Update train-on-remote-vm.ipynb

Update raw features explanation notebook

README.md

@@ -11,7 +11,8 @@ pip install azureml-sdk
 Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
 
 ## How to navigate and use the example notebooks?
-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. 
+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. 
+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. 
 
 If you want to...
 
@@ -53,4 +54,10 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 Visit following repos to see projects contributed by Azure ML users:
 
  - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
+ 
+ 
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
+
+ 
+ 

how-to-use-azureml/README.md

@@ -2,7 +2,7 @@
 
 Learn how to use Azure Machine Learning services for experimentation and model management.
 
-As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
+If you are using an Azure Machine Learning Notebook VM, 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. Then, run the notebooks in following recommended order.
 
 * [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.

how-to-use-azureml/azure-hdi/automl_hdi_local_classification.ipynb

@@ -8,7 +8,14 @@
     "\n",
     "Licensed under the MIT License."
    ]
-  },
+  },  
+ {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-hdi/automl_hdi_local_classification.png)"
+         ] 
+    },
   {
    "cell_type": "markdown",
    "metadata": {},

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

@@ -9,6 +9,13 @@
         "Licensed under the MIT License."
       ]
     },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.png)"
+         ] 
+    },  
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -26,7 +33,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
+        "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."
       ]
     },
     {
@@ -272,4 +279,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -9,6 +9,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -28,7 +35,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise,make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {
@@ -484,4 +491,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-local/register-model-deploy-local.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -28,7 +35,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {
@@ -339,4 +346,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -0,0 +1,89 @@
+
+# Notebooks for Microsoft Azure Machine Learning Hardware Accelerated Models SDK
+
+Easily create and train a model using various deep neural networks (DNNs) as a featurizer for deployment to Azure or a Data Box Edge device for ultra-low latency inferencing using FPGA's. These models are currently available:
+
+* ResNet 50
+* ResNet 152
+* DenseNet-121
+* VGG-16
+* SSD-VGG  
+
+To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
+
+### Step 1: Create an Azure ML workspace
+Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
+  
+### Step 2: Install the Azure ML Accelerated Models SDK
+Once you have set up your environment, install the Azure ML Accel Models SDK. This package requires tensorflow >= 1.6,<2.0 to be installed. 
+
+If you already have tensorflow >= 1.6,<2.0 installed in your development environment, you can install the SDK package using: 
+
+```
+pip install azureml-accel-models
+```
+
+If you do not have tensorflow >= 1.6,<2.0 and are using a CPU-only development environment, our SDK with tensorflow can be installed using:
+
+```
+pip install azureml-accel-models[cpu]
+```
+
+If your machine supports GPU (for example, on an [Azure DSVM](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview)), then you can leverage the tensorflow-gpu functionality using:
+
+```
+pip install azureml-accel-models[gpu]
+```
+
+### Step 3: Follow our notebooks
+
+The notebooks in this repo walk through the following scenarios: 
+* [Quickstart](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
+* [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
+
+<a name="model-classes"></a>
+## Model Classes
+As stated above, we support 5 Accelerated Models. Here's more information on their input and output tensors.
+
+**Available models and output tensors**
+
+The available models and the corresponding default classifier output tensors are below. This is the value that you would use during inferencing if you used the default classifier.
+* Resnet50, QuantizedResnet50 
+``
+output_tensors = "classifier_1/resnet_v1_50/predictions/Softmax:0"
+``
+* Resnet152, QuantizedResnet152 
+``
+output_tensors = "classifier/resnet_v1_152/predictions/Softmax:0"
+``
+* Densenet121, QuantizedDensenet121
+``
+output_tensors = "classifier/densenet121/predictions/Softmax:0"
+``
+* Vgg16, QuantizedVgg16 
+``
+output_tensors = "classifier/vgg_16/fc8/squeezed:0"
+``
+* SsdVgg, QuantizedSsdVgg
+``
+output_tensors = ['ssd_300_vgg/block4_box/Reshape_1:0', 'ssd_300_vgg/block7_box/Reshape_1:0', 'ssd_300_vgg/block8_box/Reshape_1:0', 'ssd_300_vgg/block9_box/Reshape_1:0', 'ssd_300_vgg/block10_box/Reshape_1:0', 'ssd_300_vgg/block11_box/Reshape_1:0', 'ssd_300_vgg/block4_box/Reshape:0', 'ssd_300_vgg/block7_box/Reshape:0', 'ssd_300_vgg/block8_box/Reshape:0', 'ssd_300_vgg/block9_box/Reshape:0', 'ssd_300_vgg/block10_box/Reshape:0', 'ssd_300_vgg/block11_box/Reshape:0']
+``
+
+For more information, please reference the azureml.accel.models package in the [Azure ML Python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.models?view=azure-ml-py).
+
+**Input tensors**
+
+The input_tensors value defaults to "Placeholder:0" and is created in the [Image Preprocessing](#construct-model) step in the line: 
+``
+in_images = tf.placeholder(tf.string)
+``
+
+You can change the input_tensors name by doing this: 
+``
+in_images = tf.placeholder(tf.string, name="images")
+``
+
+
+## Resources
+*  [Read more about FPGAs](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas)

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

@@ -0,0 +1,490 @@
+{
+ "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": [
+    "# 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 it fails, you can run wait_for_completion again with show_output=True.\n",
+    "convert_request.wait_for_completion(show_output=False)\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",
+    "\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",
+    "# Authentication is enabled by default, but for testing we specify False\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
+    "                                                    agent_count = 1)\n",
+    "\n",
+    "aks_name = 'my-aks-pb6-ssd-vgg'\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": [
+    "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": [
+    "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",
+    "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
+    "                                                num_replicas=1,\n",
+    "                                                auth_enabled = False)\n",
+    "\n",
+    "aks_service_name ='my-aks-service'\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 have a client that can call into the docker image to get predictions. \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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using the grpc client in AzureML Accelerated Models SDK\n",
+    "from azureml.accel.client import PredictionClient\n",
+    "\n",
+    "address = aks_service.scoring_uri\n",
+    "ssl_enabled = address.startswith(\"https\")\n",
+    "address = address[address.find('/')+2:].strip('/')\n",
+    "port = 443 if ssl_enabled else 80\n",
+    "\n",
+    "# Initialize AzureML Accelerated Models client\n",
+    "client = PredictionClient(address=address,\n",
+    "                          port=port,\n",
+    "                          use_ssl=ssl_enabled,\n",
+    "                          service_name=aks_service.name)"
+   ]
+  },
+  {
+   "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": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,544 @@
+{
+ "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": [
+    "# 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",
+    "# If it fails, you can run wait_for_completion again with show_output=True.\n",
+    "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')"
+   ]
+  },
+  {
+   "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": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import AksCompute, ComputeTarget\n",
+    "\n",
+    "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
+    "# Authentication is enabled by default, but for testing we specify False\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
+    "                                                    agent_count = 1)\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": [
+    "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": [
+    "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",
+    "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
+    "                                                num_replicas=1,\n",
+    "                                                auth_enabled = False)\n",
+    "\n",
+    "aks_service_name ='my-aks-service'\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 have a client that can call into the docker image to get predictions.\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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using the grpc client in AzureML Accelerated Models SDK\n",
+    "from azureml.accel.client import PredictionClient\n",
+    "\n",
+    "address = aks_service.scoring_uri\n",
+    "ssl_enabled = address.startswith(\"https\")\n",
+    "address = address[address.find('/')+2:].strip('/')\n",
+    "port = 443 if ssl_enabled else 80\n",
+    "\n",
+    "# Initialize AzureML Accelerated Models client\n",
+    "client = PredictionClient(address=address,\n",
+    "                          port=port,\n",
+    "                          use_ssl=ssl_enabled,\n",
+    "                          service_name=aks_service.name)"
+   ]
+  },
+  {
+   "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": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,858 @@
+{
+ "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": [
+    "# 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 Model](#deploy-model)\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": [
+    "# 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 it fails, you can run wait_for_completion again with show_output=True.\n",
+    "convert_request.wait_for_completion(show_output=False)\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",
+    "\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",
+    "# Authentication is enabled by default, but for testing we specify False\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
+    "                                                    agent_count = 1)\n",
+    "\n",
+    "aks_name = 'my-aks-pb6-training'\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": [
+    "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": [
+    "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",
+    "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
+    "                                                num_replicas=1,\n",
+    "                                                auth_enabled = False)\n",
+    "\n",
+    "aks_service_name ='my-aks-service'\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 have a client that can call into the docker image to get predictions. \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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using the grpc client in AzureML Accelerated Models SDK\n",
+    "from azureml.accel.client import PredictionClient\n",
+    "\n",
+    "address = aks_service.scoring_uri\n",
+    "ssl_enabled = address.startswith(\"https\")\n",
+    "address = address[address.find('/')+2:].strip('/')\n",
+    "port = 443 if ssl_enabled else 80\n",
+    "\n",
+    "# Initialize AzureML Accelerated Models client\n",
+    "client = PredictionClient(address=address,\n",
+    "                          port=port,\n",
+    "                          use_ssl=ssl_enabled,\n",
+    "                          service_name=aks_service.name)"
+   ]
+  },
+  {
+   "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": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -25,6 +25,13 @@
         "3. Build new image and deploy it. "
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -488,4 +495,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb

@@ -24,6 +24,13 @@
         "4. Build new image and deploy it. "
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -468,4 +475,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/deployment/onnx/README.md

@@ -1,10 +1,11 @@
 # ONNX on Azure Machine Learning
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/README.png)
 
 These tutorials show how to create and deploy Open Neural Network eXchange ([ONNX](http://onnx.ai)) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
 
 ## Tutorials
 
-0. [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
+0. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
 
 #### Obtain pretrained models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime
 1. [MNIST - Handwritten Digit Classification with ONNX Runtime](onnx-inference-mnist-deploy.ipynb)
@@ -34,3 +35,5 @@ Licensed under the MIT License.
 
 ## Acknowledgements
 These tutorials were developed by Vinitra Swamy and Prasanth Pulavarthi of the Microsoft AI Frameworks team and adapted for presentation at Microsoft Ignite 2018.
+
+

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

@@ -9,6 +9,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -33,7 +40,7 @@
         "To make the best use of your time, make sure you have done the following:\n",
         "\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [configuration](../../../configuration.ipynb) notebook to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (config.json)"
       ]
@@ -433,4 +440,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -7,6 +7,13 @@
         "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -34,7 +41,7 @@
         "## Prerequisites\n",
         "\n",
         "### 1. Install Azure ML SDK and create a new workspace\n",
-        "Please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
         "\n",
         "### 2. Install additional packages needed for this Notebook\n",
         "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n",
@@ -806,4 +813,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -34,7 +41,7 @@
         "## Prerequisites\n",
         "\n",
         "### 1. Install Azure ML SDK and create a new workspace\n",
-        "Please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
         "\n",
         "### 2. Install additional packages needed for this tutorial notebook\n",
         "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n",
@@ -810,4 +817,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -9,6 +9,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -33,7 +40,7 @@
         "To make the best use of your time, make sure you have done the following:\n",
         "\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (config.json)"
       ]
@@ -417,4 +424,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb

@@ -9,6 +9,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -30,7 +37,7 @@
       "source": [
         "## Prerequisites\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -663,4 +670,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -0,0 +1,407 @@
+{
+ "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": [
+    "# 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": [
+    "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.image import Image\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. 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. You can download a [pretrained resnet50](https://github.com/tensorflow/models/tree/master/official/resnet#pre-trained-model) and unpack it to that directory."
+   ]
+  },
+  {
+   "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 = \"resnet50\", # this points to a local file\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": [
+    "# Create an image\n",
+    "Create an image using the registered model the script that will load and run the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import ujson\n",
+    "from azureml.core.model import Model\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",
+    "    model_path = Model.get_model_path('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",
+    "    if len(model.signature_def['serving_default'].outputs) > 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()][0]\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 ujson.dumps(result[0])\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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy','ujson','azureml-contrib-services'])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  gpu_enabled = True\n",
+    "                                                 )\n",
+    "\n",
+    "image = ContainerImage.create(name = \"GpuImage\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "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": [
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\")\n",
+    "\n",
+    "aks_name = 'my-aks-9' \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": [
+    "# Create AKS Cluster in an existing virtual network (optional)\n",
+    "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "from azureml.core.compute import ComputeTarget, AksCompute\n",
+    "\n",
+    "# Create the compute configuration and set virtual network information\n",
+    "config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\", location=\"eastus2\")\n",
+    "config.vnet_resourcegroup_name = \"mygroup\"\n",
+    "config.vnet_name = \"mynetwork\"\n",
+    "config.subnet_name = \"default\"\n",
+    "config.service_cidr = \"10.0.0.0/16\"\n",
+    "config.dns_service_ip = \"10.0.0.10\"\n",
+    "config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
+    "\n",
+    "# Create the compute target\n",
+    "aks_target = ComputeTarget.create(workspace = ws,\n",
+    "                                  name = \"myaks\",\n",
+    "                                  provisioning_configuration = config)\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enable SSL on the AKS Cluster (optional)\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": [
+    "# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")"
+   ]
+  },
+  {
+   "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": [
+    "## Optional step: Attach existing AKS cluster\n",
+    "\n",
+    "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
+    "\n",
+    "create_name='my-existing-aks' \n",
+    "# Create the cluster\n",
+    "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
+    "aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
+    "# Wait for the operation to complete\n",
+    "aks_target.wait_for_completion(True)\n",
+    "'''"
+   ]
+  },
+  {
+   "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",
+    "aks_config = AksWebservice.deploy_configuration()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='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)\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",
+    "key1, key2 = aks_service.get_keys()\n",
+    "\n",
+    "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+    "test_sampe = open('test_image.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",
+    "image.delete()\n",
+    "model.delete()\n",
+    "aks_target.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "aashishb"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -8,6 +8,13 @@
         "\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",
@@ -467,4 +474,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+       {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -34,7 +41,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {
@@ -443,4 +450,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/explain-model/README.md

@@ -2,10 +2,7 @@
 
 Follow these sample notebooks to learn:
 
-1. [Explain tabular data](explain-tabular-data): Basic example of explaining model trained on tabular data.
-2. [Explain local classification](explain-local-sklearn-classification): Explain a scikit-learn classification model.
-3. [Explain local regression](explain-local-sklearn-regression): Explain a scikit-learn regression model.
+1. [Explain tabular data locally](explain-tabular-data-local): Basic example of explaining model trained on tabular data.
 4. [Explain on remote AMLCompute](explain-on-amlcompute): Explain a model on a remote AMLCompute target.
-5. [Explain classification using Run History](explain-run-history-sklearn-classification): Explain a scikit-learn classification model with Run History.
-6. [Explain regression using Run History](explain-run-history-sklearn-regression): Explain a scikit-learn regression model with Run History.
-7. [Explain scikit-learn raw features](explain-sklearn-raw-features): Explain the raw features of a trained scikit-learn model.
+5. [Explain tabular data with Run History](explain-tabular-data-run-history): Explain a model with Run History.
+7. [Explain raw features](explain-tabular-data-raw-features): Explain the raw features of a trained model.

