Merge pull request #376 from rastala/master

version 1.0.39

NBSETUP.md

@@ -24,8 +24,8 @@ pip install azureml-sdk
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 
 # below steps are optional
-# install the base SDK and a Jupyter notebook server
+# install the base SDK, Jupyter notebook server and tensorboard
-pip install azureml-sdk[notebooks]
+pip install azureml-sdk[notebooks,tensorboard]
 
 # install model explainability component
 pip install azureml-sdk[explain]

README.md

@@ -11,7 +11,7 @@ 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, 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 want to...
 
@@ -20,7 +20,7 @@ If you want to...
  * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
  * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
  * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
- * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](./how-to-use-azureml/machine-learning-pipelines/pipeline-mpi-batch-prediction.ipynb).
+ * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
  * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) and [model data collection](./how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb).
 
 ## Tutorials
@@ -54,3 +54,5 @@ 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)
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)

configuration.ipynb

@@ -32,7 +32,6 @@
         "    1. Workspace parameters\n",
         "    1. Access your workspace\n",
         "    1. Create a new workspace\n",
-        "    1. Create compute resources\n",
         "1. [Next steps](#Next%20steps)\n",
         "\n",
         "---\n",
@@ -96,7 +95,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.23 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.39 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -235,97 +234,6 @@
         "ws.write_config()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create compute resources for your training experiments\n",
-        "\n",
-        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
-        "\n",
-        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
-        "\n",
-        "The cluster parameters are:\n",
-        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
-        "\n",
-        "```shell\n",
-        "az vm list-skus -o tsv\n",
-        "```\n",
-        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while note in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
-        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
-        "\n",
-        "\n",
-        "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpucluster\"\n",
-        "\n",
-        "# Verify that cluster does not exist already\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print(\"Found existing cpucluster\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"Creating new cpucluster\")\n",
-        "    \n",
-        "    # Specify the configuration for the new cluster\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
-        "                                                           min_nodes=0,\n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # Create the cluster with the specified name and configuration\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "    \n",
-        "    # Wait for the cluster to complete, show the output log\n",
-        "    cpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your GPU cluster\n",
-        "gpu_cluster_name = \"gpucluster\"\n",
-        "\n",
-        "# Verify that cluster does not exist already\n",
-        "try:\n",
-        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
-        "    print(\"Found existing gpu cluster\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"Creating new gpucluster\")\n",
-        "    \n",
-        "    # Specify the configuration for the new cluster\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
-        "                                                           min_nodes=0,\n",
-        "                                                           max_nodes=4)\n",
-        "    # Create the cluster with the specified name and configuration\n",
-        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
-        "\n",
-        "    # Wait for the cluster to complete, show the output log\n",
-        "    gpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

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/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb

@@ -249,7 +249,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl._vendor.automl.client.core.common.onnx_convert import OnnxConverter\n",
+        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
         "onnx_fl_path = \"./best_model.onnx\"\n",
         "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
       ]

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

@@ -328,6 +328,12 @@
         "            print()\n",
         "            for estimator in step[1].estimators:\n",
         "                print_model(estimator[1], estimator[0]+ ' - ')\n",
+        "        elif hasattr(step[1], '_base_learners') and hasattr(step[1], '_meta_learner'):\n",
+        "            print(\"\\nMeta Learner\")\n",
+        "            pprint(step[1]._meta_learner)\n",
+        "            print()\n",
+        "            for estimator in step[1]._base_learners:\n",
+        "                print_model(estimator[1], estimator[0]+ ' - ')\n",
         "        else:\n",
         "            pprint(step[1].get_params())\n",
         "            print()\n",

