross_entropy_loss(y, logits):
+    # Convert labels to int 64 for tf cross-entropy function.
+    y = tf.cast(y, tf.int64)
+    # Apply softmax to logits and compute cross-entropy.
+    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
+    # Average loss across the batch.
+    return tf.reduce_mean(loss)
+
+
+# Accuracy metric.
+def accuracy(y_pred, y_true):
+    # Predicted class is the index of highest score in prediction vector (i.e. argmax).
+    correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))
+    return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)
+
+
+# Optimization process.
+def run_optimization(x, y):
+    # Wrap computation inside a GradientTape for automatic differentiation.
+    with tf.GradientTape() as g:
+        # Forward pass.
+        logits = neural_net(x, is_training=True)
+        # Compute loss.
+        loss = cross_entropy_loss(y, logits)
+
+    # Variables to update, i.e. trainable variables.
+    trainable_variables = neural_net.trainable_variables
+
+    # Compute gradients.
+    gradients = g.gradient(loss, trainable_variables)
+
+    # Update W and b following gradients.
+    optimizer.apply_gradients(zip(gradients, trainable_variables))
+
+
+print("TensorFlow version:", tf.__version__)
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
+parser.add_argument('--data-folder', type=str, dest='data_folder', default='data', help='data folder mounting point')
-parser.add_argument('--batch-size', type=int, dest='batch_size', default=50, help='mini batch size for training')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=128, help='mini batch size for training')
-parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=100,
+parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=128,
                     help='# of neurons in the first layer')
-parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=100,
+parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=128,
                     help='# of neurons in the second layer')
 parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate')
+parser.add_argument('--resume-from', type=str, default=None,
+                    help='location of the model or checkpoint files from where to resume the training')
 args = parser.parse_args()
 
-data_folder = os.path.join(args.data_folder, 'mnist')
+previous_model_location = args.resume_from
+# You can also use environment variable to get the model/checkpoint files location
+# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None))
 
-print('training dataset is stored here:', data_folder)
+data_folder = args.data_folder
+print('Data folder:', data_folder)
 
+# load train and test set into numpy arrays
+# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster.
 X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
 X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
-
 y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
 y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
 
 print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
+
 training_set_size = X_train.shape[0]
 
 n_inputs = 28 * 28
@@ -39,36 +108,31 @@ n_h1 = args.n_hidden_1
 n_h2 = args.n_hidden_2
 n_outputs = 10
 learning_rate = args.learning_rate
-n_epochs = 50
+n_epochs = 20
 batch_size = args.batch_size
 
-with tf.name_scope('network'):
+# Build neural network model.
-    # construct the DNN
+neural_net = NeuralNet()
-    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
-    y = tf.placeholder(tf.int64, shape=(None), name='y')
-    h1 = tf.layers.dense(X, n_h1, activation=tf.nn.relu, name='h1')
-    h2 = tf.layers.dense(h1, n_h2, activation=tf.nn.relu, name='h2')
-    output = tf.layers.dense(h2, n_outputs, name='output')
 
-with tf.name_scope('train'):
+# Stochastic gradient descent optimizer.
-    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output)
+optimizer = tf.optimizers.SGD(learning_rate)
-    loss = tf.reduce_mean(cross_entropy, name='loss')
-    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
-    train_op = optimizer.minimize(loss)
-
-with tf.name_scope('eval'):
-    correct = tf.nn.in_top_k(output, y, 1)
-    acc_op = tf.reduce_mean(tf.cast(correct, tf.float32))
-
-init = tf.global_variables_initializer()
-saver = tf.train.Saver()
 
 # start an Azure ML run
 run = Run.get_context()
 
-with tf.Session() as sess:
+if previous_model_location:
-    init.run()
+    # Restore variables from latest checkpoint.
-    for epoch in range(n_epochs):
+    checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer)
+    checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location)
+    checkpoint.restore(checkpoint_file_path)
+    checkpoint_filename = os.path.basename(checkpoint_file_path)
+    num_found = re.search(r'\d+', checkpoint_filename)
+    if num_found:
+        start_epoch = int(num_found.group(0))
+        print("Resuming from epoch {}".format(str(start_epoch)))
+
+start_time = time.perf_counter()
+for epoch in range(0, n_epochs):
 
     # randomly shuffle training set
     indices = np.random.permutation(training_set_size)
@@ -87,20 +151,36 @@ with tf.Session() as sess:
         b_end = min(b_start + batch_size, training_set_size)
 
         # train
-            sess.run(train_op, feed_dict={X: X_batch, y: y_batch})
+        run_optimization(X_batch, y_batch)
+
     # evaluate training set
-        acc_train = acc_op.eval(feed_dict={X: X_batch, y: y_batch})
+    pred = neural_net(X_batch, is_training=False)
+    acc_train = accuracy(pred, y_batch)
+
     # evaluate validation set
-        acc_val = acc_op.eval(feed_dict={X: X_test, y: y_test})
+    pred = neural_net(X_test, is_training=False)
+    acc_val = accuracy(pred, y_test)
 
     # log accuracies
     run.log('training_acc', np.float(acc_train))
     run.log('validation_acc', np.float(acc_val))
     print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val)
-        y_hat = np.argmax(output.eval(feed_dict={X: X_test}), axis=1)
 
-    run.log('final_acc', np.float(acc_val))
+    # Save checkpoints in the "./outputs" folder so that they are automatically uploaded into run history.
+    checkpoint_dir = './outputs/'
+    checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer)
 
-    os.makedirs('./outputs/model', exist_ok=True)
+    if epoch % 2 == 0:
-    # files saved in the "./outputs" folder are automatically uploaded into run history
+        checkpoint.save(checkpoint_dir)
-    saver.save(sess, './outputs/model/mnist-tf.model')
+
+run.log('final_acc', np.float(acc_val))
+os.makedirs('./outputs/model', exist_ok=True)
+
+# files saved in the "./outputs" folder are automatically uploaded into run history
+# this is workaround for https://github.com/tensorflow/tensorflow/issues/33913 and will be fixed once we move to >tf2.1
+neural_net._set_inputs(X_train)
+tf.saved_model.save(neural_net, './outputs/model/')
+
+stop_time = time.perf_counter()
+training_time = (stop_time - start_time) * 1000
+print("Total time in milliseconds for training: {}".format(str(training_time)))

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

@@ -1,5 +1,13 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -1062,7 +1070,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "anshirga"
       }
     ],
     "kernelspec": {

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/transform.py

@@ -29,8 +29,8 @@ print("Argument 2(output final transformed taxi data): %s" % args.output_transfo
 # use the drop_columns() function to delete the original fields as the newly generated features are preferred.
 # Rename the rest of the fields to use meaningful descriptions.
 
