update samples from Release-57 as a part of SDK release

update samples from Release-56 as a part of  SDK release

README.md

@@ -13,7 +13,7 @@ Read more detailed instructions on [how to set up your environment](./NBSETUP.md
 
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
 
 If you want to...
 
@@ -40,6 +40,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
 - [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
+- [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
 
 ---
 ## Documentation

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/fairness/fairlearn-azureml-mitigation.ipynb

@@ -0,0 +1,549 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/fairlearn-azureml-mitigation.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Unfairness Mitigation with Fairlearn and Azure Machine Learning\n",
+        "**This notebook shows how to upload results from Fairlearn's GridSearch mitigation algorithm into a dashboard in Azure Machine Learning Studio**\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Loading the Data](#LoadingData)\n",
+        "1. [Training an Unmitigated Model](#UnmitigatedModel)\n",
+        "1. [Mitigation with GridSearch](#Mitigation)\n",
+        "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
+        "    1. Registering models\n",
+        "    1. Computing Fairness Metrics\n",
+        "    1. Uploading to Azure\n",
+        "1. [Conclusion](#Conclusion)\n",
+        "\n",
+        "<a id=\"Introduction\"></a>\n",
+        "## Introduction\n",
+        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.github.io) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.github.io/).\n",
+        "\n",
+        "We will apply the [grid search algorithm](https://fairlearn.github.io/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
+        "\n",
+        "### Setup\n",
+        "\n",
+        "To use this notebook, an Azure Machine Learning workspace is required.\n",
+        "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
+        "This notebook also requires the following packages:\n",
+        "* `azureml-contrib-fairness`\n",
+        "* `fairlearn==0.4.6`\n",
+        "* `joblib`\n",
+        "* `shap`\n",
+        "\n",
+        "\n",
+        "<a id=\"LoadingData\"></a>\n",
+        "## Loading the Data\n",
+        "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
+        "from fairlearn.widget import FairlearnDashboard\n",
+        "from sklearn import svm\n",
+        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "import pandas as pd\n",
+        "import shap"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now load and inspect the data from the `shap` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_raw, Y = shap.datasets.adult()\n",
+        "X_raw[\"Race\"].value_counts().to_dict()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). We also separate out the Race column, but we will not perform any mitigation based on it. Finally, we perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['Sex','Race']]\n",
+        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
+        "X = pd.get_dummies(X)\n",
+        "\n",
+        "\n",
+        "le = LabelEncoder()\n",
+        "Y = le.fit_transform(Y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "With our data prepared, we can make the conventional split in to 'test' and 'train' subsets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_raw, \n",
+        "                                                    Y, \n",
+        "                                                    A,\n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=Y)\n",
+        "\n",
+        "# Work around indexing issue\n",
+        "X_train = X_train.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "X_test = X_test.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)\n",
+        "\n",
+        "# Improve labels\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+        "\n",
+        "\n",
+        "A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
+        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
+        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
+        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
+        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"UnmitigatedModel\"></a>\n",
+        "## Training an Unmitigated Model\n",
+        "\n",
+        "So we have a point of comparison, we first train a model (specifically, logistic regression from scikit-learn) on the raw data, without applying any mitigation algorithm:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
+        "\n",
+        "unmitigated_predictor.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can view this model in the fairness dashboard, and see the disparities which appear:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=Y_test,\n",
+        "                   y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Looking at the disparity in accuracy when we select 'Sex' as the sensitive feature, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n",
+        "\n",
+        "Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Mitigation\"></a>\n",
+        "## Mitigation with GridSearch\n",
+        "\n",
+        "The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox - for this simple example, we shall use the logistic regression estimator from scikit-learn. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n",
+        "\n",
+        "For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). *We are using this metric for the sake of simplicity* in this example; the appropriate fairness metric can only be selected after *careful examination of the broader context* in which the model is to be used."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),\n",
+        "                   constraints=DemographicParity(),\n",
+        "                   grid_size=71)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "With our estimator created, we can fit it to the data. After `fit()` completes, we extract the full set of predictors from the `GridSearch` object.\n",
+        "\n",
+        "The following cell trains a many copies of the underlying estimator, and may take a minute or two to run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sweep.fit(X_train, Y_train,\n",
+        "          sensitive_features=A_train.Sex)\n",
+        "\n",
+        "predictors = sweep._predictors"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the protected attribute; other potentially protected attributes will *not* be mitigated). In general, one might not want to do this, since there may be other considerations beyond the strict optimisation of error and disparity (of the given protected attribute)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "errors, disparities = [], []\n",
+        "for m in predictors:\n",
+        "    classifier = lambda X: m.predict(X)\n",
+        "    \n",
+        "    error = ErrorRate()\n",
+        "    error.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.Sex)\n",
+        "    disparity = DemographicParity()\n",
+        "    disparity.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.Sex)\n",
+        "    \n",
+        "    errors.append(error.gamma(classifier)[0])\n",
+        "    disparities.append(disparity.gamma(classifier).max())\n",
+        "    \n",
+        "all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
+        "\n",
+        "dominant_models_dict = dict()\n",
+        "base_name_format = \"census_gs_model_{0}\"\n",
+        "row_id = 0\n",
+        "for row in all_results.itertuples():\n",
+        "    model_name = base_name_format.format(row_id)\n",
+        "    errors_for_lower_or_eq_disparity = all_results[\"error\"][all_results[\"disparity\"]<=row.disparity]\n",
+        "    if row.error <= errors_for_lower_or_eq_disparity.min():\n",
+        "        dominant_models_dict[model_name] = row.predictor\n",
+        "    row_id = row_id + 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can construct predictions for the dominant models (we include the unmitigated predictor as well, for comparison):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "predictions_dominant = {\"census_unmitigated\": unmitigated_predictor.predict(X_test)}\n",
+        "models_dominant = {\"census_unmitigated\": unmitigated_predictor}\n",
+        "for name, predictor in dominant_models_dict.items():\n",
+        "    value = predictor.predict(X_test)\n",
+        "    predictions_dominant[name] = value\n",
+        "    models_dominant[name] = predictor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These predictions may then be viewed in the fairness dashboard. We include the race column from the dataset, as an alternative basis for assessing the models. However, since we have not based our mitigation on it, the variation in the models with respect to race can be large."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "FairlearnDashboard(sensitive_features=A_test, \n",
+        "                   sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=Y_test.tolist(),\n",
+        "                   y_pred=predictions_dominant)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When using sex as the sensitive feature, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
+        "\n",
+        "By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"AzureUpload\"></a>\n",
+        "## Uploading a Fairness Dashboard to Azure\n",
+        "\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
+        "1. Register the dominant models\n",
+        "1. Precompute all the required metrics\n",
+        "1. Upload to Azure\n",
+        "\n",
+        "Before that, we need to connect to Azure Machine Learning Studio:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"RegisterModels\"></a>\n",
+        "### Registering Models\n",
+        "\n",
+        "The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `<name>:<version>` pairs) of registered models in the workspace. We register each of the models in the `models_dominant` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import os\n",
+        "\n",
+        "os.makedirs('models', exist_ok=True)\n",
+        "def register_model(name, model):\n",
+        "    print(\"Registering \", name)\n",
+        "    model_path = \"models/{0}.pkl\".format(name)\n",
+        "    joblib.dump(value=model, filename=model_path)\n",
+        "    registered_model = Model.register(model_path=model_path,\n",
+        "                                      model_name=name,\n",
+        "                                      workspace=ws)\n",
+        "    print(\"Registered \", registered_model.id)\n",
+        "    return registered_model.id\n",
+        "\n",
+        "model_name_id_mapping = dict()\n",
+        "for name, model in models_dominant.items():\n",
+        "    m_id = register_model(name, model)\n",
+        "    model_name_id_mapping[name] = m_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, produce new predictions dictionaries, with the updated names:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "predictions_dominant_ids = dict()\n",
+        "for name, y_pred in predictions_dominant.items():\n",
+        "    predictions_dominant_ids[model_name_id_mapping[name]] = y_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"PrecomputeMetrics\"></a>\n",
+        "### Precomputing Metrics\n",
+        "\n",
+        "We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `predictions_dominant_ids` dictionary we just created:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sf = { 'sex': A_test.Sex, 'race': A_test.Race }\n",
+        "\n",
+        "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
+        "\n",
+        "\n",
+        "dash_dict = _create_group_metric_set(y_true=Y_test,\n",
+        "                                     predictions=predictions_dominant_ids,\n",
+        "                                     sensitive_features=sf,\n",
+        "                                     prediction_type='binary_classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"DashboardUpload\"></a>\n",
+        "### Uploading the Dashboard\n",
+        "\n",
+        "Now, we import our `contrib` package which contains the routine to perform the upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can create an Experiment, then a Run, and upload our dashboard to it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "exp = Experiment(ws, \"Test_Fairlearn_GridSearch_Census_Demo\")\n",
+        "print(exp)\n",
+        "\n",
+        "run = exp.start_logging()\n",
+        "try:\n",
+        "    dashboard_title = \"Dominant Models from GridSearch\"\n",
+        "    upload_id = upload_dashboard_dictionary(run,\n",
+        "                                            dash_dict,\n",
+        "                                            dashboard_name=dashboard_title)\n",
+        "    print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
+        "\n",
+        "    downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
+        "finally:\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The dashboard can be viewed in the Run Details page.\n",
+        "\n",
+        "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(dash_dict == downloaded_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Conclusion\"></a>\n",
+        "## Conclusion\n",
+        "\n",
+        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.github.io/) provides that discussion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "riedgar"
+      }
+    ],
+    "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": "3.6.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -0,0 +1,8 @@
+name: fairlearn-azureml-mitigation
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-fairness
+  - fairlearn==0.4.6
+  - joblib
+  - shap

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -0,0 +1,494 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/upload-fairness-dashboard.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Upload a Fairness Dashboard to Azure Machine Learning Studio\n",
+        "**This notebook shows how to generate and upload a fairness assessment dashboard from Fairlearn to AzureML Studio**\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Loading the Data](#LoadingData)\n",
+        "1. [Processing the Data](#ProcessingData)\n",
+        "1. [Training Models](#TrainingModels)\n",
+        "1. [Logging in to AzureML](#LoginAzureML)\n",
+        "1. [Registering the Models](#RegisterModels)\n",
+        "1. [Using the Fairlearn Dashboard](#LocalDashboard)\n",
+        "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
+        "    1. Computing Fairness Metrics\n",
+        "    1. Uploading to Azure\n",
+        "1. [Conclusion](#Conclusion)\n",
+        "    \n",
+        "\n",
+        "<a id=\"Introduction\"></a>\n",
+        "## Introduction\n",
+        "\n",
+        "In this notebook, we walk through a simple example of using the `azureml-contrib-fairness` package to upload a collection of fairness statistics for a fairness dashboard. It is an example of integrating the [open source Fairlearn package](https://www.github.com/fairlearn/fairlearn) with Azure Machine Learning. This is not an example of fairness analysis or mitigation - this notebook simply shows how to get a fairness dashboard into the Azure Machine Learning portal. We will load the data and train a couple of simple models. We will then use Fairlearn to generate data for a Fairness dashboard, which we can upload to Azure Machine Learning portal and view there.\n",
+        "\n",
+        "### Setup\n",
+        "\n",
+        "To use this notebook, an Azure Machine Learning workspace is required.\n",
+        "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
+        "This notebook also requires the following packages:\n",
+        "* `azureml-contrib-fairness`\n",
+        "* `fairlearn==0.4.6`\n",
+        "* `joblib`\n",
+        "* `shap`\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "<a id=\"LoadingData\"></a>\n",
+        "## Loading the Data\n",
+        "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn import svm\n",
+        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "import pandas as pd\n",
+        "import shap"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can load the data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_raw, Y = shap.datasets.adult()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can take a look at some of the data. For example, the next cells shows the counts of the different races identified in the dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(X_raw[\"Race\"].value_counts().to_dict())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"ProcessingData\"></a>\n",
+        "## Processing the Data\n",
+        "\n",
+        "With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and put the rest of the feature data into `X`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['Sex','Race']]\n",
+        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
+        "X = pd.get_dummies(X)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we apply a standard set of scalings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sc = StandardScaler()\n",
+        "X_scaled = sc.fit_transform(X)\n",
+        "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
+        "\n",
+        "le = LabelEncoder()\n",
+        "Y = le.fit_transform(Y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can then split our data into training and test sets, and also make the labels on our test portion of `A` human-readable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled, \n",
+        "                                                    Y, \n",
+        "                                                    A,\n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=Y)\n",
+        "\n",
+        "# Work around indexing issue\n",
+        "X_train = X_train.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "X_test = X_test.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)\n",
+        "\n",
+        "# Improve labels\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+        "\n",
+        "\n",
+        "A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
+        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
+        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
+        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
+        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"TrainingModels\"></a>\n",
+        "## Training Models\n",
+        "\n",
+        "We now train a couple of different models on our data. The `adult` census dataset is a classification problem - the goal is to predict whether a particular individual exceeds an income threshold. For the purpose of generating a dashboard to upload, it is sufficient to train two basic classifiers. First, a logistic regression classifier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
+        "\n",
+        "lr_predictor.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And for comparison, a support vector classifier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "svm_predictor = svm.SVC()\n",
+        "\n",
+        "svm_predictor.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoginAzureML\"></a>\n",
+        "## Logging in to AzureML\n",
+        "\n",
+        "With our two classifiers trained, we can log into our AzureML workspace:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"RegisterModels\"></a>\n",
+        "## Registering the Models\n",
+        "\n",
+        "Next, we register our models. By default, the subroutine which uploads the models checks that the names provided correspond to registered models in the workspace. We define a utility routine to do the registering:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import os\n",
+        "\n",
+        "os.makedirs('models', exist_ok=True)\n",
+        "def register_model(name, model):\n",
+        "    print(\"Registering \", name)\n",
+        "    model_path = \"models/{0}.pkl\".format(name)\n",
+        "    joblib.dump(value=model, filename=model_path)\n",
+        "    registered_model = Model.register(model_path=model_path,\n",
+        "                                      model_name=name,\n",
+        "                                      workspace=ws)\n",
+        "    print(\"Registered \", registered_model.id)\n",
+        "    return registered_model.id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we register the models. For convenience in subsequent method calls, we store the results in a dictionary, which maps the `id` of the registered model (a string in `name:version` format) to the predictor itself:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_dict = {}\n",
+        "\n",
+        "lr_reg_id = register_model(\"fairness_linear_regression\", lr_predictor)\n",
+        "model_dict[lr_reg_id] = lr_predictor\n",
+        "svm_reg_id = register_model(\"fairness_svm\", svm_predictor)\n",
+        "model_dict[svm_reg_id] = svm_predictor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LocalDashboard\"></a>\n",
+        "## Using the Fairlearn Dashboard\n",
+        "\n",
+        "We can now examine the fairness of the two models we have training, both as a function of race and (binary) sex. Before uploading the dashboard to the AzureML portal, we will first instantiate a local instance of the Fairlearn dashboard.\n",
+        "\n",
+        "Regardless of the viewing location, the dashboard is based on three things - the true values, the model predictions and the sensitive feature values. The dashboard can use predictions from multiple models and multiple sensitive features if desired (as we are doing here).\n",
+        "\n",
+        "Our first step is to generate a dictionary mapping the `id` of the registered model to the corresponding array of predictions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ys_pred = {}\n",
+        "for n, p in model_dict.items():\n",
+        "    ys_pred[n] = p.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can examine these predictions in a locally invoked Fairlearn dashboard. This can be compared to the dashboard uploaded to the portal (in the next section):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairlearn.widget import FairlearnDashboard\n",
+        "\n",
+        "FairlearnDashboard(sensitive_features=A_test, \n",
+        "                   sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=Y_test.tolist(),\n",
+        "                   y_pred=ys_pred)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"AzureUpload\"></a>\n",
+        "## Uploading a Fairness Dashboard to Azure\n",
+        "\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
+        "1. Precompute all the required metrics\n",
+        "1. Upload to Azure\n",
+        "\n",
+        "\n",
+        "### Computing Fairness Metrics\n",
+        "We use Fairlearn to create a dictionary which contains all the data required to display a dashboard. This includes both the raw data (true values, predicted values and sensitive features), and also the fairness metrics. The API is similar to that used to invoke the Dashboard locally. However, there are a few minor changes to the API, and the type of problem being examined (binary classification, regression etc.) needs to be specified explicitly:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sf = { 'Race': A_test.Race, 'Sex': A_test.Sex }\n",
+        "\n",
+        "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
+        "\n",
+        "dash_dict = _create_group_metric_set(y_true=Y_test,\n",
+        "                                     predictions=ys_pred,\n",
+        "                                     sensitive_features=sf,\n",
+        "                                     prediction_type='binary_classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `_create_group_metric_set()` method is currently underscored since its exact design is not yet final in Fairlearn."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Uploading to Azure\n",
+        "\n",
+        "We can now import the `azureml.contrib.fairness` package itself. We will round-trip the data, so there are two required subroutines:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can upload the generated dictionary to AzureML. The upload method requires a run, so we first create an experiment and a run. The uploaded dashboard can be seen on the corresponding Run Details page in AzureML Studio. For completeness, we also download the dashboard dictionary which we uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "exp = Experiment(ws, \"notebook-01\")\n",
+        "print(exp)\n",
+        "\n",
+        "run = exp.start_logging()\n",
+        "try:\n",
+        "    dashboard_title = \"Sample notebook upload\"\n",
+        "    upload_id = upload_dashboard_dictionary(run,\n",
+        "                                            dash_dict,\n",
+        "                                            dashboard_name=dashboard_title)\n",
+        "    print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
+        "\n",
+        "    downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
+        "finally:\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(dash_dict == downloaded_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Conclusion\"></a>\n",
+        "## Conclusion\n",
+        "\n",
+        "In this notebook we have demonstrated how to generate and upload a fairness dashboard to AzureML Studio. We have not discussed how to analyse the results and apply mitigations. Those topics will be covered elsewhere."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "riedgar"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

contrib/fairness/upload-fairness-dashboard.yml

@@ -0,0 +1,8 @@
+name: upload-fairness-dashboard
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-fairness
+  - fairlearn==0.4.6
+  - joblib
+  - shap

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

@@ -1,8 +1,8 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
-1. [Setup using Azure Notebooks](#jupyter)
+1. [Setup using Compute Instances](#jupyter)
-1. [Setup using Azure Databricks](#databricks)
 1. [Setup using a Local Conda environment](#localconda)
+1. [Setup using Azure Databricks](#databricks)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
@@ -21,13 +21,13 @@ Below are the three execution environments supported by automated ML.
 
 
  <a name="jupyter"></a>
-## Setup using Notebook VMs - Jupyter based notebooks from a Azure VM
+## Setup using Compute Instances - Jupyter based notebooks from a Azure Virtual Machine
 
 1. Open the [ML Azure portal](https://ml.azure.com)
 1. Select Compute
-1. Select Notebook VMs
+1. Select Compute Instances
 1. Click New
-1. Type a name for the Vm and select a VM type
+1. Type a Compute Name, select a Virtual Machine type and select a Virtual Machine size
 1. Click Create
 
 <a name="localconda"></a>
@@ -117,7 +117,7 @@ jupyter notebook
     - Simple example of using automated ML for regression
     - Uses azure compute for training
 
-- [auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb](regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb)
+- [auto-ml-regression-explanation-featurization.ipynb](regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb)
     - Dataset: Hardware Performance Dataset
     - Shows featurization and excplanation
     - Uses azure compute for training
@@ -144,7 +144,7 @@ jupyter notebook
     - Dataset: forecasting for a bike-sharing
     - Example of training an automated ML forecasting model on multiple time-series
 
-- [automl-forecasting-function.ipynb](forecasting-high-frequency/automl-forecasting-function.ipynb)
+- [auto-ml-forecasting-function.ipynb](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
@@ -152,7 +152,7 @@ jupyter notebook
     - Beer Production Forecasting
 
 - [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
-    - Continous retraining using Pipelines and Time-Series TabularDataset
+    - 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

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

@@ -4,19 +4,18 @@ dependencies:
   # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
 - python>=3.5.2,<3.6.8
-- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
 - cython
 - urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- scipy==1.4.1
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
-- py-xgboost<=0.80
+- py-xgboost<=0.90
-- fbprophet==0.5
+- conda-forge::fbprophet==0.5
-- pytorch=1.1.0
+- pytorch::pytorch=1.4.0
-- cudatoolkit=9.0
+- cudatoolkit=10.1.243
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
@@ -25,13 +24,8 @@ dependencies:
   - azureml-train
   - azureml-widgets
   - azureml-pipeline
-  - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
+  - pyarrow==0.17.0
-  - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 
-channels:
-- conda-forge
-- pytorch

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

@@ -5,18 +5,17 @@ dependencies:
 - pip<=19.3.1
 - nomkl
 - python>=3.5.2,<3.6.8
-- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
 - cython
 - urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- scipy==1.4.1
 - scikit-learn>=0.19.0,<=0.20.3
-- pandas>=0.22.0,<0.23.0
+- pandas>=0.22.0,<=0.23.4
-- py-xgboost<=0.80
+- py-xgboost<=0.90
-- fbprophet==0.5
+- conda-forge::fbprophet==0.5
-- pytorch=1.1.0
+- pytorch::pytorch=1.4.0
 - cudatoolkit=9.0
 
 - pip:
@@ -26,13 +25,7 @@ dependencies:
   - azureml-train
   - azureml-widgets
   - azureml-pipeline
-  - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
+  - pyarrow==0.17.0
-  - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
-
-channels:
-- conda-forge
-- pytorch

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

@@ -41,7 +41,7 @@
         "\n",
         "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, 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",
+        "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",
         "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\n",
         "\n",
@@ -92,6 +92,23 @@
         "from azureml.explain.model._internal.explanation_client import ExplanationClient"
       ]
     },
+    {
+      "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -132,7 +149,6 @@
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -160,35 +176,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
+        "# Choose a name for your CPU cluster\n",
-        "amlcompute_cluster_name = \"cpu-cluster-4\"\n",
+        "cpu_cluster_name = \"cpu-cluster-4\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=6)\n",
-        "    \n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
         "\n",
-        "    # Create the cluster.\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -320,7 +323,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\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)"
       ]
@@ -352,7 +354,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -396,8 +397,6 @@
       "outputs": [],
       "source": [
         "#from azureml.train.automl.run import AutoMLRun\n",
-        "#experiment_name = 'automl-classification-bmarketing'\n",
-        "#experiment = Experiment(ws, experiment_name)\n",
         "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
         "#remote_run"
       ]
@@ -500,11 +499,11 @@
       "outputs": [],
       "source": [
         "# Wait for the best model explanation run to complete\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
+        "from azureml.core.run import Run\n",
         "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
         "print(model_explainability_run_id)\n",
         "if model_explainability_run_id is not None:\n",
-        "    model_explainability_run = AutoMLRun(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",
         "\n",
         "# Get the best run object\n",
@@ -644,7 +643,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select the best pipeline from our iterations.  The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -676,10 +675,8 @@
         "model_name = best_run.properties['model_name']\n",
         "\n",
         "script_file_name = 'inference/score.py'\n",
-        "conda_env_file_name = 'inference/env.yml'\n",
         "\n",
-        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
+        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')"
-        "best_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/env.yml')"
       ]
     },
     {
@@ -722,8 +719,7 @@
         "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name)\n",
-        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",

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

@@ -2,10 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - onnxruntime==1.0.0
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -42,7 +42,7 @@
         "\n",
         "This notebook is using remote compute to train the model.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), 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",
+        "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 using an existing workspace.\n",
@@ -80,6 +80,23 @@
         "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 1.9.0 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,
@@ -94,7 +111,6 @@
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -122,35 +138,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your AmlCompute cluster.\n",
+        "# Choose a name for your CPU cluster\n",
-        "amlcompute_cluster_name = \"cpu-cluster-1\"\n",
+        "cpu_cluster_name = \"cpu-cluster-1\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'cpu-cluster-1':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=6)\n",
-        "    \n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
         "\n",
-        "    # Create the cluster.\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -319,7 +322,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {

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

@@ -2,8 +2,3 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - azureml-explain-model

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

@@ -47,8 +47,8 @@
         "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. Evaluating the final model on a test set\n",
+        "3. Registering the best model for future use\n",
-        "4. Deploying the model on ACI"
+        "4. Evaluating the final model on a test set"
       ]
     },
     {
@@ -84,6 +84,23 @@
         "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -105,7 +122,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -132,34 +148,25 @@
       "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",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
-        "    compute_target = cts[amlcompute_cluster_name]\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",
-        "if not found:\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_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",
-        "\n",
-        "    # Create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -187,8 +194,8 @@
         "    '''\n",
         "    remove = ('headers', 'footers', 'quotes')\n",
         "    categories = [\n",
-        "        'alt.atheism',\n",
+        "        'rec.sport.baseball',\n",
-        "        'talk.religion.misc',\n",
+        "        'rec.sport.hockey',\n",
         "        'comp.graphics',\n",
         "        'sci.space',\n",
         "        ]\n",
@@ -338,7 +345,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can test the model locally to get a feel of the input/output. This step may require additional package installations such as pytorch."
+        "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"
       ]
     },
     {
@@ -373,8 +381,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploying the model\n",
+        "### Registering the best model\n",
-        "We now use the best fitted model from the AutoML Run to make predictions on the test set.  "
+        "We now register the best fitted model from the AutoML Run for use in future deployments.  "
       ]
     },
     {
@@ -474,7 +482,7 @@
       "source": [
         "script_folder = os.path.join(os.getcwd(), 'inference')\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy2('infer.py', script_folder)"
+        "shutil.copy('infer.py', script_folder)"
       ]
     },
     {
@@ -483,8 +491,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset,\n",
+        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
-        "                 target_column_name, model_name)"
+        "                         train_dataset, test_dataset, target_column_name, model_name)"
       ]
     },
     {