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb

@@ -117,21 +117,12 @@
       "outputs": [],
       "source": [
         "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
-        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
-        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
-        "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
-        "\n",
         "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
         "# and convert column types manually.\n",
-        "# Here we read a comma delimited file and convert all columns to integers.\n",
+        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-      ]
+        "dflow.get_profile()"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
       ]
     },
     {
@@ -140,7 +131,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X.skip(1).head(5)"
+        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
+        "dflow = dflow.drop_nulls('Primary Type')\n",
+        "dflow.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Review the Data Preparation Result\n",
+        "\n",
+        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
+        "\n",
+        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
       ]
     },
     {
@@ -162,9 +176,8 @@
         "    \"iteration_timeout_minutes\" : 10,\n",
         "    \"iterations\" : 2,\n",
         "    \"primary_metric\" : 'AUC_weighted',\n",
-        "    \"preprocess\" : False,\n",
+        "    \"preprocess\" : True,\n",
-        "    \"verbosity\" : logging.INFO,\n",
+        "    \"verbosity\" : logging.INFO\n",
-        "    \"n_cross_validations\": 3\n",
         "}"
       ]
     },
@@ -181,7 +194,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dsvm_name = 'mydsvmc'\n",
+        "dsvm_name = 'mydsvmb'\n",
         "\n",
         "try:\n",
         "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
@@ -257,6 +270,23 @@
         "remote_run"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Pre-process cache cleanup\n",
+        "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.clean_preprocessor_cache()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -376,7 +406,8 @@
       "source": [
         "## Test\n",
         "\n",
-        "#### Load Test Data"
+        "#### Load Test Data\n",
+        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
       ]
     },
     {
@@ -385,12 +416,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from sklearn import datasets\n",
+        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "\n",
+        "dflow_test = dflow_test.drop_nulls('Primary Type')"
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
       ]
     },
     {
@@ -398,7 +425,7 @@
       "metadata": {},
       "source": [
         "#### Testing Our Best Fitted Model\n",
-        "We will try to predict 2 digits and see how our model works."
+        "We will use confusion matrix to see how our model works."
       ]
     },
     {
@@ -407,65 +434,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Randomly select digits and test\n",
+        "from pandas_ml import ConfusionMatrix\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
         "\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
-        "    print(index)\n",
+        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(1, figsize=(3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Appendix"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
         "\n",
-        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+        "\n",
-      ]
+        "ypred = fitted_model.predict(X_test)\n",
-    },
+        "\n",
-    {
+        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
-      "cell_type": "code",
+        "\n",
-      "execution_count": null,
+        "print(cm)\n",
-      "metadata": {},
+        "\n",
-      "outputs": [],
+        "cm.plot()"
-      "source": [
-        "# sklearn.digits.data + target\n",
-        "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(digits_complete.to_pandas_dataframe().shape)\n",
-        "labels_column = 'Column64'\n",
-        "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
-        "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
       ]
     }
   ],

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

@@ -115,23 +115,12 @@
       "outputs": [],
       "source": [
         "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
-        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
-        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
-        "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
-        "\n",
         "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
         "# and convert column types manually.\n",
-        "# Here we read a comma delimited file and convert all columns to integers.\n",
+        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-      ]
+        "dflow.get_profile()"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Review the Data Preparation Result\n",
-        "\n",
-        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
       ]
     },
     {
@@ -140,7 +129,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X.skip(1).head(5)"
+        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
+        "dflow = dflow.drop_nulls('Primary Type')\n",
+        "dflow.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Review the Data Preparation Result\n",
+        "\n",
+        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
+        "\n",
+        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
       ]
     },
     {
@@ -162,7 +174,7 @@
         "    \"iteration_timeout_minutes\" : 10,\n",
         "    \"iterations\" : 2,\n",
         "    \"primary_metric\" : 'AUC_weighted',\n",
-        "    \"preprocess\" : False,\n",
+        "    \"preprocess\" : True,\n",
         "    \"verbosity\" : logging.INFO\n",
         "}"
       ]
@@ -326,7 +338,8 @@
       "source": [
         "## Test\n",
         "\n",
-        "#### Load Test Data"
+        "#### Load Test Data\n",
+        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
       ]
     },
     {
@@ -335,12 +348,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from sklearn import datasets\n",
+        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "\n",
+        "dflow_test = dflow_test.drop_nulls('Primary Type')"
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
       ]
     },
     {
@@ -348,7 +357,7 @@
       "metadata": {},
       "source": [
         "#### Testing Our Best Fitted Model\n",
-        "We will try to predict 2 digits and see how our model works."
+        "We will use confusion matrix to see how our model works."
       ]
     },
     {
@@ -357,65 +366,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Randomly select digits and test\n",
+        "from pandas_ml import ConfusionMatrix\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
         "\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
-        "    print(index)\n",
+        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(1, figsize=(3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Appendix"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
         "\n",
-        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+        "ypred = fitted_model.predict(X_test)\n",
-      ]
+        "\n",
-    },
+        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
-    {
+        "\n",
-      "cell_type": "code",
+        "print(cm)\n",
-      "execution_count": null,
+        "\n",
-      "metadata": {},
+        "cm.plot()"
-      "outputs": [],
-      "source": [
-        "# sklearn.digits.data + target\n",
-        "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(digits_complete.to_pandas_dataframe().shape)\n",
-        "labels_column = 'Column64'\n",
-        "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
-        "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
       ]
     }
   ],