-normalized_df = normalized_df.astype({"pickup_date": 'datetime64', "dropoff_date": 'datetime64',
+normalized_df = normalized_df.astype({"pickup_date": 'datetime64[ns]', "dropoff_date": 'datetime64[ns]',
-                                      "pickup_time": 'datetime64', "dropoff_time": 'datetime64',
+                                      "pickup_time": 'datetime64[us]', "dropoff_time": 'datetime64[us]',
                                       "distance": 'float64', "cost": 'float64'})
 
 normalized_df["pickup_weekday"] = normalized_df["pickup_date"].dt.dayofweek

how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py

@@ -6,10 +6,15 @@ import numpy as np
 from azureml.core.model import Model
 from sklearn.linear_model import LogisticRegression
 
+from azureml_user.parallel_run import EntryScript
+
 
 def init():
     global iris_model
 
+    logger = EntryScript().logger
+    logger.info("init() is called.")
+
     parser = argparse.ArgumentParser(description="Iris model serving")
     parser.add_argument('--model_name', dest="model_name", required=True)
     args, unknown_args = parser.parse_known_args()
@@ -20,6 +25,9 @@ def init():
 
 
 def run(input_data):
+    logger = EntryScript().logger
+    logger.info("run() is called with: {}.".format(input_data))
+
     # make inference
     num_rows, num_cols = input_data.shape
     pred = iris_model.predict(input_data).reshape((num_rows, 1))

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

@@ -51,7 +51,23 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": []
+      },
       "outputs": [],
       "source": [
         "from azureml.core import Workspace\n",
@@ -397,7 +413,7 @@
         "    parallel_run_config=parallel_run_config,\n",
         "    inputs=[ input_mnist_ds_consumption ],\n",
         "    output=output_dir,\n",
-        "    allow_reuse=True\n",
+        "    allow_reuse=False\n",
         ")"
       ]
     },
@@ -426,7 +442,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Monitor the run"
+        "### Monitor the run\n",
+        "\n",
+        "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object."
       ]
     },
     {
@@ -435,8 +453,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.widgets import RunDetails\n",
+        "# This will output information of the pipeline run, including the link to the details page of portal.\n",
-        "RunDetails(pipeline_run).show()"
+        "pipeline_run"
       ]
     },
     {
@@ -452,9 +470,40 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# Wait the run for completion and show output log to console\n",
         "pipeline_run.wait_for_completion(show_output=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### View the prediction results per input image\n",
+        "In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from  all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import tempfile\n",
+        "\n",
+        "batch_run = pipeline_run.find_step_run(parallelrun_step.name)[0]\n",
+        "batch_output = batch_run.get_output_data(output_dir.name)\n",
+        "\n",
+        "target_dir = tempfile.mkdtemp()\n",
+        "batch_output.download(local_path=target_dir)\n",
+        "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
+        "\n",
+        "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
+        "df.columns = [\"Filename\", \"Prediction\"]\n",
+        "print(\"Prediction has \", df.shape[0], \" rows\")\n",
+        "df.head(10) "
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -492,15 +541,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "pipeline_run_2.wait_for_completion(show_output=True)"
+        "# This will output information of the pipeline run, including the link to the details page of portal.\n",
-      ]
+        "pipeline_run_2"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### View the prediction results per input image\n",
-        "In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from  all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
       ]
     },
     {
@@ -509,20 +551,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import pandas as pd\n",
+        "# Wait the run for completion and show output log to console\n",
-        "import tempfile\n",
+        "pipeline_run_2.wait_for_completion(show_output=True)"
-        "\n",
-        "batch_run = pipeline_run.find_step_run(parallelrun_step.name)[0]\n",
-        "batch_output = batch_run.get_output_data(output_dir.name)\n",
-        "\n",
-        "target_dir = tempfile.mkdtemp()\n",
-        "batch_output.download(local_path=target_dir)\n",
-        "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
-        "\n",
-        "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
-        "df.columns = [\"Filename\", \"Prediction\"]\n",
-        "print(\"Prediction has \", df.shape[0], \" rows\")\n",
-        "df.head(10) "
       ]
     },
     {

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

@@ -49,7 +49,23 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": []
+      },
       "outputs": [],
       "source": [
         "from azureml.core import Workspace\n",
@@ -318,7 +334,7 @@
         "parallel_run_config = ParallelRunConfig(\n",
         "    source_directory=scripts_folder,\n",
         "    entry_script=script_file,  # the user script to run against each input\n",
-        "    mini_batch_size='5MB',\n",
+        "    mini_batch_size='1KB',\n",
         "    error_threshold=5,\n",
         "    output_action='append_row',\n",
         "    append_row_file_name=\"iris_outputs.txt\",\n",
@@ -349,7 +365,7 @@
         "    output=output_folder,\n",
         "    parallel_run_config=parallel_run_config,\n",
         "    arguments=['--model_name', 'iris-prs'],\n",
-        "    allow_reuse=True\n",
+        "    allow_reuse=False\n",
         ")"
       ]
     },
@@ -375,13 +391,22 @@
         "pipeline_run = Experiment(ws, 'iris-prs').submit(pipeline)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View progress of Pipeline run\n",
+        "\n",
+        "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "# this will output a table with link to the run details in azure portal\n",
+        "# This will output information of the pipeline run, including the link to the details page of portal.\n",
         "pipeline_run"
       ]
     },
@@ -389,29 +414,18 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## View progress of Pipeline run\n",
+        "### Optional: View detailed logs (streaming) "
-        "\n",
-        "The progress of the pipeline is able to be viewed either through azureml.widgets or a console feed from PipelineRun.wait_for_completion()."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
-      "outputs": [],
+        "tags": []
-      "source": [
-        "# GUI\n",
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show() "
-      ]
       },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
       "outputs": [],
       "source": [
-        "# Console logs\n",
+        "## Wait the run for completion and show output log to console\n",
         "pipeline_run.wait_for_completion(show_output=True)"
       ]
     },