how-to-use-azureml/explain-model/explain-local-sklearn-classification/explain-local-sklearn-classification.ipynb

@@ -1,243 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Breast cancer diagnosis classification with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
-        "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "# per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# local feature importance information\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
-        "print('local importance for first instance: {}'.format(local_importance_values[y_test[0]]))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('local importance feature names: {}'.format(list(local_explanation.features)))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(local_explanation.features, local_explanation.local_importance_values[y_test[0]]))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "wamartin"
-      }
-    ],
-    "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": 2
-}

how-to-use-azureml/explain-model/explain-local-sklearn-regression/explain-local-sklearn-regression.ipynb

@@ -1,231 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Boston Housing Price Prediction with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a GradientBoosting Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features = boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "help(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values \n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# local feature importance information\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "wamartin"
-      }
-    ],
-    "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": 2
-}

how-to-use-azureml/explain-model/explain-run-history-sklearn-classification/explain-run-history-sklearn-classification.ipynb

@@ -1,255 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Breast cancer diagnosis classification with scikit-learn (save model explanations via AML Run History)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Save Model Explanation With AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uploading model explanation data for storage or visualization in webUX\n",
-        "# The explanation can then be downloaded on any compute\n",
-        "client.upload_model_explanation(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get model explanation data\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()\n",
-        "per_class_names = explanation.get_ranked_per_class_names()[0]\n",
-        "per_class_values = explanation.get_ranked_per_class_values()[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('per class feature importance values: {}'.format(per_class_values))\n",
-        "print('per class feature importance names: {}'.format(per_class_names))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(per_class_names, per_class_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "wamartin"
-      }
-    ],
-    "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": 2
-}

how-to-use-azureml/explain-model/explain-run-history-sklearn-regression/explain-run-history-sklearn-regression.ipynb

@@ -1,269 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Boston Housing Price Prediction with scikit-learn (save model explanations via AML Run History)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Save Model Explanation With AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Import Iris dataset\n",
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a GradientBoosting Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uploading model explanation data for storage or visualization in webUX\n",
-        "# The explanation can then be downloaded on any compute\n",
-        "client.upload_model_explanation(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get model explanation data\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print the values\n",
-        "print('expected values: {}'.format(expected_values))\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('global importance values: {}'.format(global_importance_values))\n",
-        "print('global importance names: {}'.format(global_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain individual instance predictions (local explanation) ##### needs to get updated with the new build"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# local feature importance information\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "wamartin"
-      }
-    ],
-    "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": 2
-}

how-to-use-azureml/explain-model/explain-sklearn-raw-features/explain-sklearn-raw-features.ipynb

@@ -1,221 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Summary\n",
-        "From raw data that is a mixture of categoricals and numeric, featurize the categoricals using one hot encoding. Use tabular explainer to get explain object and then get raw feature importances"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Load titanic dataset. Impute missing values by filling both backward and forward since some data is at the first/last row. This is just for illustration and not a recommended way to impute missing data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
-        "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
-        "data = pd.read_csv(titanic_url)\n",
-        "# fill missing values\n",
-        "data = data.fillna(method=\"ffill\")\n",
-        "data = data.fillna(method=\"bfill\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data.columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "numeric_features = ['age', 'fare']\n",
-        "categorical_features = ['embarked', 'sex', 'pclass']\n",
-        "\n",
-        "y = data['survived'].values\n",
-        "X = data[categorical_features + numeric_features]\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "One hot encode the categorical features"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.preprocessing import OneHotEncoder\n",
-        "one_enc = OneHotEncoder()\n",
-        "one_enc.fit(X_train[categorical_features])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Columnwise concatenate one hot encoded categoricals and numerical features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from scipy import sparse\n",
-        "def get_feats(X):\n",
-        "    a = one_enc.transform(X[categorical_features])\n",
-        "    b = X[numeric_features]\n",
-        "    return sparse.hstack((one_enc.transform(X[categorical_features]), X[numeric_features].values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Train a logistic regression model on featurized training data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.linear_model import LogisticRegression\n",
-        "\n",
-        "X_train_transformed = get_feats(X_train)\n",
-        "X_test_transformed = get_feats(X_test)\n",
-        "\n",
-        "clf = LogisticRegression(solver='lbfgs', max_iter=200)\n",
-        "clf.fit(X_train_transformed, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get feature mapping between raw and generated features. Using the order in which features are concatenated in `get_feats` and using `categories_` in `OneHotEncoder` we are able to compute this mapping."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "raw_feat_mapping = []\n",
-        "start_index = 0\n",
-        "for cat_list in one_enc.categories_:\n",
-        "    raw_feat_mapping.append([start_index + i for i in range(len(cat_list))])\n",
-        "    start_index += len(cat_list)\n",
-        "for i in range(len(numeric_features)):\n",
-        "    raw_feat_mapping.append([start_index])\n",
-        "    start_index += 1    "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "\n",
-        "explainer = TabularExplainer(clf, X_train_transformed)\n",
-        "global_explanation = explainer.explain_global(X_test_transformed)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "raw_feat_imps = global_explanation.get_raw_feature_importances(raw_feat_mapping)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "feature_names = categorical_features + numeric_features\n",
-        "sorted_indices = np.argsort(raw_feat_imps)[::-1]\n",
-        "\n",
-        "for i in sorted_indices:\n",
-        "    print(\"{}: {}\".format(feature_names[i], raw_feat_imps[i]))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "hichando"
-      }
-    ],
-    "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": 2
-}

how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.ipynb

@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Breast cancer diagnosis classification with scikit-learn (run model explainer locally)"
+   ]
+  },
+  {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.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": [
+    "Explain a model with the AML explain-model package\n",
+    "\n",
+    "1. Train a SVM classification model using Scikit-learn\n",
+    "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
+    "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
+    "4. Visualize the global and local explanations with the visualization dashboard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from sklearn import svm\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Run model explainer locally with full data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the breast cancer diagnosis data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breast_cancer_data = load_breast_cancer()\n",
+    "classes = breast_cancer_data.target_names.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into train and test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a SVM classification model, which you want to explain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
+    "model = clf.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain predictions on your local machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions (global explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+    "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+    "global_explanation = tabular_explainer.explain_global(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sorted SHAP values\n",
+    "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+    "# Corresponding feature names\n",
+    "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+    "# feature ranks (based on original order of features)\n",
+    "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+    "# per class feature names\n",
+    "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+    "# per class feature importance values\n",
+    "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions as a collection of local (instance-level) explanations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# feature shap values for all features and all data points in the training data\n",
+    "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain local data points (individual instances)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# explain the first member of the test set\n",
+    "instance_num = 0\n",
+    "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the prediction for the first member of the test set and explain why model made that prediction\n",
+    "prediction_value = clf.predict(x_test)[instance_num]\n",
+    "\n",
+    "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+    "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+    "\n",
+    "\n",
+    "dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Load visualization dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ExplanationDashboard(global_explanation, model, x_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "mesameki"
+   }
+  ],
+  "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": 2
+}