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

@@ -220,7 +220,7 @@
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**country**|The country used to generate holiday features. These should be ISO 3166 two-letter country codes (i.e. 'US', 'GB').|\n",
+        "|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
         "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
       ]
     },
@@ -235,8 +235,8 @@
         "    \"time_column_name\": time_column_name,\n",
         "    # these columns are a breakdown of the total and therefore a leak\n",
         "    \"drop_column_names\": ['casual', 'registered'],\n",
-        "    # knowing the country allows Automated ML to bring in holidays\n",
+        "    # knowing the country/region allows Automated ML to bring in holidays\n",
-        "    \"country\" : 'US',\n",
+        "    \"country_or_region\" : 'US',\n",
         "    \"max_horizon\" : max_horizon,\n",
         "    \"target_lags\": 1    \n",
         "}\n",

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

@@ -112,9 +112,7 @@
       "metadata": {},
       "source": [
         "### Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you attach the default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -125,34 +123,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(\"CPU\")"
-        "from azureml.core.compute import ComputeTarget\n",
-        "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"automlcl\"\n",
-        "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {

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

@@ -130,7 +130,7 @@
         "    print('Found an existing DSVM.')\n",
         "except:\n",
         "    print('Creating a new DSVM.')\n",
-        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2s_v3\")\n",
+        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
         "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
         "    dsvm_compute.wait_for_completion(show_output = True)\n",
         "    print(\"Waiting one minute for ssh to be accessible\")\n",

how-to-use-azureml/azure-hdi/automl_hdi_local_classification.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/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, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {

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."
       ]
     },
     {

how-to-use-azureml/deploy-to-local/register-model-deploy-local.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.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."
       ]
     },
     {