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

@@ -2,12 +2,3 @@ name: auto-ml-classification-text-dnn
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azurmel-train
-  - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
-  - sentencepiece==0.1.82
-  - pytorch-transformers==1.0
-  - spacy==2.1.8
-  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz

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

@@ -6,7 +6,7 @@ from azureml.core.run import Run
 
 
 def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  test_dataset, target_column_name, model_name):
+                  train_dataset, test_dataset, target_column_name, model_name):
 
     train_run.download_file('outputs/conda_env_v_1_0_0.yml',
                             'inference/condafile.yml')
@@ -22,7 +22,10 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--target_column_name': target_column_name,
                         '--model_name': model_name
                     },
-                    inputs=[test_dataset.as_named_input('test_data')],
+                    inputs=[
+                        train_dataset.as_named_input('train_data'),
+                        test_dataset.as_named_input('test_data')
+                    ],
                     compute_target=compute_target,
                     environment_definition=inference_env)
 

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

@@ -1,9 +1,11 @@
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+
 from sklearn.externals import joblib
-from azureml.automl.core._vendor.automl.client.core.common import metrics
+
-from automl.client.core.common import constants
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
 from azureml.core.model import Model
 
 
@@ -30,22 +32,26 @@ 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 automl metrics module
+# Use the AutoML scoring module
-scores = metrics.compute_metrics_classification(
+class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
-    np.array(predicted),
+train_labels = model.classes_
-    np.array(y_test_df),
+classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
-    class_labels=model.classes_,
+scores = scoring.score_classification(y_test_df.values, predicted,
-    metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
+                                      classification_metrics,
-)
+                                      class_labels, train_labels)
 
 print("scores:")
 print(scores)

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

@@ -20,7 +20,7 @@
       "metadata": {},
       "source": [
         "# Automated Machine Learning \n",
-        "**Continous retraining using Pipelines and Time-Series TabularDataset**\n",
+        "**Continuous retraining using Pipelines and Time-Series TabularDataset**\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
         "2. [Setup](#Setup)\n",
@@ -75,6 +75,23 @@
         "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -112,7 +129,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -143,33 +159,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
+        "# Choose a name for your CPU cluster\n",
-        "amlcompute_cluster_name = \"cpu-cluster-42\"\n",
+        "amlcompute_cluster_name = \"cont-cluster\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=4)\n",
-        "    \n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
         "\n",
-        "    # Create the cluster.\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_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 = 0, timeout_in_minutes = 10)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -196,7 +201,7 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets'], \n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n",
         "                              conda_packages=['numpy==1.16.2'], \n",
         "                              pin_sdk_version=False)\n",
         "#cd.add_pip_package('azureml-explain-model')\n",
@@ -343,7 +348,7 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 10,\n",

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

@@ -2,7 +2,3 @@ name: auto-ml-continuous-retraining
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azureml-pipeline

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

@@ -101,6 +101,23 @@
         "from azureml.train.estimator import Estimator"
       ]
     },
+    {
+      "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -128,7 +145,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -163,7 +179,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "cpu_cluster_name = \"beer-cluster\"\n",
         "\n",
         "# Verify that cluster does not exist already\n",
         "try:\n",
@@ -218,19 +234,18 @@
         "import pandas as pd\n",
         "from pandas import DataFrame\n",
         "from pandas import Grouper\n",
-        "from matplotlib import pyplot\n",
         "from pandas import concat\n",
-        "from matplotlib import pyplot\n",
         "from pandas.plotting import register_matplotlib_converters\n",
+        "\n",
         "register_matplotlib_converters()\n",
-        "plt.tight_layout()\n",
         "plt.figure(figsize=(20, 10))\n",
+        "plt.tight_layout()\n",
         "\n",
         "plt.subplot(2, 1, 1)\n",
         "plt.title('Beer Production By Year')\n",
         "df = pd.read_csv(\"Beer_no_valid_split_train.csv\", parse_dates=True, index_col= 'DATE').drop(columns='grain')\n",
         "test_df = pd.read_csv(\"Beer_no_valid_split_test.csv\", parse_dates=True, index_col= 'DATE').drop(columns='grain')\n",
-        "pyplot.plot(df)\n",
+        "plt.plot(df)\n",
         "\n",
         "plt.subplot(2, 1, 2)\n",
         "plt.title('Beer Production By Month')\n",
@@ -239,7 +254,8 @@
         "months = DataFrame(months)\n",
         "months.columns = range(1,13)\n",
         "months.boxplot()\n",
-        "pyplot.show()\n"
+        "\n",
+        "plt.show()"
       ]
     },
     {
@@ -538,7 +554,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "compute_target = ws.compute_targets['cpu-cluster']\n",
+        "compute_target = ws.compute_targets['beer-cluster']\n",
         "test_experiment = Experiment(ws, experiment_name + \"_test\")"
       ]
     },
@@ -556,7 +572,7 @@
         "\n",
         "script_folder = os.path.join(os.getcwd(), 'inference')\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy2('infer.py', script_folder)"
+        "shutil.copy('infer.py', script_folder)"
       ]
     },
     {

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

@@ -1,10 +1,4 @@
 name: auto-ml-forecasting-beer-remote
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azureml-train

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

@@ -1,12 +1,14 @@
-import pandas as pd
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+import pandas as pd
+
+from pandas.tseries.frequencies import to_offset
 from sklearn.externals import joblib
 from sklearn.metrics import mean_absolute_error, mean_squared_error
-from azureml.automl.core._vendor.automl.client.core.common import metrics
+
-from automl.client.core.common import constants
+from azureml.automl.runtime.shared.score import scoring, constants
-from pandas.tseries.frequencies import to_offset
+from azureml.core import Run
 
 
 def align_outputs(y_predicted, X_trans, X_test, y_test,
@@ -300,12 +302,11 @@ print(df_all[target_column_name])
 print("predicted values:::")
 print(df_all['predicted'])
 
-# use automl metrics module
+# Use the AutoML scoring module
-scores = metrics.compute_metrics_regression(
+regression_metrics = list(constants.REGRESSION_SCALAR_SET)
-    df_all['predicted'],
+y_test = np.array(df_all[target_column_name])
-    df_all[target_column_name],
+y_pred = np.array(df_all['predicted'])
-    list(constants.Metric.SCALAR_REGRESSION_SET),
+scores = scoring.score_regression(y_test, y_pred, regression_metrics)
-    None, None, None)
 
 print("scores:")
 print(scores)

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

@@ -74,6 +74,23 @@
         "from datetime import datetime"
       ]
     },
+    {
+      "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -95,7 +112,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['SKU'] = ws.sku\n",
@@ -124,35 +140,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-bike\"\n",
+        "amlcompute_cluster_name = \"bike-cluster\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=4)\n",
-        "    \n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
         "\n",
-        "    # Create the cluster.\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -450,8 +453,8 @@
         "\n",
         "script_folder = os.path.join(os.getcwd(), 'forecast')\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy2('forecasting_script.py', script_folder)\n",
+        "shutil.copy('forecasting_script.py', script_folder)\n",
-        "shutil.copy2('forecasting_helper.py', script_folder)"
+        "shutil.copy('forecasting_helper.py', script_folder)"
       ]
     },
     {
@@ -507,17 +510,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

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

@@ -1,9 +1,4 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -1,6 +1,6 @@
 import argparse
 import azureml.train.automl
-from azureml.automl.runtime._vendor.automl.client.core.runtime import forecasting_models
+from azureml.automl.runtime.shared import forecasting_models
 from azureml.core import Run
 from sklearn.externals import joblib
 import forecasting_helper

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

@@ -28,7 +28,6 @@
         "1. [Setup](#Setup)\n",
         "1. [Data and Forecasting Configurations](#Data)\n",
         "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
         "\n",
         "Advanced Forecasting\n",
         "1. [Advanced Training](#advanced_training)\n",
@@ -43,7 +42,7 @@
         "\n",
         "In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\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, to establish your connection to the AzureML Workspace.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Creating an Experiment using an existing Workspace\n",
@@ -85,6 +84,23 @@
         "from datetime import datetime"
       ]
     },
+    {
+      "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -109,7 +125,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -140,35 +155,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"aml-compute\"\n",
+        "amlcompute_cluster_name = \"energy-cluster\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "if not found:\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -463,7 +465,7 @@
       "metadata": {},
       "source": [
         "### Forecast Function\n",
-        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb)."
+        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see the [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -505,16 +507,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",
@@ -666,16 +667,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

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

@@ -2,9 +2,3 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -35,7 +35,6 @@
         "Terminology:\n",
         "* forecast origin: the last period when the target value is known\n",
         "* forecast periods(s): the period(s) for which the value of the target is desired.\n",
-        "* forecast horizon: the number of forecast periods\n",
         "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n",
         "* prediction context: `lookback` periods immediately preceding the forecast origin\n",
         "\n",
@@ -68,6 +67,7 @@
         "import logging\n",
         "import warnings\n",
         "\n",
+        "import azureml.core\n",
         "from azureml.core.dataset import Dataset\n",
         "from pandas.tseries.frequencies import to_offset\n",
         "from azureml.core.compute import AmlCompute\n",
@@ -81,13 +81,29 @@
         "np.set_printoptions(precision=4, suppress=True, linewidth=120)"
       ]
     },
+    {
+      "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 1.9.0 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": [
-        "import azureml.core\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.train.automl import AutoMLConfig\n",
@@ -100,7 +116,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['SKU'] = ws.sku\n",
@@ -258,29 +273,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "amlcompute_cluster_name = \"cpu-cluster-fcfn\"\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "        \n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
         "\n",
-        "if not found:\n",
+        "# Choose a name for your CPU cluster\n",
-        "    print('Creating a new compute target...')\n",
+        "amlcompute_cluster_name = \"fcfn-cluster\"\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
         "\n",
-        "    # Create the cluster.\\n\",\n",
+        "# Verify that cluster does not exist already\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "print('Checking cluster status...')\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "# 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)"
       ]
     },
     {
@@ -346,9 +354,24 @@
         "                             label_column_name=target_label,\n",
         "                             **time_series_settings)\n",
         "\n",
-        "remote_run = experiment.submit(automl_config, show_output=False)\n",
+        "remote_run = experiment.submit(automl_config, show_output=False)"
-        "remote_run.wait_for_completion()\n",
+      ]
-        "\n",
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "# Retrieve the best model to use it further.\n",
         "_, fitted_model = remote_run.get_output()"
       ]
@@ -459,8 +482,8 @@
         "# use forecast_quantiles function, not the forecast() one\n",
         "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# it all nicely aligns column-wise\n",
+        "# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
-        "pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
+        "y_pred_quantiles"
       ]
     },
     {
@@ -696,12 +719,96 @@
         "X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
         "# prediction is in _automl_target_col"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting farther than the maximum horizon <a id=\"recursive forecasting\"></a>\n",
+        "When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified maximum horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
+        "\n",
+        "To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the maximum horizon given at training time.\n",
+        "\n",
+        "![Recursive_forecast_overview](recursive_forecast_overview_small.png)\n",
+        "\n",
+        "Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first max-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next max-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
+        "\n",
+        "A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n",
+        "\n",
+        "![Recursive_forecast_iter1](recursive_forecast_iter1.png)\n",
+        "![Recursive_forecast_iter2](recursive_forecast_iter2.png)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# generate the same kind of test data we trained on, but with a single grain/time-series and test period twice as long as the max_horizon\n",
+        "_, _, X_test_long, y_test_long = get_timeseries(train_len=n_train_periods,\n",
+        "                                                  test_len=max_horizon*2,\n",
+        "                                                  time_column_name=TIME_COLUMN_NAME,\n",
+        "                                                  target_column_name=TARGET_COLUMN_NAME,\n",
+        "                                                  grain_column_name=GRAIN_COLUMN_NAME,\n",
+        "                                                  grains=1)\n",
+        "\n",
+        "print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
+        "print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# forecast() function will invoke the recursive forecast method internally.\n",
+        "y_pred_long, X_trans_long = fitted_model.forecast(X_test_long)\n",
+        "y_pred_long"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following. \n",
+        "y_pred1, _ = fitted_model.forecast(X_test_long[:max_horizon])\n",
+        "y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(max_horizon, np.nan))))\n",
+        "np.array_equal(y_pred_all, y_pred_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Confidence interval and distributional forecasts\n",
+        "AutoML cannot currently estimate forecast errors beyond the maximum horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the maximum horizon. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fitted_model.forecast_quantiles(X_test_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Similarly with the simple senarios illustrated above, forecasting farther than the max horizon in other senarios like 'multiple grain', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "erwright, nirovins"
+        "name": "erwright"
       }
     ],
     "category": "tutorial",

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

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

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml

@@ -1,9 +0,0 @@
-name: automl-forecasting-function
-dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -65,7 +65,25 @@
         "\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.experiment import Experiment\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig"
+      ]
+    },
+    {
+      "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
     {
@@ -89,7 +107,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['SKU'] = ws.sku\n",
@@ -118,35 +135,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
+        "# Choose a name for your CPU cluster\n",
-        "amlcompute_cluster_name = \"cpu-cluster-oj\"\n",
+        "amlcompute_cluster_name = \"oj-cluster\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=6)\n",
-        "    \n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
         "\n",
-        "    # Create the cluster.\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -315,17 +319,58 @@
         "target_column_name = 'Quantity'"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Customization\n",
+        "\n",
+        "The featurization customization in forecasting is an advanced feature in AutoML which allows our customers to change the default forecasting featurization behaviors and column types through `FeaturizationConfig`. The supported scenarios include,\n",
+        "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",
+        "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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "featurization_config = FeaturizationConfig()\n",
+        "featurization_config.drop_columns = ['logQuantity']  # 'logQuantity' is a leaky feature, so we remove it.\n",
+        "# Force the CPWVOL5 feature to be numeric type.\n",
+        "featurization_config.add_column_purpose('CPWVOL5', 'Numeric')\n",
+        "# Fill missing values in the target column, Quantity, with zeros.\n",
+        "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\"})"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "## Train\n",
         "\n",
-        "The AutoMLConfig 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",
+        "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 a grain is 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: 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",
+        "\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",
+        "\n",
+        "We note here that AutoML can sweep over two types of time-series models:\n",
+        "* Models that are trained for each series such as ARIMA and Facebook's Prophet. Note that these models are only available for [Enterprise Edition Workspaces](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace#upgrade).\n",
+        "* Models trained across multiple time-series using a regression approach.\n",
+        "\n",
+        "In the first case, AutoML loops over all time-series in your dataset and trains one model (e.g. AutoArima or Prophet, as the case may be) for each series. This can result in long runtimes to train these models if there are a lot of series in the data. One way to mitigate this problem is to fit models for different series in parallel if you have multiple compute cores available. To enable this behavior, set the `max_cores_per_iteration` parameter in your AutoMLConfig as shown in the example in the next cell. \n",
         "\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 organizaion that needs to estimate the next month of sales would set the horizon accordingly. 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",
         "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
         "\n",
@@ -346,8 +391,9 @@
         "|**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",
-        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
+        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|"
+        "|**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.|"
       ]
     },
     {
@@ -359,7 +405,6 @@
         "time_series_settings = {\n",
         "    'time_column_name': time_column_name,\n",
         "    'grain_column_names': grain_column_names,\n",
-        "    'drop_column_names': ['logQuantity'],  # 'logQuantity' is a leaky feature, so we remove it.\n",
         "    'max_horizon': n_test_periods\n",
         "}\n",
         "\n",
@@ -371,8 +416,10 @@
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
         "                             enable_early_stopping=True,\n",
+        "                             featurization=featurization_config,\n",
         "                             n_cross_validations=3,\n",
         "                             verbosity=logging.INFO,\n",
+        "                             max_cores_per_iteration=-1,\n",
         "                             **time_series_settings)"
       ]
     },
@@ -422,6 +469,33 @@
         "model_name = best_run.properties['model_name']"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Transparency\n",
+        "\n",
+        "View updated featurization summary"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "custom_featurizer = fitted_model.named_steps['timeseriestransformer']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "custom_featurizer.get_featurization_summary()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -475,7 +549,7 @@
       "source": [
         "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
         "\n",
-        "The [energy demand forecasting notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) demonstrates the use of the forecast function in more detail in the context of using lags and rolling window features. "
+        "The [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -506,16 +580,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
-        "# use automl metrics module\n",
+        "# use automl scoring module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

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

@@ -1,9 +1,4 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -28,7 +28,8 @@
         "1. [Setup](#Setup)\n",
         "1. [Train](#Train)\n",
         "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
+        "1. [Test](#Tests)\n",
+        "1. [Explanation](#Explanation)\n",
         "1. [Acknowledgements](#Acknowledgements)"
       ]
     },
@@ -42,14 +43,16 @@
         "\n",
         "This notebook is using the local machine compute to train the model.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), 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",
+        "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 using an existing workspace.\n",
         "2. Configure AutoML using `AutoMLConfig`.\n",
         "3. Train the model.\n",
         "4. Explore the results.\n",
-        "5. Test the fitted model."
+        "5. Test the fitted model.\n",
+        "6. Explore any model's explanation and explore feature importance in azure portal.\n",
+        "7. Create an AKS cluster, deploy the webservice of AutoML scoring model and the explainer model to the AKS and consume the web service."
       ]
     },
     {
@@ -71,13 +74,30 @@
         "\n",
         "from matplotlib import pyplot as plt\n",
         "import pandas as pd\n",
-        "import os\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"
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+      ]
+    },
+    {
+      "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 1.9.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
     {
@@ -94,7 +114,6 @@
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -237,9 +256,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Analyze results\n",
+        "### Analyze results\n",
         "\n",
-        "### Retrieve the Best Model\n",
+        "#### Retrieve the Best Model\n",
         "\n",
         "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
@@ -266,7 +285,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Test the fitted model\n",
+        "## Tests\n",
         "\n",
         "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
       ]
@@ -330,6 +349,407 @@
         "plt.show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "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",
+        "\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",
+        "### Run the explanation\n",
+        "#### Download engineered feature importance from artifact store\n",
+        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Retrieve any other AutoML model from training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run, fitted_model = local_run.get_output(metric='accuracy')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Setup the model explanations for AutoML models\n",
+        "The fitted_model can generate the following which will be used for getting the engineered explanations using automl_setup_model_explanations:-\n",
+        "\n",
+        "1. Featurized data from train samples/test samples\n",
+        "2. Gather engineered name lists\n",
+        "3. Find the classes in your labeled column in classification scenarios\n",
+        "\n",
+        "The automl_explainer_setup_obj contains all the structures from above list."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = training_data.drop_columns(columns=[label_column_name])\n",
+        "y_train = training_data.keep_columns(columns=[label_column_name], validate=True)\n",
+        "X_test = validation_data.drop_columns(columns=[label_column_name])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
+        "\n",
+        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
+        "                                                             X_test=X_test, y=y_train, \n",
+        "                                                             task='classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.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",
+        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
+        "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
+        "                         classes=automl_explainer_setup_obj.classes,\n",
+        "                         explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
+        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Compute the engineered explanations\n",
+        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Initialize the scoring Explainer, save and upload it for later use in scoring explanation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer\n",
+        "import joblib\n",
+        "\n",
+        "# Initialize the ScoringExplainer\n",
+        "scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
+        "\n",
+        "# Pickle scoring explainer locally to './scoring_explainer.pkl'\n",
+        "scoring_explainer_file_name = 'scoring_explainer.pkl'\n",
+        "with open(scoring_explainer_file_name, 'wb') as stream:\n",
+        "    joblib.dump(scoring_explainer, stream)\n",
+        "\n",
+        "# Upload the scoring explainer to the automl run\n",
+        "automl_run.upload_file('outputs/scoring_explainer.pkl', scoring_explainer_file_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Register trained automl model present in the 'outputs' folder in the artifacts\n",
+        "original_model = automl_run.register_model(model_name='automl_model', \n",
+        "                                           model_path='outputs/model.pkl')\n",
+        "scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer',\n",
+        "                                                    model_path='outputs/scoring_explainer.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Create the conda dependencies for setting up the service\n",
+        "\n",
+        "We need to create the conda dependencies comprising of the azureml-explain-model, azureml-train-automl and azureml-defaults packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.core.environment import Environment\n",
+        "\n",
+        "automl_run.download_file(constants.CONDA_ENV_FILE_PATH, 'myenv.yml')\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "myenv"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Write the Entry Script\n",
+        "Write the script that will be used to predict on your model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "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",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "\n",
+        "def init():\n",
+        "    global automl_model\n",
+        "    global scoring_explainer\n",
+        "\n",
+        "    # Retrieve the path to the model file using the model name\n",
+        "    # Assume original model is named original_prediction_model\n",
+        "    automl_model_path = Model.get_model_path('automl_model')\n",
+        "    scoring_explainer_path = Model.get_model_path('scoring_explainer')\n",
+        "\n",
+        "    automl_model = joblib.load(automl_model_path)\n",
+        "    scoring_explainer = joblib.load(scoring_explainer_path)\n",
+        "\n",
+        "\n",
+        "def run(raw_data):\n",
+        "    data = pd.read_json(raw_data, orient='records')    \n",
+        "    # Make prediction\n",
+        "    predictions = automl_model.predict(data)\n",
+        "    # Setup for inferencing explanations\n",
+        "    automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,\n",
+        "                                                                 X_test=data, task='classification')\n",
+        "    # Retrieve model explanations for engineered explanations\n",
+        "    engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)    \n",
+        "    # You can return any data type as long as it is JSON-serializable\n",
+        "    return {'predictions': predictions.tolist(),\n",
+        "            'engineered_local_importance_values': engineered_local_importance_values}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Create the InferenceConfig \n",
+        "Create the inference config that will be used when deploying the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Provision the AKS Cluster\n",
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "aks_name = 'scoring-explain'\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    prov_config = AksCompute.provisioning_configuration(vm_size='STANDARD_D3_V2')\n",
+        "    aks_target = ComputeTarget.create(workspace=ws, \n",
+        "                                      name=aks_name,\n",
+        "                                      provisioning_configuration=prov_config)\n",
+        "aks_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Deploy web service to AKS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set the web service configuration (using default here)\n",
+        "from azureml.core.webservice import AksWebservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "aks_config = AksWebservice.deploy_configuration()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service_name ='model-scoring-local-aks'\n",
+        "\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[scoring_explainer_model, original_model],\n",
+        "                           inference_config=inf_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=aks_target)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### View the service logs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service.get_logs()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Consume the web service using run method to do the scoring and explanation of scoring.\n",
+        "We test the web sevice by passing data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Serialize the first row of the test data into json\n",
+        "X_test_json = X_test_df[:1].to_json(orient='records')\n",
+        "print(X_test_json)\n",
+        "\n",
+        "# Call the service to get the predictions and the engineered and raw explanations\n",
+        "output = aks_service.run(X_test_json)\n",
+        "\n",
+        "# Print the predicted value\n",
+        "print('predictions:\\n{}\\n'.format(output['predictions']))\n",
+        "# Print the engineered feature importances for the predicted value\n",
+        "print('engineered_local_importance_values:\\n{}\\n'.format(output['engineered_local_importance_values']))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Clean up\n",
+        "Delete the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service.delete()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -358,7 +778,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "tzvikei"
+        "name": "anumamah"
       }
     ],
     "category": "tutorial",

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

@@ -2,8 +2,3 @@ name: auto-ml-classification-credit-card-fraud-local
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - azureml-explain-model