how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.ipynb

@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Iris flower classification with scikit-learn (run model explainer locally)"
+   ]
+  },
+  {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.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": [
+    "Explain a model with the AML explain-model package\n",
+    "\n",
+    "1. Train a SVM classification model using Scikit-learn\n",
+    "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
+    "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
+    "4. Visualize the global and local explanations with the visualization dashboard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn import svm\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Run model explainer locally with full data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the breast cancer diagnosis data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = load_iris()\n",
+    "X = iris['data']\n",
+    "y = iris['target']\n",
+    "classes = iris['target_names']\n",
+    "feature_names = iris['feature_names']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into train and test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a SVM classification model, which you want to explain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
+    "model = clf.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain predictions on your local machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabular_explainer = TabularExplainer(model, x_train, features = feature_names, classes=classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions (global explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "global_explanation = tabular_explainer.explain_global(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sorted SHAP values\n",
+    "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+    "# Corresponding feature names\n",
+    "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+    "# feature ranks (based on original order of features)\n",
+    "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+    "# per class feature names\n",
+    "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+    "# per class feature importance values\n",
+    "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions as a collection of local (instance-level) explanations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# feature shap values for all features and all data points in the training data\n",
+    "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain local data points (individual instances)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# explain the first member of the test set\n",
+    "instance_num = 0\n",
+    "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the prediction for the first member of the test set and explain why model made that prediction\n",
+    "prediction_value = clf.predict(x_test)[instance_num]\n",
+    "\n",
+    "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+    "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+    "\n",
+    "\n",
+    "dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load visualization dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ExplanationDashboard(global_explanation, model, x_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "mesameki"
+   }
+  ],
+  "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": 2
+}

how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.ipynb

@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Boston Housing Price Prediction with scikit-learn (run model explainer locally)"
+   ]
+  },
+  {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.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": [
+    "Explain a model with the AML explain-model package\n",
+    "\n",
+    "1. Train a GradientBoosting regression model using Scikit-learn\n",
+    "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
+    "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
+    "4. Visualize the global and local explanations with the visualization dashboard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Run model explainer locally with full data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the Boston house price data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boston_data = datasets.load_boston()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into train and test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a GradientBoosting Regression model, which you want to explain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
+    "                                learning_rate=0.1, loss='huber',\n",
+    "                                random_state=1)\n",
+    "model = reg.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain predictions on your local machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabular_explainer = TabularExplainer(model, x_train, features = boston_data.feature_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions (global explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+    "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+    "global_explanation = tabular_explainer.explain_global(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sorted SHAP values \n",
+    "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+    "# Corresponding feature names\n",
+    "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+    "# feature ranks (based on original order of features)\n",
+    "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions as a collection of local (instance-level) explanations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# feature shap values for all features and all data points in the training data\n",
+    "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain local data points (individual instances)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sorted local feature importance information; reflects the original feature order\n",
+    "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
+    "sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
+    "\n",
+    "print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
+    "print('sorted local importance values: {}'.format(sorted_local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load visualization dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ExplanationDashboard(global_explanation, model, x_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "mesameki"
+   }
+  ],
+  "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": 2
+}

how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb

@@ -0,0 +1,288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "From raw data that is a mixture of categoricals and numeric, featurize the categoricals using one hot encoding. Use tabular explainer to get explain object and then get raw feature importances"
+   ]
+  },
+  {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.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": [
+    "Explain a model with the AML explain-model package on raw features\n",
+    "\n",
+    "1. Train a Logistic Regression model using Scikit-learn\n",
+    "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
+    "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
+    "4. Visualize the global and local explanations with the visualization dashboard."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example needs sklearn-pandas. If it is not installed, uncomment and run the following line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install sklearn-pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+    "from sklearn_pandas import DataFrameMapper\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
+    "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
+    "data = pd.read_csv(titanic_url)\n",
+    "# fill missing values\n",
+    "data = data.fillna(method=\"ffill\")\n",
+    "data = data.fillna(method=\"bfill\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Run model explainer locally with full data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "numeric_features = ['age', 'fare']\n",
+    "categorical_features = ['embarked', 'sex', 'pclass']\n",
+    "\n",
+    "y = data['survived'].values\n",
+    "X = data[categorical_features + numeric_features]\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn_pandas import DataFrameMapper\n",
+    "\n",
+    "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+    "\n",
+    "transformations = [\n",
+    "    ([\"age\", \"fare\"], Pipeline(steps=[\n",
+    "        ('imputer', SimpleImputer(strategy='median')),\n",
+    "        ('scaler', StandardScaler())\n",
+    "    ])),\n",
+    "    ([\"embarked\"], Pipeline(steps=[\n",
+    "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
+    "        (\"encoder\", OneHotEncoder(sparse=False))])),\n",
+    "    ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False))    \n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Append classifier to preprocessing pipeline.\n",
+    "# Now we have a full prediction pipeline.\n",
+    "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
+    "                      ('classifier', LogisticRegression(solver='lbfgs'))])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a Logistic Regression model, which you want to explain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = clf.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain predictions on your local machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabular_explainer = TabularExplainer(clf.steps[-1][1], initialization_examples=x_train, features=x_train.columns, transformations=transformations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+    "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+    "global_explanation = tabular_explainer.explain_global(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_global_importance_values = global_explanation.get_ranked_global_values()\n",
+    "sorted_global_importance_names = global_explanation.get_ranked_global_names()\n",
+    "dict(zip(sorted_global_importance_names, sorted_global_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions as a collection of local (instance-level) explanations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# explain the first member of the test set\n",
+    "local_explanation = tabular_explainer.explain_local(x_test[:1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the prediction for the first member of the test set and explain why model made that prediction\n",
+    "prediction_value = clf.predict(x_test)[0]\n",
+    "\n",
+    "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+    "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+    "\n",
+    "# Sorted local SHAP values\n",
+    "print('ranked local importance values: {}'.format(sorted_local_importance_values))\n",
+    "# Corresponding feature names\n",
+    "print('ranked local importance names: {}'.format(sorted_local_importance_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Load visualization dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ExplanationDashboard(global_explanation, model, x_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "mesameki"
+   }
+  ],
+  "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": 2
+}

how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.ipynb

@@ -0,0 +1,262 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Breast cancer diagnosis classification with scikit-learn (save model explanations via AML Run History)"
+   ]
+  },
+  {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.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": [
+    "Explain a model with the AML explain-model package\n",
+    "\n",
+    "1. Train a SVM classification model using Scikit-learn\n",
+    "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from sklearn import svm\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Run model explainer locally with full data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the breast cancer diagnosis data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breast_cancer_data = load_breast_cancer()\n",
+    "classes = breast_cancer_data.target_names.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into train and test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a SVM classification model, which you want to explain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
+    "model = clf.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain predictions on your local machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions (global explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+    "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+    "global_explanation = tabular_explainer.explain_global(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Save Model Explanation With AML Run History"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "from azureml.core import Workspace, Experiment, Run\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+    "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'explain_model'\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "run = experiment.start_logging()\n",
+    "client = ExplanationClient.from_run(run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uploading model explanation data for storage or visualization in webUX\n",
+    "# The explanation can then be downloaded on any compute\n",
+    "client.upload_model_explanation(global_explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get model explanation data\n",
+    "explanation = client.download_model_explanation()\n",
+    "local_importance_values = explanation.local_importance_values\n",
+    "expected_values = explanation.expected_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the top k (e.g., 4) most important features with their importance values\n",
+    "explanation = client.download_model_explanation(top_k=4)\n",
+    "global_importance_values = explanation.get_ranked_global_values()\n",
+    "global_importance_names = explanation.get_ranked_global_names()\n",
+    "per_class_names = explanation.get_ranked_per_class_names()[0]\n",
+    "per_class_values = explanation.get_ranked_per_class_values()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('per class feature importance values: {}'.format(per_class_values))\n",
+    "print('per class feature importance names: {}'.format(per_class_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict(zip(per_class_names, per_class_values))"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "mesameki"
+   }
+  ],
+  "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": 2
+}