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

@@ -0,0 +1,102 @@
+
+# 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: Check your FPGA quota
+Use the Azure CLI to check whether you have quota.
+
+```shell
+az vm list-usage --location "eastus" -o table
+```
+
+The other locations are ``southeastasia``, ``westeurope``, and ``westus2``.
+
+Under the "Name" column, look for "Standard PBS Family vCPUs" and ensure you have at least 6 vCPUs under "CurrentValue."
+
+If you do not have quota, then submit a request form [here](https://aka.ms/accelerateAI).
+
+### Step 3: 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 4: 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 +1,492 @@
+{
+ "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",
+    "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
+    "                                                    agent_count = 1, \n",
+    "                                                    location = \"eastus\")\n",
+    "\n",
+    "aks_name = 'aks-pb6-obj'\n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "# Authentication is enabled by default, but for testing we specify False\n",
+    "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
+    "                                                num_replicas=1,\n",
+    "                                                auth_enabled = False)\n",
+    "\n",
+    "aks_service_name ='my-aks-service'\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 +1,546 @@
+{
+ "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",
+    "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
+    "                                                    agent_count = 1, \n",
+    "                                                    location = \"eastus\")\n",
+    "\n",
+    "aks_name = 'my-aks-pb6'\n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can also check the status in your Workspace under Compute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "# Authentication is enabled by default, but for testing we specify False\n",
+    "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
+    "                                                num_replicas=1,\n",
+    "                                                auth_enabled = False)\n",
+    "\n",
+    "aks_service_name ='my-aks-service'\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 +1,860 @@
+{
+ "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 Image](#deploy-image)\n",
+    "* [Test the service](#test-service)\n",
+    "* [Clean-up](#cleanup)\n",
+    "* [Appendix](#appendix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"setup\"></a>\n",
+    "## 1. Setup Environment\n",
+    "#### 1.a. Please set up your environment as described in the [Quickstart](./accelerated-models-quickstart.ipynb), meaning:\n",
+    "* Make sure your Workspace config.json exists and has the correct info\n",
+    "* Install Tensorflow\n",
+    "\n",
+    "#### 1.b. Download dataset into ~/catsanddogs \n",
+    "The dataset we will be using for training can be downloaded [here](https://www.microsoft.com/en-us/download/details.aspx?id=54765). Download the zip and extract to a directory named 'catsanddogs' under your user directory (\"~/catsanddogs\"). \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.c. Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from keras import backend as K\n",
+    "import sklearn\n",
+    "import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.d. Create directories for later use\n",
+    "After you train your model in float32, you'll write the weights to a place on disk. We also need a location to store the models that get downloaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "custom_weights_dir = os.path.expanduser(\"~/custom-weights\")\n",
+    "saved_model_dir = os.path.expanduser(\"~/models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"prepare-data\"></a>\n",
+    "## 2. Prepare Data\n",
+    "Load the files we are going to use for training and testing. By default this notebook uses only a very small subset of the Cats and Dogs dataset. That makes it run relatively quickly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import imghdr\n",
+    "datadir = os.path.expanduser(\"~/catsanddogs\")\n",
+    "\n",
+    "cat_files = glob.glob(os.path.join(datadir, 'PetImages', 'Cat', '*.jpg'))\n",
+    "dog_files = glob.glob(os.path.join(datadir, 'PetImages', 'Dog', '*.jpg'))\n",
+    "\n",
+    "# Limit the data set to make the notebook execute quickly.\n",
+    "cat_files = cat_files[:64]\n",
+    "dog_files = dog_files[:64]\n",
+    "\n",
+    "# The data set has a few images that are not jpeg. Remove them.\n",
+    "cat_files = [f for f in cat_files if imghdr.what(f) == 'jpeg']\n",
+    "dog_files = [f for f in dog_files if imghdr.what(f) == 'jpeg']\n",
+    "\n",
+    "if(not len(cat_files) or not len(dog_files)):\n",
+    "    print(\"Please download the Kaggle Cats and Dogs dataset form https://www.microsoft.com/en-us/download/details.aspx?id=54765 and extract the zip to \" + datadir)    \n",
+    "    raise ValueError(\"Data not found\")\n",
+    "else:\n",
+    "    print(cat_files[0])\n",
+    "    print(dog_files[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct a numpy array as labels\n",
+    "image_paths = cat_files + dog_files\n",
+    "total_files = len(cat_files) + len(dog_files)\n",
+    "labels = np.zeros(total_files)\n",
+    "labels[len(cat_files):] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split images data as training data and test data\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "onehot_labels = np.array([[0,1] if i else [1,0] for i in labels])\n",
+    "img_train, img_test, label_train, label_test = train_test_split(image_paths, onehot_labels, random_state=42, shuffle=True)\n",
+    "\n",
+    "print(len(img_train), len(img_test), label_train.shape, label_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"construct-model\"></a>\n",
+    "## 3. Construct Model\n",
+    "We will define the functions to handle creating the preprocessor and the classifier first, and then run them together to actually construct the model with the Resnet50 featurizer in a single Tensorflow session in a separate cell.\n",
+    "\n",
+    "We use ResNet50 for the featurizer and build our own classifier using Keras layers. We train the featurizer and the classifier as one model. We will provide parameters to determine whether we are using the quantized version and whether we are using custom weights in training or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.a. Define image preprocessing step\n",
+    "Same as in the Quickstart, before passing image dataset to the ResNet50 featurizer, we need to preprocess the input file to get it into the form expected by ResNet50. ResNet50 expects float tensors representing the images in BGR, channel last order. We've provided a default implementation of the preprocessing that you can use.\n",
+    "\n",
+    "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.accel.models.utils as utils\n",
+    "\n",
+    "def preprocess_images(scaling_factor=1.0):\n",
+    "    # Convert images to 3D tensors [width,height,channel] - channels are in BGR order.\n",
+    "    in_images = tf.placeholder(tf.string)\n",
+    "    image_tensors = utils.preprocess_array(in_images, 'RGB', scaling_factor)\n",