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

@@ -40,7 +40,7 @@
         "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n",
         "After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, 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",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade) \n",
         "\n",
@@ -51,8 +51,8 @@
         "4. Explore the results and featurization transparency options\n",
         "5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
         "6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n",
-        "7. Download the feature importance for engineered features and visualize the explanations for engineered features. \n",
+        "7. Download the feature importance for engineered features and visualize the explanations for engineered features on azure portal. \n",
-        "8. Download the feature importance for raw features and visualize the explanations for raw features. \n"
+        "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n"
       ]
     },
     {
@@ -85,6 +85,23 @@
         "from azureml.core.dataset import Dataset"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.9.0 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,
@@ -98,7 +115,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -127,35 +143,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-5\"\n",
+        "amlcompute_cluster_name = \"hardware-cluster\"\n",
         "\n",
-        "found = False\n",
+        "# Verify that cluster does not exist already\n",
-        "# Check if this compute target already exists in the workspace.\n",
+        "try:\n",
-        "cts = ws.compute_targets\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    print('Found existing cluster, use it.')\n",
-        "    found = True\n",
+        "except ComputeTargetException:\n",
-        "    print('Found existing compute target.')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "                                                           max_nodes=4)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "if not found:\n",
+        "compute_target.wait_for_completion(show_output=True)"
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
-        "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\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()."
       ]
     },
     {
@@ -239,7 +242,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks2"
+        ]
+      },
       "outputs": [],
       "source": [
         "featurization_config = FeaturizationConfig()\n",
@@ -257,7 +264,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks3"
+        ]
+      },
       "outputs": [],
       "source": [
         "automl_settings = {\n",
@@ -320,8 +331,6 @@
       "outputs": [],
       "source": [
         "#from azureml.train.automl.run import AutoMLRun\n",
-        "#experiment_name = 'automl-regression-hardware'\n",
-        "#experiment = Experiment(ws, experiment_name)\n",
         "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
         "#remote_run"
       ]
@@ -514,7 +523,7 @@
         "    content = cefr.read()\n",
         "\n",
         "# Replace the values in train_explainer.py file with the appropriate values\n",
-        "content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
+        "content = content.replace('<<experiment_name>>', automl_run.experiment.name) # your experiment name.\n",
         "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
         "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
         "content = content.replace('<<task>>', 'regression') # Training task type\n",
@@ -532,8 +541,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Create conda configuration for model explanations experiment\n",
+        "#### Create conda configuration for model explanations experiment from automl_run object"
-        "We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute."
       ]
     },
     {
@@ -552,13 +560,9 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n",
-        "]\n",
         "\n",
         "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
+        "conda_run_config.environment.python.conda_dependencies = automl_run.get_environment().python.conda_dependencies"
-        "    pip_packages=azureml_pip_packages)"
       ]
     },
     {
@@ -603,38 +607,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Feature importance  and  explanation dashboard\n",
+        "### Feature importance  and  visualizing explanation dashboard\n",
-        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model. "
+        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model on the azure portal."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Setup for visualizing the model explanation results\n",
-        "For visualizing the explanation results for the *fitted_model* we need to perform the following steps:-\n",
-        "1. Featurize test data samples.\n",
-        "\n",
-        "The *automl_explainer_setup_obj* contains all the structures from above list. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test = test_data.drop_columns([label]).to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
-        "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
       ]
     },
     {
@@ -642,7 +616,7 @@
       "metadata": {},
       "source": [
         "#### Download engineered feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the engineered features."
+        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
       ]
     },
     {
@@ -652,11 +626,10 @@
       "outputs": [],
       "source": [
         "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
-        "from interpret_community.widget import ExplanationDashboard\n",
         "client = ExplanationClient.from_run(automl_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, datasetX=explainer_setup_class.X_test_transform)"
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {
@@ -664,7 +637,7 @@
       "metadata": {},
       "source": [
         "#### Download raw feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the raw features."
+        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the raw features."
       ]
     },
     {
@@ -673,9 +646,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "raw_explanations = client.download_model_explanation(raw=True)\n",
+        "raw_explanations = client.download_model_explanation(raw=True, comment='raw explanations')\n",
         "print(raw_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, datasetX=explainer_setup_class.X_test_raw)"
+        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {
@@ -808,6 +781,7 @@
       "outputs": [],
       "source": [
         "if service.state == 'Healthy':\n",
+        "    X_test = test_data.drop_columns([label]).to_pandas_dataframe()\n",
         "    # Serialize the first row of the test data into json\n",
         "    X_test_json = X_test[:1].to_json(orient='records')\n",
         "    print(X_test_json)\n",

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

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

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

@@ -7,7 +7,7 @@ import azureml.train.automl
 import azureml.explain.model
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
     automl_setup_model_explanations
-from sklearn.externals import joblib
+import joblib
 from azureml.core.model import Model
 
 

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

@@ -4,15 +4,14 @@ import os
 
 from azureml.core.run import Run
 from azureml.core.experiment import Experiment
-from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
     automl_setup_model_explanations, automl_check_model_if_explainable
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
-from automl.client.core.common.constants import MODEL_PATH
+from azureml.automl.core.shared.constants import MODEL_PATH
-from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save
+from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer
-
+import joblib
 
 OUTPUT_DIR = './outputs/'
 os.makedirs(OUTPUT_DIR, exist_ok=True)
@@ -22,7 +21,7 @@ run = Run.get_context()
 ws = run.experiment.workspace
 
 # Get the AutoML run object from the experiment name and the workspace
-experiment = Experiment(ws, '<<experimnet_name>>')
+experiment = Experiment(ws, '<<experiment_name>>')
 automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
 # Check if this AutoML model is explainable
@@ -60,22 +59,22 @@ explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMEx
                          classes=automl_explainer_setup_obj.classes)
 
 # Compute the engineered explanations
-engineered_explanations = explainer.explain(['local', 'global'],
+engineered_explanations = explainer.explain(['local', 'global'], tag='engineered explanations',
                                             eval_dataset=automl_explainer_setup_obj.X_test_transform)
 
 # Compute the raw explanations
-raw_explanations = explainer.explain(['local', 'global'], get_raw=True,
+raw_explanations = explainer.explain(['local', 'global'], get_raw=True, tag='raw explanations',
                                      raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
                                      eval_dataset=automl_explainer_setup_obj.X_test_transform)
 
 print("Engineered and raw explanations computed successfully")
 
-
 # Initialize the ScoringExplainer
 scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])
 
 # Pickle scoring explainer locally
-save(scoring_explainer, exist_ok=True)
+with open('scoring_explainer.pkl', 'wb') as stream:
+    joblib.dump(scoring_explainer, stream)
 
 # Upload the scoring explainer to the automl run
 automl_run.upload_file('outputs/scoring_explainer.pkl', 'scoring_explainer.pkl')

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

@@ -1,11 +0,0 @@
-name: auto-ml-regression-hardware-performance-explanation-and-featurization
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - azureml-explain-model
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -40,7 +40,7 @@
         "## 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 Notebook VM, 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",
+        "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",
@@ -79,6 +79,23 @@
         "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 1.9.0 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,
@@ -93,7 +110,6 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
         "output['Subscription ID'] = ws.subscription_id\n",
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -122,7 +138,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster-2\"\n",
+        "cpu_cluster_name = \"reg-cluster\"\n",
         "\n",
         "# Verify that cluster does not exist already\n",
         "try:\n",

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

@@ -2,6 +2,3 @@ name: auto-ml-regression
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/ForecastEnergyDemand.sql

@@ -1,23 +0,0 @@
--- This shows using the AutoMLForecast stored procedure to predict using a forecasting model for the nyc_energy dataset.
-
-DECLARE @Model NVARCHAR(MAX) = (SELECT TOP 1 Model FROM dbo.aml_model
-                                WHERE ExperimentName = 'automl-sql-forecast'
-								ORDER BY CreatedDate DESC)
-
-DECLARE @max_horizon INT = 48
-DECLARE @split_time NVARCHAR(22) = (SELECT DATEADD(hour, -@max_horizon, MAX(timeStamp)) FROM nyc_energy WHERE demand IS NOT NULL)
-
-DECLARE @TestDataQuery NVARCHAR(MAX) = '
-SELECT CAST(timeStamp AS NVARCHAR(30)) AS timeStamp,
-       demand,
-	   precip,
-	   temp
-FROM nyc_energy
-WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
-AND timeStamp > ''' + @split_time + ''''
-
-EXEC dbo.AutoMLForecast @input_query=@TestDataQuery,
-@label_column='demand',
-@time_column_name='timeStamp',
-@model=@model
-WITH RESULT SETS ((timeStamp DATETIME, grain NVARCHAR(255), predicted_demand FLOAT, precip FLOAT, temp FLOAT, actual_demand FLOAT))

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/GetMetrics.sql

@@ -1,10 +0,0 @@
--- This lists all the metrics for all iterations for the most recent run.
-
-DECLARE @RunId NVARCHAR(43)
-DECLARE @ExperimentName NVARCHAR(255)
-
-SELECT TOP 1 @ExperimentName=ExperimentName, @RunId=SUBSTRING(RunId, 1, 43)
-FROM aml_model
-ORDER BY CreatedDate DESC
-
-EXEC dbo.AutoMLGetMetrics @RunId, @ExperimentName

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/TrainEnergyDemand.sql

@@ -1,25 +0,0 @@
--- This shows using the AutoMLTrain stored procedure to create a forecasting model for the nyc_energy dataset.
-
-DECLARE @max_horizon INT = 48
-DECLARE @split_time NVARCHAR(22) = (SELECT DATEADD(hour, -@max_horizon, MAX(timeStamp)) FROM nyc_energy WHERE demand IS NOT NULL)
-
-DECLARE @TrainDataQuery NVARCHAR(MAX) = '
-SELECT CAST(timeStamp as NVARCHAR(30)) as timeStamp,
-       demand,
-	   precip,
-	   temp
-FROM nyc_energy
-WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
-and timeStamp < ''' + @split_time + ''''
-
-INSERT INTO dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
-EXEC dbo.AutoMLTrain @input_query= @TrainDataQuery,
-@label_column='demand',
-@task='forecasting',
-@iterations=10,
-@iteration_timeout_minutes=5,
-@time_column_name='timeStamp',
-@max_horizon=@max_horizon,
-@experiment_name='automl-sql-forecast',
-@primary_metric='normalized_root_mean_squared_error'
-

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

@@ -1,161 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Train a model and use it for prediction\r\n",
-        "\r\n",
-        "Before running this notebook, run the auto-ml-sql-setup.ipynb notebook."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set the default database"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "USE [automl]\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use the AutoMLTrain stored procedure to create a forecasting model for the nyc_energy dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "INSERT INTO dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)\r\n",
-        "EXEC dbo.AutoMLTrain @input_query='\r\n",
-        "SELECT CAST(timeStamp as NVARCHAR(30)) as timeStamp,\r\n",
-        "       demand,\r\n",
-        "\t   precip,\r\n",
-        "\t   temp,\r\n",
-        "\t   CASE WHEN timeStamp < ''2017-01-01'' THEN 0 ELSE 1 END AS is_validate_column\r\n",
-        "FROM nyc_energy\r\n",
-        "WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL\r\n",
-        "and timeStamp < ''2017-02-01''',\r\n",
-        "@label_column='demand',\r\n",
-        "@task='forecasting',\r\n",
-        "@iterations=10,\r\n",
-        "@iteration_timeout_minutes=5,\r\n",
-        "@time_column_name='timeStamp',\r\n",
-        "@is_validate_column='is_validate_column',\r\n",
-        "@experiment_name='automl-sql-forecast',\r\n",
-        "@primary_metric='normalized_root_mean_squared_error'"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use the AutoMLPredict stored procedure to predict using the forecasting model for the nyc_energy dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "DECLARE @Model NVARCHAR(MAX) = (SELECT TOP 1 Model FROM dbo.aml_model\r\n",
-        "                                WHERE ExperimentName = 'automl-sql-forecast'\r\n",
-        "\t\t\t\t\t\t\t\tORDER BY CreatedDate DESC)\r\n",
-        "\r\n",
-        "EXEC dbo.AutoMLPredict @input_query='\r\n",
-        "SELECT CAST(timeStamp AS NVARCHAR(30)) AS timeStamp,\r\n",
-        "       demand,\r\n",
-        "\t   precip,\r\n",
-        "\t   temp\r\n",
-        "FROM nyc_energy\r\n",
-        "WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL\r\n",
-        "AND timeStamp >= ''2017-02-01''',\r\n",
-        "@label_column='demand',\r\n",
-        "@model=@model\r\n",
-        "WITH RESULT SETS ((timeStamp NVARCHAR(30), actual_demand FLOAT, precip FLOAT, temp FLOAT, predicted_demand FLOAT))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## List all the metrics for all iterations for the most recent training run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "DECLARE @RunId NVARCHAR(43)\r\n",
-        "DECLARE @ExperimentName NVARCHAR(255)\r\n",
-        "\r\n",
-        "SELECT TOP 1 @ExperimentName=ExperimentName, @RunId=SUBSTRING(RunId, 1, 43)\r\n",
-        "FROM aml_model\r\n",
-        "ORDER BY CreatedDate DESC\r\n",
-        "\r\n",
-        "EXEC dbo.AutoMLGetMetrics @RunId, @ExperimentName"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "Local"
-    ],
-    "datasets": [
-      "NYC Energy"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Azure ML AutoML"
-    ],
-    "tags": [
-      ""
-    ],
-    "friendly_name": "Forecasting with automated ML SQL integration",
-    "index_order": 1,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "sql",
-      "name": "python36"
-    },
-    "language_info": {
-      "name": "sql",
-      "version": ""
-    },
-    "task": "Forecasting"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLForecast.sql

@@ -1,92 +0,0 @@
--- This procedure forecast values based on a forecasting model returned by AutoMLTrain.
--- It returns a dataset with the forecasted values.
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLForecast]
- (
-   @input_query NVARCHAR(MAX),          -- A SQL query returning data to predict on.
-   @model NVARCHAR(MAX),                -- A model returned from AutoMLTrain.
-   @time_column_name  NVARCHAR(255)='', -- The name of the timestamp column for forecasting.
-   @label_column  NVARCHAR(255)='',     -- Optional name of the column from input_query, which should be ignored when predicting
-   @y_query_column NVARCHAR(255)='',    -- Optional value column that can be used for predicting.
-                                        -- If specified, this can contain values for past times (after the model was trained)
-									    -- and contain Nan for future times.
-   @forecast_column_name NVARCHAR(255) = 'predicted'
-                                        -- The name of the output column containing the forecast value.
- ) AS 
-BEGIN 
-  
-    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd 
-import azureml.core  
-import numpy as np 
-from azureml.train.automl import AutoMLConfig  
-import pickle 
-import codecs 
-  
-model_obj = pickle.loads(codecs.decode(model.encode(), "base64")) 
-  
-test_data = input_data.copy() 
-
-if label_column != "" and label_column is not None:
-    y_test = test_data.pop(label_column).values
-else:
-    y_test = None 
-
-if y_query_column != "" and y_query_column is not None:
-    y_query = test_data.pop(y_query_column).values
-else:
-    y_query = np.repeat(np.nan, len(test_data))
-
-X_test = test_data 
-
-if time_column_name != "" and time_column_name is not None:
-    X_test[time_column_name] = pd.to_datetime(X_test[time_column_name])
-
-y_fcst, X_trans = model_obj.forecast(X_test, y_query) 
-
-def align_outputs(y_forecast, X_trans, X_test, y_test, forecast_column_name):
-    # Demonstrates how to get the output aligned to the inputs
-    # using pandas indexes. Helps understand what happened if
-    # the output 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
-
-    df_fcst = pd.DataFrame({forecast_column_name : y_forecast})
-    # 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()
-    if y_test is not None:
-        X_test_full[label_column] = y_test
-
-    # X_test_full 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[[label_column, forecast_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-combined_output = align_outputs(y_fcst, X_trans, X_test, y_test, forecast_column_name)
-  
-' 
-    , @input_data_1 = @input_query 
-    , @input_data_1_name = N'input_data' 
-    , @output_data_1_name = N'combined_output' 
-    , @params = N'@model NVARCHAR(MAX), @time_column_name  NVARCHAR(255), @label_column NVARCHAR(255), @y_query_column NVARCHAR(255), @forecast_column_name NVARCHAR(255)' 
-    , @model = @model 
-	, @time_column_name = @time_column_name
-	, @label_column = @label_column
-	, @y_query_column = @y_query_column
-	, @forecast_column_name = @forecast_column_name
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLGetMetrics.sql

@@ -1,70 +0,0 @@
--- This procedure returns a list of metrics for each iteration of a run.
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLGetMetrics]
- (
-	@run_id NVARCHAR(250),                           -- The RunId
-    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.
-    @connection_name NVARCHAR(255)='default'         -- The AML connection to use.
- ) AS
-BEGIN
-    DECLARE @tenantid NVARCHAR(255)
-    DECLARE @appid NVARCHAR(255)
-    DECLARE @password NVARCHAR(255)
-    DECLARE @config_file NVARCHAR(255)
-
-	SELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile
-	FROM aml_connection
-	WHERE ConnectionName = @connection_name;
-
-    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd
-import logging 
-import azureml.core 
-import numpy as np
-from azureml.core.experiment import Experiment 
-from azureml.train.automl.run import AutoMLRun
-from azureml.core.authentication import ServicePrincipalAuthentication 
-from azureml.core.workspace import Workspace 
-
-auth = ServicePrincipalAuthentication(tenantid, appid, password) 
- 
-ws = Workspace.from_config(path=config_file, auth=auth) 
- 
-experiment = Experiment(ws, experiment_name) 
-
-ml_run = AutoMLRun(experiment = experiment, run_id = run_id)
-
-children = list(ml_run.get_children())
-iterationlist = []
-metricnamelist = []
-metricvaluelist = []
-
-for run in children:
-    properties = run.get_properties()
-    if "iteration" in properties:
-        iteration = int(properties["iteration"])
-        for metric_name, metric_value in run.get_metrics().items():
-            if isinstance(metric_value, float):
-                iterationlist.append(iteration)
-                metricnamelist.append(metric_name)
-                metricvaluelist.append(metric_value)
-             
-metrics = pd.DataFrame({"iteration": iterationlist, "metric_name": metricnamelist, "metric_value": metricvaluelist})
-'
-    , @output_data_1_name = N'metrics'
-	, @params = N'@run_id NVARCHAR(250), 
-				  @experiment_name NVARCHAR(32),
-  				  @tenantid NVARCHAR(255),
-				  @appid NVARCHAR(255),
-				  @password NVARCHAR(255),
-				  @config_file NVARCHAR(255)'
-    , @run_id = @run_id
-	, @experiment_name = @experiment_name
-	, @tenantid = @tenantid
-	, @appid = @appid
-	, @password = @password
-	, @config_file = @config_file
-WITH RESULT SETS ((iteration INT, metric_name NVARCHAR(100), metric_value FLOAT))
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLPredict.sql

@@ -1,41 +0,0 @@
--- This procedure predicts values based on a model returned by AutoMLTrain and a dataset.
--- It returns the dataset with a new column added, which is the predicted value.
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLPredict]
- (
-   @input_query NVARCHAR(MAX),      -- A SQL query returning data to predict on.
-   @model NVARCHAR(MAX),            -- A model returned from AutoMLTrain.
-   @label_column  NVARCHAR(255)=''  -- Optional name of the column from input_query, which should be ignored when predicting
- ) AS 
-BEGIN 
-  
-    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd 
-import azureml.core  
-import numpy as np 
-from azureml.train.automl import AutoMLConfig  
-import pickle 
-import codecs 
-  
-model_obj = pickle.loads(codecs.decode(model.encode(), "base64")) 
-  
-test_data = input_data.copy() 
-
-if label_column != "" and label_column is not None:
-    y_test = test_data.pop(label_column).values 
-X_test = test_data 
-  
-predicted = model_obj.predict(X_test) 
-  
-combined_output = input_data.assign(predicted=predicted)
-  
-' 
-    , @input_data_1 = @input_query 
-    , @input_data_1_name = N'input_data' 
-    , @output_data_1_name = N'combined_output' 
-    , @params = N'@model NVARCHAR(MAX), @label_column  NVARCHAR(255)' 
-    , @model = @model 
-	, @label_column = @label_column
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLTrain.sql

@@ -1,240 +0,0 @@
--- This stored procedure uses automated machine learning to train several models
--- and returns the best model.
---
--- The result set has several columns:
---   best_run - iteration ID for the best model
---   experiment_name - experiment name pass in with the @experiment_name parameter
---   fitted_model - best model found
---   log_file_text - AutoML debug_log contents
---   workspace - name of the Azure ML workspace where run history is stored
---
--- An example call for a classification problem is:
---    insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
---    exec dbo.AutoMLTrain @input_query='
---    SELECT top 100000 
---          CAST([pickup_datetime] AS NVARCHAR(30)) AS pickup_datetime
---          ,CAST([dropoff_datetime] AS NVARCHAR(30)) AS dropoff_datetime
---          ,[passenger_count]
---          ,[trip_time_in_secs]
---          ,[trip_distance]
---          ,[payment_type]
---          ,[tip_class]
---      FROM [dbo].[nyctaxi_sample] order by [hack_license] ',
---      @label_column = 'tip_class',
---      @iterations=10
--- 
--- An example call for forecasting is:
---      insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
---      exec dbo.AutoMLTrain @input_query='
---      select cast(timeStamp as nvarchar(30)) as timeStamp,
---             demand,
---      	   precip,
---      	   temp,
---             case when timeStamp < ''2017-01-01'' then 0 else 1 end as is_validate_column
---      from nyc_energy
---      where demand is not null and precip is not null and temp is not null
---      and timeStamp < ''2017-02-01''',
---      @label_column='demand',
---      @task='forecasting',
---      @iterations=10,
---      @iteration_timeout_minutes=5,
---      @time_column_name='timeStamp',
---      @is_validate_column='is_validate_column',
---      @experiment_name='automl-sql-forecast',
---      @primary_metric='normalized_root_mean_squared_error'
-
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]
- (
-    @input_query NVARCHAR(MAX),                      -- The SQL Query that will return the data to train and validate the model.
-    @label_column NVARCHAR(255)='Label',             -- The name of the column in the result of @input_query that is the label.
-    @primary_metric NVARCHAR(40)='AUC_weighted',     -- The metric to optimize.
-    @iterations INT=100,                             -- The maximum number of pipelines to train.
-    @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.
-    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.
-    @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. 
-    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.
-    @n_cross_validations INT = 3,                    -- The number of cross validations.
-    @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.
-                                                     -- The list of possible models can be found at:
-                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings
-    @whitelist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that can be used.
-                                                     -- The list of possible models can be found at:
-                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings
-    @experiment_exit_score FLOAT = 0,                -- Stop the experiment if this score is acheived.
-    @sample_weight_column NVARCHAR(255)='',          -- The name of the column in the result of  @input_query that gives a sample weight.
-    @is_validate_column NVARCHAR(255)='',            -- The name of the column in the result of  @input_query that indicates if the row is for training or validation.
-	                                                 -- In the values of the column, 0 means for training and 1 means for validation.
-    @time_column_name  NVARCHAR(255)='',             -- The name of the timestamp column for forecasting.
-    @connection_name NVARCHAR(255)='default',        -- The AML connection to use.
-    @max_horizon INT = 0                             -- A forecast horizon is a time span into the future (or just beyond the latest date in the training data)
-                                                     -- where forecasts of the target quantity are needed.
-                                                     -- For example, if data is recorded daily and max_horizon is 5, we will predict 5 days ahead.
- ) AS
-BEGIN
-
-    DECLARE @tenantid NVARCHAR(255)
-    DECLARE @appid NVARCHAR(255)
-    DECLARE @password NVARCHAR(255)
-    DECLARE @config_file NVARCHAR(255)
-
-	SELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile
-	FROM aml_connection
-	WHERE ConnectionName = @connection_name;
-
-	EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd
-import logging 
-import azureml.core 
-import pandas as pd
-import numpy as np
-from azureml.core.experiment import Experiment 
-from azureml.train.automl import AutoMLConfig 
-from sklearn import datasets 
-import pickle
-import codecs
-from azureml.core.authentication import ServicePrincipalAuthentication 
-from azureml.core.workspace import Workspace 
-
-if __name__.startswith("sqlindb"):
-    auth = ServicePrincipalAuthentication(tenantid, appid, password) 
- 
-    ws = Workspace.from_config(path=config_file, auth=auth) 
- 
-    project_folder = "./sample_projects/" + experiment_name
- 
-    experiment = Experiment(ws, experiment_name) 
-
-    data_train = input_data
-    X_valid = None
-    y_valid = None
-    sample_weight_valid = None
-
-    if is_validate_column != "" and is_validate_column is not None:
-        data_train = input_data[input_data[is_validate_column] <= 0]
-        data_valid = input_data[input_data[is_validate_column] > 0]
-        data_train.pop(is_validate_column)
-        data_valid.pop(is_validate_column)
-        y_valid = data_valid.pop(label_column).values
-        if sample_weight_column != "" and sample_weight_column is not None:
-            sample_weight_valid = data_valid.pop(sample_weight_column).values
-        X_valid = data_valid
-        n_cross_validations = None
-
-    y_train = data_train.pop(label_column).values
-
-    sample_weight = None
-    if sample_weight_column != "" and sample_weight_column is not None:
-        sample_weight = data_train.pop(sample_weight_column).values
-
-    X_train = data_train
-
-    if experiment_timeout_hours == 0:
-        experiment_timeout_hours = None
-
-    if experiment_exit_score == 0:
-        experiment_exit_score = None
-
-    if blacklist_models == "":
-        blacklist_models = None
-
-    if blacklist_models is not None:
-        blacklist_models = blacklist_models.replace(" ", "").split(",")
-
-    if whitelist_models == "":
-        whitelist_models = None
-
-    if whitelist_models is not None:
-        whitelist_models = whitelist_models.replace(" ", "").split(",")
-
-    automl_settings = {}
-    preprocess = True
-    if time_column_name != "" and time_column_name is not None:
-        automl_settings = { "time_column_name": time_column_name }
-        preprocess = False
-        if max_horizon > 0:
-            automl_settings["max_horizon"] = max_horizon
-
-    log_file_name = "automl_sqlindb_errors.log"
-	 
-    automl_config = AutoMLConfig(task = task, 
-                                 debug_log = log_file_name, 
-                                 primary_metric = primary_metric, 
-                                 iteration_timeout_minutes = iteration_timeout_minutes, 
-                                 experiment_timeout_hours = experiment_timeout_hours,
-                                 iterations = iterations, 
-                                 n_cross_validations = n_cross_validations, 
-                                 preprocess = preprocess,
-                                 verbosity = logging.INFO, 
-                                 X = X_train,  
-                                 y = y_train, 
-                                 path = project_folder,
-                                 blacklist_models = blacklist_models,
-                                 whitelist_models = whitelist_models,
-                                 experiment_exit_score = experiment_exit_score,
-                                 sample_weight = sample_weight,
-                                 X_valid = X_valid,
-                                 y_valid = y_valid,
-                                 sample_weight_valid = sample_weight_valid,
-                                 **automl_settings) 
- 
-    local_run = experiment.submit(automl_config, show_output = True) 
-
-    best_run, fitted_model = local_run.get_output()
-
-    pickled_model = codecs.encode(pickle.dumps(fitted_model), "base64").decode()
-
-    log_file_text = ""
-
-    try:
-        with open(log_file_name, "r") as log_file:
-            log_file_text = log_file.read()
-    except:
-        log_file_text = "Log file not found"
-
-    returned_model = pd.DataFrame({"best_run": [best_run.id], "experiment_name": [experiment_name], "fitted_model": [pickled_model], "log_file_text": [log_file_text], "workspace": [ws.name]}, dtype=np.dtype(np.str))
-'
-	, @input_data_1 = @input_query
-	, @input_data_1_name = N'input_data'
-	, @output_data_1_name = N'returned_model'
-	, @params = N'@label_column NVARCHAR(255), 
-	              @primary_metric NVARCHAR(40),
-				  @iterations INT, @task NVARCHAR(40),
-				  @experiment_name NVARCHAR(32),
-				  @iteration_timeout_minutes INT,
-				  @experiment_timeout_hours FLOAT,
-				  @n_cross_validations INT,
-				  @blacklist_models NVARCHAR(MAX),
-				  @whitelist_models NVARCHAR(MAX),
-				  @experiment_exit_score FLOAT,
-				  @sample_weight_column NVARCHAR(255),
-				  @is_validate_column NVARCHAR(255),
-				  @time_column_name  NVARCHAR(255),
-				  @tenantid NVARCHAR(255),
-				  @appid NVARCHAR(255),
-				  @password NVARCHAR(255),
-				  @config_file NVARCHAR(255),
-				  @max_horizon INT'
-	, @label_column = @label_column
-	, @primary_metric = @primary_metric
-	, @iterations = @iterations
-	, @task = @task
-	, @experiment_name = @experiment_name
-	, @iteration_timeout_minutes = @iteration_timeout_minutes
-	, @experiment_timeout_hours = @experiment_timeout_hours
-	, @n_cross_validations = @n_cross_validations
-	, @blacklist_models = @blacklist_models
-	, @whitelist_models = @whitelist_models
-	, @experiment_exit_score = @experiment_exit_score
-	, @sample_weight_column = @sample_weight_column
-	, @is_validate_column = @is_validate_column
-	, @time_column_name = @time_column_name
-	, @tenantid = @tenantid
-	, @appid = @appid
-	, @password = @password
-	, @config_file = @config_file
-	, @max_horizon = @max_horizon
-WITH RESULT SETS ((best_run NVARCHAR(250), experiment_name NVARCHAR(100), fitted_model VARCHAR(MAX), log_file_text NVARCHAR(MAX), workspace NVARCHAR(100)))
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/aml_connection.sql