how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.ipynb

@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Boston Housing Price Prediction with scikit-learn (save model explanations via AML Run History)"
+   ]
+  },
+   {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.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": [
+    "Explain a model with the AML explain-model package\n",
+    "\n",
+    "1. Train a GradientBoosting regression model using Scikit-learn\n",
+    "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Save Model Explanation With AML Run History"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import Iris dataset\n",
+    "from sklearn import datasets\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core import Workspace, Experiment, Run\n",
+    "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+    "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'explain_model'\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "run = experiment.start_logging()\n",
+    "client = ExplanationClient.from_run(run)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the Boston house price data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boston_data = datasets.load_boston()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into train and test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a GradientBoosting Regression model, which you want to explain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
+    "                                learning_rate=0.1, loss='huber',\n",
+    "                                random_state=1)\n",
+    "model = clf.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain predictions on your local machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabular_explainer = TabularExplainer(model, x_train, features=boston_data.feature_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain overall model predictions (global explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+    "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+    "global_explanation = tabular_explainer.explain_global(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uploading model explanation data for storage or visualization in webUX\n",
+    "# The explanation can then be downloaded on any compute\n",
+    "client.upload_model_explanation(global_explanation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get model explanation data\n",
+    "explanation = client.download_model_explanation()\n",
+    "local_importance_values = explanation.local_importance_values\n",
+    "expected_values = explanation.expected_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print the values\n",
+    "print('expected values: {}'.format(expected_values))\n",
+    "print('local importance values: {}'.format(local_importance_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the top k (e.g., 4) most important features with their importance values\n",
+    "explanation = client.download_model_explanation(top_k=4)\n",
+    "global_importance_values = explanation.get_ranked_global_values()\n",
+    "global_importance_names = explanation.get_ranked_global_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('global importance values: {}'.format(global_importance_values))\n",
+    "print('global importance names: {}'.format(global_importance_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain individual instance predictions (local explanation) ##### needs to get updated with the new build"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# local feature importance information\n",
+    "local_importance_values = local_explanation.local_importance_values\n",
+    "print('local importance values: {}'.format(local_importance_values))"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "mesameki"
+   }
+  ],
+  "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": 2
+}

how-to-use-azureml/explain-model/explain-tabular-data/explain-tabular-data.ipynb

@@ -1,267 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uncomment these if explanation packages are not already installed in your environment\n",
-        "#!pip install --upgrade azureml-sdk[explain]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM model using Scikit-learn\n",
-        "2. Run 'explain_model' in local mode, which doesn't contact any Azure services\n",
-        "3. Run 'explain_model' with AML Run History, which leverages Run History Service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Disclaimer: this notebook is a preview of model explainability, and the APIs shown below are subject to breaking changes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM model, which we will try to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Import Iris dataset\n",
-        "from sklearn import datasets\n",
-        "iris = datasets.load_iris()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Import scikit learn, fit a SVM model\n",
-        "def create_scikit_learn_model(X, y):\n",
-        "    from sklearn import svm\n",
-        "    clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "    model = clf.fit(X, y)\n",
-        "    return model\n",
-        "model = create_scikit_learn_model(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run model explainer locally"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import time\n",
-        "start = time.time()\n",
-        "\n",
-        "explainer = TabularExplainer(model, x_train, features=iris.feature_names)\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# importance values for each class, test example, and feature (local importance)\n",
-        "local_imp_values = global_explanation.local_importance_values\n",
-        "# base prediction with feature importances ignored\n",
-        "expected_values = global_explanation.expected_values\n",
-        "# global feature importance information\n",
-        "global_imp_values = global_explanation.global_importance_values\n",
-        "ranked_global_imp_names = global_explanation.get_ranked_global_names()\n",
-        "# global per-class feature importance information\n",
-        "per_class_imp_values = global_explanation.per_class_values\n",
-        "ranked_per_class_imp_names = global_explanation.get_ranked_per_class_names()\n",
-        "\n",
-        "end = time.time()\n",
-        "print(end - start)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run model explainer with AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import time\n",
-        "start = time.time()\n",
-        "explainer = TabularExplainer(model, x_train, features=iris.feature_names, classes=iris.target_names)\n",
-        "explanation = explainer.explain_global(x_test)\n",
-        "client.upload_model_explanation(explanation)\n",
-        "end = time.time()\n",
-        "print(end - start)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "explanation_from_run = client.download_model_explanation()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# global feature importance information\n",
-        "global_imp_values = explanation_from_run.global_importance_values\n",
-        "global_imp_names = explanation_from_run.get_ranked_global_names()\n",
-        "# global per-class feature importance information\n",
-        "per_class_imp_values = explanation_from_run.per_class_values\n",
-        "per_class_imp_names = explanation_from_run.get_ranked_per_class_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## This visualization is unsupported, and is not guaranteed to work in the future"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the shap values and explore locally\n",
-        "import shap\n",
-        "import numpy as np\n",
-        "shap.initjs()\n",
-        "display(shap.force_plot(explanation_from_run.expected_values[1], np.asarray(explanation_from_run.local_importance_values[1]), x_test))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.complete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "wamartin"
-      }
-    ],
-    "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": 2
-}

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

@@ -54,3 +54,6 @@ In this directory, there are two types of notebooks:
 
 1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score): This notebook demonstrates how to run a batch scoring job using Azure Machine Learning pipelines.
 2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans)
+
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/README.png)

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

@@ -13,3 +13,6 @@ These notebooks below are designed to go in sequence.
 8. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
 9. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
 10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore. 
+
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.png)

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

@@ -8,6 +8,15 @@
         "Licensed under the MIT License."
       ]
     },
+    
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.png)"
+         ] 
+    },
+     
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -53,7 +62,7 @@
       "source": [
         "## Initialize Workspace\n",
         "\n",
-        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n",
+        "Initialize a workspace object from persisted configuration.If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n",
         "\n",
         "If you don't have a config.json file, please go through the configuration Notebook located here:\n",
         "https://github.com/Azure/MachineLearningNotebooks. \n",
@@ -466,4 +475,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,15 @@
         "Licensed under the MIT License."
       ]
     },
+    
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)"
+         ] 
+    },
+        
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -37,7 +46,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
       ]
     },
     {
@@ -634,4 +643,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -67,7 +74,7 @@
       "source": [
         "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n",
         "\n",
-        "If you don't have a config.json file, please go through the configuration Notebook located [here](https://github.com/Azure/MachineLearningNotebooks).  \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, if you don't have a config.json file, please go through the configuration Notebook located [here](https://github.com/Azure/MachineLearningNotebooks).  \n",
         "\n",
         "This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
@@ -373,4 +380,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -9,6 +9,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -20,7 +27,7 @@
         "\n",
         "## Prerequisite:\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise,go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -278,4 +285,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -50,7 +57,7 @@
       "source": [
         "## Initialize workspace\n",
         "\n",
-        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
+        "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json"
       ]
     },
     {
@@ -434,4 +441,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -21,7 +28,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
         "\n",
         "### Initialization Steps"
       ]
@@ -413,4 +420,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -21,7 +28,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and AML Basics\n",
-        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n",
         "\n",
         "### Initialization Steps"
       ]
@@ -444,4 +451,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.png)"
+         ] 
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -52,7 +59,7 @@
       "source": [
         "## Initialize Workspace\n",
         "\n",
-        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
+        "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json"
       ]
     },
     {
@@ -364,4 +371,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -7,6 +7,13 @@
         "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -105,7 +112,7 @@
       "source": [
         "## Initialize Workspace\n",
         "\n",
-        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
+        "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json"
       ]
     },
     {
@@ -705,4 +712,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -7,6 +7,13 @@
         "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -23,7 +30,7 @@
         "## Introduction\n",
         "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you would see\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -514,4 +521,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -7,6 +7,13 @@
         "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
         "Licensed under the MIT License."
       ]
+    },
+      {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -21,7 +28,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
         "\n",
         "### Azure Machine Learning and Pipeline SDK-specific Imports"
       ]
@@ -464,4 +471,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb

@@ -7,6 +7,13 @@
         "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -28,7 +35,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
     },
     {
@@ -593,4 +600,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

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

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -25,7 +32,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwsie, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
     },
     {
@@ -643,4 +650,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azure-ml.ipynb

@@ -18,6 +18,13 @@
         " \n",
         "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.png)"
+         ] 
     },
     {
       "cell_type": "code",
@@ -250,4 +257,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/README.md

@@ -7,4 +7,6 @@ Follow these sample notebooks to learn:
 3. [Train on remote VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
 4. [Train on AmlCompute](train-on-amlcompute): train a model using an AmlCompute cluster as compute target.
 5. [Train in an HDI Spark cluster](train-in-spark): train a Spark ML model using an HDInsight Spark cluster as compute target.
-6. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
+6. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/README.png)

how-to-use-azureml/training/logging-api/logging-api.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/logging-api/logging-api.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -62,7 +69,7 @@
         "\n",
         "## Setup\n",
         "\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\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. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
         "\n",
         "```\n",
         "(myenv) $ conda install -y tqdm matplotlib\n",
@@ -527,4 +534,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/manage-runs/manage-runs.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/manage-runs/manage-runs.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -45,7 +52,7 @@
       "source": [
         "## Setup\n",
         "\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't. Also, if you're new to Azure ML, we recommend that you go through [the tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml) first to learn the basic concepts.\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) first if you haven't. Also, if you're new to Azure ML, we recommend that you go through [the tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml) first to learn the basic concepts.\n",
         "\n",
         "Let's first import required packages, check Azure ML SDK version, connect to your workspace and create an Experiment to hold the runs."
       ]
@@ -592,4 +599,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-in-spark/train-in-spark.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -25,7 +32,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "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) first if you haven't."
       ]
     },
     {
@@ -275,4 +282,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -31,7 +38,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "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) first if you haven't."
       ]
     },
     {
@@ -519,4 +526,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/train-on-local/train-on-local.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-local/train-on-local.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -29,7 +36,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "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) first if you haven't."
       ]
     },
     {
@@ -532,4 +539,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb

@@ -8,6 +8,13 @@
         "\n",
         "Licensed under the MIT License."
       ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+          "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -30,7 +37,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "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) first if you haven't."
       ]
     },
     {
@@ -638,4 +645,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/using-environments/example/example.py

@@ -0,0 +1,8 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License
+
+# Very simple script to demonstrate run in environment
+# Print message passed in as environment variable
+import os
+
+print(os.environ.get("MESSAGE"))

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

@@ -0,0 +1,364 @@
+{
+ "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": [
+    "# Using environments\n",
+    "\n",
+    "\n",
+    "## Contents\n",
+    "\n",
+    "1. [Introduction](#Introduction)\n",
+    "1. [Setup](#Setup)\n",
+    "1. [Create environment](#Create-environment)\n",
+    "    1. Add Python packages\n",
+    "    1. Specify environment variables\n",
+    "1. [Submit run using environment](#Submit-run-using-environment)\n",
+    "1. [Register environment](#Register-environment)\n",
+    "1. [List and get existing environments](#List-and-get-existing-environments)\n",
+    "1. [Other ways to create environments](#Other-ways-to-create-environments)\n",
+    "    1. From existing Conda environment\n",
+    "    1. From Conda or pip files\n",
+    "1. [Docker settings](#Docker-settings)\n",
+    "1. [Spark and Azure Databricks settings](#Spark-and-Azure-Databricks-settings)\n",
+    "1. [Next steps](#Next-steps)\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "Azure ML environments are an encapsulation of the environment where your machine learning training happens. They define Python packages, environment variables, Docker settings and other attributes in declarative fashion. Environments are versioned: you can update them and retrieve old versions to revist and review your work.\n",
+    "\n",
+    "Environments allow you to:\n",
+    "* Encapsulate dependencies of your training process, such as Python packages and their versions.\n",
+    "* Reproduce the Python environment on your local computer in a remote run on VM or ML Compute cluster\n",
+    "* Reproduce your experimentation environment in production setting.\n",
+    "* Revisit and audit the environment in which an existing model was trained.\n",
+    "\n",
+    "Environment, compute target and training script together form run configuration: the full specification of training run.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n",
+    "\n",
+    "First, let's validate Azure ML SDK version and connect to workspace."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "import azureml.core\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "ws.get_details()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create environment\n",
+    "\n",
+    "You can create an environment by instantiating ```Environment``` object and then setting its attributes: set of Python packages, environment variables and others.\n",
+    "\n",
+    "### Add Python packages\n",
+    "\n",
+    "The recommended way is to specify Conda packages, as they typically come with complete set of pre-built binaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Environment\n",
+    "from azureml.core.environment import CondaDependencies\n",
+    "\n",
+    "myenv = Environment(name=\"myenv\")\n",
+    "conda_dep = CondaDependencies()\n",
+    "conda_dep.add_conda_package(\"scikit-learn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also add pip packages, and specify the version of package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conda_dep.add_pip_package(\"pillow==5.4.1\")\n",
+    "myenv.python.conda_dependencies=conda_dep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Specify environment variables\n",
+    "\n",
+    "You can add environment variables to your environment. These then become available using ```os.environ.get``` in your training script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myenv.environment_variables = {\"MESSAGE\":\"Hello from Azure Machine Learning\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submit run using environment\n",
+    "\n",
+    "When you submit a run, you can specify which environment to use. \n",
+    "\n",
+    "On the first run in given environment, Azure ML spends some time building the environment. On the subsequent runs, Azure ML keeps track of changes and uses the existing environment, resulting in faster run completion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig, Experiment\n",
+    "\n",
+    "myexp = Experiment(workspace=ws, name = \"environment-example\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To submit a run, create a run configuration that combines the script file and environment, and pass it to ```Experiment.submit```. In this example, the script is submitted to local computer, but you can specify other compute targets such as remote clusters as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "runconfig = ScriptRunConfig(source_directory=\"example\", script=\"example.py\")\n",
+    "runconfig.run_config.target = \"local\"\n",
+    "runconfig.run_config.environment = myenv\n",
+    "run = myexp.submit(config=runconfig)\n",
+    "\n",
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register environment\n",
+    "\n",
+    "You can manage environments by registering them. This allows you to track their versions, and reuse them in future runs. For example, once you've constructed an environment that meets your requirements, you can register it and use it in other experiments so as to standardize your workflow.\n",
+    "\n",
+    "If you register the environment with same name, the version number is increased by one. Note that Azure ML keeps track of differences between the version, so if you re-register an identical version, the version number is not increased."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myenv.register(workspace=ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List and get existing environments\n",
+    "\n",
+    "Your workspace contains a dictionary of registered environments. You can then use ```Environment.get``` to retrieve a specific environment with specific version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name,env in ws.environments.items():\n",
+    "    print(\"Name {} \\t version {}\".format(name,env.version))\n",
+    "\n",
+    "restored_environment = Environment.get(workspace=ws,name=\"myenv\",version=\"1\")\n",
+    "\n",
+    "print(\"Attributes of restored environment\")\n",
+    "restored_environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other ways to create environments\n",
+    "\n",
+    "### From existing Conda environment\n",
+    "\n",
+    "You can create an environment from existing conda environment. This make it easy to reuse your local interactive environment in Azure ML remote runs. For example, if you've created conda environment using\n",
+    "```\n",
+    "conda create -n mycondaenv\n",
+    "```\n",
+    "you can create Azure ML environment out of that conda environment using\n",
+    "```\n",
+    "myenv = Environment.from_existing_conda_environment(name=\"myenv\",conda_environment_name=\"mycondaenv\")\n",
+    "```\n",
+    "\n",
+    "### From conda or pip files\n",
+    "\n",
+    "You can create environments from conda specification or pip requirements files using\n",
+    "```\n",
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"path-to-conda-specification-file\")\n",
+    "\n",
+    "myenv = Environment.from_pip_requirements(name=\"myenv\", file_path=\"path-to-pip-requirements-file\")\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Docker settings\n",
+    "\n",
+    "Docker container provides an efficient way to encapsulate the dependencies. When you enable Docker, Azure ML builds a Docker image and creates a Python environment within that container, given your specifications. The Docker images are reused: the first run in a new environment typically takes longer as the image is build.\n",
+    "\n",
+    "**Note:** For runs on local computer or attached virtual machine, that computer must have Docker installed and enabled. Machine Learning Compute has Docker pre-installed.\n",
+    "\n",
+    "Attribute ```docker.enabled``` controls whether to use Docker container or host OS for execution. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myenv.docker.enabled = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can specify custom Docker base image and registry. This allows you to customize and control in detail the guest OS in which your training run executes. whether to use GPU, whether to use shared volumes, and shm size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myenv.docker.base_image\n",
+    "myenv.docker.base_image_registry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also specify whether to use GPU or shared volumes, and shm size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myenv.docker.gpu_support\n",
+    "myenv.docker.shared_volumes\n",
+    "myenv.docker.shm_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spark and Azure Databricks settings\n",
+    "\n",
+    "In addition to Python and Docker settings, Environment also contains attributes for Spark and Azure Databricks runs. These attributes become relevant when you submit runs on those compute targets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "Learn more about remote runs on different compute targets:\n",
+    "\n",
+    "* [Train on ML Compute](../../train-on-amlcompute)\n",
+    "\n",
+    "* [Train on remote VM](../../train-on-remote-vm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
+  "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/work-with-data/datasets/datasets-tutorial.ipynb