+    "    return in_images, image_tensors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.b. Define classifier\n",
+    "We use Keras layer APIs to construct the classifier. Because we're using the tensorflow backend, we can train this classifier in one session with our Resnet50 model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def construct_classifier(in_tensor, seed=None):\n",
+    "    from keras.layers import Dropout, Dense, Flatten\n",
+    "    from keras.initializers import glorot_uniform\n",
+    "    K.set_session(tf.get_default_session())\n",
+    "\n",
+    "    FC_SIZE = 1024\n",
+    "    NUM_CLASSES = 2\n",
+    "\n",
+    "    x = Dropout(0.2, input_shape=(1, 1, int(in_tensor.shape[3]),), seed=seed)(in_tensor)\n",
+    "    x = Dense(FC_SIZE, activation='relu', input_dim=(1, 1, int(in_tensor.shape[3]),),\n",
+    "              kernel_initializer=glorot_uniform(seed=seed), bias_initializer='zeros')(x)\n",
+    "    x = Flatten()(x)\n",
+    "    preds = Dense(NUM_CLASSES, activation='softmax', input_dim=FC_SIZE, name='classifier_output',\n",
+    "                  kernel_initializer=glorot_uniform(seed=seed), bias_initializer='zeros')(x)\n",
+    "    return preds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.c. Define model construction\n",
+    "Now that the preprocessor and classifier for the model are defined, we can define how we want to construct the model. \n",
+    "\n",
+    "Constructing the model has these steps: \n",
+    "1. Get preprocessing steps\n",
+    "* Get featurizer using the Azure ML Accel Models SDK:\n",
+    "    * import the graph definition\n",
+    "    * restore the weights of the model into a Tensorflow session\n",
+    "* Get classifier\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def construct_model(quantized, starting_weights_directory = None):\n",
+    "    from azureml.accel.models import Resnet50, QuantizedResnet50\n",
+    "    \n",
+    "    # Convert images to 3D tensors [width,height,channel]\n",
+    "    in_images, image_tensors = preprocess_images(1.0)\n",
+    "\n",
+    "    # Construct featurizer using quantized or unquantized ResNet50 model\n",
+    "    if not quantized:\n",
+    "        featurizer = Resnet50(saved_model_dir)\n",
+    "    else:\n",
+    "        featurizer = QuantizedResnet50(saved_model_dir, custom_weights_directory = starting_weights_directory)\n",
+    "\n",
+    "    features = featurizer.import_graph_def(input_tensor=image_tensors)\n",
+    "    \n",
+    "    # Construct classifier\n",
+    "    preds = construct_classifier(features)\n",
+    "    \n",
+    "    # Initialize weights\n",
+    "    sess = tf.get_default_session()\n",
+    "    tf.global_variables_initializer().run()\n",
+    "\n",
+    "    featurizer.restore_weights(sess)\n",
+    "\n",
+    "    return in_images, image_tensors, features, preds, featurizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"train-model\"></a>\n",
+    "## 4. Train Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_files(files):\n",
+    "    \"\"\" Read files to array\"\"\"\n",
+    "    contents = []\n",
+    "    for path in files:\n",
+    "        with open(path, 'rb') as f:\n",
+    "            contents.append(f.read())\n",
+    "    return contents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(preds, in_images, img_train, label_train, is_retrain = False, train_epoch = 10, learning_rate=None):\n",
+    "    \"\"\" training model \"\"\"\n",
+    "    from keras.objectives import binary_crossentropy\n",
+    "    from tqdm import tqdm\n",
+    "    \n",
+    "    learning_rate = learning_rate if learning_rate else 0.001 if is_retrain else 0.01\n",
+    "        \n",
+    "    # Specify the loss function\n",
+    "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))   \n",
+    "    cross_entropy = tf.reduce_mean(binary_crossentropy(in_labels, preds))\n",
+    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n",
+    "\n",
+    "    def chunks(a, b, n):\n",
+    "        \"\"\"Yield successive n-sized chunks from a and b.\"\"\"\n",
+    "        if (len(a) != len(b)):\n",
+    "            print(\"a and b are not equal in chunks(a,b,n)\")\n",
+    "            raise ValueError(\"Parameter error\")\n",
+    "\n",
+    "        for i in range(0, len(a), n):\n",
+    "            yield a[i:i + n], b[i:i + n]\n",
+    "\n",
+    "    chunk_size = 16\n",
+    "    chunk_num = len(label_train) / chunk_size\n",
+    "\n",
+    "    sess = tf.get_default_session()\n",
+    "    for epoch in range(train_epoch):\n",
+    "        avg_loss = 0\n",
+    "        for img_chunk, label_chunk in tqdm(chunks(img_train, label_train, chunk_size)):\n",
+    "            contents = read_files(img_chunk)\n",
+    "            _, loss = sess.run([optimizer, cross_entropy],\n",
+    "                                feed_dict={in_images: contents,\n",
+    "                                           in_labels: label_chunk,\n",
+    "                                           K.learning_phase(): 1})\n",
+    "            avg_loss += loss / chunk_num\n",
+    "        print(\"Epoch:\", (epoch + 1), \"loss = \", \"{:.3f}\".format(avg_loss))\n",
+    "            \n",
+    "        # Reach desired performance\n",
+    "        if (avg_loss < 0.001):\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"test-model\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"test-model\"></a>\n",
+    "## 5. Test Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_model(preds, in_images, img_test, label_test):\n",
+    "    \"\"\"Test the model\"\"\"\n",
+    "    from keras.metrics import categorical_accuracy\n",
+    "\n",
+    "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))\n",
+    "    accuracy = tf.reduce_mean(categorical_accuracy(in_labels, preds))\n",
+    "    contents = read_files(img_test)\n",
+    "\n",
+    "    accuracy = accuracy.eval(feed_dict={in_images: contents,\n",
+    "                                        in_labels: label_test,\n",
+    "                                        K.learning_phase(): 0})\n",
+    "    return accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"execution\"></a>\n",
+    "## 6. Execute steps\n",
+    "You can run through the Transfer Learning section, then skip to Create AccelContainerImage. By default, because the custom weights section takes much longer for training twice, it is not saved as executable cells. You can copy the code or change cell type to 'Code'.\n",
+    "\n",
+    "<a id=\"transfer-learning\"></a>\n",
+    "### 6.a. Training using Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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",
+    "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
+    "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
+    "                                                    agent_count = 1,\n",
+    "                                                    location = \"eastus\")\n",
+    "\n",
+    "aks_name = 'aks-pb6-tl'\n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "# Authentication is enabled by default, but for testing we specify False\n",
+    "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
+    "                                                num_replicas=1,\n",
+    "                                                auth_enabled = False)\n",
+    "\n",
+    "aks_service_name ='my-aks-service'\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": {},