@@ -1,18 +0,0 @@
--- This is a table to store the Azure ML connection information.
-SET ANSI_NULLS ON
-GO
-
-SET QUOTED_IDENTIFIER ON
-GO
-
-CREATE TABLE [dbo].[aml_connection](
-    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,
-	[ConnectionName] [nvarchar](255) NULL,
-	[TenantId] [nvarchar](255) NULL,
-	[AppId] [nvarchar](255) NULL,
-	[Password] [nvarchar](255) NULL,
-	[ConfigFile] [nvarchar](255) NULL
-) ON [PRIMARY]
-GO
-
-

how-to-use-azureml/automated-machine-learning/sql-server/setup/aml_model.sql

@@ -1,22 +0,0 @@
--- This is a table to hold the results from the AutoMLTrain procedure.
-SET ANSI_NULLS ON
-GO
-
-SET QUOTED_IDENTIFIER ON
-GO
-
-CREATE TABLE [dbo].[aml_model](
-    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,
-    [Model] [varchar](max) NOT NULL,        -- The model, which can be passed to AutoMLPredict for testing or prediction.
-    [RunId] [nvarchar](250) NULL,           -- The RunId, which can be used to view the model in the Azure Portal.
-    [CreatedDate] [datetime] NULL,
-    [ExperimentName] [nvarchar](100) NULL,  -- Azure ML Experiment Name
-    [WorkspaceName] [nvarchar](100) NULL,   -- Azure ML Workspace Name
-	[LogFileText] [nvarchar](max) NULL
-) 
-GO
-
-ALTER TABLE [dbo].[aml_model] ADD  DEFAULT (getutcdate()) FOR [CreatedDate]
-GO
-
-

how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb

@@ -1,581 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Set up Azure ML Automated Machine Learning on SQL Server 2019 CTP 2.4 big data cluster\r\n",
-        "\r\n",
-        "\\# Prerequisites:  \r\n",
-        "\\# - An Azure subscription and resource group  \r\n",
-        "\\# - An Azure Machine Learning workspace  \r\n",
-        "\\# - A SQL Server 2019 CTP 2.4 big data cluster with Internet access and a database named 'automl'  \r\n",
-        "\\# - Azure CLI  \r\n",
-        "\\# - kubectl command  \r\n",
-        "\\# - The https://github.com/Azure/MachineLearningNotebooks repository downloaded (cloned) to your local machine\r\n",
-        "\r\n",
-        "\\# In the 'automl' database, create a table named 'dbo.nyc_energy' as follows:  \r\n",
-        "\\# - In SQL Server Management Studio, right-click the 'automl' database, select Tasks, then Import Flat File.  \r\n",
-        "\\# - Select the file AzureMlCli\\notebooks\\how-to-use-azureml\\automated-machine-learning\\forecasting-energy-demand\\nyc_energy.csv.  \r\n",
-        "\\# - Using the \"Modify Columns\" page, allow nulls for all columns. \r\n",
-        "\r\n",
-        "\\# Create an Azure Machine Learning Workspace using the instructions at https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace \r\n",
-        "\r\n",
-        "\\# Create an Azure service principal.  You can do this with the following commands: \r\n",
-        "\r\n",
-        "az login  \r\n",
-        "az account set --subscription *subscriptionid*  \r\n",
-        "\r\n",
-        "\\# The following command prints out the **appId** and **tenant**,  \r\n",
-        "\\# which you insert into the indicated cell later in this notebook  \r\n",
-        "\\# to allow AutoML to authenticate with Azure:  \r\n",
-        "\r\n",
-        "az ad sp create-for-rbac --name *principlename* --password *password*\r\n",
-        "\r\n",
-        "\\# Log into the master instance of SQL Server 2019 CTP 2.4:  \r\n",
-        "kubectl exec -it mssql-master-pool-0 -n *clustername* -c mssql-server -- /bin/bash\r\n",
-        "\r\n",
-        "mkdir /tmp/aml\r\n",
-        "\r\n",
-        "cd /tmp/aml\r\n",
-        "\r\n",
-        "\\# **Modify** the following with your subscription_id, resource_group, and workspace_name:  \r\n",
-        "cat > config.json << EOF  \r\n",
-        "{  \r\n",
-        "    \"subscription_id\": \"123456ab-78cd-0123-45ef-abcd12345678\",  \r\n",
-        "    \"resource_group\": \"myrg1\",  \r\n",
-        "    \"workspace_name\": \"myws1\"  \r\n",
-        "}  \r\n",
-        "EOF\r\n",
-        "\r\n",
-        "\\# The directory referenced below is appropriate for the master instance of SQL Server 2019 CTP 2.4.\r\n",
-        "\r\n",
-        "cd /opt/mssql/mlservices/runtime/python/bin\r\n",
-        "\r\n",
-        "./python -m pip install azureml-sdk[automl]\r\n",
-        "\r\n",
-        "./python -m pip install --upgrade numpy \r\n",
-        "\r\n",
-        "./python -m pip install --upgrade sklearn\r\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.png)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- Enable external scripts to allow invoking Python\r\n",
-        "sp_configure 'external scripts enabled',1 \r\n",
-        "reconfigure with override \r\n",
-        "GO\r\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- Use database 'automl'\r\n",
-        "USE [automl]\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This is a table to hold the Azure ML connection information.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "CREATE TABLE [dbo].[aml_connection](\r\n",
-        "    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,\r\n",
-        "\t[ConnectionName] [nvarchar](255) NULL,\r\n",
-        "\t[TenantId] [nvarchar](255) NULL,\r\n",
-        "\t[AppId] [nvarchar](255) NULL,\r\n",
-        "\t[Password] [nvarchar](255) NULL,\r\n",
-        "\t[ConfigFile] [nvarchar](255) NULL\r\n",
-        ") ON [PRIMARY]\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Copy the values from create-for-rbac above into the cell below"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- Use the following values:\r\n",
-        "-- Leave the name as 'Default'\r\n",
-        "-- Insert <tenant> returned by create-for-rbac above\r\n",
-        "-- Insert <AppId> returned by create-for-rbac above\r\n",
-        "-- Insert <password> used in create-for-rbac above\r\n",
-        "-- Leave <path> as '/tmp/aml/config.json'\r\n",
-        "INSERT INTO [dbo].[aml_connection]  \r\n",
-        "VALUES (\r\n",
-        "    N'Default', -- Name\r\n",
-        "    N'11111111-2222-3333-4444-555555555555', -- Tenant\r\n",
-        "    N'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee', -- AppId\r\n",
-        "    N'insertpasswordhere', -- Password\r\n",
-        "    N'/tmp/aml/config.json' -- Path\r\n",
-        "    );\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This is a table to hold the results from the AutoMLTrain procedure.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "CREATE TABLE [dbo].[aml_model](\r\n",
-        "    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,\r\n",
-        "    [Model] [varchar](max) NOT NULL,        -- The model, which can be passed to AutoMLPredict for testing or prediction.\r\n",
-        "    [RunId] [nvarchar](250) NULL,           -- The RunId, which can be used to view the model in the Azure Portal.\r\n",
-        "    [CreatedDate] [datetime] NULL,\r\n",
-        "    [ExperimentName] [nvarchar](100) NULL,  -- Azure ML Experiment Name\r\n",
-        "    [WorkspaceName] [nvarchar](100) NULL,   -- Azure ML Workspace Name\r\n",
-        "\t[LogFileText] [nvarchar](max) NULL\r\n",
-        ") \r\n",
-        "GO\r\n",
-        "\r\n",
-        "ALTER TABLE [dbo].[aml_model] ADD  DEFAULT (getutcdate()) FOR [CreatedDate]\r\n",
-        "GO\r\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This stored procedure uses automated machine learning to train several models\r\n",
-        "-- and return the best model.\r\n",
-        "--\r\n",
-        "-- The result set has several columns:\r\n",
-        "--   best_run - ID of the best model found\r\n",
-        "--   experiment_name - training run name\r\n",
-        "--   fitted_model - best model found\r\n",
-        "--   log_file_text - console output\r\n",
-        "--   workspace - name of the Azure ML workspace where run history is stored\r\n",
-        "--\r\n",
-        "-- An example call for a classification problem is:\r\n",
-        "--    insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)\r\n",
-        "--    exec dbo.AutoMLTrain @input_query='\r\n",
-        "--    SELECT top 100000 \r\n",
-        "--          CAST([pickup_datetime] AS NVARCHAR(30)) AS pickup_datetime\r\n",
-        "--          ,CAST([dropoff_datetime] AS NVARCHAR(30)) AS dropoff_datetime\r\n",
-        "--          ,[passenger_count]\r\n",
-        "--          ,[trip_time_in_secs]\r\n",
-        "--          ,[trip_distance]\r\n",
-        "--          ,[payment_type]\r\n",
-        "--          ,[tip_class]\r\n",
-        "--      FROM [dbo].[nyctaxi_sample] order by [hack_license] ',\r\n",
-        "--      @label_column = 'tip_class',\r\n",
-        "--      @iterations=10\r\n",
-        "-- \r\n",
-        "-- An example call for forecasting is:\r\n",
-        "--      insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)\r\n",
-        "--      exec dbo.AutoMLTrain @input_query='\r\n",
-        "--      select cast(timeStamp as nvarchar(30)) as timeStamp,\r\n",
-        "--             demand,\r\n",
-        "--      \t   precip,\r\n",
-        "--      \t   temp,\r\n",
-        "--             case when timeStamp < ''2017-01-01'' then 0 else 1 end as is_validate_column\r\n",
-        "--      from nyc_energy\r\n",
-        "--      where demand is not null and precip is not null and temp is not null\r\n",
-        "--      and timeStamp < ''2017-02-01''',\r\n",
-        "--      @label_column='demand',\r\n",
-        "--      @task='forecasting',\r\n",
-        "--      @iterations=10,\r\n",
-        "--      @iteration_timeout_minutes=5,\r\n",
-        "--      @time_column_name='timeStamp',\r\n",
-        "--      @is_validate_column='is_validate_column',\r\n",
-        "--      @experiment_name='automl-sql-forecast',\r\n",
-        "--      @primary_metric='normalized_root_mean_squared_error'\r\n",
-        "\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]\r\n",
-        " (\r\n",
-        "    @input_query NVARCHAR(MAX),                      -- The SQL Query that will return the data to train and validate the model.\r\n",
-        "    @label_column NVARCHAR(255)='Label',             -- The name of the column in the result of @input_query that is the label.\r\n",
-        "    @primary_metric NVARCHAR(40)='AUC_weighted',     -- The metric to optimize.\r\n",
-        "    @iterations INT=100,                             -- The maximum number of pipelines to train.\r\n",
-        "    @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.\r\n",
-        "    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.\r\n",
-        "    @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. \r\n",
-        "    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.\r\n",
-        "    @n_cross_validations INT = 3,                    -- The number of cross validations.\r\n",
-        "    @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.\r\n",
-        "                                                     -- The list of possible models can be found at:\r\n",
-        "                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings\r\n",
-        "    @whitelist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that can be used.\r\n",
-        "                                                     -- The list of possible models can be found at:\r\n",
-        "                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings\r\n",
-        "    @experiment_exit_score FLOAT = 0,                -- Stop the experiment if this score is acheived.\r\n",
-        "    @sample_weight_column NVARCHAR(255)='',          -- The name of the column in the result of  @input_query that gives a sample weight.\r\n",
-        "    @is_validate_column NVARCHAR(255)='',            -- The name of the column in the result of  @input_query that indicates if the row is for training or validation.\r\n",
-        "\t                                                 -- In the values of the column, 0 means for training and 1 means for validation.\r\n",
-        "    @time_column_name  NVARCHAR(255)='',             -- The name of the timestamp column for forecasting.\r\n",
-        "\t@connection_name NVARCHAR(255)='default'         -- The AML connection to use.\r\n",
-        " ) AS\r\n",
-        "BEGIN\r\n",
-        "\r\n",
-        "    DECLARE @tenantid NVARCHAR(255)\r\n",
-        "    DECLARE @appid NVARCHAR(255)\r\n",
-        "    DECLARE @password NVARCHAR(255)\r\n",
-        "    DECLARE @config_file NVARCHAR(255)\r\n",
-        "\r\n",
-        "\tSELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile\r\n",
-        "\tFROM aml_connection\r\n",
-        "\tWHERE ConnectionName = @connection_name;\r\n",
-        "\r\n",
-        "\tEXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd\r\n",
-        "import logging \r\n",
-        "import azureml.core \r\n",
-        "import pandas as pd\r\n",
-        "import numpy as np\r\n",
-        "from azureml.core.experiment import Experiment \r\n",
-        "from azureml.train.automl import AutoMLConfig \r\n",
-        "from sklearn import datasets \r\n",
-        "import pickle\r\n",
-        "import codecs\r\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication \r\n",
-        "from azureml.core.workspace import Workspace \r\n",
-        "\r\n",
-        "if __name__.startswith(\"sqlindb\"):\r\n",
-        "    auth = ServicePrincipalAuthentication(tenantid, appid, password) \r\n",
-        " \r\n",
-        "    ws = Workspace.from_config(path=config_file, auth=auth) \r\n",
-        " \r\n",
-        "    project_folder = \"./sample_projects/\" + experiment_name\r\n",
-        " \r\n",
-        "    experiment = Experiment(ws, experiment_name) \r\n",
-        "\r\n",
-        "    data_train = input_data\r\n",
-        "    X_valid = None\r\n",
-        "    y_valid = None\r\n",
-        "    sample_weight_valid = None\r\n",
-        "\r\n",
-        "    if is_validate_column != \"\" and is_validate_column is not None:\r\n",
-        "        data_train = input_data[input_data[is_validate_column] <= 0]\r\n",
-        "        data_valid = input_data[input_data[is_validate_column] > 0]\r\n",
-        "        data_train.pop(is_validate_column)\r\n",
-        "        data_valid.pop(is_validate_column)\r\n",
-        "        y_valid = data_valid.pop(label_column).values\r\n",
-        "        if sample_weight_column != \"\" and sample_weight_column is not None:\r\n",
-        "            sample_weight_valid = data_valid.pop(sample_weight_column).values\r\n",
-        "        X_valid = data_valid\r\n",
-        "        n_cross_validations = None\r\n",
-        "\r\n",
-        "    y_train = data_train.pop(label_column).values\r\n",
-        "\r\n",
-        "    sample_weight = None\r\n",
-        "    if sample_weight_column != \"\" and sample_weight_column is not None:\r\n",
-        "        sample_weight = data_train.pop(sample_weight_column).values\r\n",
-        "\r\n",
-        "    X_train = data_train\r\n",
-        "\r\n",
-        "    if experiment_timeout_hours == 0:\r\n",
-        "        experiment_timeout_hours = None\r\n",
-        "\r\n",
-        "    if experiment_exit_score == 0:\r\n",
-        "        experiment_exit_score = None\r\n",
-        "\r\n",
-        "    if blacklist_models == \"\":\r\n",
-        "        blacklist_models = None\r\n",
-        "\r\n",
-        "    if blacklist_models is not None:\r\n",
-        "        blacklist_models = blacklist_models.replace(\" \", \"\").split(\",\")\r\n",
-        "\r\n",
-        "    if whitelist_models == \"\":\r\n",
-        "        whitelist_models = None\r\n",
-        "\r\n",
-        "    if whitelist_models is not None:\r\n",
-        "        whitelist_models = whitelist_models.replace(\" \", \"\").split(\",\")\r\n",
-        "\r\n",
-        "    automl_settings = {}\r\n",
-        "    preprocess = True\r\n",
-        "    if time_column_name != \"\" and time_column_name is not None:\r\n",
-        "        automl_settings = { \"time_column_name\": time_column_name }\r\n",
-        "        preprocess = False\r\n",
-        "\r\n",
-        "    log_file_name = \"automl_errors.log\"\r\n",
-        "\t \r\n",
-        "    automl_config = AutoMLConfig(task = task, \r\n",
-        "                                 debug_log = log_file_name, \r\n",
-        "                                 primary_metric = primary_metric, \r\n",
-        "                                 iteration_timeout_minutes = iteration_timeout_minutes, \r\n",
-        "                                 experiment_timeout_hours = experiment_timeout_hours,\r\n",
-        "                                 iterations = iterations, \r\n",
-        "                                 n_cross_validations = n_cross_validations, \r\n",
-        "                                 preprocess = preprocess,\r\n",
-        "                                 verbosity = logging.INFO, \r\n",
-        "                                 X = X_train,  \r\n",
-        "                                 y = y_train, \r\n",
-        "                                 path = project_folder,\r\n",
-        "                                 blacklist_models = blacklist_models,\r\n",
-        "                                 whitelist_models = whitelist_models,\r\n",
-        "                                 experiment_exit_score = experiment_exit_score,\r\n",
-        "                                 sample_weight = sample_weight,\r\n",
-        "                                 X_valid = X_valid,\r\n",
-        "                                 y_valid = y_valid,\r\n",
-        "                                 sample_weight_valid = sample_weight_valid,\r\n",
-        "                                 **automl_settings) \r\n",
-        " \r\n",
-        "    local_run = experiment.submit(automl_config, show_output = True) \r\n",
-        "\r\n",
-        "    best_run, fitted_model = local_run.get_output()\r\n",
-        "\r\n",
-        "    pickled_model = codecs.encode(pickle.dumps(fitted_model), \"base64\").decode()\r\n",
-        "\r\n",
-        "    log_file_text = \"\"\r\n",
-        "\r\n",
-        "    try:\r\n",
-        "        with open(log_file_name, \"r\") as log_file:\r\n",
-        "            log_file_text = log_file.read()\r\n",
-        "    except:\r\n",
-        "        log_file_text = \"Log file not found\"\r\n",
-        "\r\n",
-        "    returned_model = pd.DataFrame({\"best_run\": [best_run.id], \"experiment_name\": [experiment_name], \"fitted_model\": [pickled_model], \"log_file_text\": [log_file_text], \"workspace\": [ws.name]}, dtype=np.dtype(np.str))\r\n",
-        "'\r\n",
-        "\t, @input_data_1 = @input_query\r\n",
-        "\t, @input_data_1_name = N'input_data'\r\n",
-        "\t, @output_data_1_name = N'returned_model'\r\n",
-        "\t, @params = N'@label_column NVARCHAR(255), \r\n",
-        "\t              @primary_metric NVARCHAR(40),\r\n",
-        "\t\t\t\t  @iterations INT, @task NVARCHAR(40),\r\n",
-        "\t\t\t\t  @experiment_name NVARCHAR(32),\r\n",
-        "\t\t\t\t  @iteration_timeout_minutes INT,\r\n",
-        "\t\t\t\t  @experiment_timeout_hours FLOAT,\r\n",
-        "\t\t\t\t  @n_cross_validations INT,\r\n",
-        "\t\t\t\t  @blacklist_models NVARCHAR(MAX),\r\n",
-        "\t\t\t\t  @whitelist_models NVARCHAR(MAX),\r\n",
-        "\t\t\t\t  @experiment_exit_score FLOAT,\r\n",
-        "\t\t\t\t  @sample_weight_column NVARCHAR(255),\r\n",
-        "\t\t\t\t  @is_validate_column NVARCHAR(255),\r\n",
-        "\t\t\t\t  @time_column_name  NVARCHAR(255),\r\n",
-        "\t\t\t\t  @tenantid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @appid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @password NVARCHAR(255),\r\n",
-        "\t\t\t\t  @config_file NVARCHAR(255)'\r\n",
-        "\t, @label_column = @label_column\r\n",
-        "\t, @primary_metric = @primary_metric\r\n",
-        "\t, @iterations = @iterations\r\n",
-        "\t, @task = @task\r\n",
-        "\t, @experiment_name = @experiment_name\r\n",
-        "\t, @iteration_timeout_minutes = @iteration_timeout_minutes\r\n",
-        "\t, @experiment_timeout_hours = @experiment_timeout_hours\r\n",
-        "\t, @n_cross_validations = @n_cross_validations\r\n",
-        "\t, @blacklist_models = @blacklist_models\r\n",
-        "\t, @whitelist_models = @whitelist_models\r\n",
-        "\t, @experiment_exit_score = @experiment_exit_score\r\n",
-        "\t, @sample_weight_column = @sample_weight_column\r\n",
-        "\t, @is_validate_column = @is_validate_column\r\n",
-        "\t, @time_column_name = @time_column_name\r\n",
-        "\t, @tenantid = @tenantid\r\n",
-        "\t, @appid = @appid\r\n",
-        "\t, @password = @password\r\n",
-        "\t, @config_file = @config_file\r\n",
-        "WITH RESULT SETS ((best_run NVARCHAR(250), experiment_name NVARCHAR(100), fitted_model VARCHAR(MAX), log_file_text NVARCHAR(MAX), workspace NVARCHAR(100)))\r\n",
-        "END"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This procedure returns a list of metrics for each iteration of a training run.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "CREATE OR ALTER PROCEDURE [dbo].[AutoMLGetMetrics]\r\n",
-        " (\r\n",
-        "\t@run_id NVARCHAR(250),                           -- The RunId\r\n",
-        "    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.\r\n",
-        "    @connection_name NVARCHAR(255)='default'         -- The AML connection to use.\r\n",
-        " ) AS\r\n",
-        "BEGIN\r\n",
-        "    DECLARE @tenantid NVARCHAR(255)\r\n",
-        "    DECLARE @appid NVARCHAR(255)\r\n",
-        "    DECLARE @password NVARCHAR(255)\r\n",
-        "    DECLARE @config_file NVARCHAR(255)\r\n",
-        "\r\n",
-        "\tSELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile\r\n",
-        "\tFROM aml_connection\r\n",
-        "\tWHERE ConnectionName = @connection_name;\r\n",
-        "\r\n",
-        "    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd\r\n",
-        "import logging \r\n",
-        "import azureml.core \r\n",
-        "import numpy as np\r\n",
-        "from azureml.core.experiment import Experiment \r\n",
-        "from azureml.train.automl.run import AutoMLRun\r\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication \r\n",
-        "from azureml.core.workspace import Workspace \r\n",
-        "\r\n",
-        "auth = ServicePrincipalAuthentication(tenantid, appid, password) \r\n",
-        " \r\n",
-        "ws = Workspace.from_config(path=config_file, auth=auth) \r\n",
-        " \r\n",
-        "experiment = Experiment(ws, experiment_name) \r\n",
-        "\r\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\r\n",
-        "\r\n",
-        "children = list(ml_run.get_children())\r\n",
-        "iterationlist = []\r\n",
-        "metricnamelist = []\r\n",
-        "metricvaluelist = []\r\n",
-        "\r\n",
-        "for run in children:\r\n",
-        "    properties = run.get_properties()\r\n",
-        "    if \"iteration\" in properties:\r\n",
-        "        iteration = int(properties[\"iteration\"])\r\n",
-        "        for metric_name, metric_value in run.get_metrics().items():\r\n",
-        "            if isinstance(metric_value, float):\r\n",
-        "                iterationlist.append(iteration)\r\n",
-        "                metricnamelist.append(metric_name)\r\n",
-        "                metricvaluelist.append(metric_value)\r\n",
-        "             \r\n",
-        "metrics = pd.DataFrame({\"iteration\": iterationlist, \"metric_name\": metricnamelist, \"metric_value\": metricvaluelist})\r\n",
-        "'\r\n",
-        "    , @output_data_1_name = N'metrics'\r\n",
-        "\t, @params = N'@run_id NVARCHAR(250), \r\n",
-        "\t\t\t\t  @experiment_name NVARCHAR(32),\r\n",
-        "  \t\t\t\t  @tenantid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @appid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @password NVARCHAR(255),\r\n",
-        "\t\t\t\t  @config_file NVARCHAR(255)'\r\n",
-        "    , @run_id = @run_id\r\n",
-        "\t, @experiment_name = @experiment_name\r\n",
-        "\t, @tenantid = @tenantid\r\n",
-        "\t, @appid = @appid\r\n",
-        "\t, @password = @password\r\n",
-        "\t, @config_file = @config_file\r\n",
-        "WITH RESULT SETS ((iteration INT, metric_name NVARCHAR(100), metric_value FLOAT))\r\n",
-        "END"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This procedure predicts values based on a model returned by AutoMLTrain and a dataset.\r\n",
-        "-- It returns the dataset with a new column added, which is the predicted value.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "CREATE OR ALTER PROCEDURE [dbo].[AutoMLPredict]\r\n",
-        " (\r\n",
-        "   @input_query NVARCHAR(MAX),      -- A SQL query returning data to predict on.\r\n",
-        "   @model NVARCHAR(MAX),            -- A model returned from AutoMLTrain.\r\n",
-        "   @label_column  NVARCHAR(255)=''  -- Optional name of the column from input_query, which should be ignored when predicting\r\n",
-        " ) AS \r\n",
-        "BEGIN \r\n",
-        "  \r\n",
-        "    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd \r\n",
-        "import azureml.core  \r\n",
-        "import numpy as np \r\n",
-        "from azureml.train.automl import AutoMLConfig  \r\n",
-        "import pickle \r\n",
-        "import codecs \r\n",
-        "  \r\n",
-        "model_obj = pickle.loads(codecs.decode(model.encode(), \"base64\")) \r\n",
-        "  \r\n",
-        "test_data = input_data.copy() \r\n",
-        "\r\n",
-        "if label_column != \"\" and label_column is not None:\r\n",
-        "    y_test = test_data.pop(label_column).values \r\n",
-        "X_test = test_data \r\n",
-        "  \r\n",
-        "predicted = model_obj.predict(X_test) \r\n",
-        "  \r\n",
-        "combined_output = input_data.assign(predicted=predicted)\r\n",
-        "  \r\n",
-        "' \r\n",
-        "    , @input_data_1 = @input_query \r\n",
-        "    , @input_data_1_name = N'input_data' \r\n",
-        "    , @output_data_1_name = N'combined_output' \r\n",
-        "    , @params = N'@model NVARCHAR(MAX), @label_column  NVARCHAR(255)' \r\n",
-        "    , @model = @model \r\n",
-        "\t, @label_column = @label_column\r\n",
-        "END"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "None"
-    ],
-    "datasets": [
-      "None"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Azure ML AutoML"
-    ],
-    "friendly_name": "Setup automated ML SQL integration",
-    "index_order": 1,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "sql",
-      "name": "python36"
-    },
-    "language_info": {
-      "name": "sql",
-      "version": ""
-    },
-    "tags": [
-      ""
-    ],
-    "task": "None"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -512,9 +512,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the Best Model after the above run is complete \n",
+        "## Deploy\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "### Retrieve the Best Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -523,17 +525,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
+        "best_run, fitted_model = local_run.get_output()"
-        "print(best_run)\n",
-        "print(fitted_model)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
+        "### Download the conda environment file\n",
-        "Show the run and the model that has the smallest `log_loss` value:"
+        "From the *best_run* download the conda environment file that was used to train the AutoML model."
       ]
     },
     {
@@ -542,10 +542,34 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "lookup_metric = \"log_loss\"\n",
+        "from azureml.automl.core.shared import constants\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+        "conda_env_file_name = 'conda_env.yml'\n",
-        "print(best_run)\n",
+        "best_run.download_file(name=\"outputs/conda_env_v_1_0_0.yml\", output_file_path=conda_env_file_name)\n",
-        "print(fitted_model)"
+        "with open(conda_env_file_name, \"r\") as conda_file:\n",
+        "    conda_file_contents = conda_file.read()\n",
+        "    print(conda_file_contents)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download the model scoring file\n",
+        "From the *best_run* download the scoring file to get the predictions from the AutoML model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.shared import constants\n",
+        "script_file_name = 'scoring_file.py'\n",
+        "best_run.download_file(name=\"outputs/scoring_file_v_1_0_0.py\", output_file_path=script_file_name)\n",
+        "with open(script_file_name, \"r\") as scoring_file:\n",
+        "    scoring_file_contents = scoring_file.read()\n",
+        "    print(scoring_file_contents)"
       ]
     },
     {
@@ -572,8 +596,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create Scoring Script\n",
+        "### Deploy the model as a Web Service on Azure Container Instance\n",
-        "Replace model_id with name of model from output of above register cell"
+        "\n",
+        "Create the configuration needed for deploying the model as a web service service."
       ]
     },
     {
@@ -582,123 +607,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy as np\n",
-        "import azureml.train.automl\n",
-        "from sklearn.externals import joblib\n",
-        "from azureml.core.model import Model\n",
-        "import pandas as pd\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    model_path = Model.get_model_path(model_name = '<<model_id>>') # this name is model.id of model that we want to deploy\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "def run(raw_data):\n",
-        "    try:\n",
-        "        data = (pd.DataFrame(np.array(json.loads(raw_data)['data']), columns=[str(i) for i in range(0,64)]))\n",
-        "        result = model.predict(data)\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return json.dumps({\"error\": result})\n",
-        "    return json.dumps({\"result\":result.tolist()})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Replace <<model_id>>\n",
-        "content = \"\"\n",
-        "with open(\"score.py\", \"r\") as fo:\n",
-        "    content = fo.read()\n",
-        "\n",
-        "new_content = content.replace(\"<<model_id>>\", local_run.model_id)\n",
-        "with open(\"score.py\", \"w\") as fw:\n",
-        "    fw.write(new_content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create a YAML File for the Environment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-defaults', 'azureml-sdk[automl]'])\n",
-        "\n",
-        "conda_env_file_name = 'myenv.yml'\n",
-        "myenv.save_to_file('.', conda_env_file_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy the model as a Web Service on Azure Container Instance\n",
-        "Replace servicename with any meaningful name of service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# this will take 10-15 minutes to finish\n",
-        "\n",
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
-        "from azureml.core.model import Model\n",
+        "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.environment import Environment\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import uuid\n",
         "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
         "\n",
-        "myaci_config = AciWebservice.deploy_configuration(\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "    cpu_cores = 2, \n",
+        "                                               memory_gb = 1, \n",
-        "    memory_gb = 2, \n",
+        "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
-        "    tags = {'name':'Databricks Azure ML ACI'}, \n",
+        "                                               description = 'sample service for Automl Classification')"
-        "    description = 'This is for ADB and AutoML example.')\n",
-        "\n",
-        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
-        "# we need to add extra packages to procured environment\n",
-        "# in order to deploy amended environment we need to rename it\n",
-        "myenv.name = 'myenv'\n",
-        "model_dependencies = CondaDependencies('myenv.yml')\n",
-        "for pip_dep in model_dependencies.pip_packages:\n",
-        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
-        "for conda_dep in model_dependencies.conda_packages:\n",
-        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
-        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
-        "\n",
-        "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
-        "service_name = \"myservice-{}\".format(guid)\n",
-        "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "print(\"Creating service with name: {}\".format(service_name))\n",
-        "\n",
-        "myservice = Model.deploy(ws, service_name, [model], inference_config, myaci_config)\n",
-        "myservice.wait_for_deployment(show_output=True)"
       ]
     },
     {
@@ -707,8 +626,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#for using the Web HTTP API \n",
+        "from azureml.core.webservice import Webservice\n",
-        "print(myservice.scoring_uri)"
+        "from azureml.core.model import Model\n",
+        "\n",
+        "aci_service_name = 'automl-databricks-local'\n",
+        "print(aci_service_name)\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service.wait_for_deployment(True)\n",
+        "print(aci_service.state)"
       ]
     },
     {
@@ -752,7 +677,7 @@
         "for index in np.random.choice(len(y_test), 2, replace = False):\n",
         "    print(index)\n",
         "    test_sample = json.dumps({'data':X_test[index:index + 1].values.tolist()})\n",
-        "    predicted = myservice.run(input_data = test_sample)\n",
+        "    predicted = aci_service.run(input_data = test_sample)\n",
         "    label = y_test.values[index]\n",
         "    predictedDict = json.loads(predicted)\n",
         "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])    \n",