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb

@@ -461,7 +461,7 @@
         "    output=processed_images,  # Output file share/blob container\n",
         "    arguments=[\"--style\", style_param],\n",
         "    parallel_run_config=parallel_run_config,\n",
-        "    allow_reuse=True #[optional - default value True]\n",
+        "    allow_reuse=False #[optional - default value True]\n",
         ")"
       ]
     },
@@ -497,7 +497,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Monitor using widget"
+        "# Monitor pipeline run\n",
+        "\n",
+        "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object.\n",
+        "\n"
       ]
     },
     {
@@ -506,25 +509,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Track pipeline run progress\n",
+        "# This will output information of the pipeline run, including the link to the details page of portal.\n",
-        "from azureml.widgets import RunDetails\n",
+        "pipeline_run"
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_run.wait_for_completion()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Downloads the video in `output_video` folder"
+        "### Optional: View detailed logs (streaming) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait the run for completion and show output log to console\n",
+        "pipeline_run.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -534,6 +537,13 @@
         "# Download output video"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Downloads the video in `output_video` folder"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -674,7 +684,8 @@
         "from azureml.pipeline.core.run import PipelineRun\n",
         "published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n",
         "\n",
-        "RunDetails(published_pipeline_run_candy).show()"
+        "# Show detail information of run\n",
+        "published_pipeline_run_candy"
       ]
     },
     {

how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb

@@ -418,7 +418,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.hyperdrive.runconfig import HyperDriveRunConfig\n",
+        "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
         "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
         "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
         "from azureml.train.hyperdrive.parameter_expressions import choice\n",
@@ -430,7 +430,7 @@
         "    }\n",
         ")\n",
         "\n",
-        "hyperdrive_run_config = HyperDriveRunConfig(estimator=estimator,\n",
+        "hyperdrive_config = HyperDriveConfig(estimator=estimator,\n",
         "                                     hyperparameter_sampling=param_sampling, \n",
         "                                     primary_metric_name='Accuracy',\n",
         "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
@@ -452,7 +452,7 @@
       "outputs": [],
       "source": [
         "# start the HyperDrive run\n",
-        "hyperdrive_run = experiment.submit(hyperdrive_run_config)"
+        "hyperdrive_run = experiment.submit(hyperdrive_config)"
       ]
     },
     {

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

@@ -630,52 +630,44 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Predict on the test set\n",
+        "## Predict on the test set (Optional)\n",
         "Now load the saved TensorFlow graph, and list all operations under the `network` scope. This way we can discover the input tensor `network/X:0` and the output tensor `network/output/MatMul:0`, and use them in the scoring script in the next step.\n",
         "\n",
         "Note: if your local TensorFlow version is different than the version running in the cluster where the model is trained, you might see a \"compiletime version mismatch\" warning. You can ignore it."
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
-      "execution_count": null,
       "metadata": {},
-      "outputs": [],
       "source": [
-        "import tensorflow as tf\n",
+        "    import tensorflow as tf\n",
-        "imported_model = tf.saved_model.load('./model')"
+        "    imported_model = tf.saved_model.load('./model')"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
-      "execution_count": null,
       "metadata": {},
-      "outputs": [],
       "source": [
-        "pred =imported_model(X_test)\n",
+        "    pred = imported_model(X_test)\n",
-        "y_hat = np.argmax(pred, axis=1)\n",
+        "    y_hat = np.argmax(pred, axis=1)\n",
         "\n",
-        "# print the first 30 labels and predictions\n",
+        "    # print the first 30 labels and predictions\n",
-        "print('labels:  \\t', y_test[:30])\n",
+        "    print('labels:  \\t', y_test[:30])\n",
-        "print('predictions:\\t', y_hat[:30])"
+        "    print('predictions:\\t', y_hat[:30])"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
-      "execution_count": null,
       "metadata": {},
-      "outputs": [],
       "source": [
-        "print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
+        "    print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
-      "execution_count": null,
       "metadata": {},
-      "outputs": [],
       "source": [
-        "print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
+        "    print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
       ]
     },
     {
@@ -1055,7 +1047,7 @@
         "    font_color = 'red' if y_test[s] != result[i] else 'black'\n",
         "    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
         "    \n",
-        "    plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)\n",
+        "    plt.text(x=10, y=-10, s=result[i], fontsize=18, color=font_color)\n",
         "    plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
         "    \n",
         "    i = i + 1\n",

how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb

@@ -158,7 +158,7 @@
       "source": [
         "from azureml.core import Dataset\n",
         "\n",
-        "web_paths = ['http://mattmahoney.net/dc/text8.zip']\n",
+        "web_paths = ['https://azureopendatastorage.blob.core.windows.net/testpublic/text8.zip']\n",
         "dataset = Dataset.File.from_files(path=web_paths)"
       ]
     },

how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/scripts/train.py

@@ -0,0 +1,78 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import mlflow
+import mlflow.keras
+import numpy as np
+import warnings
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.optimizers import RMSprop
+
+print("Keras version:", keras.__version__)
+
+# Enable auto-logging to MLflow to capture Keras metrics.
+mlflow.keras.autolog()
+
+# Model / data parameters
+n_inputs = 28 * 28
+n_h1 = 300
+n_h2 = 100
+n_outputs = 10
+learning_rate = 0.001
+
+# the data, split between train and test sets
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+# Scale images to the [0, 1] range
+x_train = x_train.astype("float32") / 255
+x_test = x_test.astype("float32") / 255
+
+# Flatten image to be (n, 28 * 28)
+x_train = x_train.reshape(len(x_train), -1)
+x_test = x_test.reshape(len(x_test), -1)
+
+print("x_train shape:", x_train.shape)
+print(x_train.shape[0], "train samples")
+print(x_test.shape[0], "test samples")
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, n_outputs)
+y_test = keras.utils.to_categorical(y_test, n_outputs)
+
+
+def driver():
+    warnings.filterwarnings("ignore")
+
+    with mlflow.start_run() as run:
+
+        # Build a simple MLP model
+        model = Sequential()
+        # first hidden layer
+        model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,)))
+        # second hidden layer
+        model.add(Dense(n_h2, activation='relu'))
+        # output layer
+        model.add(Dense(n_outputs, activation='softmax'))
+        model.summary()
+
+        batch_size = 128
+        epochs = 5
+
+        model.compile(loss='categorical_crossentropy',
+                      optimizer=RMSprop(lr=learning_rate),
+                      metrics=['accuracy'])
+
+        model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+
+        score = model.evaluate(x_test, y_test, verbose=0)
+        print('Test loss:', score[0])
+        print('Test accuracy:', score[1])
+
+    return run
+
+
+if __name__ == "__main__":
+    driver()