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

@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "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": {},

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

@@ -4,7 +4,7 @@ These tutorials show how to create and deploy Open Neural Network eXchange ([ONN
 
 ## 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 +34,6 @@ 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.
+
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/README.png)

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)"
       ]

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-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-facial-expression-recognition-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 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",

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",

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)"
       ]

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`)"
       ]

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

@@ -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/production-deploy-to-aks/production-deploy-to-aks.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.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/register-model-create-image-deploy-service/register-model-create-image-deploy-service.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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."
       ]
     },
     {

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.
+1. [Explain tabular data locally](explain-tabular-data-local): 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.
 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.
+5. [Explain tabular data with Run History](explain-tabular-data-run-history): Explain a 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 raw features](explain-tabular-data-raw-features): Explain the raw features of a trained model.
-7. [Explain scikit-learn raw features](explain-sklearn-raw-features): Explain the raw features of a trained scikit-learn model.

how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.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/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -32,7 +39,7 @@
       "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."
       ]
     },
     {
@@ -196,9 +203,6 @@
         "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-explain-model'\n",
@@ -576,7 +580,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "wamartin"
+        "name": "mesameki"
       }
     ],
     "kernelspec": {

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

@@ -7,6 +7,13 @@
         "# 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": {},
@@ -24,7 +31,8 @@
         "\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"
+        "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."
       ]
     },
     {
@@ -181,7 +189,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
+        "# explain the first member of the test set\n",
+        "instance_num = 0\n",
+        "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
       ]
     },
     {
@@ -190,9 +200,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# local feature importance information\n",
+        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "print('local importance for first instance: {}'.format(local_importance_values[y_test[0]]))"
+        "\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"
       ]
     },
     {
@@ -201,7 +223,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print('local importance feature names: {}'.format(list(local_explanation.features)))"
+        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
+        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "# Or, in Jupyter Labs, uncomment below\n",
+        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "# jupyter labextension install microsoft-mli-widget"
       ]
     },
     {
@@ -210,14 +237,23 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dict(zip(local_explanation.features, local_explanation.local_importance_values[y_test[0]]))"
+        "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": "wamartin"
+        "name": "mesameki"
       }
     ],
     "kernelspec": {

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

@@ -0,0 +1,280 @@
+{
+  "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": [
+        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
+        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "# Or, in Jupyter Labs, uncomment below\n",
+        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "# jupyter labextension install microsoft-mli-widget"
+      ]
+    },
+    {
+      "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

@@ -7,6 +7,13 @@
         "# 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": {},
@@ -24,7 +31,8 @@
         "\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."
+        "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."
       ]
     },
     {
@@ -85,10 +93,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
+        "reg = 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)"
+        "model = reg.fit(x_train, y_train)"
       ]
     },
     {
@@ -125,15 +133,6 @@
         "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,
@@ -196,16 +195,58 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# local feature importance information\n",
+        "# sorted local feature importance information; reflects the original feature order\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
-        "print('local importance values: {}'.format(local_importance_values))"
+        "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": [
+        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
+        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "# Or, in Jupyter Labs, uncomment below\n",
+        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "# jupyter labextension install microsoft-mli-widget"
+      ]
+    },
+    {
+      "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": "wamartin"
+        "name": "mesameki"
       }
     ],
     "kernelspec": {

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

@@ -0,0 +1,302 @@
+{
+  "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": [
+        "![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": [
+        "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": [
+        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
+        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "# Or, in Jupyter Labs, uncomment below\n",
+        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "# jupyter labextension install microsoft-mli-widget"
+      ]
+    },
+    {
+      "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

@@ -7,6 +7,13 @@
         "# 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": {},
@@ -229,7 +236,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "wamartin"
+        "name": "mesameki"
       }
     ],
     "kernelspec": {

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

@@ -7,6 +7,13 @@
         "# 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": {},
@@ -243,7 +250,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "wamartin"
+        "name": "mesameki"
       }
     ],
     "kernelspec": {

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

@@ -37,20 +37,11 @@ Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency.
 In this directory, there are two types of notebooks: 
 
 * The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder:
-
+Take a look at [intro-to-pipelines](./intro-to-pipelines/) for the list of notebooks that introduce Azure Machine Learning concepts for you.
-1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started): Start with this notebook to understand the concepts of using Azure Machine Learning Pipelines. This notebook will show you how to runs steps in parallel and in sequence.
-2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep): This notebooks shows how to connect steps in your pipeline using data. Data produced by one step is used by subsequent steps to force an explicit dependency between steps. 
-3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep): Once you are satisfied with your iterative runs in, you could publish your pipeline to get a REST endpoint which could be invoked from non-Pythons clients as well. 
-4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
-5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
-6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
-7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
-7. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
-8. [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.
-9. [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. 
-10. [aml-pipelines-with-automated-machine-learning-step.ipynb](https://aka.ms/pl-automl): AutoMLStep in Pipelines shows how you can do automated machine learning using Pipelines.
 