how-to-use-azureml/deployment/accelerated-models/README.md

@@ -50,10 +50,12 @@ pip install azureml-accel-models[gpu]
 
 ### Step 4: Follow our notebooks
 
-The notebooks in this repo walk through the following scenarios: 
+We provide notebooks to walk through the following scenarios, linked below: 
-* [Quickstart](accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
+* [Quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
-* [Object Detection](accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
+* [Object Detection](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
-* [Training models](accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
+* [Training models](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
+
+**Note**: the above notebooks work only for tensorflow >= 1.6,<2.0.
 
 <a name="model-classes"></a>
 ## Model Classes

how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb

@@ -1,497 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure ML Hardware Accelerated Object Detection"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This tutorial will show you how to deploy an object detection service based on the SSD-VGG model in just a few minutes using the Azure Machine Learning Accelerated AI service.\n",
-        "\n",
-        "We will use the SSD-VGG model accelerated on an FPGA. Our Accelerated Models Service handles translating deep neural networks (DNN) into an FPGA program.\n",
-        "\n",
-        "The steps in this notebook are: \n",
-        "1. [Setup Environment](#set-up-environment)\n",
-        "* [Construct Model](#construct-model)\n",
-        "    * Image Preprocessing\n",
-        "    * Featurizer\n",
-        "    * Save Model\n",
-        "    * Save input and output tensor names\n",
-        "* [Create Image](#create-image)\n",
-        "* [Deploy Image](#deploy-image)\n",
-        "* [Test the Service](#test-service)\n",
-        "    * Create Client\n",
-        "    * Serve the model\n",
-        "* [Cleanup](#cleanup)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"set-up-environment\"></a>\n",
-        "## 1. Set up Environment\n",
-        "### 1.a. Imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1.b. Retrieve Workspace\n",
-        "If you haven't created a Workspace, please follow [this notebook](\"../../../configuration.ipynb\") to do so. If you have, run the codeblock below to retrieve it. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"construct-model\"></a>\n",
-        "## 2. Construct model\n",
-        "### 2.a. Image preprocessing\n",
-        "We'd like our service to accept JPEG images as input. However the input to SSD-VGG is a float tensor of shape \\[1, 300, 300, 3\\]. The first dimension is batch, then height, width, and channels (i.e. NHWC). To bridge this gap, we need code that decodes JPEG images and resizes them appropriately for input to SSD-VGG. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as TensorFlow strings) and produces a tensor that is ready to be featurized by SSD-VGG.\n",
-        "\n",
-        "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
-        "import azureml.accel.models.utils as utils\n",
-        "tf.reset_default_graph()\n",
-        "\n",
-        "in_images = tf.placeholder(tf.string)\n",
-        "image_tensors = utils.preprocess_array(in_images, output_width=300, output_height=300, preserve_aspect_ratio=False)\n",
-        "print(image_tensors.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.b. Featurizer\n",
-        "The SSD-VGG model is different from our other models in that it generates 12 tensor outputs. These corresponds to x,y displacements of the anchor boxes and the detection confidence (for 21 classes). Because these outputs are not convenient to work with, we will later use a pre-defined post-processing utility to transform the outputs into a simplified list of bounding boxes with their respective class and confidence.\n",
-        "\n",
-        "For more information about the output tensors, take this example: the output tensor 'ssd_300_vgg/block4_box/Reshape_1:0' has a shape of [None, 37, 37, 4, 21]. This gives the pre-softmax confidence for 4 anchor boxes situated at each site of a 37 x 37 grid imposed on the image, one confidence score for each of the 21 classes. The first dimension is the batch dimension. Likewise, 'ssd_300_vgg/block4_box/Reshape:0' has shape [None, 37, 37, 4, 4] and encodes the (cx, cy) center shift and rescaling (sw, sh) relative to each anchor box. Refer to the [SSD-VGG paper](https://arxiv.org/abs/1512.02325) to understand how these are computed. The other 10 tensors are defined similarly."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.accel.models import SsdVgg\n",
-        "\n",
-        "saved_model_dir = os.path.join(os.path.expanduser('~'), 'models')\n",
-        "model_graph = SsdVgg(saved_model_dir, is_frozen = True)\n",
-        "\n",
-        "print('SSD-VGG Input Tensors:')\n",
-        "for idx, input_name in enumerate(model_graph.input_tensor_list):\n",
-        "    print('{}, {}'.format(input_name, model_graph.get_input_dims(idx)))\n",
-        "    \n",
-        "print('SSD-VGG Output Tensors:')\n",
-        "for idx, output_name in enumerate(model_graph.output_tensor_list):\n",
-        "    print('{}, {}'.format(output_name, model_graph.get_output_dims(idx)))\n",
-        "\n",
-        "ssd_outputs = model_graph.import_graph_def(image_tensors, is_training=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.c. Save Model\n",
-        "Now that we loaded both parts of the tensorflow graph (preprocessor and SSD-VGG featurizer), we can save the graph and associated variables to a directory which we can register as an Azure ML Model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_name = \"ssdvgg\"\n",
-        "model_save_path = os.path.join(saved_model_dir, model_name, \"saved_model\")\n",
-        "print(\"Saving model in {}\".format(model_save_path))\n",
-        "\n",
-        "output_map = {}\n",
-        "for i, output in enumerate(ssd_outputs):\n",
-        "    output_map['out_{}'.format(i)] = output\n",
-        "\n",
-        "with tf.Session() as sess:\n",
-        "    model_graph.restore_weights(sess)\n",
-        "    tf.saved_model.simple_save(sess, \n",
-        "                               model_save_path, \n",
-        "                               inputs={'images': in_images}, \n",
-        "                               outputs=output_map)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.d. Important! Save names of input and output tensors\n",
-        "\n",
-        "These input and output tensors that were created during the preprocessing and classifier steps are also going to be used when **converting the model** to an Accelerated Model that can run on FPGA's and for **making an inferencing request**. It is very important to save this information!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "input_tensors = in_images.name\n",
-        "# We will use the list of output tensors during inferencing\n",
-        "output_tensors = [output.name for output in ssd_outputs]\n",
-        "# However, for multiple output tensors, our AccelOnnxConverter will \n",
-        "#    accept comma-delimited strings (lists will cause error)\n",
-        "output_tensors_str = \",\".join(output_tensors)\n",
-        "\n",
-        "print(input_tensors)\n",
-        "print(output_tensors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"create-image\"></a>\n",
-        "## 3. Create AccelContainerImage\n",
-        "Below we will execute all the same steps as in the [Quickstart](./accelerated-models-quickstart.ipynb#create-image) to package the model we have saved locally into an accelerated Docker image saved in our workspace. To complete all the steps, it may take a few minutes. For more details on each step, check out the [Quickstart section on model registration](./accelerated-models-quickstart.ipynb#register-model)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.core.image import Image\n",
-        "from azureml.accel import AccelOnnxConverter\n",
-        "from azureml.accel import AccelContainerImage\n",
-        "\n",
-        "# Retrieve workspace\n",
-        "ws = Workspace.from_config()\n",
-        "print(\"Successfully retrieved workspace:\", ws.name, ws.resource_group, ws.location, ws.subscription_id, '\\n')\n",
-        "\n",
-        "# Register model\n",
-        "registered_model = Model.register(workspace = ws,\n",
-        "                                  model_path = model_save_path,\n",
-        "                                  model_name = model_name)\n",
-        "print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
-        "\n",
-        "# Convert model\n",
-        "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
-        "if convert_request.wait_for_completion(show_output = False):\n",
-        "    # If the above call succeeded, get the converted model\n",
-        "    converted_model = convert_request.result\n",
-        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "          converted_model.id, converted_model.created_time, '\\n')\n",
-        "else:\n",
-        "    print(\"Model conversion failed. Showing output.\")\n",
-        "    convert_request.wait_for_completion(show_output = True)\n",
-        "\n",
-        "# Package into AccelContainerImage\n",
-        "image_config = AccelContainerImage.image_configuration()\n",
-        "# Image name must be lowercase\n",
-        "image_name = \"{}-image\".format(model_name)\n",
-        "image = Image.create(name = image_name,\n",
-        "                     models = [converted_model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "image.wait_for_creation()\n",
-        "print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"deploy-image\"></a>\n",
-        "## 4. Deploy image\n",
-        "Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
-        "\n",
-        "### 4.a. Deploy to Databox Edge Machine using IoT Hub\n",
-        "See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
-        "\n",
-        "### 4.b. Deploy to AKS Cluster\n",
-        "Same as in the [Quickstart section on image deployment](./accelerated-models-quickstart.ipynb#deploy-image), we are going to create an AKS cluster with FPGA-enabled machines, then deploy our service to it.\n",
-        "#### Create AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
-        "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
-        "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
-        "                                                    agent_count = 1, \n",
-        "                                                    location = \"eastus\")\n",
-        "\n",
-        "aks_name = 'aks-pb6-obj'\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy AccelContainerImage to AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "\n",
-        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
-        "# Authentication is enabled by default, but for testing we specify False\n",
-        "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
-        "                                                num_replicas=1,\n",
-        "                                                auth_enabled = False)\n",
-        "\n",
-        "aks_service_name ='my-aks-service-3'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-service\"></a>\n",
-        "## 5. Test the service\n",
-        "<a id=\"create-client\"></a>\n",
-        "### 5.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
-        "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel import client_from_service\n",
-        "\n",
-        "# Initialize AzureML Accelerated Models client\n",
-        "client = client_from_service(aks_service)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can adapt the client [code](https://github.com/Azure/aml-real-time-ai/blob/master/pythonlib/amlrealtimeai/client.py) to meet your needs. There is also an example C# [client](https://github.com/Azure/aml-real-time-ai/blob/master/sample-clients/csharp).\n",
-        "\n",
-        "The service provides an API that is compatible with TensorFlow Serving. There are instructions to download a sample client [here](https://www.tensorflow.org/serving/setup)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"serve-model\"></a>\n",
-        "### 5.b. Serve the model\n",
-        "The SSD-VGG model returns the confidence and bounding boxes for all possible anchor boxes. As mentioned earlier, we will use a post-processing routine to transform this into a list of bounding boxes (y1, x1, y2, x2) where x, y are fractional coordinates measured from left and top respectively. A respective list of classes and scores is also returned to tag each bounding box. Below we make use of this information to draw the bounding boxes on top the original image. Note that in the post-processing routine we select a confidence threshold of 0.5."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import cv2\n",
-        "from matplotlib import pyplot as plt\n",
-        "\n",
-        "colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
-        "                  (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
-        "                  (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
-        "                  (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
-        "                  (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
-        "\n",
-        "\n",
-        "def draw_boxes_on_img(img, classes, scores, bboxes, thickness=2):\n",
-        "    shape = img.shape\n",
-        "    for i in range(bboxes.shape[0]):\n",
-        "        bbox = bboxes[i]\n",
-        "        color = colors_tableau[classes[i]]\n",
-        "        # Draw bounding box...\n",
-        "        p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))\n",
-        "        p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))\n",
-        "        cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)\n",
-        "        # Draw text...\n",
-        "        s = '%s/%.3f' % (classes[i], scores[i])\n",
-        "        p1 = (p1[0]-5, p1[1])\n",
-        "        cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.accel._external.ssdvgg_utils as ssdvgg_utils\n",
-        "\n",
-        "result = client.score_file(path=\"meeting.jpg\", input_name=input_tensors, outputs=output_tensors)\n",
-        "classes, scores, bboxes = ssdvgg_utils.postprocess(result, select_threshold=0.5)\n",
-        "\n",
-        "img = cv2.imread('meeting.jpg', 1)\n",
-        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
-        "draw_boxes_on_img(img, classes, scores, bboxes)\n",
-        "plt.imshow(img)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"cleanup\"></a>\n",
-        "## 6. Cleanup\n",
-        "It's important to clean up your resources, so that you won't incur unnecessary costs. In the [next notebook](./accelerated-models-training.ipynb) you will learn how to train a classfier on a new dataset using transfer learning."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.delete()\n",
-        "aks_target.delete()\n",
-        "image.delete()\n",
-        "registered_model.delete()\n",
-        "converted_model.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      },
-      {
-        "name": "paledger"
-      },
-      {
-        "name": "sukha"
-      }
-    ],
-    "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": "3.5.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml

@@ -1,7 +0,0 @@
-name: accelerated-models-object-detection
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-accel-models[cpu]
-  - opencv-python
-  - matplotlib

how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb

@@ -1,555 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure ML Hardware Accelerated Models Quickstart"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This tutorial will show you how to deploy an image recognition service based on the ResNet 50 classifier using the Azure Machine Learning Accelerated Models service.  Get more information about our service from our [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas), [API reference](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py), or [forum](https://aka.ms/aml-forum).\n",
-        "\n",
-        "We will use an accelerated ResNet50 featurizer running on an FPGA. Our Accelerated Models Service handles translating deep neural networks (DNN) into an FPGA program.\n",
-        "\n",
-        "For more information about using other models besides Resnet50, see the [README](./README.md).\n",
-        "\n",
-        "The steps covered in this notebook are: \n",
-        "1. [Set up environment](#set-up-environment)\n",
-        "* [Construct model](#construct-model)\n",
-        "    * Image Preprocessing\n",
-        "    * Featurizer (Resnet50)\n",
-        "    * Classifier\n",
-        "    * Save Model\n",
-        "* [Register Model](#register-model)\n",
-        "* [Convert into Accelerated Model](#convert-model)\n",
-        "* [Create Image](#create-image)\n",
-        "* [Deploy](#deploy-image)\n",
-        "* [Test service](#test-service)\n",
-        "* [Clean-up](#clean-up)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"set-up-environment\"></a>\n",
-        "## 1. Set up environment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve Workspace\n",
-        "If you haven't created a Workspace, please follow [this notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) to do so. If you have, run the codeblock below to retrieve it. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"construct-model\"></a>\n",
-        "## 2. Construct model\n",
-        "\n",
-        "There are three parts to the model we are deploying: pre-processing, featurizer with ResNet50, and classifier with ImageNet dataset. Then we will save this complete Tensorflow model graph locally before registering it to your Azure ML Workspace.\n",
-        "\n",
-        "### 2.a. Image preprocessing\n",
-        "We'd like our service to accept JPEG images as input. However the input to ResNet50 is a tensor. So we need code that decodes JPEG images and does the preprocessing required by ResNet50. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as strings) and produces a tensor that is ready to be featurized by ResNet50.\n",
-        "\n",
-        "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
-        "import azureml.accel.models.utils as utils\n",
-        "tf.reset_default_graph()\n",
-        "\n",
-        "in_images = tf.placeholder(tf.string)\n",
-        "image_tensors = utils.preprocess_array(in_images)\n",
-        "print(image_tensors.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.b. Featurizer\n",
-        "We use ResNet50 as a featurizer. In this step we initialize the model. This downloads a TensorFlow checkpoint of the quantized ResNet50."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.accel.models import QuantizedResnet50\n",
-        "save_path = os.path.expanduser('~/models')\n",
-        "model_graph = QuantizedResnet50(save_path, is_frozen = True)\n",
-        "feature_tensor = model_graph.import_graph_def(image_tensors)\n",
-        "print(model_graph.version)\n",
-        "print(feature_tensor.name)\n",
-        "print(feature_tensor.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.c. Classifier\n",
-        "The model we downloaded includes a classifier which takes the output of the ResNet50 and identifies an image. This classifier is trained on the ImageNet dataset. We are going to use this classifier for our service. The next [notebook](./accelerated-models-training.ipynb) shows how to train a classifier for a different data set. The input to the classifier is a tensor matching the output of our ResNet50 featurizer."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "classifier_output = model_graph.get_default_classifier(feature_tensor)\n",
-        "print(classifier_output)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.d. Save Model\n",
-        "Now that we loaded all three parts of the tensorflow graph (preprocessor, resnet50 featurizer, and the classifier), we can save the graph and associated variables to a directory which we can register as an Azure ML Model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# model_name must be lowercase\n",
-        "model_name = \"resnet50\"\n",
-        "model_save_path = os.path.join(save_path, model_name)\n",
-        "print(\"Saving model in {}\".format(model_save_path))\n",
-        "\n",
-        "with tf.Session() as sess:\n",
-        "    model_graph.restore_weights(sess)\n",
-        "    tf.saved_model.simple_save(sess, model_save_path,\n",
-        "                                   inputs={'images': in_images},\n",
-        "                                   outputs={'output_alias': classifier_output})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.e. Important! Save names of input and output tensors\n",
-        "\n",
-        "These input and output tensors that were created during the preprocessing and classifier steps are also going to be used when **converting the model** to an Accelerated Model that can run on FPGA's and for **making an inferencing request**. It is very important to save this information! You can see our defaults for all the models in the [README](./README.md).\n",
-        "\n",
-        "By default for Resnet50, these are the values you should see when running the cell below: \n",
-        "* input_tensors = \"Placeholder:0\"\n",
-        "* output_tensors = \"classifier/resnet_v1_50/predictions/Softmax:0\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "input_tensors = in_images.name\n",
-        "output_tensors = classifier_output.name\n",
-        "\n",
-        "print(input_tensors)\n",
-        "print(output_tensors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"register-model\"></a>\n",
-        "## 3. Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "registered_model = Model.register(workspace = ws,\n",
-        "                                  model_path = model_save_path,\n",
-        "                                  model_name = model_name)\n",
-        "\n",
-        "print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, sep = '\\t')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"convert-model\"></a>\n",
-        "## 4. Convert Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For conversion you need to provide names of input and output tensors. This information can be found from the model_graph you saved in step 2.e. above.\n",
-        "\n",
-        "**Note**: Conversion may take a while and on average for FPGA model it is about 1-3 minutes and it depends on model type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.accel import AccelOnnxConverter\n",
-        "\n",
-        "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "\n",
-        "if convert_request.wait_for_completion(show_output = False):\n",
-        "    # If the above call succeeded, get the converted model\n",
-        "    converted_model = convert_request.result\n",
-        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "          converted_model.id, converted_model.created_time, '\\n')\n",
-        "else:\n",
-        "    print(\"Model conversion failed. Showing output.\")\n",
-        "    convert_request.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"create-image\"></a>\n",
-        "## 5. Package the model into an Image"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to image. Also, for FPGA model an image can only contain **single** model.\n",
-        "\n",
-        "**Note**: The following command can take few minutes. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import Image\n",
-        "from azureml.accel import AccelContainerImage\n",
-        "\n",
-        "image_config = AccelContainerImage.image_configuration()\n",
-        "# Image name must be lowercase\n",
-        "image_name = \"{}-image\".format(model_name)\n",
-        "\n",
-        "image = Image.create(name = image_name,\n",
-        "                     models = [converted_model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "image.wait_for_creation(show_output = False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"deploy-image\"></a>\n",
-        "## 6. Deploy\n",
-        "Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
-        "\n",
-        "### 6.a. Databox Edge Machine using IoT Hub\n",
-        "See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
-        "\n",
-        "### 6.b. Azure Kubernetes Service (AKS) using Azure ML Service\n",
-        "We are going to create an AKS cluster with FPGA-enabled machines, then deploy our service to it. For more information, see [AKS official docs](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#aks).\n",
-        "\n",
-        "#### Create AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-akscompute-provision"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
-        "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
-        "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
-        "                                                    agent_count = 1, \n",
-        "                                                    location = \"eastus\")\n",
-        "\n",
-        "aks_name = 'my-aks-pb6'\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can also check the status in your Workspace under Compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy AccelContainerImage to AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "\n",
-        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
-        "# Authentication is enabled by default, but for testing we specify False\n",
-        "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
-        "                                                num_replicas=1,\n",
-        "                                                auth_enabled = False)\n",
-        "\n",
-        "aks_service_name ='my-aks-service-1'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-service\"></a>\n",
-        "## 7. Test the service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice, see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
-        "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel import client_from_service\n",
-        "\n",
-        "# Initialize AzureML Accelerated Models client\n",
-        "client = client_from_service(aks_service)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can adapt the client [code](https://github.com/Azure/aml-real-time-ai/blob/master/pythonlib/amlrealtimeai/client.py) to meet your needs. There is also an example C# [client](https://github.com/Azure/aml-real-time-ai/blob/master/sample-clients/csharp).\n",
-        "\n",
-        "The service provides an API that is compatible with TensorFlow Serving. There are instructions to download a sample client [here](https://www.tensorflow.org/serving/setup)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.b. Serve the model\n",
-        "To understand the results we need a mapping to the human readable imagenet classes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import requests\n",
-        "classes_entries = requests.get(\"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\").text.splitlines()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Score image with input and output tensor names\n",
-        "results = client.score_file(path=\"./snowleopardgaze.jpg\", \n",
-        "                             input_name=input_tensors, \n",
-        "                             outputs=output_tensors)\n",
-        "\n",
-        "# map results [class_id] => [confidence]\n",
-        "results = enumerate(results)\n",
-        "# sort results by confidence\n",
-        "sorted_results = sorted(results, key=lambda x: x[1], reverse=True)\n",
-        "# print top 5 results\n",
-        "for top in sorted_results[:5]:\n",
-        "    print(classes_entries[top[0]], 'confidence:', top[1])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"clean-up\"></a>\n",
-        "## 8. Clean-up\n",
-        "Run the cell below to delete your webservice, image, and model (must be done in that order). In the [next notebook](./accelerated-models-training.ipynb) you will learn how to train a classfier on a new dataset using transfer learning and finetune the weights."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.delete()\n",
-        "aks_target.delete()\n",
-        "image.delete()\n",
-        "registered_model.delete()\n",
-        "converted_model.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      },
-      {
-        "name": "paledger"
-      },
-      {
-        "name": "aibhalla"
-      }
-    ],
-    "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": "3.7.3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml

@@ -1,5 +0,0 @@
-name: accelerated-models-quickstart
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-accel-models[cpu]

how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb

@@ -1,870 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Training with the Azure Machine Learning Accelerated Models Service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This notebook will introduce how to apply common machine learning techniques, like transfer learning, custom weights, and unquantized vs. quantized models, when working with our Azure Machine Learning Accelerated Models Service (Azure ML Accel Models).\n",
-        "\n",
-        "We will use Tensorflow for the preprocessing steps, ResNet50 for the featurizer, and the Keras API (built on Tensorflow backend) to build the classifier layers instead of the default ImageNet classifier used in Quickstart. Then we will train the model, evaluate it, and deploy it to run on an FPGA.\n",
-        "\n",
-        "#### Transfer Learning and Custom weights\n",
-        "We will walk you through two ways to build and train a ResNet50 model on the Kaggle Cats and Dogs dataset: transfer learning only and then transfer learning with custom weights.\n",
-        "\n",
-        "In using transfer learning, our goal is to re-purpose the ResNet50 model already trained on the [ImageNet image dataset](http://www.image-net.org/) as a basis for our training of the Kaggle Cats and Dogs dataset. The ResNet50 featurizer will be imported as frozen, so only the Keras classifier will be trained.\n",
-        "\n",
-        "With the addition of custom weights, we will build the model so that the ResNet50 featurizer weights as not frozen. This will let us retrain starting with custom weights trained with ImageNet on ResNet50 and then use the Kaggle Cats and Dogs dataset to retrain and fine-tune the quantized version of the model.\n",
-        "\n",
-        "#### Unquantized vs. Quantized models\n",
-        "The unquantized version of our models (ie. Resnet50, Resnet152, Densenet121, Vgg16, SsdVgg) uses native float precision (32-bit floats), which will be faster at training. We will use this for our first run through, then fine-tune the weights with the quantized version. The quantized version of our models (i.e. QuantizedResnet50, QuantizedResnet152, QuantizedDensenet121, QuantizedVgg16, QuantizedSsdVgg) will have the same node names as the unquantized version, but use quantized operations and will match the performance of the model when running on an FPGA.\n",
-        "\n",
-        "#### Contents\n",
-        "1. [Setup Environment](#setup)\n",
-        "* [Prepare Data](#prepare-data)\n",
-        "* [Construct Model](#construct-model)\n",
-        "    * Preprocessor\n",
-        "    * Classifier\n",
-        "    * Model construction\n",
-        "* [Train Model](#train-model)\n",
-        "* [Test Model](#test-model)\n",
-        "* [Execution](#execution)\n",
-        "    * [Transfer Learning](#transfer-learning)\n",
-        "    * [Transfer Learning with Custom Weights](#custom-weights)\n",
-        "* [Create Image](#create-image)\n",
-        "* [Deploy Image](#deploy-image)\n",
-        "* [Test the service](#test-service)\n",
-        "* [Clean-up](#cleanup)\n",
-        "* [Appendix](#appendix)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"setup\"></a>\n",
-        "## 1. Setup Environment\n",
-        "#### 1.a. Please set up your environment as described in the [Quickstart](./accelerated-models-quickstart.ipynb), meaning:\n",
-        "* Make sure your Workspace config.json exists and has the correct info\n",
-        "* Install Tensorflow\n",
-        "\n",
-        "#### 1.b. Download dataset into ~/catsanddogs \n",
-        "The dataset we will be using for training can be downloaded [here](https://www.microsoft.com/en-us/download/details.aspx?id=54765). Download the zip and extract to a directory named 'catsanddogs' under your user directory (\"~/catsanddogs\"). \n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### 1.c. Import packages"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import sys\n",
-        "import tensorflow as tf\n",
-        "import numpy as np\n",
-        "from keras import backend as K\n",
-        "import sklearn\n",
-        "import tqdm"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### 1.d. Create directories for later use\n",
-        "After you train your model in float32, you'll write the weights to a place on disk. We also need a location to store the models that get downloaded."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "custom_weights_dir = os.path.expanduser(\"~/custom-weights\")\n",
-        "saved_model_dir = os.path.expanduser(\"~/models\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"prepare-data\"></a>\n",
-        "## 2. Prepare Data\n",
-        "Load the files we are going to use for training and testing. By default this notebook uses only a very small subset of the Cats and Dogs dataset. That makes it run relatively quickly."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import glob\n",
-        "import imghdr\n",
-        "datadir = os.path.expanduser(\"~/catsanddogs\")\n",
-        "\n",
-        "cat_files = glob.glob(os.path.join(datadir, 'PetImages', 'Cat', '*.jpg'))\n",
-        "dog_files = glob.glob(os.path.join(datadir, 'PetImages', 'Dog', '*.jpg'))\n",
-        "\n",
-        "# Limit the data set to make the notebook execute quickly.\n",
-        "cat_files = cat_files[:64]\n",
-        "dog_files = dog_files[:64]\n",
-        "\n",
-        "# The data set has a few images that are not jpeg. Remove them.\n",
-        "cat_files = [f for f in cat_files if imghdr.what(f) == 'jpeg']\n",
-        "dog_files = [f for f in dog_files if imghdr.what(f) == 'jpeg']\n",
-        "\n",
-        "if(not len(cat_files) or not len(dog_files)):\n",
-        "    print(\"Please download the Kaggle Cats and Dogs dataset form https://www.microsoft.com/en-us/download/details.aspx?id=54765 and extract the zip to \" + datadir)    \n",
-        "    raise ValueError(\"Data not found\")\n",
-        "else:\n",
-        "    print(cat_files[0])\n",
-        "    print(dog_files[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Construct a numpy array as labels\n",
-        "image_paths = cat_files + dog_files\n",
-        "total_files = len(cat_files) + len(dog_files)\n",
-        "labels = np.zeros(total_files)\n",
-        "labels[len(cat_files):] = 1"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split images data as training data and test data\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "onehot_labels = np.array([[0,1] if i else [1,0] for i in labels])\n",
-        "img_train, img_test, label_train, label_test = train_test_split(image_paths, onehot_labels, random_state=42, shuffle=True)\n",
-        "\n",
-        "print(len(img_train), len(img_test), label_train.shape, label_test.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"construct-model\"></a>\n",
-        "## 3. Construct Model\n",
-        "We will define the functions to handle creating the preprocessor and the classifier first, and then run them together to actually construct the model with the Resnet50 featurizer in a single Tensorflow session in a separate cell.\n",
-        "\n",
-        "We use ResNet50 for the featurizer and build our own classifier using Keras layers. We train the featurizer and the classifier as one model. We will provide parameters to determine whether we are using the quantized version and whether we are using custom weights in training or not."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3.a. Define image preprocessing step\n",
-        "Same as in the Quickstart, before passing image dataset to the ResNet50 featurizer, we need to preprocess the input file to get it into the form expected by ResNet50. ResNet50 expects float tensors representing the images in BGR, channel last order. We've provided a default implementation of the preprocessing that you can use.\n",
-        "\n",
-        "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.accel.models.utils as utils\n",
-        "\n",
-        "def preprocess_images(scaling_factor=1.0):\n",
-        "    # Convert images to 3D tensors [width,height,channel] - channels are in BGR order.\n",
-        "    in_images = tf.placeholder(tf.string)\n",
-        "    image_tensors = utils.preprocess_array(in_images, 'RGB', scaling_factor)\n",
-        "    return in_images, image_tensors"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3.b. Define classifier\n",
-        "We use Keras layer APIs to construct the classifier. Because we're using the tensorflow backend, we can train this classifier in one session with our Resnet50 model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def construct_classifier(in_tensor, seed=None):\n",
-        "    from keras.layers import Dropout, Dense, Flatten\n",
-        "    from keras.initializers import glorot_uniform\n",
-        "    K.set_session(tf.get_default_session())\n",
-        "\n",
-        "    FC_SIZE = 1024\n",
-        "    NUM_CLASSES = 2\n",
-        "\n",
-        "    x = Dropout(0.2, input_shape=(1, 1, int(in_tensor.shape[3]),), seed=seed)(in_tensor)\n",
-        "    x = Dense(FC_SIZE, activation='relu', input_dim=(1, 1, int(in_tensor.shape[3]),),\n",
-        "              kernel_initializer=glorot_uniform(seed=seed), bias_initializer='zeros')(x)\n",
-        "    x = Flatten()(x)\n",
-        "    preds = Dense(NUM_CLASSES, activation='softmax', input_dim=FC_SIZE, name='classifier_output',\n",
-        "                  kernel_initializer=glorot_uniform(seed=seed), bias_initializer='zeros')(x)\n",
-        "    return preds"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3.c. Define model construction\n",
-        "Now that the preprocessor and classifier for the model are defined, we can define how we want to construct the model. \n",
-        "\n",
-        "Constructing the model has these steps: \n",
-        "1. Get preprocessing steps\n",
-        "* Get featurizer using the Azure ML Accel Models SDK:\n",
-        "    * import the graph definition\n",
-        "    * restore the weights of the model into a Tensorflow session\n",
-        "* Get classifier\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def construct_model(quantized, starting_weights_directory = None):\n",
-        "    from azureml.accel.models import Resnet50, QuantizedResnet50\n",
-        "    \n",
-        "    # Convert images to 3D tensors [width,height,channel]\n",
-        "    in_images, image_tensors = preprocess_images(1.0)\n",
-        "\n",
-        "    # Construct featurizer using quantized or unquantized ResNet50 model\n",
-        "    if not quantized:\n",
-        "        featurizer = Resnet50(saved_model_dir)\n",
-        "    else:\n",
-        "        featurizer = QuantizedResnet50(saved_model_dir, custom_weights_directory = starting_weights_directory)\n",
-        "\n",
-        "    features = featurizer.import_graph_def(input_tensor=image_tensors)\n",
-        "    \n",
-        "    # Construct classifier\n",
-        "    preds = construct_classifier(features)\n",
-        "    \n",
-        "    # Initialize weights\n",
-        "    sess = tf.get_default_session()\n",
-        "    tf.global_variables_initializer().run()\n",
-        "\n",
-        "    featurizer.restore_weights(sess)\n",
-        "\n",
-        "    return in_images, image_tensors, features, preds, featurizer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"train-model\"></a>\n",
-        "## 4. Train Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def read_files(files):\n",
-        "    \"\"\" Read files to array\"\"\"\n",
-        "    contents = []\n",
-        "    for path in files:\n",
-        "        with open(path, 'rb') as f:\n",
-        "            contents.append(f.read())\n",
-        "    return contents"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def train_model(preds, in_images, img_train, label_train, is_retrain = False, train_epoch = 10, learning_rate=None):\n",
-        "    \"\"\" training model \"\"\"\n",
-        "    from keras.objectives import binary_crossentropy\n",
-        "    from tqdm import tqdm\n",
-        "    \n",
-        "    learning_rate = learning_rate if learning_rate else 0.001 if is_retrain else 0.01\n",
-        "        \n",
-        "    # Specify the loss function\n",
-        "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))   \n",
-        "    cross_entropy = tf.reduce_mean(binary_crossentropy(in_labels, preds))\n",
-        "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n",
-        "\n",
-        "    def chunks(a, b, n):\n",
-        "        \"\"\"Yield successive n-sized chunks from a and b.\"\"\"\n",
-        "        if (len(a) != len(b)):\n",
-        "            print(\"a and b are not equal in chunks(a,b,n)\")\n",
-        "            raise ValueError(\"Parameter error\")\n",
-        "\n",
-        "        for i in range(0, len(a), n):\n",
-        "            yield a[i:i + n], b[i:i + n]\n",
-        "\n",
-        "    chunk_size = 16\n",
-        "    chunk_num = len(label_train) / chunk_size\n",
-        "\n",
-        "    sess = tf.get_default_session()\n",
-        "    for epoch in range(train_epoch):\n",
-        "        avg_loss = 0\n",
-        "        for img_chunk, label_chunk in tqdm(chunks(img_train, label_train, chunk_size)):\n",
-        "            contents = read_files(img_chunk)\n",
-        "            _, loss = sess.run([optimizer, cross_entropy],\n",
-        "                                feed_dict={in_images: contents,\n",
-        "                                           in_labels: label_chunk,\n",
-        "                                           K.learning_phase(): 1})\n",
-        "            avg_loss += loss / chunk_num\n",
-        "        print(\"Epoch:\", (epoch + 1), \"loss = \", \"{:.3f}\".format(avg_loss))\n",
-        "            \n",
-        "        # Reach desired performance\n",
-        "        if (avg_loss < 0.001):\n",
-        "            break"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-model\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-model\"></a>\n",
-        "## 5. Test Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def test_model(preds, in_images, img_test, label_test):\n",
-        "    \"\"\"Test the model\"\"\"\n",
-        "    from keras.metrics import categorical_accuracy\n",
-        "\n",
-        "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))\n",
-        "    accuracy = tf.reduce_mean(categorical_accuracy(in_labels, preds))\n",
-        "    contents = read_files(img_test)\n",
-        "\n",
-        "    accuracy = accuracy.eval(feed_dict={in_images: contents,\n",
-        "                                        in_labels: label_test,\n",
-        "                                        K.learning_phase(): 0})\n",
-        "    return accuracy"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"execution\"></a>\n",
-        "## 6. Execute steps\n",
-        "You can run through the Transfer Learning section, then skip to Create AccelContainerImage. By default, because the custom weights section takes much longer for training twice, it is not saved as executable cells. You can copy the code or change cell type to 'Code'.\n",
-        "\n",
-        "<a id=\"transfer-learning\"></a>\n",
-        "### 6.a. Training using Transfer Learning"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "# Launch the training\n",
-        "tf.reset_default_graph()\n",
-        "sess = tf.Session(graph=tf.get_default_graph())\n",
-        "\n",
-        "with sess.as_default():\n",
-        "    in_images, image_tensors, features, preds, featurizer = construct_model(quantized=True)\n",
-        "    train_model(preds, in_images, img_train, label_train, is_retrain=False, train_epoch=10, learning_rate=0.01)    \n",
-        "    accuracy = test_model(preds, in_images, img_test, label_test)  \n",
-        "    print(\"Accuracy:\", accuracy)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Save Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_name = 'resnet50-catsanddogs-tl'\n",
-        "model_save_path = os.path.join(saved_model_dir, model_name)\n",
-        "\n",
-        "tf.saved_model.simple_save(sess, model_save_path,\n",
-        "                               inputs={'images': in_images},\n",
-        "                               outputs={'output_alias': preds})\n",
-        "\n",
-        "input_tensors = in_images.name\n",
-        "output_tensors = preds.name\n",
-        "\n",
-        "print(input_tensors)\n",
-        "print(output_tensors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"custom-weights\"></a>\n",
-        "### 6.b. Traning using Custom Weights\n",
-        "\n",
-        "Because the quantized graph defintion and the float32 graph defintion share the same node names in the graph definitions, we can initally train the weights in float32, and then reload them with the quantized operations (which take longer) to fine-tune the model.\n",
-        "\n",
-        "First we train the model with custom weights but without quantization. Training is done with native float precision (32-bit floats). We load the training data set and batch the training with 10 epochs. When the performance reaches desired level or starts decredation, we stop the training iteration and save the weights as tensorflow checkpoint files. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Launch the training\n",
-        "```\n",
-        "tf.reset_default_graph()\n",
-        "sess = tf.Session(graph=tf.get_default_graph())\n",
-        "\n",
-        "with sess.as_default():\n",
-        "    in_images, image_tensors, features, preds, featurizer = construct_model(quantized=False)\n",
-        "    train_model(preds, in_images, img_train, label_train, is_retrain=False, train_epoch=10)    \n",
-        "    accuracy = test_model(preds, in_images, img_test, label_test)  \n",
-        "    print(\"Accuracy:\", accuracy)\n",
-        "    featurizer.save_weights(custom_weights_dir + \"/rn50\", tf.get_default_session())\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Test Model\n",
-        "After training, we evaluate the trained model's accuracy on test dataset with quantization. So that we know the model's performance if it is deployed on the FPGA."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```\n",
-        "tf.reset_default_graph()\n",
-        "sess = tf.Session(graph=tf.get_default_graph())\n",
-        "\n",
-        "with sess.as_default():\n",
-        "    print(\"Testing trained model with quantization\")\n",
-        "    in_images, image_tensors, features, preds, quantized_featurizer = construct_model(quantized=True, starting_weights_directory=custom_weights_dir)\n",
-        "    accuracy = test_model(preds, in_images, img_test, label_test)      \n",
-        "    print(\"Accuracy:\", accuracy)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Fine-Tune Model\n",
-        "Sometimes, the model's accuracy can drop significantly after quantization. In those cases, we need to retrain the model enabled with quantization to get better model accuracy."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```\n",
-        "if (accuracy < 0.93):\n",
-        "    with sess.as_default():\n",
-        "        print(\"Fine-tuning model with quantization\")\n",
-        "        train_model(preds, in_images, img_train, label_train, is_retrain=True, train_epoch=10)\n",
-        "        accuracy = test_model(preds, in_images, img_test, label_test)        \n",
-        "        print(\"Accuracy:\", accuracy)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Save Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```\n",
-        "model_name = 'resnet50-catsanddogs-cw'\n",
-        "model_save_path = os.path.join(saved_model_dir, model_name)\n",
-        "\n",
-        "tf.saved_model.simple_save(sess, model_save_path,\n",
-        "                               inputs={'images': in_images},\n",
-        "                               outputs={'output_alias': preds})\n",
-        "\n",
-        "input_tensors = in_images.name\n",
-        "output_tensors = preds.name\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"create-image\"></a>\n",
-        "## 7. Create AccelContainerImage\n",
-        "\n",
-        "Below we will execute all the same steps as in the [Quickstart](./accelerated-models-quickstart.ipynb#create-image) to package the model we have saved locally into an accelerated Docker image saved in our workspace. To complete all the steps, it may take a few minutes. For more details on each step, check out the [Quickstart section on model registration](./accelerated-models-quickstart.ipynb#register-model)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.core.image import Image\n",
-        "from azureml.accel import AccelOnnxConverter\n",
-        "from azureml.accel import AccelContainerImage\n",
-        "\n",
-        "# Retrieve workspace\n",
-        "ws = Workspace.from_config()\n",
-        "print(\"Successfully retrieved workspace:\", ws.name, ws.resource_group, ws.location, ws.subscription_id, '\\n')\n",
-        "\n",
-        "# Register model\n",
-        "registered_model = Model.register(workspace = ws,\n",
-        "                                  model_path = model_save_path,\n",
-        "                                  model_name = model_name)\n",
-        "print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
-        "\n",
-        "# Convert model\n",
-        "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "if convert_request.wait_for_completion(show_output = False):\n",
-        "    # If the above call succeeded, get the converted model\n",
-        "    converted_model = convert_request.result\n",
-        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "          converted_model.id, converted_model.created_time, '\\n')\n",
-        "else:\n",
-        "    print(\"Model conversion failed. Showing output.\")\n",
-        "    convert_request.wait_for_completion(show_output = True)\n",
-        "\n",
-        "# Package into AccelContainerImage\n",
-        "image_config = AccelContainerImage.image_configuration()\n",
-        "# Image name must be lowercase\n",
-        "image_name = \"{}-image\".format(model_name)\n",
-        "image = Image.create(name = image_name,\n",
-        "                     models = [converted_model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "image.wait_for_creation()\n",
-        "print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"deploy-image\"></a>\n",
-        "## 8. Deploy image\n",
-        "Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
-        "\n",
-        "### 8.a. Deploy to Databox Edge Machine using IoT Hub\n",
-        "See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
-        "\n",
-        "### 8.b. Deploy to AKS Cluster"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
-        "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
-        "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
-        "                                                    agent_count = 1,\n",
-        "                                                    location = \"eastus\")\n",
-        "\n",
-        "aks_name = 'aks-pb6-tl'\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy AccelContainerImage to AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-akswebservice-deploy-from-image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "\n",
-        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
-        "# Authentication is enabled by default, but for testing we specify False\n",
-        "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
-        "                                                num_replicas=1,\n",
-        "                                                auth_enabled = False)\n",
-        "\n",
-        "aks_service_name ='my-aks-service-2'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-service\"></a>\n",
-        "## 9. Test the service\n",
-        "\n",
-        "<a id=\"create-client\"></a>\n",
-        "### 9.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
-        "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel import client_from_service\n",
-        "\n",
-        "# Initialize AzureML Accelerated Models client\n",
-        "client = client_from_service(aks_service)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"serve-model\"></a>\n",
-        "### 9.b. Serve the model\n",
-        "Let's see how our service does on a few images. It may get a few wrong."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Specify an image to classify\n",
-        "print('CATS')\n",
-        "for image_file in cat_files[:8]:\n",
-        "    results = client.score_file(path=image_file, \n",
-        "                                 input_name=input_tensors, \n",
-        "                                 outputs=output_tensors)\n",
-        "    result = 'CORRECT ' if results[0] > results[1] else 'WRONG '\n",
-        "    print(result + str(results))\n",
-        "print('DOGS')\n",
-        "for image_file in dog_files[:8]:\n",
-        "    results = client.score_file(path=image_file, \n",
-        "                                 input_name=input_tensors, \n",
-        "                                 outputs=output_tensors)\n",
-        "    result = 'CORRECT ' if results[1] > results[0] else 'WRONG '\n",
-        "    print(result + str(results))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"cleanup\"></a>\n",
-        "## 10. Cleanup\n",
-        "It's important to clean up your resources, so that you won't incur unnecessary costs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.delete()\n",
-        "aks_target.delete()\n",
-        "image.delete()\n",
-        "registered_model.delete()\n",
-        "converted_model.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"appendix\"></a>\n",
-        "## 11. Appendix"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "License for plot_confusion_matrix:\n",
-        "\n",
-        "New BSD License\n",
-        "\n",
-        "Copyright (c) 2007-2018 The scikit-learn developers.\n",
-        "All rights reserved.\n",
-        "\n",
-        "\n",
-        "Redistribution and use in source and binary forms, with or without\n",
-        "modification, are permitted provided that the following conditions are met:\n",
-        "\n",
-        "  a. Redistributions of source code must retain the above copyright notice,\n",
-        "     this list of conditions and the following disclaimer.\n",
-        "  b. Redistributions in binary form must reproduce the above copyright\n",
-        "     notice, this list of conditions and the following disclaimer in the\n",
-        "     documentation and/or other materials provided with the distribution.\n",
-        "  c. Neither the name of the Scikit-learn Developers  nor the names of\n",
-        "     its contributors may be used to endorse or promote products\n",
-        "     derived from this software without specific prior written\n",
-        "     permission. \n",
-        "\n",
-        "\n",
-        "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n",
-        "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n",
-        "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n",
-        "ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR\n",
-        "ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n",
-        "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n",
-        "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n",
-        "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n",
-        "LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\n",
-        "OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\n",
-        "DAMAGE.\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      },
-      {
-        "name": "paledger"
-      }
-    ],
-    "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": "3.5.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml

@@ -1,8 +0,0 @@
-name: accelerated-models-training
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-accel-models[cpu]
-  - keras
-  - tqdm 
-  - sklearn

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

@@ -86,7 +86,37 @@
       "source": [
         "In this example, we will be using and registering two models. \n",
         "\n",
-        "You wil need to have a `first_model.pkl` file and `second_model.pkl` file in the current directory. The below call registers the files as Models with the names `my_first_model` and `my_second_model` in the workspace."
+        "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import sklearn\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import BayesianRidge, Ridge\n",
+        "\n",
+        "x, y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "first_model = Ridge().fit(x, y)\n",
+        "second_model = BayesianRidge().fit(x, y)\n",
+        "\n",
+        "joblib.dump(first_model, \"first_model.pkl\")\n",
+        "joblib.dump(second_model, \"second_model.pkl\")\n",
+        "\n",
+        "print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace."
       ]
     },
     {
@@ -102,12 +132,12 @@
         "from azureml.core.model import Model\n",
         "\n",
         "my_model_1 = Model.register(model_path=\"first_model.pkl\",\n",
-        "                       model_name=\"my_first_model\",\n",
+        "                            model_name=\"my_first_model\",\n",
-        "                       workspace=ws)\n",
+        "                            workspace=ws)\n",
         "\n",
         "my_model_2 = Model.register(model_path=\"second_model.pkl\",\n",
-        "                       model_name=\"my_second_model\",\n",
+        "                            model_name=\"my_second_model\",\n",
-        "                       workspace=ws)"
+        "                            workspace=ws)"
       ]
     },
     {
@@ -149,25 +179,24 @@
       "outputs": [],
       "source": [
         "%%writefile score.py\n",
-        "import pickle\n",
+        "import joblib\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "\n",
-        "from sklearn.linear_model import Ridge\n",
         "from azureml.core.model import Model\n",
         "\n",
         "def init():\n",
         "    global model_1, model_2\n",
-        "    # note here \"my_first_model\" is the name of the model registered under the workspace\n",
+        "    # Here \"my_first_model\" is the name of the model registered under the workspace.\n",
-        "    # this call should return the path to the model.pkl file on the local disk.\n",
+        "    # This call will return the path to the .pkl file on the local disk.\n",
         "    model_1_path = Model.get_model_path(model_name='my_first_model')\n",
         "    model_2_path = Model.get_model_path(model_name='my_second_model')\n",
         "    \n",
-        "    # deserialize the model files back into a sklearn model\n",
+        "    # Deserialize the model files back into scikit-learn models.\n",
         "    model_1 = joblib.load(model_1_path)\n",
         "    model_2 = joblib.load(model_2_path)\n",
         "\n",
-        "# note you can pass in multiple rows for scoring\n",
+        "# Note you can pass in multiple rows for scoring.\n",
         "def run(raw_data):\n",
         "    try:\n",
         "        data = json.loads(raw_data)['data']\n",
@@ -177,7 +206,7 @@
         "        result_1 = model_1.predict(data)\n",
         "        result_2 = model_2.predict(data)\n",
         "\n",
-        "        # you can return any data type as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable value.\n",
         "        return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n",
         "    except Exception as e:\n",
         "        result = str(e)\n",
@@ -208,10 +237,10 @@
       "source": [
         "from azureml.core import Environment\n",
         "\n",
-        "env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
+        "env = Environment(\"deploytocloudenv\")\n",
-        "\n",
+        "env.python.conda_dependencies.add_pip_package(\"joblib\")\n",
-        "# This is optional at this point\n",
+        "env.python.conda_dependencies.add_pip_package(\"numpy\")\n",
-        "# env.register(workspace=ws)"
+        "env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
       ]
     },
     {
@@ -281,25 +310,15 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.exceptions import WebserviceException\n",
+        "\n",
+        "aci_service_name = \"aciservice-multimodel\"\n",
         "\n",
         "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
-        "aci_service_name = 'aciservice1'\n",
-        "\n",
-        "try:\n",
-        "    # if you want to get existing service below is the command\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
-        "    # we use aci_service_name to create azure aci\n",
-        "    service = Webservice(ws, name=aci_service_name)\n",
-        "    if service:\n",
-        "        service.delete()\n",
-        "except WebserviceException as e:\n",
-        "    print()\n",
-        "\n",
-        "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config)\n",
         "\n",
+        "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n",
         "service.wait_for_deployment(True)\n",
+        "\n",
         "print(service.state)"
       ]
     },
@@ -317,13 +336,11 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
         "\n",
-        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "test_sample = json.dumps({'data': x[0:2].tolist()})\n",
-        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "\n",
+        "prediction = service.run(test_sample)\n",
+        "\n",
         "print(prediction)"
       ]
     },

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

@@ -2,3 +2,5 @@ name: multi-model-register-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy
+  - scikit-learn

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

@@ -1,8 +0,0 @@
-name: project_environment
-dependencies:
-  - python=3.6.2
-  - pip:
-    - azureml-defaults
-    - scikit-learn
-    - numpy
-    - inference-schema[numpy-support]

how-to-use-azureml/deployment/deploy-to-cloud/features.csv

@@ -1,442 +0,0 @@
-3.807590643342410180e-02,5.068011873981870252e-02,6.169620651868849837e-02,2.187235499495579841e-02,-4.422349842444640161e-02,-3.482076283769860309e-02,-4.340084565202689815e-02,-2.592261998182820038e-03,1.990842087631829876e-02,-1.764612515980519894e-02
--1.882016527791040067e-03,-4.464163650698899782e-02,-5.147406123880610140e-02,-2.632783471735180084e-02,-8.448724111216979540e-03,-1.916333974822199970e-02,7.441156407875940126e-02,-3.949338287409189657e-02,-6.832974362442149896e-02,-9.220404962683000083e-02
-8.529890629667830071e-02,5.068011873981870252e-02,4.445121333659410312e-02,-5.670610554934250001e-03,-4.559945128264750180e-02,-3.419446591411950259e-02,-3.235593223976569732e-02,-2.592261998182820038e-03,2.863770518940129874e-03,-2.593033898947460017e-02
--8.906293935226029801e-02,-4.464163650698899782e-02,-1.159501450521270051e-02,-3.665644679856060184e-02,1.219056876180000040e-02,2.499059336410210108e-02,-3.603757004385269719e-02,3.430885887772629900e-02,2.269202256674450122e-02,-9.361911330135799444e-03
-5.383060374248070309e-03,-4.464163650698899782e-02,-3.638469220447349689e-02,2.187235499495579841e-02,3.934851612593179802e-03,1.559613951041610019e-02,8.142083605192099172e-03,-2.592261998182820038e-03,-3.199144494135589684e-02,-4.664087356364819692e-02
--9.269547780327989928e-02,-4.464163650698899782e-02,-4.069594049999709917e-02,-1.944209332987930153e-02,-6.899064987206669775e-02,-7.928784441181220555e-02,4.127682384197570165e-02,-7.639450375000099436e-02,-4.118038518800790082e-02,-9.634615654166470144e-02
--4.547247794002570037e-02,5.068011873981870252e-02,-4.716281294328249912e-02,-1.599922263614299983e-02,-4.009563984984299695e-02,-2.480001206043359885e-02,7.788079970179680352e-04,-3.949338287409189657e-02,-6.291294991625119570e-02,-3.835665973397880263e-02
-6.350367559056099842e-02,5.068011873981870252e-02,-1.894705840284650021e-03,6.662967401352719310e-02,9.061988167926439408e-02,1.089143811236970016e-01,2.286863482154040048e-02,1.770335448356720118e-02,-3.581672810154919867e-02,3.064409414368320182e-03
-4.170844488444359899e-02,5.068011873981870252e-02,6.169620651868849837e-02,-4.009931749229690007e-02,-1.395253554402150001e-02,6.201685656730160021e-03,-2.867429443567860031e-02,-2.592261998182820038e-03,-1.495647502491130078e-02,1.134862324403770016e-02
--7.090024709716259699e-02,-4.464163650698899782e-02,3.906215296718960200e-02,-3.321357610482440076e-02,-1.257658268582039982e-02,-3.450761437590899733e-02,-2.499265663159149983e-02,-2.592261998182820038e-03,6.773632611028609918e-02,-1.350401824497050006e-02
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how-to-use-azureml/deployment/deploy-to-cloud/labels.csv

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

@@ -80,9 +80,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Register input and output datasets\n",
+        "## Create trained model\n",
         "\n",
-        "For this example, we have provided a small model (`sklearn_regression_model.pkl` in the notebook's directory) that was trained on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). Here, you will register the data used to create this model in your workspace."
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
       ]
     },
     {
@@ -91,9 +91,42 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import joblib\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "\n",
+        "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "model = Ridge().fit(dataset_x, dataset_y)\n",
+        "\n",
+        "joblib.dump(model, 'sklearn_regression_model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register input and output datasets\n",
+        "\n",
+        "Here, you will register the data used to create the model in your workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
         "from azureml.core import Dataset\n",
         "\n",
         "\n",
+        "np.savetxt('features.csv', dataset_x, delimiter=',')\n",
+        "np.savetxt('labels.csv', dataset_y, delimiter=',')\n",
+        "\n",
         "datastore = ws.get_default_datastore()\n",
         "datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
         "                       target_path='sklearn_regression/',\n",
@@ -125,6 +158,8 @@
       },
       "outputs": [],
       "source": [
+        "import sklearn\n",
+        "\n",
         "from azureml.core import Model\n",
         "from azureml.core.resource_configuration import ResourceConfiguration\n",
         "\n",
@@ -133,7 +168,7 @@
         "                       model_name='my-sklearn-model',                # Name of the registered model in your workspace.\n",
         "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
         "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
-        "                       model_framework_version='0.19.1',             # Version of scikit-learn used to create the model.\n",
+        "                       model_framework_version=sklearn.__version__,  # Version of scikit-learn used to create the model.\n",
         "                       sample_input_dataset=input_dataset,\n",
         "                       sample_output_dataset=output_dataset,\n",
         "                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
@@ -174,19 +209,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "\n",
-        "\n",
         "service_name = 'my-sklearn-service'\n",
         "\n",
-        "# Remove any existing service under the same name.\n",
+        "service = Model.deploy(ws, service_name, [model], overwrite=True)\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "service = Model.deploy(ws, service_name, [model])\n",
         "service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -207,10 +232,7 @@
         "\n",
         "\n",
         "input_payload = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist(),\n",
-        "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
-        "         -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
-        "    ],\n",
         "    'method': 'predict'  # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
         "})\n",
         "\n",
@@ -262,7 +284,7 @@
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",
         "    'numpy',\n",
-        "    'scikit-learn'\n",
+        "    'scikit-learn=={}'.format(sklearn.__version__)\n",
         "])"
       ]
     },
@@ -303,20 +325,12 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "service_name = 'my-custom-env-service'\n",
         "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
         "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
         "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
         "\n",
@@ -324,7 +338,8 @@
         "                       name=service_name,\n",
         "                       models=[model],\n",
         "                       inference_config=inference_config,\n",
-        "                       deployment_config=aci_config)\n",
+        "                       deployment_config=aci_config,\n",
+        "                       overwrite=True)\n",
         "service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -341,14 +356,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
-        "\n",
-        "\n",
         "input_payload = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist()\n",
-        "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
-        "         -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
-        "    ]\n",
         "})\n",
         "\n",
         "output = service.run(input_payload)\n",
@@ -376,16 +385,121 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Model profiling\n",
+        "### Model Profiling\n",
         "\n",
-        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
+        "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
         "\n",
-        "```python\n",
+        "In order to profile your model you will need:\n",
-        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
+        "- a registered model\n",
-        "profile.wait_for_profiling(True)\n",
+        "- an entry script\n",
-        "profiling_results = profile.get_results()\n",
+        "- an inference configuration\n",
-        "print(profiling_results)\n",
+        "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
-        "```"
+        "\n",
+        "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
+        "\n",
+        "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
+        "\n",
+        "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
+        "You can try get the dataset first to see if it's already registered."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Datastore\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.data import dataset_type_definitions\n",
+        "\n",
+        "dataset_name='diabetes_sample_request_data'\n",
+        "\n",
+        "dataset_registered = False\n",
+        "try:\n",
+        "    sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
+        "    dataset_registered = True\n",
+        "except:\n",
+        "    print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
+        "\n",
+        "if not dataset_registered:\n",
+        "    # create a string that can be utf-8 encoded and\n",
+        "    # put in the body of the request\n",
+        "    serialized_input_json = json.dumps({\n",
+        "        'data': [\n",
+        "            [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
+        "            -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
+        "        ]\n",
+        "    })\n",
+        "    dataset_content = []\n",
+        "    for i in range(100):\n",
+        "        dataset_content.append(serialized_input_json)\n",
+        "    dataset_content = '\\n'.join(dataset_content)\n",
+        "    file_name = \"{}.txt\".format(dataset_name)\n",
+        "    f = open(file_name, 'w')\n",
+        "    f.write(dataset_content)\n",
+        "    f.close()\n",
+        "\n",
+        "    # upload the txt file created above to the Datastore and create a dataset from it\n",
+        "    data_store = Datastore.get_default(ws)\n",
+        "    data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
+        "    datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
+        "    sample_request_data = Dataset.Tabular.from_delimited_files(\n",
+        "        datastore_path,\n",
+        "        separator='\\n',\n",
+        "        infer_column_types=True,\n",
+        "        header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
+        "    sample_request_data = sample_request_data.register(workspace=ws,\n",
+        "                                                    name=dataset_name,\n",
+        "                                                    create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "\n",
+        "\n",
+        "environment = Environment('my-sklearn-environment')\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "    'azureml-defaults',\n",
+        "    'inference-schema[numpy-support]',\n",
+        "    'joblib',\n",
+        "    'numpy',\n",
+        "    'scikit-learn=={}'.format(sklearn.__version__)\n",
+        "])\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "# if cpu and memory_in_gb parameters are not provided\n",
+        "# the model will be profiled on default configuration of\n",
+        "# 3.5CPU and 15GB memory\n",
+        "profile = Model.profile(ws,\n",
+        "            'rgrsn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+        "            [model],\n",
+        "            inference_config,\n",
+        "            input_dataset=sample_request_data,\n",
+        "            cpu=1.0,\n",
+        "            memory_in_gb=0.5)\n",
+        "\n",
+        "# profiling is a long running operation and may take up to 25 min\n",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
       ]
     },
     {
@@ -430,7 +544,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "category": "deployment",

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

@@ -2,3 +2,5 @@ name: model-register-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy
+  - scikit-learn

how-to-use-azureml/deployment/deploy-to-local/myenv.yml

@@ -1,8 +0,0 @@
-name: project_environment
-dependencies:
-  - python=3.6.2
-  - pip:
-    - azureml-defaults
-    - scikit-learn
-    - numpy
-    - inference-schema[numpy-support]

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

@@ -75,6 +75,33 @@
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create trained model\n",
+        "\n",
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "sk_model = Ridge().fit(dataset_x, dataset_y)\n",
+        "\n",
+        "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -86,7 +113,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model_local_adv` in the workspace.\n",
+        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
         "\n",
         "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
       ]
@@ -105,7 +132,7 @@
         "from azureml.core.model import Model\n",
         "\n",
         "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
-        "                       model_name=\"sklearn_regression_model_local_adv\",\n",
+        "                       model_name=\"sklearn_regression_model\",\n",
         "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
         "                       description=\"Ridge regression model to predict diabetes\",\n",
         "                       workspace=ws)"
@@ -126,12 +153,12 @@
       "source": [
         "import os\n",
         "\n",
-        "source_directory = \"C:/abc\"\n",
+        "source_directory = \"source_directory\"\n",
         "\n",
         "os.makedirs(source_directory, exist_ok=True)\n",
-        "os.makedirs(\"C:/abc/x/y\", exist_ok=True)\n",
+        "os.makedirs(os.path.join(source_directory, \"x/y\"), exist_ok=True)\n",
-        "os.makedirs(\"C:/abc/env\", exist_ok=True)\n",
+        "os.makedirs(os.path.join(source_directory, \"env\"), exist_ok=True)\n",
-        "os.makedirs(\"C:/abc/dockerstep\", exist_ok=True)"
+        "os.makedirs(os.path.join(source_directory, \"dockerstep\"), exist_ok=True)"
       ]
     },
     {
@@ -147,14 +174,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile C:/abc/x/y/score.py\n",
+        "%%writefile source_directory/x/y/score.py\n",
-        "import os\n",
+        "import joblib\n",
-        "import pickle\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "import os\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
         "\n",
         "from inference_schema.schema_decorators import input_schema, output_schema\n",
         "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
@@ -165,16 +189,17 @@
         "    # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
         "    model = joblib.load(model_path)\n",
+        "\n",
         "    global name\n",
-        "    # note here, entire source directory on inference config gets added into image\n",
+        "    # Note here, the entire source directory from inference config gets added into image.\n",
-        "    # bellow is the example how you can use any extra files in image\n",
+        "    # Below is an example of how you can use any extra files in image.\n",
-        "    with open('./abc/extradata.json') as json_file:  \n",
+        "    with open('./source_directory/extradata.json') as json_file:\n",
         "        data = json.load(json_file)\n",
         "        name = data[\"people\"][0][\"name\"]\n",
         "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
         "output_sample = np.array([3726.995])\n",
         "\n",
         "@input_schema('data', NumpyParameterType(input_sample))\n",
@@ -182,46 +207,20 @@
         "def run(data):\n",
         "    try:\n",
         "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable object.\n",
         "        return \"Hello \" + name + \" here is your result = \" + str(result)\n",
         "    except Exception as e:\n",
         "        error = str(e)\n",
         "        return error"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency for your environemnt. This package contains the functionality needed to host the model as a web service."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile C:/abc/env/myenv.yml\n",
+        "%%writefile source_directory/extradata.json\n",
-        "name: project_environment\n",
-        "dependencies:\n",
-        "  - python=3.6.2\n",
-        "  - pip:\n",
-        "    - azureml-defaults\n",
-        "    - scikit-learn\n",
-        "    - numpy\n",
-        "    - inference-schema[numpy-support]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/extradata.json\n",
         "{\n",
         "    \"people\": [\n",
         "        {\n",
@@ -251,19 +250,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import sklearn\n",
+        "\n",
         "from azureml.core.environment import Environment\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n",
+        "myenv = Environment('myenv')\n",
+        "myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
+        "myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n",
+        "myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n",
         "\n",
         "# explicitly set base_image to None when setting base_dockerfile\n",
         "myenv.docker.base_image = None\n",
-        "myenv.docker.base_dockerfile = \"RUN echo \\\"this is test\\\"\"\n",
+        "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\\nRUN echo \\\"this is test\\\"\"\n",
+        "myenv.inferencing_stack_version = \"latest\"\n",
         "\n",
-        "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
+        "inference_config = InferenceConfig(source_directory=source_directory,\n",
         "                                   entry_script=\"x/y/score.py\",\n",
-        "                                   environment=myenv)\n"
+        "                                   environment=myenv)"
       ]
     },
     {
@@ -353,15 +358,10 @@
         "import json\n",
         "\n",
         "sample_input = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist()\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
         "})\n",
         "\n",
-        "sample_input = bytes(sample_input, encoding='utf-8')\n",
+        "print(local_service.run(sample_input))"
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
       ]
     },
     {
@@ -379,13 +379,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile C:/abc/x/y/score.py\n",
+        "%%writefile source_directory/x/y/score.py\n",
-        "import os\n",
+        "import joblib\n",
-        "import pickle\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "import os\n",
-        "from sklearn.linear_model import Ridge\n",
         "\n",
         "from inference_schema.schema_decorators import input_schema, output_schema\n",
         "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
@@ -396,17 +394,18 @@
         "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
         "    model = joblib.load(model_path)\n",
+        "\n",
         "    global name, from_location\n",
-        "    # note here, entire source directory on inference config gets added into image\n",
+        "    # Note here, the entire source directory from inference config gets added into image.\n",
-        "    # bellow is the example how you can use any extra files in image\n",
+        "    # Below is an example of how you can use any extra files in image.\n",
-        "    with open('./abc/extradata.json') as json_file:  \n",
+        "    with open('source_directory/extradata.json') as json_file:  \n",
         "        data = json.load(json_file)\n",
         "        name = data[\"people\"][0][\"name\"]\n",
         "        from_location = data[\"people\"][0][\"from\"]\n",
         "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
         "output_sample = np.array([3726.995])\n",
         "\n",
         "@input_schema('data', NumpyParameterType(input_sample))\n",
@@ -414,8 +413,8 @@
         "def run(data):\n",
         "    try:\n",
         "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable object.\n",
-        "        return \"Hello  \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
+        "        return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
         "    except Exception as e:\n",
         "        error = str(e)\n",
         "        return error"
@@ -431,7 +430,7 @@
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
         "# After calling reload(), run() will return the updated message.\n",
-        "local_service.run(input_data=sample_input)"
+        "local_service.run(sample_input)"
       ]
     },
     {

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

@@ -0,0 +1,5 @@
+name: register-model-deploy-local-advanced
+dependencies:
+- pip:
+  - azureml-sdk
+  - scikit-learn

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

@@ -71,6 +71,33 @@
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create trained model\n",
+        "\n",
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "sk_model = Ridge().fit(dataset_x, dataset_y)\n",
+        "\n",
+        "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -82,9 +109,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model_local` in the workspace.\n",
+        "Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
         "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
+        "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
       ]
     },
     {
@@ -100,7 +127,7 @@
         "from azureml.core.model import Model\n",
         "\n",
         "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
-        "                       model_name=\"sklearn_regression_model_local\",\n",
+        "                       model_name=\"sklearn_regression_model\",\n",
         "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
         "                       description=\"Ridge regression model to predict diabetes\",\n",
         "                       workspace=ws)"
@@ -119,11 +146,62 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import sklearn\n",
+        "\n",
         "from azureml.core.environment import Environment\n",
         "\n",
         "environment = Environment(\"LocalDeploy\")\n",
-        "environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")"
+        "environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
+        "environment.python.conda_dependencies.add_pip_package(\"joblib\")\n",
+        "environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Provide the Scoring Script\n",
+        "\n",
+        "This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import joblib\n",
+        "import json\n",
+        "import numpy as np\n",
+        "import os\n",
+        "\n",
+        "from inference_schema.schema_decorators import input_schema, output_schema\n",
+        "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
+        "\n",
+        "def init():\n",
+        "    global model\n",
+        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
+        "    model = joblib.load(model_path)\n",
+        "\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
+        "output_sample = np.array([3726.995])\n",
+        "\n",
+        "@input_schema('data', NumpyParameterType(input_sample))\n",
+        "@output_schema(NumpyParameterType(output_sample))\n",
+        "def run(data):\n",
+        "    try:\n",
+        "        result = model.predict(data)\n",
+        "        # You can return any JSON-serializable object.\n",
+        "        return result.tolist()\n",
+        "    except Exception as e:\n",
+        "        error = str(e)\n",
+        "        return error"
       ]
     },
     {
@@ -231,15 +309,10 @@
         "import json\n",
         "\n",
         "sample_input = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist()\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
         "})\n",
         "\n",
-        "sample_input = bytes(sample_input, encoding='utf-8')\n",
+        "local_service.run(sample_input)"
-        "\n",
-        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -258,12 +331,10 @@
       "outputs": [],
       "source": [
         "%%writefile score.py\n",
-        "import os\n",
+        "import joblib\n",
-        "import pickle\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "import os\n",
-        "from sklearn.linear_model import Ridge\n",
         "\n",
         "from inference_schema.schema_decorators import input_schema, output_schema\n",
         "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
@@ -274,10 +345,10 @@
         "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
         "    model = joblib.load(model_path)\n",
         "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
         "output_sample = np.array([3726.995])\n",
         "\n",
         "@input_schema('data', NumpyParameterType(input_sample))\n",
@@ -285,8 +356,8 @@
         "def run(data):\n",
         "    try:\n",
         "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable object.\n",
-        "        return 'hello from updated score.py'\n",
+        "        return 'Hello from the updated score.py: ' + str(result.tolist())\n",
         "    except Exception as e:\n",
         "        error = str(e)\n",
         "        return error"
@@ -302,7 +373,7 @@
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
         "# After calling reload(), run() will return the updated message.\n",
-        "local_service.run(input_data=sample_input)"
+        "local_service.run(sample_input)"
       ]
     },
     {