how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb

@@ -0,0 +1,455 @@
+{
+  "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/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier\n",
+        "\n",
+        "This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a Keras model to classify MNIST digit images and deploy the model as a web service. You'll learn how to:\n",
+        "\n",
+        " 1. Set up MLflow tracking URI so as to use Azure ML\n",
+        " 2. Create experiment\n",
+        " 3. Instrument your model with MLflow tracking\n",
+        " 4. Train a Keras model locally with MLflow auto logging\n",
+        " 5. Train a model on GPU compute on Azure with MLflow auto logging\n",
+        " 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
+        " 7. Deploy the model as a web service on Azure Container Instance\n",
+        " 8. Call the model to make predictions\n",
+        " \n",
+        "### Pre-requisites\n",
+        " \n",
+        "If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
+        "\n",
+        "Install TensorFlow and Keras, this notebook has been tested with TensorFlow version 2.1.0 and Keras version 2.3.1.\n",
+        "\n",
+        "Also, install azureml-mlflow package using ```pip install azureml-mlflow```. Note that azureml-mlflow installs mlflow package itself as a dependency if you haven't done so previously.\n",
+        "\n",
+        "### Set-up\n",
+        "\n",
+        "Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys, os\n",
+        "import mlflow\n",
+        "import mlflow.azureml\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)\n",
+        "print(\"MLflow version:\", mlflow.version.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set tracking URI\n",
+        "\n",
+        "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Experiment\n",
+        "\n",
+        "In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment_name = \"keras-with-mlflow\"\n",
+        "mlflow.set_experiment(experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train model locally while logging metrics and artifacts\n",
+        "\n",
+        "The ```scripts/train.py``` program contains the code to load the image dataset, train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
+        "\n",
+        "Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, MLflow's automatic logging is used to log metrics, parameters and model for the Keras run.\n",
+        "\n",
+        "Let's add the program to search path, import it as a module and invoke the driver function. Note that the training can take few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lib_path = os.path.abspath(\"scripts\")\n",
+        "sys.path.append(lib_path)\n",
+        "\n",
+        "import train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = train.driver()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train model on GPU compute on Azure\n",
+        "\n",
+        "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
+        "\n",
+        "Clone an environment object from the Tensorflow 2.1 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.get(workspace=ws, name=\"AzureML-TensorFlow-2.1-GPU\").clone(\"mlflow-env\")\n",
+        "\n",
+        "env.python.conda_dependencies.add_pip_package(\"azureml-mlflow\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"keras==2.3.1\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"numpy\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a ScriptRunConfig to specify the training configuration: script, compute as well as environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=\"./scripts\", script=\"train.py\")\n",
+        "src.run_config.environment = env\n",
+        "src.run_config.target = \"gpu-cluster\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get a reference to the experiment you created previously, but this time, as an Azure Machine Learning experiment object.\n",
+        "\n",
+        "Then, use the ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as the cached image is used."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "exp = Experiment(ws, experiment_name)\n",
+        "run = exp.submit(src)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can monitor the run and its metrics on Azure Portal."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Also, you can wait for run to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy model as web service\n",
+        "\n",
+        "The ```mlflow.azureml.deploy``` function registers the logged Keras+Tensorflow model and deploys the model in a framework-aware manner. It automatically creates the Tensorflow-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
+        "\n",
+        "In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
+        "\n",
+        "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
+        "\n",
+        "Note that the service deployment can take several minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "\n",
+        "model_path = \"model\"\n",
+        "\n",
+        "aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
+        "                                                memory_gb=5, \n",
+        "                                                tags={\"data\": \"MNIST\",  \"method\" : \"keras\"}, \n",
+        "                                                description=\"Predict using webservice\")\n",
+        "\n",
+        "webservice, azure_model = mlflow.azureml.deploy(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
+        "                                                      workspace=ws,\n",
+        "                                                      deployment_config=aci_config,\n",
+        "                                                      service_name=\"keras-mnist-1\",\n",
+        "                                                      model_name=\"keras_mnist\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once the deployment has completed you can check the scoring URI of the web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Make predictions using a web service\n",
+        "\n",
+        "To make the web service, create a test data set as normalized NumPy array. \n",
+        "\n",
+        "Then, let's define a utility function that takes a random image and converts it into a format and shape suitable for input to the Keras inferencing end-point. The conversion is done by: \n",
+        "\n",
+        " 1. Select a random (image, label) tuple\n",
+        " 2. Take the image and converting to to NumPy array \n",
+        " 3. Reshape array into 1 x 1 x N array\n",
+        "    * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
+        "    * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
+        " 4. Convert the NumPy array to list to make it into a built-in type.\n",
+        " 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import random\n",
+        "import numpy as np\n",
+        "\n",
+        "# the data, split between train and test sets\n",
+        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
+        "\n",
+        "# Scale images to the [0, 1] range\n",
+        "x_test = x_test.astype(\"float32\") / 255\n",
+        "x_test = x_test.reshape(len(x_test), -1)\n",
+        "\n",
+        "# convert class vectors to binary class matrices\n",
+        "y_test = keras.utils.to_categorical(y_test, 10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import json\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# send a random row from the test set to score\n",
+        "random_index = np.random.randint(0, len(x_test)-1)\n",
+        "input_data = \"{\\\"data\\\": [\" + str(list(x_test[random_index])) + \"]}\"\n",
+        "\n",
+        "response = webservice.run(input_data)\n",
+        "\n",
+        "response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
+        "\n",
+        "print(\"Predicted label:\", response[0][0])\n",
+        "plt.imshow(x_test[random_index].reshape(28,28), cmap = \"gray\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also call the web service using a raw POST method against the web service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "\n",
+        "response = requests.post(url=webservice.scoring_uri, data=input_data,headers={\"Content-type\": \"application/json\"})\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up\n",
+        "You can delete the ACI deployment with a delete API call."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "webservice.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "hancwang"
+      }
+    ],
+    "category": "tutorial",
+    "celltoolbar": "Edit Metadata",
+    "compute": [
+      "Local",
+      "AML Compute"
+    ],
+    "datasets": [
+      "MNIST"
+    ],
+    "deployment": [
+      "Azure Container Instance"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Keras"
+    ],
+    "friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier",
+    "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": "3.7.7"
+    },
+    "tags": [
+      "mlflow",
+      "keras"
+    ],
+    "task": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier, leveraging MLflow auto logging"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py