 * The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
 
 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)
+2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans): This notebook demonstrates a multi-step pipeline that uses GPU compute.
+
+ ![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 +8,13 @@
         "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-data-transfer.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -53,7 +60,7 @@
       "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",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.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-getting-started.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -37,7 +44,7 @@
       "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](../../../configuration.ipynb) 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"
       ]
     },
     {
@@ -146,7 +153,7 @@
       "metadata": {},
       "source": [
         "### Datastore concepts\n",
-        "A [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py) is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target. \n",
+        "A [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target. \n",
         "\n",
         "A Datastore can either be backed by an Azure File Storage (default) or by an Azure Blob Storage.\n",
         "\n",
@@ -189,7 +196,7 @@
       "metadata": {},
       "source": [
         "#### (Optional) See your files using Azure Portal\n",
-        "Once you successfully uploaded the files, you can browse to them (or upload more files) using [Azure Portal](https://portal.azure.com). At the portal, make sure you have selected **AzureML Nursery** as your subscription (click *Resource Groups* and then select the subscription). Then look for your **Machine Learning Workspace** (it has your *alias* as the name). It has a link to your storage. Click on the storage link. It will take you to a page where you can see [Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), [Tables](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview), and [Queues](https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction). We have just uploaded a file to the Blob storage and another one to the File storage. You should be able to see both of these files in their respective locations. "
+        "Once you successfully uploaded the files, you can browse to them (or upload more files) using [Azure Portal](https://portal.azure.com). At the portal, make sure you have selected your subscription (click *Resource Groups* and then select the subscription). Then look for your **Machine Learning Workspace** name. It has a link to your storage. Click on the storage link. It will take you to a page where you can see [Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), [Tables](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview), and [Queues](https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction). We have uploaded a file each to the Blob storage and to the File storage in the above step. You should be able to see both of these files in their respective locations. "
       ]
     },
     {
@@ -226,15 +233,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Retrieve or create a Azure Machine Learning compute\n",
+        "#### Retrieve default Azure Machine Learning compute\n",
-        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
+        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Azure Machine Learning Compute in the current workspace. We will then run the training script on this compute target."
-        "\n",
-        "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
-        "\n",
-        "1. Create the configuration\n",
-        "2. Create the Azure Machine Learning compute\n",
-        "\n",
-        "**This process will take about 3 minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**"
       ]
     },
     {
@@ -243,22 +243,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute_target import ComputeTargetException\n",
+        "aml_compute = ws.get_default_compute_target(\"CPU\")"
-        "\n",
-        "aml_compute_target = \"aml-compute\"\n",
-        "try:\n",
-        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
-        "    print(\"found existing compute target.\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"creating new compute target\")\n",
-        "    \n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
-        "                                                                min_nodes = 1, \n",
-        "                                                                max_nodes = 4)    \n",
-        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
-        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "    \n",
-        "print(\"Azure Machine Learning Compute attached\")\n"
       ]
     },
     {
@@ -295,11 +280,15 @@
         "## Creating a Step in a Pipeline\n",
         "A Step is a unit of execution. Step typically needs a target of execution (compute target), a script to execute, and may require script arguments and inputs, and can produce outputs. The step also could take a number of other parameters. Azure Machine Learning Pipelines provides the following built-in Steps:\n",
         "\n",
-        "- [**PythonScriptStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py): Add a step to run a Python script in a Pipeline.\n",
+        "- [**PythonScriptStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py): Adds a step to run a Python script in a Pipeline.\n",
         "- [**AdlaStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py): Adds a step to run U-SQL script using Azure Data Lake Analytics.\n",
         "- [**DataTransferStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep?view=azure-ml-py): Transfers data between Azure Blob and Data Lake accounts.\n",
         "- [**DatabricksStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py): Adds a DataBricks notebook as a step in a Pipeline.\n",
         "- [**HyperDriveStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivestep?view=azure-ml-py): Creates a Hyper Drive step for Hyper Parameter Tuning in a Pipeline.\n",
+        "- [**AzureBatchStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatch_step.azurebatchstep?view=azure-ml-py): Creates a step for submitting jobs to Azure Batch\n",
+        "- [**EstimatorStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py): Adds a step to run Estimator in a Pipeline.\n",
+        "- [**MpiStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step.mpistep?view=azure-ml-py): Adds a step to run a MPI job in a Pipeline.\n",
+        "- [**AutoMLStep**](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automlstep?view=azure-ml-py): Creates a AutoML step in a Pipeline.\n",
         "\n",
         "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the project folder.\n",
         "\n",
@@ -386,9 +375,6 @@
         "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
         "# specify CondaDependencies obj\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
         "\n",
@@ -467,8 +453,8 @@
       "source": [
         "# Submit syntax\n",
         "# submit(experiment_name, \n",
-        "#        pipeline_parameters=None, \n",
+        "#        pipeline_params=None, \n",
-        "#        continue_on_node_failure=False, \n",
+        "#        continue_on_step_failure=False, \n",
         "#        regenerate_outputs=False)\n",
         "\n",
         "pipeline_run1 = Experiment(ws, 'Hello_World1').submit(pipeline1, regenerate_outputs=False)\n",