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

@@ -0,0 +1,5 @@
+name: register-model-deploy-local
+dependencies:
+- pip:
+  - azureml-sdk
+  - scikit-learn

how-to-use-azureml/deployment/deploy-to-local/score.py

@@ -1,35 +0,0 @@
-import os
-import pickle
-import json
-import numpy as np
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-
-from inference_schema.schema_decorators import input_schema, output_schema
-from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
-
-
-def init():
-    global model
-    # AZUREML_MODEL_DIR is an environment variable created during deployment.
-    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
-    # For multiple models, it points to the folder containing all deployed models (./azureml-models)
-    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')
-    # deserialize the model file back into a sklearn model
-    model = joblib.load(model_path)
-
-
-input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
-output_sample = np.array([3726.995])
-
-
-@input_schema('data', NumpyParameterType(input_sample))
-@output_schema(NumpyParameterType(output_sample))
-def run(data):
-    try:
-        result = model.predict(data)
-        # you can return any datatype as long as it is JSON-serializable
-        return result.tolist()
-    except Exception as e:
-        error = str(e)
-        return error

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

@@ -108,9 +108,9 @@
         "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
-        "    'joblib',\n",
         "    'numpy',\n",
-        "    'scikit-learn'\n",
+        "    'scikit-learn==0.19.1',\n",
+        "    'scipy'\n",
         "])"
       ]
     },

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

@@ -5,7 +5,7 @@
       "metadata": {},
       "source": [
         "# Enabling App Insights for Services in Production\n",
-        "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
+        "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n",
         "\n",
         "\n",
         "## What does Application Insights monitor?\n",
@@ -45,11 +45,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import azureml.core\n",
+        "import json\n",
+        "\n",
         "from azureml.core import Workspace\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
         "from azureml.core.webservice import AksWebservice\n",
-        "import azureml.core\n",
+        "\n",
-        "import json\n",
         "print(azureml.core.VERSION)"
       ]
     },
@@ -67,7 +69,7 @@
       "outputs": [],
       "source": [
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -84,13 +86,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Register the model\n",
+        "from azureml.core import Model\n",
-        "from azureml.core.model import Model\n",
+        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n",
-        "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+        "                       model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)\n",
+        "                       workspace=ws)\n",
         "\n",
         "print(model.name, model.description, model.version)"
       ]
@@ -120,7 +122,7 @@
         "import os\n",
         "import pickle\n",
         "import json\n",
-        "import numpy \n",
+        "import numpy\n",
         "from sklearn.externals import joblib\n",
         "from sklearn.linear_model import Ridge\n",
         "import time\n",
@@ -129,15 +131,15 @@
         "    global model\n",
         "    #Print statement for appinsights custom traces:\n",
         "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
-        "    \n",
+        "\n",
         "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
         "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    \n",
+        "\n",
         "    # deserialize the model file back into a sklearn model\n",
         "    model = joblib.load(model_path)\n",
-        "  \n",
+        "\n",
         "\n",
         "# note you can pass in multiple rows for scoring\n",
         "def run(raw_data):\n",
@@ -168,7 +170,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'],\n",
         "                                 pip_packages=['azureml-defaults'])\n",
@@ -190,9 +192,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.environment import Environment\n",
-        "\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
         "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
         "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
@@ -213,11 +214,11 @@
       "source": [
         "from azureml.core.webservice import AciWebservice\n",
         "\n",
-        "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n",
-        "                                               memory_gb = 1, \n",
+        "                                                           memory_gb=1,\n",
-        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
+        "                                                           tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                                               description = 'Predict diabetes using regression model',\n",
+        "                                                           description=\"Predict diabetes using regression model\",\n",
-        "                                               enable_app_insights = True)"
+        "                                                           enable_app_insights=True)"
       ]
     },
     {
@@ -226,29 +227,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import Webservice\n",
+        "aci_service_name = \"aci-service-appinsights\"\n",
+        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n",
+        "aci_service.wait_for_deployment(show_output=True)\n",
         "\n",
-        "aci_service_name = 'my-aci-service-4'\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config)\n",
-        "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,28,13,45,54,6,57,8,8,10], \n",
-        "    [101,9,8,37,6,45,4,3,2,41]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding='utf8')"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -256,7 +242,15 @@
       "outputs": [],
       "source": [
         "if aci_service.state == \"Healthy\":\n",
-        "    prediction = aci_service.run(input_data=test_sample)\n",
+        "    test_sample = json.dumps({\n",
+        "        \"data\": [\n",
+        "            [1,28,13,45,54,6,57,8,8,10],\n",
+        "            [101,9,8,37,6,45,4,3,2,41]\n",
+        "        ]\n",
+        "    })\n",
+        "\n",
+        "    prediction = aci_service.run(test_sample)\n",
+        "\n",
         "    print(prediction)\n",
         "else:\n",
         "    raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)"
@@ -282,14 +276,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Use the default configuration (can also provide parameters to customize)\n",
+        "from azureml.exceptions import ComputeTargetException\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
         "\n",
-        "aks_name = 'my-aks-test3' \n",
+        "aks_name = \"my-aks\"\n",
-        "# Create the cluster\n",
+        "\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "try:\n",
-        "                                  name = aks_name, \n",
+        "    aks_target = ComputeTarget(ws, aks_name)\n",
-        "                                  provisioning_configuration = prov_config)"
+        "    print(\"Using existing AKS cluster {}.\".format(aks_name))\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating a new AKS cluster {}.\".format(aks_name))\n",
+        "\n",
+        "    # Use the default configuration (can also provide parameters to customize).\n",
+        "    prov_config = AksCompute.provisioning_configuration()\n",
+        "    aks_target = ComputeTarget.create(workspace=ws,\n",
+        "                                      name=aks_name,\n",
+        "                                      provisioning_configuration=prov_config)"
       ]
     },
     {
@@ -299,7 +300,8 @@
       "outputs": [],
       "source": [
         "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)"
+        "if aks_target.provisioning_state != \"Succeeded\":\n",
+        "    aks_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -323,13 +325,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "```python \n",
+        "```python\n",
         "%%time\n",
         "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
         "create_name= 'myaks4'\n",
         "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
-        "aks_target = ComputeTarget.attach(workspace = ws, \n",
+        "aks_target = ComputeTarget.attach(workspace=ws,\n",
-        "                                  name = create_name, \n",
+        "                                  name=create_name,\n",
         "                                  attach_configuration=attach_config)\n",
         "## Wait for the operation to complete\n",
         "aks_target.wait_for_provisioning(True)```"
@@ -349,7 +351,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Set the web service configuration\n",
+        "# Set the web service configuration.\n",
         "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
       ]
     },
@@ -366,15 +368,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "if aks_target.provisioning_state== \"Succeeded\": \n",
+        "if aks_target.provisioning_state == \"Succeeded\":\n",
-        "    aks_service_name ='aks-w-dc5'\n",
+        "    aks_service_name = \"aks-service-appinsights\"\n",
         "    aks_service = Model.deploy(ws,\n",
-        "                               aks_service_name, \n",
+        "                               aks_service_name,\n",
-        "                               [model], \n",
+        "                               [model],\n",
-        "                               inference_config, \n",
+        "                               inference_config,\n",
-        "                               aks_deployment_config, \n",
+        "                               aks_deployment_config,\n",
-        "                               deployment_target = aks_target)    \n",
+        "                               deployment_target=aks_target,\n",
-        "    aks_service.wait_for_deployment(show_output = True)\n",
+        "                               overwrite=True)\n",
+        "    aks_service.wait_for_deployment(show_output=True)\n",
         "    print(aks_service.state)\n",
         "else:\n",
         "    raise ValueError(\"AKS provisioning failed. Error: \", aks_service.error)"
@@ -395,13 +398,14 @@
       "source": [
         "%%time\n",
         "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,28,13,45,54,6,57,8,8,10], \n",
-        "    [101,9,8,37,6,45,4,3,2,41]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding='utf8')\n",
-        "\n",
         "if aks_service.state == \"Healthy\":\n",
+        "    test_sample = json.dumps({\n",
+        "        \"data\": [\n",
+        "            [1,28,13,45,54,6,57,8,8,10],\n",
+        "            [101,9,8,37,6,45,4,3,2,41]\n",
+        "        ]\n",
+        "    })\n",
+        "\n",
         "    prediction = aks_service.run(input_data=test_sample)\n",
         "    print(prediction)\n",
         "else:\n",
@@ -435,7 +439,7 @@
       "outputs": [],
       "source": [
         "aks_service.update(enable_app_insights=False)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
+        "aks_service.wait_for_deployment(show_output=True)"
       ]
     },
     {

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

@@ -115,6 +115,11 @@
         "# Convert from CoreML into ONNX\n",
         "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
         "\n",
+        "# Fix the preprocessor bias in the ImageScaler\n",
+        "for init in onnx_model.graph.initializer:\n",
+        "    if init.name == 'scalerPreprocessor_bias':\n",
+        "        init.dims[1] = 1\n",
+        "\n",
         "# Save ONNX model\n",
         "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
         "\n",
@@ -255,7 +260,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==0.4.0\", \"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -316,7 +321,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "aci_service_name = 'my-aci-service-15ad'\n",
+        "aci_service_name = 'my-aci-service-tiny-yolo'\n",
         "print(\"Service\", aci_service_name)\n",
         "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",

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

@@ -4,4 +4,5 @@ dependencies:
   - azureml-sdk
   - numpy
   - git+https://github.com/apple/coremltools@v2.1
-  - onnxmltools==1.3.1
+  - onnx<1.7.0
+  - onnxmltools

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

@@ -5,5 +5,5 @@ dependencies:
   - azureml-widgets
   - matplotlib
   - numpy
-  - onnx
+  - onnx<1.7.0
-  - opencv-python
+  - opencv-python-headless

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

@@ -5,5 +5,5 @@ dependencies:
   - azureml-widgets
   - matplotlib
   - numpy
-  - onnx
+  - onnx<1.7.0
-  - opencv-python
+  - opencv-python-headless

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

@@ -202,7 +202,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "kernelspec": {

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

@@ -0,0 +1,350 @@
+{
+  "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/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+        "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+        "We then test and delete the service, image and model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Get workspace\n",
+        "Load existing workspace from the config file info."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Download the model\n",
+        "\n",
+        "Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. This cell will download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import requests\n",
+        "import shutil\n",
+        "import tarfile\n",
+        "import tempfile\n",
+        "\n",
+        "from io import BytesIO\n",
+        "\n",
+        "model_url = \"http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz\"\n",
+        "\n",
+        "archive_prefix = \"./resnet_v1_fp32_savedmodel_NCHW_jpg/1538686758/\"\n",
+        "target_folder = \"resnet50\"\n",
+        "\n",
+        "if not os.path.exists(target_folder):\n",
+        "    response = requests.get(model_url)\n",
+        "    archive = tarfile.open(fileobj=BytesIO(response.content))\n",
+        "    with tempfile.TemporaryDirectory() as temp_folder:\n",
+        "        archive.extractall(temp_folder)\n",
+        "        shutil.copytree(os.path.join(temp_folder, archive_prefix), target_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register the model\n",
+        "Register an existing trained model, add description and tags."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "model = Model.register(model_path=\"resnet50\", # This points to the local directory to upload.\n",
+        "                       model_name=\"resnet50\", # This is the name the model is registered as.\n",
+        "                       tags={'area': \"Image classification\", 'type': \"classification\"},\n",
+        "                       description=\"Image classification trained on Imagenet Dataset\",\n",
+        "                       workspace=ws)\n",
+        "\n",
+        "print(model.name, model.description, model.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Provision the AKS Cluster\n",
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your GPU cluster\n",
+        "gpu_cluster_name = \"aks-gpu-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+        "    print(\"Found existing gpu cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new gpu-cluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n",
+        "                                                           agent_count=1,\n",
+        "                                                           vm_size=\"Standard_NV6\")\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+        "\n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    gpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploy the model as a web service to AKS\n",
+        "\n",
+        "First create a scoring script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "import json\n",
+        "import os\n",
+        "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
+        "from azureml.contrib.services.aml_response import AMLResponse\n",
+        "\n",
+        "def init():\n",
+        "    global session\n",
+        "    global input_name\n",
+        "    global output_name\n",
+        "    \n",
+        "    session = tf.Session()\n",
+        "\n",
+        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n",
+        "    model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
+        "    if len(model.signature_def['serving_default'].inputs) > 1:\n",
+        "        raise ValueError(\"This score.py only supports one input\")\n",
+        "    input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
+        "    output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n",
+        "    \n",
+        "\n",
+        "@rawhttp\n",
+        "def run(request):\n",
+        "    if request.method == 'POST':\n",
+        "        reqBody = request.get_data(False)\n",
+        "        resp = score(reqBody)\n",
+        "        return AMLResponse(resp, 200)\n",
+        "    if request.method == 'GET':\n",
+        "        respBody = str.encode(\"GET is not supported\")\n",
+        "        return AMLResponse(respBody, 405)\n",
+        "    return AMLResponse(\"bad request\", 500)\n",
+        "\n",
+        "def score(data):\n",
+        "    result = session.run(output_name, {input_name: [data]})\n",
+        "    return json.dumps(result[1].tolist())\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    init()\n",
+        "    with open(\"test_image.jpg\", 'rb') as f:\n",
+        "        content = f.read()\n",
+        "        print(score(content))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now create the deployment configuration objects and deploy the model as a webservice."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set the web service configuration (using default here)\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import AksWebservice\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n",
+        "\n",
+        "env = Environment('deploytocloudenv')\n",
+        "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n",
+        "# for open-sourced GPU base images.\n",
+        "env.docker.base_image = DEFAULT_GPU_IMAGE\n",
+        "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n",
+        "                                 pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
+        "aks_config = AksWebservice.deploy_configuration()\n",
+        "\n",
+        "# # Enable token auth and disable (key) auth on the webservice\n",
+        "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service_name ='gpu-rn50'\n",
+        "\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[model],\n",
+        "                           inference_config=inference_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=gpu_cluster)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Test the web service\n",
+        "We test the web sevice by passing the test images content."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "import requests\n",
+        "\n",
+        "# if (key) auth is enabled, fetch keys and include in the request\n",
+        "key1, key2 = aks_service.get_keys()\n",
+        "\n",
+        "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+        "\n",
+        "# # if token auth is enabled, fetch token and include in the request\n",
+        "# access_token, fetch_after = aks_service.get_token()\n",
+        "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
+        "\n",
+        "test_sample = open('snowleopardgaze.jpg', 'rb').read()\n",
+        "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Clean up\n",
+        "Delete the service, image, model and compute target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service.delete()\n",
+        "model.delete()\n",
+        "gpu_cluster.delete()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "vaidyas"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

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

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

@@ -0,0 +1,354 @@
+{
+  "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/deployment/production-deploy-to-aks/production-deploy-to-aks.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+        "This notebook shows the steps for deploying a service: registering a model, provisioning a cluster with ssl (one time action), and deploying a service to it. \n",
+        "We then test and delete the service, image and model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "from azureml.core.compute import AksCompute, ComputeTarget\n",
+        "from azureml.core.webservice import Webservice, AksWebservice\n",
+        "from azureml.core.model import Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Get workspace\n",
+        "Load existing workspace from the config file info."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register the model\n",
+        "Register an existing trained model, add descirption and tags."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Register the model\n",
+        "from azureml.core.model import Model\n",
+        "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+        "                       model_name = \"sklearn_model\", # this is the name the model is registered as\n",
+        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description = \"Ridge regression model to predict diabetes\",\n",
+        "                       workspace = ws)\n",
+        "\n",
+        "print(model.name, model.description, model.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Create the Environment\n",
+        "Create an environment that the model will be deployed with"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.19.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
+        "myenv = Environment(name='myenv')\n",
+        "myenv.python.conda_dependencies = conda_deps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use a custom Docker image\n",
+        "\n",
+        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
+        "\n",
+        "Only supported with `python` runtime.\n",
+        "```python\n",
+        "# use an image available in public Container Registry without authentication\n",
+        "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
+        "\n",
+        "# or, use an image available in a private Container Registry\n",
+        "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
+        "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+        "myenv.docker.base_image_registry.username = \"username\"\n",
+        "myenv.docker.base_image_registry.password = \"password\"\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Write the Entry Script\n",
+        "Write the script that will be used to predict on your model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import os\n",
+        "import pickle\n",
+        "import json\n",
+        "import numpy\n",
+        "from sklearn.externals import joblib\n",
+        "from sklearn.linear_model import Ridge\n",
+        "from inference_schema.schema_decorators import input_schema, output_schema\n",
+        "from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType\n",
+        "\n",
+        "def init():\n",
+        "    global model\n",
+        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
+        "    # deserialize the model file back into a sklearn model\n",
+        "    model = joblib.load(model_path)\n",
+        "\n",
+        "\n",
+        "standard_sample_input = {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }\n",
+        "standard_sample_output = {'outcome': 1}\n",
+        "\n",
+        "@input_schema('param', StandardPythonParameterType(standard_sample_input))\n",
+        "@output_schema(StandardPythonParameterType(standard_sample_output))\n",
+        "def run(param):\n",
+        "    try:\n",
+        "        raw_data = [param['a'], param['b'], param['c'], param['d'], param['e'], param['f'], param['g'], param['h'], param['i'], param['j']]\n",
+        "        data = numpy.array([raw_data])\n",
+        "        result = model.predict(data)\n",
+        "        return { 'outcome' : result[0] }\n",
+        "    except Exception as e:\n",
+        "        error = str(e)\n",
+        "        return error"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Create the InferenceConfig\n",
+        "Create the inference config that will be used when deploying the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Provision the AKS Cluster with SSL\n",
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
+        "\n",
+        "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Use the default configuration (can also provide parameters to customize)\n",
+        "\n",
+        "provisioning_config = AksCompute.provisioning_configuration()\n",
+        "# Leaf domain label generates a name using the formula\n",
+        "#  \"<leaf-domain-label>######.<azure-region>.cloudapp.azure.net\"\n",
+        "#  where \"######\" is a random series of characters\n",
+        "provisioning_config.enable_ssl(leaf_domain_label = \"contoso\")\n",
+        "\n",
+        "aks_name = 'my-aks-ssl-1' \n",
+        "# Create the cluster\n",
+        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "                                  name = aks_name, \n",
+        "                                  provisioning_configuration = provisioning_config)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_target.wait_for_completion(show_output = True)\n",
+        "print(aks_target.provisioning_state)\n",
+        "print(aks_target.provisioning_errors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploy web service to AKS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "sample-deploy-to-aks"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "\n",
+        "aks_config = AksWebservice.deploy_configuration()\n",
+        "\n",
+        "aks_service_name ='aks-service-ssl-1'\n",
+        "\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[model],\n",
+        "                           inference_config=inf_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=aks_target,\n",
+        "                          overwrite=True)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Test the web service using run method\n",
+        "We test the web sevice by passing data.\n",
+        "Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "import json\n",
+        "\n",
+        "standard_sample_input = json.dumps({'param': {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }})\n",
+        "\n",
+        "aks_service.run(input_data=standard_sample_input)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Clean up\n",
+        "Delete the service, image and model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service.delete()\n",
+        "model.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "vaidyas"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

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

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

@@ -109,7 +109,7 @@
         "from azureml.core import Environment\n",
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-defaults'])\n",
+        "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults'])\n",
         "myenv = Environment(name='myenv')\n",
         "myenv.python.conda_dependencies = conda_deps"
       ]
@@ -198,6 +198,128 @@
         "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Model Profiling\n",
+        "\n",
+        "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
+        "\n",
+        "In order to profile your model you will need:\n",
+        "- a registered model\n",
+        "- an entry script\n",
+        "- an inference configuration\n",
+        "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
+        "\n",
+        "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
+        "\n",
+        "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
+        "\n",
+        "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
+        "You can try get the dataset first to see if it's already registered."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "from azureml.core import Datastore\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.data import dataset_type_definitions\n",
+        "\n",
+        "dataset_name='sample_request_data'\n",
+        "\n",
+        "dataset_registered = False\n",
+        "try:\n",
+        "    sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
+        "    dataset_registered = True\n",
+        "except:\n",
+        "    print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
+        "\n",
+        "if not dataset_registered:\n",
+        "    input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+        "                        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n",
+        "    # create a string that can be put in the body of the request\n",
+        "    serialized_input_json = json.dumps(input_json)\n",
+        "    dataset_content = []\n",
+        "    for i in range(100):\n",
+        "        dataset_content.append(serialized_input_json)\n",
+        "    sample_request_data = '\\n'.join(dataset_content)\n",
+        "    file_name = \"{}.txt\".format(dataset_name)\n",
+        "    f = open(file_name, 'w')\n",
+        "    f.write(sample_request_data)\n",
+        "    f.close()\n",
+        "\n",
+        "    # upload the txt file created above to the Datastore and create a dataset from it\n",
+        "    data_store = Datastore.get_default(ws)\n",
+        "    data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
+        "    datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
+        "    sample_request_data = Dataset.Tabular.from_delimited_files(\n",
+        "        datastore_path,\n",
+        "        separator='\\n',\n",
+        "        infer_column_types=True,\n",
+        "        header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
+        "    sample_request_data = sample_request_data.register(workspace=ws,\n",
+        "                                                    name=dataset_name,\n",
+        "                                                    create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.model import Model, InferenceConfig\n",
+        "\n",
+        "\n",
+        "environment = Environment('my-sklearn-environment')\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "    'azureml-defaults',\n",
+        "    'inference-schema[numpy-support]',\n",
+        "    'joblib',\n",
+        "    'numpy',\n",
+        "    'scikit-learn==0.19.1',\n",
+        "    'scipy'\n",
+        "])\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "# if cpu and memory_in_gb parameters are not provided\n",
+        "# the model will be profiled on default configuration of\n",
+        "# 3.5CPU and 15GB memory\n",
+        "profile = Model.profile(ws,\n",
+        "            'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+        "            [model],\n",
+        "            inference_config,\n",
+        "            input_dataset=sample_request_data,\n",
+        "            cpu=1.0,\n",
+        "            memory_in_gb=0.5)\n",
+        "\n",
+        "# profiling is a long running operation and may take up to 25 min\n",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -460,7 +582,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "kernelspec": {

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

@@ -302,7 +302,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "category": "deployment",

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

@@ -1,260 +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/deployment/tensorflow/tensorflow-model-register-and-deploy.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Register TensorFlow SavedModel and deploy as webservice\n",
-        "\n",
-        "Following this notebook, you will:\n",
-        "\n",
-        " - Learn how to register a TF SavedModel in your Azure Machine Learning Workspace.\n",
-        " - Deploy your model as a web service in an Azure Container Instance."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "# Check core SDK version number.\n",
-        "print('SDK version:', azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "\n",
-        "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download the Model\n",
-        "\n",
-        "Download and extract the model from https://amlsamplenotebooksdata.blob.core.windows.net/data/flowers_model.tar.gz to \"models\" directory"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import tarfile\n",
-        "import urllib.request\n",
-        "\n",
-        "# create directory for model\n",
-        "model_dir = 'models'\n",
-        "if not os.path.isdir(model_dir):\n",
-        "    os.mkdir(model_dir)\n",
-        "\n",
-        "url=\"https://amlsamplenotebooksdata.blob.core.windows.net/data/flowers_model.tar.gz\"\n",
-        "response = urllib.request.urlretrieve(url, model_dir + \"/flowers_model.tar.gz\")\n",
-        "tar = tarfile.open(model_dir + \"/flowers_model.tar.gz\", \"r:gz\")\n",
-        "tar.extractall(model_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register model\n",
-        "\n",
-        "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-). For this example, we have provided a TensorFlow SavedModel (`flowers_model` in the notebook's directory).\n",
-        "\n",
-        "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Model\n",
-        "\n",
-        "model = Model.register(workspace=ws,\n",
-        "                       model_name='flowers',                        # Name of the registered model in your workspace.\n",
-        "                       model_path= model_dir + '/flowers_model',    # Local Tensorflow SavedModel folder to upload and register as a model.\n",
-        "                       model_framework=Model.Framework.TENSORFLOW,  # Framework used to create the model.\n",
-        "                       model_framework_version='1.14.0',            # Version of Tensorflow used to create the model.\n",
-        "                       description='Flowers model')\n",
-        "\n",
-        "print('Name:', model.name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy model\n",
-        "\n",
-        "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets.\n",
-        "\n",
-        "For this example, we will deploy your TensorFlow SavedModel to an Azure Container Instance (ACI)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Use a default environment (for supported models)\n",
-        "\n",
-        "The Azure Machine Learning service provides a default environment for supported model frameworks, including TensorFlow, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n",
-        "\n",
-        "**Note**: This step can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "\n",
-        "service_name = 'tensorflow-flower-service'\n",
-        "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "service = Model.deploy(ws, service_name, [model])\n",
-        "service.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "After your model is deployed, perform a call to the web service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import requests\n",
-        "\n",
-        "headers = {'Content-Type': 'application/json'}\n",
-        "\n",
-        "if service.auth_enabled:\n",
-        "    headers['Authorization'] = 'Bearer '+ service.get_keys()[0]\n",
-        "elif service.token_auth_enabled:\n",
-        "    headers['Authorization'] = 'Bearer '+ service.get_token()[0]\n",
-        "\n",
-        "scoring_uri = service.scoring_uri # If you have a SavedModel with classify and regress, \n",
-        "                                  #  you can change the scoring_uri from 'uri:predict' to 'uri:classify' or 'uri:regress'.\n",
-        "print(scoring_uri)\n",
-        "\n",
-        "with open('tensorflow-flower-predict-input.json', 'rb') as data_file:\n",
-        "    response = requests.post(\n",
-        "        scoring_uri, data=data_file, headers=headers)\n",
-        "print(response.status_code)\n",
-        "print(response.elapsed)\n",
-        "print(response.json())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you are finished testing your service, clean up the deployment."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "aashishb"
-      }
-    ],
-    "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": "3.7.0"
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