@@ -0,0 +1,150 @@
+# Copyright (c) 2017, PyTorch Team
+# All rights reserved
+# Licensed under BSD 3-Clause License.
+
+# This example is based on PyTorch MNIST example:
+# https://github.com/pytorch/examples/blob/master/mnist/main.py
+
+import mlflow
+import mlflow.pytorch
+from mlflow.utils.environment import _mlflow_conda_env
+import warnings
+import cloudpickle
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision
+from torchvision import datasets, transforms
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 20, 5, 1)
+        self.conv2 = nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = nn.Linear(4 * 4 * 50, 500)
+        self.fc2 = nn.Linear(500, 10)
+
+    def forward(self, x):
+        # Added the view for reshaping score requests
+        x = x.view(-1, 1, 28, 28)
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4 * 4 * 50)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+            # Use MLflow logging
+            mlflow.log_metric("epoch_loss", loss.item())
+
+
+def test(args, model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction="sum").item()
+            # get the index of the max log-probability
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+    print("\n")
+    print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+        test_loss, correct, len(test_loader.dataset),
+        100. * correct / len(test_loader.dataset)))
+    # Use MLflow logging
+    mlflow.log_metric("average_loss", test_loss)
+
+
+class Args(object):
+    pass
+
+
+# Training settings
+args = Args()
+setattr(args, 'batch_size', 64)
+setattr(args, 'test_batch_size', 1000)
+setattr(args, 'epochs', 3)  # Higher number for better convergence
+setattr(args, 'lr', 0.01)
+setattr(args, 'momentum', 0.5)
+setattr(args, 'no_cuda', True)
+setattr(args, 'seed', 1)
+setattr(args, 'log_interval', 10)
+setattr(args, 'save_model', True)
+
+use_cuda = not args.no_cuda and torch.cuda.is_available()
+
+torch.manual_seed(args.seed)
+
+device = torch.device("cuda" if use_cuda else "cpu")
+
+kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+train_loader = torch.utils.data.DataLoader(
+    datasets.MNIST('../data', train=True, download=True,
+                   transform=transforms.Compose([
+                       transforms.ToTensor(),
+                       transforms.Normalize((0.1307,), (0.3081,))
+                   ])),
+    batch_size=args.batch_size, shuffle=True, **kwargs)
+test_loader = torch.utils.data.DataLoader(
+    datasets.MNIST(
+        '../data',
+        train=False,
+        transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))])),
+    batch_size=args.test_batch_size, shuffle=True, **kwargs)
+
+
+def driver():
+    warnings.filterwarnings("ignore")
+    # Dependencies for deploying the model
+    pytorch_index = "https://download.pytorch.org/whl/"
+    pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl"
+    deps = [
+        "cloudpickle=={}".format(cloudpickle.__version__),
+        pytorch_index + pytorch_version,
+        "torchvision=={}".format(torchvision.__version__),
+        "Pillow=={}".format("6.0.0")
+    ]
+    with mlflow.start_run() as run:
+        model = Net().to(device)
+        optimizer = optim.SGD(
+            model.parameters(),
+            lr=args.lr,
+            momentum=args.momentum)
+        for epoch in range(1, args.epochs + 1):
+            train(args, model, device, train_loader, optimizer, epoch)
+            test(args, model, device, test_loader)
+        # Log model to run history using MLflow
+        if args.save_model:
+            model_env = _mlflow_conda_env(additional_pip_deps=deps)
+            mlflow.pytorch.log_model(model, "model", conda_env=model_env)
+    return run
+
+
+if __name__ == "__main__":
+    driver()

how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb

@@ -0,0 +1,464 @@
+{
+  "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/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier\n",
+        "\n",
+        "This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a PyTorch model to classify MNIST digit images and deploy the model  as a web service. You'll learn how to:\n",
+        "\n",
+        " 1. Set up MLflow tracking URI so as to use Azure ML\n",
+        " 2. Create experiment\n",
+        " 3. Instrument your model with MLflow tracking\n",
+        " 4. Train a PyTorch model locally\n",
+        " 5. Train a model on GPU compute on Azure\n",
+        " 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
+        " 7. Deploy the model as a web service on Azure Container Instance\n",
+        " 8. Call the model to make predictions\n",
+        " \n",
+        "## Pre-requisites\n",
+        " \n",
+        "If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
+        "\n",
+        "Install PyTorch, this notebook has been tested with torch==1.4\n",
+        "\n",
+        "Also, install azureml-mlflow package using ```pip install azureml-mlflow```. Note that azureml-mlflow installs mlflow package itself as a dependency if you haven't done so previously.\n",
+        "\n",
+        "## Set-up\n",
+        "\n",
+        "Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys, os\n",
+        "import mlflow\n",
+        "import mlflow.azureml\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)\n",
+        "print(\"MLflow version:\", mlflow.version.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set tracking URI\n",
+        "\n",
+        "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Experiment\n",
+        "\n",
+        "In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment_name = \"pytorch-with-mlflow\"\n",
+        "mlflow.set_experiment(experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model locally while logging metrics and artifacts\n",
+        "\n",
+        "The ```scripts/train.py``` program contains the code to load the image dataset, train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
+        "\n",
+        "Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, ```mlflow.log_metric``` functions are used to track the convergence of the neural network training iterations. Finally ```mlflow.pytorch.save_model``` is used to save the trained model in framework-aware manner.\n",
+        "\n",
+        "Let's add the program to search path, import it as a module and invoke the driver function. Note that the training can take few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lib_path = os.path.abspath(\"scripts\")\n",
+        "sys.path.append(lib_path)\n",
+        "\n",
+        "import train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = train.driver()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train model on GPU compute on Azure\n",
+        "\n",
+        "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
+        "\n",
+        "Clone an environment object from the PyTorch 1.4 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.get(workspace=ws, name=\"AzureML-PyTorch-1.4-GPU\").clone(\"mlflow-env\")\n",
+        "\n",
+        "env.python.conda_dependencies.add_pip_package(\"azureml-mlflow\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"Pillow==6.0.0\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a ScriptRunConfig to specify the training configuration: script, compute as well as environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=\"./scripts\", script=\"train.py\")\n",
+        "src.run_config.environment = env\n",
+        "src.run_config.target = \"gpu-cluster\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get a reference to the experiment you created previously, but this time, as an Azure Machine Learning experiment object.\n",
+        "\n",
+        "Then, use the ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as the cached image is used."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "exp = Experiment(ws, experiment_name)\n",
+        "run = exp.submit(src)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can monitor the run and its metrics on Azure Portal."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Also, you can wait for run to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy model as web service\n",
+        "\n",
+        "The ```mlflow.azureml.deploy``` function registers the logged PyTorch model and deploys the model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
+        "\n",
+        "In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
+        "\n",
+        "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
+        "\n",
+        "Note that the service deployment can take several minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "\n",
+        "model_path = \"model\"\n",
+        "\n",
+        "aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
+        "                                                memory_gb=5, \n",
+        "                                                tags={\"data\": \"MNIST\",  \"method\" : \"pytorch\"}, \n",
+        "                                                description=\"Predict using webservice\")\n",
+        "\n",
+        "webservice, azure_model = mlflow.azureml.deploy(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
+        "                                                      workspace=ws,\n",
+        "                                                      deployment_config=aci_config,\n",
+        "                                                      service_name=\"pytorch-mnist-1\",\n",
+        "                                                      model_name=\"pytorch_mnist\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once the deployment has completed you can check the scoring URI of the web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Make predictions using a web service\n",
+        "\n",
+        "To make the web service, create a test data set as normalized PyTorch tensors. \n",
+        "\n",
+        "Then, let's define a utility function that takes a random image and converts it into a format and shape suitable for input to the PyTorch inferencing end-point. The conversion is done by: \n",
+        "\n",
+        " 1. Select a random (image, label) tuple\n",
+        " 2. Take the image and converting the tensor to NumPy array \n",
+        " 3. Reshape array into 1 x 1 x N array\n",
+        "    * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
+        "    * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
+        " 4. Convert the NumPy array to list to make it into a built-in type.\n",
+        " 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from torchvision import datasets, transforms\n",
+        "import random\n",
+        "import numpy as np\n",
+        "\n",
+        "test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
+        "                       transforms.ToTensor(),\n",
+        "                       transforms.Normalize((0.1307,), (0.3081,))]))\n",
+        "\n",
+        "\n",
+        "def get_random_image():\n",
+        "    image_idx = random.randint(0,len(test_data))\n",
+        "    image_as_tensor = test_data[image_idx][0]\n",
+        "    return {\"data\": elem for elem in image_as_tensor.numpy().reshape(1,1,-1).tolist()}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then, invoke the web service using a random test image. Convert the dictionary containing the image to JSON string before passing it to web service.\n",
+        "\n",
+        "The response contains the raw scores for each label, with greater value indicating higher probability. Sort the labels and select the one with greatest score to get the prediction. Let's also plot the image sent to web service for comparison purposes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import json\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "test_image = get_random_image()\n",
+        "\n",
+        "response = webservice.run(json.dumps(test_image))\n",
+        "\n",
+        "response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
+        "\n",
+        "\n",
+        "print(\"Predicted label:\", response[0][0])\n",
+        "plt.imshow(np.array(test_image[\"data\"]).reshape(28,28), cmap = \"gray\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also call the web service using a raw POST method against the web service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "\n",
+        "response = requests.post(url=webservice.scoring_uri, data=json.dumps(test_image),headers={\"Content-type\": \"application/json\"})\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up\n",
+        "You can delete the ACI deployment with a delete API call."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "webservice.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "shipatel"
+      }
+    ],
+    "category": "tutorial",
+    "celltoolbar": "Edit Metadata",
+    "compute": [
+      "Local",
+      "AML Compute"
+    ],
+    "datasets": [
+      "MNIST"
+    ],
+    "deployment": [
+      "Azure Container Instance"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "PyTorch"
+    ],
+    "friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier",
+    "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": "3.7.7"
+    },
+    "name": "mlflow-sparksummit-pytorch",
+    "notebookId": 2495374963457641,
+    "tags": [
+      "mlflow",
+      "pytorch"
+    ],
+    "task": "Use MLflow with Azure Machine Learning to train and deploy PyTorch image classifier model"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/monitor-models/data-drift/config.py