@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Learn how to use Datasets in Azure ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial, you learn how to use Azure ML Datasets to train a regression model with the Azure Machine Learning SDK for Python. You will\n",
+    "\n",
+    "* Explore and prepare data for training the model\n",
+    "* Register the Dataset in your workspace to share it with others\n",
+    "* Take snapshots of data to ensure models can be trained with the same data every time\n",
+    "* Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial, you:\n",
+    "\n",
+    "☑ Setup a Python environment and import packages\n",
+    "\n",
+    "☑ Load the Titanic data from your Azure Blob Storage. (The [original data](https://www.kaggle.com/c/titanic/data) can be found on Kaggle)\n",
+    "\n",
+    "☑ Explore and cleanse the data to remove anomalies\n",
+    "\n",
+    "☑ Register the Dataset in your workspace, allowing you to use it in model training \n",
+    "\n",
+    "☑ Take a Dataset snapshot for repeatability and train a model with the snapshot\n",
+    "\n",
+    "☑ Make changes to the dataset's definition without breaking the production model or the daily data pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-requisites:\n",
+    "Skip to Set up your development environment to read through the notebook steps, or use the instructions below to get the notebook and run it on Azure Notebooks or your own notebook server. To run the notebook you will need:\n",
+    "\n",
+    "A Python 3.6 notebook server with the following installed:\n",
+    "* The Azure Machine Learning SDK for Python\n",
+    "* The Azure Machine Learning Data Prep SDK for Python\n",
+    "* The tutorial notebook\n",
+    "\n",
+    "Data and train.py script to store in your Azure Blob Storage Account.\n",
+    " * [Titanic data](./train-dataset/Titanic.csv)\n",
+    " * [train.py](./train-dataset/train.py)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create and register Datasets you need:\n",
+    "\n",
+    "   * An Azure subscription. If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.\n",
+    "\n",
+    "   * An Azure Machine Learning service workspace. See the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
+    "\n",
+    "   * The Azure Machine Learning SDK for Python (version 1.0.21 or later). To install or update to the latest version of the SDK, see [Install or update the SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).\n",
+    "\n",
+    "\n",
+    "For more information on how to set up your workspace, see the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
+    "\n",
+    "The first part that needs to be done is setting up your python environment. You will need to import all of your python packages including `azureml.dataprep` and `azureml.core.dataset`. Then access your workspace through your Azure subscription and set up your compute target. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.dataprep as dprep\n",
+    "import azureml.core\n",
+    "import pandas as pd\n",
+    "import logging\n",
+    "import os\n",
+    "import shutil\n",
+    "from azureml.core import Workspace, Datastore, Dataset\n",
+    "\n",
+    "# Get existing workspace from config.json file in the same folder as the tutorial notebook\n",
+    "# You can download the config file from your workspace\n",
+    "workspace = Workspace.from_config()\n",
+    "print(\"Workspace\")\n",
+    "print(workspace)\n",
+    "print(\"Compute targets\")\n",
+    "print(workspace.compute_targets)\n",
+    "\n",
+    "# Get compute target that has already been attached to the workspace\n",
+    "# Pick the right compute target from the list of computes attached to your workspace\n",
+    "\n",
+    "compute_target_name = 'dataset-test'\n",
+    "remote_compute_target = workspace.compute_targets[compute_target_name]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To load data to your dataset, you will access the data through your datastore. After you create your dataset, you can use `get_profile()` to see your data's statistics.\n",
+    "\n",
+    "We will now upload the [original data](https://www.kaggle.com/c/titanic/data) to the default datastore(blob) within your workspace.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datastore = workspace.get_default_datastore()\n",
+    "datastore.upload_files(files=['./train-dataset/Titanic.csv'],\n",
+    "                       target_path='train-dataset/',\n",
+    "                       overwrite=True,\n",
+    "                       show_progress=True)\n",
+    "\n",
+    "dataset = Dataset.auto_read_files(path=datastore.path('train-dataset/Titanic.csv'))\n",
+    "\n",
+    "#Display Dataset Profile of the Titanic Dataset\n",
+    "dataset.get_profile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To predict if a person survived the Titanic's sinking or not, the columns that are relevant to train the model are 'Survived','Pclass', 'Sex','SibSp', and 'Parch'. You can update your dataset's deinition and only keep these columns you will need. You will also need to convert values (\"male\",\"female\") in the \"Sex\" column to 0 or 1, because the algorithm in the train.py file will be using numeric values instead of strings.\n",
+    "\n",
+    "For more examples of preparing data with Datasets, see [Explore and prepare data with the Dataset class](aka.ms/azureml/howto/exploreandpreparedata)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_def = dataset.get_definition()\n",
+    "ds_def = ds_def.keep_columns(['Survived','Pclass', 'Sex','SibSp', 'Parch', 'Fare'])\n",
+    "ds_def = ds_def.replace('Sex','male', 0)\n",
+    "ds_def = ds_def.replace('Sex','female', 1)\n",
+    "ds_def.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have cleaned your data, you can register your dataset in your workspace. \n",
+    "\n",
+    "Registering your dataset allows you to easily have access to your processed data and share it with other people in your organization using the same workspace. It can be accessed in any notebook or script that is connected to your workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = dataset.update_definition(ds_def, 'Cleaned Data')\n",
+    "\n",
+    "dataset.generate_profile(compute_target='local').get_result()\n",
+    "\n",
+    "dataset_name = 'clean_Titanic_tutorial'\n",
+    "dataset = dataset.register(workspace=workspace,\n",
+    "                           name=dataset_name,\n",
+    "                           description='training dataset',\n",
+    "                           tags = {'year':'2019', 'month':'Apr'},\n",
+    "                           exist_ok=True)\n",
+    "workspace.datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also take a snapshot of your dataset. This makes for easily reproducing your data as it is in that moment. Even if you changed the definition of your dataset, or have data that refreshes regularly, you can always go back to your snapshot to compare. Since this snapshot is being created on a compute in your workspace, it may take a signficant amount of time to provision the compute before running the action itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(dataset.get_all_snapshots())\n",
+    "snapshot_name = 'train_snapshot'\n",
+    "\n",
+    "print(\"Compute target status\")\n",
+    "print(remote_compute_target.get_status().provisioning_state)\n",
+    "\n",
+    "snapshot = dataset.create_snapshot(snapshot_name=snapshot_name, \n",
+    "                                   compute_target=remote_compute_target, \n",
+    "                                   create_data_snapshot=True)\n",
+    "snapshot.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that you have registered your dataset and created a snapshot, you can call up the dataset and it's snapshot to use it in your train.py script.\n",
+    "\n",
+    "The following code snippit will train your model locally using the train.py script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment, RunConfiguration\n",
+    "\n",
+    "experiment_name = 'training-datasets'\n",
+    "experiment = Experiment(workspace = workspace, name = experiment_name)\n",
+    "project_folder = './train-dataset/'\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "run_config = RunConfiguration()\n",
+    "\n",
+    "run_config.environment.python.user_managed_dependencies = True\n",
+    "\n",
+    "from azureml.core import Run\n",
+    "from azureml.core import ScriptRunConfig\n",
+    "\n",
+    "src = ScriptRunConfig(source_directory=project_folder, \n",
+    "                      script='train.py', \n",
+    "                      run_config=run_config) \n",
+    "run = experiment.submit(config=src)\n",
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use the same script with your dataset snapshot for your Pipeline's Python Script Step.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.pipeline.core import Pipeline, PipelineData\n",
+    "from azureml.pipeline.steps import PythonScriptStep\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "\n",
+    "trainStep = PythonScriptStep(script_name=\"train.py\",\n",
+    "                             compute_target=remote_compute_target,\n",
+    "                             source_directory=project_folder)\n",
+    "\n",
+    "pipeline = Pipeline(workspace=workspace,\n",
+    "                    steps=trainStep)\n",
+    "\n",
+    "pipeline_run = experiment.submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During any point of your workflow, you can get a previous snapshot of your dataset and use that version in your pipeline to quickly see how different versions of your data can effect your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snapshot = dataset.get_snapshot(snapshot_name=snapshot_name)\n",
+    "snapshot.to_pandas_dataframe().head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can make changes to the dataset's definition without breaking the production model or the daily data pipeline. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can call get_definitions to see that there are several versions. After each change to a dataset's version, another one is added."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.get_definitions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = Dataset.get(workspace=workspace, name=dataset_name)\n",
+    "ds_def = dataset.get_definition()\n",
+    "ds_def = ds_def.drop_columns(['Fare'])\n",
+    "dataset = dataset.update_definition(ds_def, 'Dropping Fare as PClass and Fare are strongly correlated')\n",
+    "dataset.generate_profile(compute_target='local').get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dataset definitions can be deprecated when usage is no longer recommended and a replacement is available. When a deprecated dataset definition is used in an AML Experimentation/Pipeline scenario, a warning message gets returned but execution will not be blocked. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Deprecate dataset definition 1 by the 2nd definition\n",
+    "ds_def = dataset.get_definition('1')\n",
+    "ds_def.deprecate(deprecate_by_dataset_id=dataset._id, deprecated_by_definition_version='2')\n",
+    "dataset.get_definitions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dataset definitions can be archived when definitions are not supposed to be used for any reasons (such as underlying data no longer available). When an archived dataset definition is used in an AML Experimentation/Pipeline scenario, execution will be blocked with error. No further actions can be performed on archived Dataset definitions, but the references will be kept intact. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Archive the deprecated dataset definition #1\n",
+    "ds_def = dataset.get_definition('1')\n",
+    "ds_def.archive()\n",
+    "dataset.get_definitions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also reactivate any defition that you archived for later use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_def = dataset.get_definition('1')\n",
+    "ds_def.reactivate()\n",
+    "dataset.get_definitions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now delete the current snapshot name to clean up your resource's space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.delete_snapshot(snapshot_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have now finished using a dataset from start to finish of your experiment!"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "cforbe"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "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/work-with-data/datasets/train-dataset/Titanic.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
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how-to-use-azureml/work-with-data/datasets/train-dataset/train.py