@@ -1,31 +0,0 @@
-# imports
-import pickle
-from datetime import datetime
-from azureml.opendatasets import NoaaIsdWeather
-from sklearn.linear_model import LinearRegression
-
-# get weather dataset
-start = datetime(2019, 1, 1)
-end = datetime(2019, 1, 14)
-isd = NoaaIsdWeather(start, end)
-
-# convert to pandas dataframe and filter down
-df = isd.to_pandas_dataframe().fillna(0)
-df = df[df['stationName'].str.contains('FLORIDA', regex=True, na=False)]
-
-# features for training
-X_features = ['latitude', 'longitude', 'temperature', 'windAngle', 'windSpeed']
-y_features = ['elevation']
-
-# write the training dataset to csv
-training_dataset = df[X_features + y_features]
-training_dataset.to_csv('training.csv', index=False)
-
-# train the model
-X = training_dataset[X_features]
-y = training_dataset[y_features]
-model = LinearRegression().fit(X, y)
-
-# save the model as a .pkl file
-with open('elevation-regression-model.pkl', 'wb') as f:
-    pickle.dump(model, f)

how-to-use-azureml/monitor-models/data-drift/dataset/testing.csv

@@ -1,346 +0,0 @@
-latitude,longitude,temperature,windAngle,windSpeed,elevation
-26.536,-81.755,17.8,10.0,2.1,9.0
-26.536,-81.755,16.7,360.0,1.5,9.0
-26.536,-81.755,16.1,350.0,1.5,9.0
-26.536,-81.755,15.0,0.0,0.0,9.0
-26.536,-81.755,14.4,350.0,1.5,9.0
-26.536,-81.755,0.0,0.0,0.0,9.0
-26.536,-81.755,13.9,360.0,2.1,9.0
-26.536,-81.755,13.3,350.0,1.5,9.0
-26.536,-81.755,13.3,10.0,2.1,9.0
-26.536,-81.755,13.3,360.0,1.5,9.0
-26.536,-81.755,13.3,0.0,0.0,9.0
-26.536,-81.755,12.2,0.0,0.0,9.0
-26.536,-81.755,11.7,0.0,0.0,9.0
-26.536,-81.755,14.4,0.0,0.0,9.0
-26.536,-81.755,17.2,10.0,2.6,9.0
-26.536,-81.755,20.0,20.0,2.6,9.0
-26.536,-81.755,22.2,10.0,3.6,9.0
-26.536,-81.755,23.3,30.0,4.6,9.0
-26.536,-81.755,23.3,330.0,2.6,9.0
-26.536,-81.755,24.4,0.0,0.0,9.0
-26.536,-81.755,25.0,360.0,3.1,9.0
-26.536,-81.755,24.4,20.0,4.1,9.0
-26.536,-81.755,23.3,10.0,2.6,9.0
-26.536,-81.755,21.1,30.0,2.1,9.0
-26.536,-81.755,18.3,0.0,0.0,9.0
-26.536,-81.755,17.2,30.0,2.1,9.0
-26.536,-81.755,15.6,60.0,2.6,9.0
-26.536,-81.755,15.6,0.0,0.0,9.0
-26.536,-81.755,13.9,60.0,2.6,9.0
-26.536,-81.755,12.8,70.0,2.6,9.0
-26.536,-81.755,0.0,0.0,0.0,9.0
-26.536,-81.755,11.7,70.0,2.1,9.0
-26.536,-81.755,12.2,20.0,2.1,9.0
-26.536,-81.755,11.7,30.0,1.5,9.0
-26.536,-81.755,11.1,40.0,2.1,9.0
-26.536,-81.755,12.2,40.0,2.6,9.0
-26.536,-81.755,12.2,30.0,2.6,9.0
-26.536,-81.755,12.2,0.0,0.0,9.0
-26.536,-81.755,15.0,30.0,6.2,9.0
-26.536,-81.755,17.2,50.0,3.6,9.0
-26.536,-81.755,20.6,60.0,5.1,9.0
-26.536,-81.755,22.8,50.0,4.6,9.0
-26.536,-81.755,24.4,80.0,6.2,9.0
-26.536,-81.755,25.0,100.0,5.7,9.0
-26.536,-81.755,25.6,60.0,3.1,9.0
-26.536,-81.755,25.6,80.0,4.6,9.0
-26.536,-81.755,25.0,90.0,5.1,9.0
-26.536,-81.755,24.4,80.0,5.1,9.0
-26.536,-81.755,21.1,60.0,2.6,9.0
-26.536,-81.755,19.4,70.0,3.6,9.0
-26.536,-81.755,18.3,70.0,2.6,9.0
-26.536,-81.755,18.3,80.0,2.6,9.0
-26.536,-81.755,17.2,60.0,1.5,9.0
-26.536,-81.755,16.1,70.0,2.6,9.0
-26.536,-81.755,15.6,70.0,2.6,9.0
-26.536,-81.755,0.0,0.0,0.0,9.0
-26.536,-81.755,16.1,50.0,2.6,9.0
-26.536,-81.755,15.6,50.0,2.1,9.0
-26.536,-81.755,15.0,50.0,1.5,9.0
-26.536,-81.755,15.0,0.0,0.0,9.0
-26.536,-81.755,15.0,0.0,0.0,9.0
-26.536,-81.755,14.4,0.0,0.0,9.0
-26.536,-81.755,14.4,30.0,4.1,9.0
-26.536,-81.755,16.1,40.0,1.5,9.0
-26.536,-81.755,19.4,0.0,1.5,9.0
-26.536,-81.755,22.8,90.0,2.6,9.0
-26.536,-81.755,24.4,130.0,3.6,9.0
-26.536,-81.755,25.6,100.0,4.6,9.0
-26.536,-81.755,26.1,120.0,3.1,9.0
-26.536,-81.755,26.7,0.0,2.6,9.0
-26.536,-81.755,27.2,0.0,0.0,9.0
-26.536,-81.755,27.2,40.0,3.1,9.0
-26.536,-81.755,26.1,30.0,1.5,9.0
-26.536,-81.755,22.8,310.0,2.1,9.0
-26.536,-81.755,23.3,330.0,2.1,9.0
--34.067,-56.238,17.5,30.0,3.1,68.0
--34.067,-56.238,21.2,30.0,5.7,68.0
--34.067,-56.238,24.5,30.0,3.1,68.0
--34.067,-56.238,27.5,330.0,3.6,68.0
--34.067,-56.238,29.2,30.0,4.1,68.0
--34.067,-56.238,31.0,20.0,4.6,68.0
--34.067,-56.238,33.0,360.0,2.6,68.0
--34.067,-56.238,33.6,60.0,3.1,68.0
--34.067,-56.238,33.6,30.0,3.6,68.0
--34.067,-56.238,18.6,40.0,3.1,68.0
--34.067,-56.238,22.0,120.0,1.5,68.0
--34.067,-56.238,25.0,120.0,2.6,68.0
--34.067,-56.238,28.6,50.0,3.1,68.0
--34.067,-56.238,30.6,50.0,4.1,68.0
--34.067,-56.238,31.5,30.0,6.7,68.0
--34.067,-56.238,32.0,40.0,7.2,68.0
--34.067,-56.238,33.0,30.0,5.7,68.0
--34.067,-56.238,33.2,360.0,3.6,68.0
--34.067,-56.238,20.6,30.0,3.1,68.0
--34.067,-56.238,21.2,0.0,0.0,68.0
--34.067,-56.238,22.0,210.0,3.1,68.0
--34.067,-56.238,23.0,210.0,3.6,68.0
--34.067,-56.238,24.0,180.0,6.7,68.0
--34.067,-56.238,24.5,210.0,7.2,68.0
--34.067,-56.238,21.0,180.0,8.2,68.0
--34.067,-56.238,20.0,180.0,6.7,68.0
--34.083,-56.233,20.2,180.0,7.2,68.0
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how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb

@@ -1,547 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/monitor-models/data-drift/drift-on-aks.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Monitor data drift on models deployed to Azure Kubernetes Service \n",
-        "\n",
-        "In this tutorial, you will setup a data drift monitor on a toy model that predicts elevation based on a few weather factors which will send email alerts if drift is detected."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Compute instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already established your connection to the AzureML Workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print('SDK version:', azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "ws"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup training dataset and model\n",
-        "\n",
-        "Setup the training dataset and model in preparation for deployment to the Azure Kubernetes Service. \n",
-        "\n",
-        "The next few cells will:\n",
-        "  * get the default datastore and upload the `training.csv` dataset to the datastore\n",
-        "  * create and register the dataset \n",
-        "  * register the model with the dataset\n",
-        "  \n",
-        "See the `config.py` script in this folder for details on how `training.csv` and `elevation-regression-model.pkl` are created. If you train your model in Azure ML using a Dataset, it will be automatically captured when registering the model from the run. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# use default datastore\n",
-        "dstore = ws.get_default_datastore()\n",
-        "\n",
-        "# upload weather data\n",
-        "dstore.upload('dataset', 'drift-on-aks-data', overwrite=True, show_progress=False)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Dataset\n",
-        "\n",
-        "# create dataset \n",
-        "dset = Dataset.Tabular.from_delimited_files(dstore.path('drift-on-aks-data/training.csv'))\n",
-        "# register dataset\n",
-        "dset = dset.register(ws, 'drift-demo-dataset')\n",
-        "# get the dataset by name from the workspace\n",
-        "dset = Dataset.get_by_name(ws, 'drift-demo-dataset')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "# register the model\n",
-        "model = Model.register(model_path='elevation-regression-model.pkl',\n",
-        "                       model_name='elevation-regression-model.pkl',\n",
-        "                       tags={'area': \"weather\", 'type': \"linear regression\"},\n",
-        "                       description='Linear regression model to predict elevation based on the weather',\n",
-        "                       workspace=ws,\n",
-        "                       datasets=[(Dataset.Scenario.TRAINING, dset)]) # need to register the dataset with the model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create the inference config\n",
-        "\n",
-        "Create the environment and inference config from the `myenv.yml` and `score.py` files. Notice the [Model Data Collector](https://docs.microsoft.com/azure/machine-learning/service/how-to-enable-data-collection) code included in the scoring script. This dependency is currently required to collect model data, but will be removed in the near future as data collection in Azure Machine Learning webservice endpoints is automated."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Environment\n",
-        "\n",
-        "# create the environment from the yml file \n",
-        "env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import InferenceConfig\n",
-        "\n",
-        "# create an inference config, combining the environment and entry script \n",
-        "inference_config = InferenceConfig(entry_script='score.py', environment=env)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create the AKS compute target\n",
-        "\n",
-        "Create an Azure Kubernetes Service compute target to deploy the model to. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Use the default configuration (you can also provide parameters to customize this).\n",
-        "# For example, to create a dev/test cluster, use:\n",
-        "# prov_config = AksCompute.provisioning_configuration(cluster_purpose = AksCompute.ClusterPurpose.DEV_TEST)\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
-        "\n",
-        "aks_name = 'drift-aks'\n",
-        "aks_target = ws.compute_targets.get(aks_name)\n",
-        "\n",
-        "# Create the cluster\n",
-        "if not aks_target:\n",
-        "    aks_target = ComputeTarget.create(workspace = ws,\n",
-        "                                      name = aks_name,\n",
-        "                                      provisioning_configuration = prov_config)\n",
-        "\n",
-        "    # Wait for the create process to complete\n",
-        "    aks_target.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy the model to AKS \n",
-        "\n",
-        "Deploy the model as a webservice endpoint. Be sure to enable the `collect_model_data` flag so that serving data is collected in blob storage for use by the data drift capability."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AksWebservice\n",
-        "\n",
-        "deployment_config = AksWebservice.deploy_configuration(cpu_cores=1, memory_gb=1, collect_model_data=True)\n",
-        "service_name = 'drift-aks-service'\n",
-        "\n",
-        "service = Model.deploy(ws, service_name, [model], inference_config, deployment_config, aks_target)\n",
-        "\n",
-        "service.wait_for_deployment(True)\n",
-        "print(service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run recent weather data through the webservice \n",
-        "\n",
-        "The below cells take the weather data of Florida from 2019-11-20 to 2019-11-12, filter and transform using the same processes as the training dataset, and runs the data through the service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# create dataset \n",
-        "tset = Dataset.Tabular.from_delimited_files(dstore.path('drift-on-aks-data/testing.csv'))\n",
-        "\n",
-        "df = tset.to_pandas_dataframe().fillna(0)\n",
-        "\n",
-        "X_features = ['latitude', 'longitude', 'temperature', 'windAngle', 'windSpeed']\n",
-        "y_features = ['elevation']\n",
-        "\n",
-        "X = df[X_features]\n",
-        "y = df[y_features]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "data = json.dumps({'data': X.values.tolist()})\n",
-        "\n",
-        "data_encoded = bytes(data, encoding='utf8')\n",
-        "prediction = service.run(input_data=data_encoded)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Azure Machine Learning Compute cluster\n",
-        "\n",