@@ -0,0 +1,36 @@
+import azureml.dataprep as dprep
+import azureml.core
+import pandas as pd
+import logging
+import os
+import datetime
+import shutil
+
+from azureml.core import Workspace, Datastore, Dataset, Experiment, Run
+from sklearn.model_selection import train_test_split
+from azureml.core.compute import ComputeTarget, AmlCompute
+from azureml.core.compute_target import ComputeTargetException
+from sklearn.tree import DecisionTreeClassifier
+
+run = Run.get_context()
+workspace = run.experiment.workspace
+
+dataset_name = 'training_data'
+
+dataset = Dataset.get(workspace=workspace, name=dataset_name)
+dflow = dataset.get_definition()
+dflow_val, dflow_train = dflow.random_split(percentage=0.3)
+
+y_df = dflow_train.keep_columns(['HasDetections']).to_pandas_dataframe()
+x_df = dflow_train.drop_columns(['HasDetections']).to_pandas_dataframe()
+y_val = dflow_val.keep_columns(['HasDetections']).to_pandas_dataframe()
+x_val = dflow_val.drop_columns(['HasDetections']).to_pandas_dataframe()
+
+data = {"train": {"X": x_df, "y": y_df},
+
+        "validation": {"X": x_val, "y": y_val}}
+
+clf = DecisionTreeClassifier().fit(data["train"]["X"], data["train"]["y"])
+
+print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_df, y_df)))
+print('Accuracy of Decision Tree classifier on validation set: {:.2f}'.format(clf.score(x_val, y_val)))