-        "The data drift capability needs a compute target for computing drift and other data metrics. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "\n",
-        "compute_name = 'cpu-cluster'\n",
-        "\n",
-        "if compute_name in ws.compute_targets:\n",
-        "    compute_target = ws.compute_targets[compute_name]\n",
-        "    if compute_target and type(compute_target) is AmlCompute:\n",
-        "        print('found compute target. just use it. ' + compute_name)\n",
-        "else:\n",
-        "    print('creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D3_V2', min_nodes=0, max_nodes=2)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
-        "\n",
-        "    # can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # if no min node count is provided it will use the scale settings for the cluster\n",
-        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "\n",
-        "    # For a more detailed view of current AmlCompute status, use get_status()\n",
-        "    print(compute_target.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Wait 10 minutes\n",
-        "\n",
-        "From the Model Data Collector, it can take up to (but usually less than) 10 minutes for data to arrive in your blob storage account. Wait 10 minutes to ensure cells below will run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import time\n",
-        "\n",
-        "time.sleep(600)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create and update the data drift object"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from datetime import datetime, timedelta\n",
-        "from azureml.datadrift import DataDriftDetector, AlertConfiguration\n",
-        "\n",
-        "services = [service_name]\n",
-        "start = datetime.now() - timedelta(days=2)\n",
-        "feature_list = X_features\n",
-        "alert_config = AlertConfiguration(['user@contoso.com'])\n",
-        "\n",
-        "try:\n",
-        "    monitor = DataDriftDetector.create_from_model(ws, model.name, model.version, services, \n",
-        "                                                  frequency='Day', \n",
-        "                                                  schedule_start=datetime.utcnow() + timedelta(days=1), \n",
-        "                                                  alert_config=alert_config, \n",
-        "                                                  compute_target='cpu-cluster')\n",
-        "except KeyError:\n",
-        "    monitor = DataDriftDetector.get(ws, model.name, model.version)\n",
-        "    \n",
-        "monitor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# many monitor settings can be updated \n",
-        "monitor = monitor.update(drift_threshold = 0.1)\n",
-        "\n",
-        "monitor"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run the monitor on today's scoring data\n",
-        "\n",
-        "Perform a data drift run on the data sent to the service earlier in this notebook. If you set your email address in the alert configuration and the drift threshold <=0.1 you should recieve an email alert to drift from this run.\n",
-        "\n",
-        "Wait for the run to complete before getting the results. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "now = datetime.utcnow()\n",
-        "target_date = datetime(now.year, now.month, now.day)\n",
-        "run = monitor.run(target_date, services, feature_list=feature_list, compute_target='cpu-cluster')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Get and view results and metrics\n",
-        "\n",
-        "For enterprise workspaces, the UI in the Azure Machine Learning studio can be used. Otherwise, the metrics can be queried in Python and plotted. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# The run() API initiates a pipeline run for each service in the services list. \n",
-        "# Here we retrieve the individual service run to get its output results and metrics. \n",
-        "\n",
-        "child_run = list(run.get_children())[0]\n",
-        "child_run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "child_run.wait_for_completion(wait_post_processing=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "results, metrics = monitor.get_output(run_id=child_run.id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "drift_plots = monitor.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Enable the monitor's pipeline schedule\n",
-        "\n",
-        "Turn on a scheduled pipeline which will anlayze the serving dataset for drift. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "monitor.enable_schedule()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Delete the DataDriftDetector\n",
-        "\n",
-        "Invoking the `delete()` method on the object deletes the the drift monitor permanently and cannot be undone. You will no longer be able to find it in the UI and the `list()` or `get()` methods. The object on which delete() was called will have its state set to deleted and name suffixed with deleted. The baseline and target datasets and model data that was collected, if any, are not deleted. The compute is not deleted. The DataDrift schedule pipeline is disabled and archived."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "monitor.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "  * See [our documentation](https://aka.ms/datadrift/aks) or [Python SDK reference](https://docs.microsoft.com/python/api/overview/azure/ml/intro)\n",
-        "  * [Send requests or feedback](mailto:driftfeedback@microsoft.com) on data drift directly to the team\n",
-        "  * Please open issues with data drift here on GitHub or on StackOverflow if others are likely to run into the same issue"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jamgan"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "Remote"
-    ],
-    "datasets": [
-      "NOAA"
-    ],
-    "deployment": [
-      "AKS"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Azure ML"
-    ],
-    "friendly_name": "Data drift on aks",
-    "index_order": 1.0,
-    "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": "3.7.4"
-    },
-    "star_tag": [
-      "featured"
-    ],
-    "tags": [
-      "Dataset",
-      "Timeseries",
-      "Drift"
-    ],
-    "task": "Filtering"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}

how-to-use-azureml/monitor-models/data-drift/drift-on-aks.yml

@@ -1,8 +0,0 @@
-name: drift-on-aks
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-datadrift
-  - azureml-monitoring
-  - azureml-opendatasets
-  - azureml-widgets

how-to-use-azureml/monitor-models/data-drift/myenv.yml

@@ -1,11 +0,0 @@
-name: project_environment
-dependencies:
-  - python=3.6.2
-  - pip:
-    - azureml-core
-    - azureml-defaults
-    - azureml-monitoring
-    - scikit-learn
-    - numpy
-    - packaging
-    - inference-schema[numpy-support]