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. "
       ]
@@ -106,25 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "batch_compute_name = 'mybatchcompute' # Name to associate with new compute in workspace\n",
+        "batch_compute = ws.get_default_compute_target(\"CPU\")"
-        "\n",
-        "# Batch account details needed to attach as compute to workspace\n",
-        "batch_account_name = \"<batch_account_name>\" # Name of the Batch account\n",
-        "batch_resource_group = \"<batch_resource_group>\" # Name of the resource group which contains this account\n",
-        "\n",
-        "try:\n",
-        "    # check if already attached\n",
-        "    batch_compute = BatchCompute(ws, batch_compute_name)\n",
-        "except ComputeTargetException:\n",
-        "    print('Attaching Batch compute...')\n",
-        "    provisioning_config = BatchCompute.attach_configuration(resource_group=batch_resource_group, \n",
-        "                                                            account_name=batch_account_name)\n",
-        "    batch_compute = ComputeTarget.attach(ws, batch_compute_name, provisioning_config)\n",
-        "    batch_compute.wait_for_completion()\n",
-        "    print(\"Provisioning state:{}\".format(batch_compute.provisioning_state))\n",
-        "    print(\"Provisioning errors:{}\".format(batch_compute.provisioning_errors))\n",
-        "\n",
-        "print(\"Using Batch compute:{}\".format(batch_compute.cluster_resource_id))"
       ]
     },
     {
@@ -189,8 +178,8 @@
         "\n",
         "\n",
         "def upload_file_to_datastore(datastore, file_name, content):\n",
-        "    dir = create_local_file(content=content, file_name=file_name)\n",
+        "    src_dir = create_local_file(content=content, file_name=file_name)\n",
-        "    datastore.upload(src_dir=dir, overwrite=True, show_progress=True)"
+        "    datastore.upload(src_dir=src_dir, overwrite=True, show_progress=True)"
       ]
     },
     {

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`)"
       ]
@@ -69,18 +76,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you use default `AmlCompute` as your training compute resource."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
-        "1. create the configuration (this step is local and only takes a second)\n",
-        "2. create the cluster (this step will take about **20 seconds**)\n",
-        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
       ]
     },
     {
@@ -89,25 +86,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "cpu_cluster = ws.get_default_compute_target(\"CPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"cpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it uses the scale settings for the cluster\n",
-        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(cpu_cluster.get_status().serialize())"

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"
       ]
     },
     {
@@ -128,14 +135,7 @@
       "metadata": {},
       "source": [
         "## Retrieve or create a Azure Machine Learning compute\n",
-        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
+        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Azure Machine Learning Compute in the current workspace. We will then run the training script on this compute target."
-        "\n",
-        "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:\n",
-        "\n",
-        "1. Create the configuration\n",
-        "2. Create the Azure Machine Learning compute\n",
-        "\n",
-        "**This process will take a few minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**\n"
       ]
     },
     {
@@ -144,20 +144,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "cluster_name = \"gpucluster\"\n",
+        "compute_target = ws.get_default_compute_target(\"GPU\")"
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target {}.'.format(cluster_name))\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
-        "                                                               max_nodes=4)\n",
-        "\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "    compute_target.wait_for_completion(show_output=True, timeout_in_minutes=20)\n",
-        "\n",
-        "print(\"Azure Machine Learning Compute attached\")"
       ]
     },
     {
@@ -259,7 +246,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "hd_config = HyperDriveRunConfig(estimator=est, \n",
+        "hd_config = HyperDriveConfig(estimator=est, \n",
         "                             hyperparameter_sampling=ps,\n",
         "                             policy=early_termination_policy,\n",
         "                             primary_metric_name='validation_acc', \n",
@@ -295,7 +282,7 @@
         "### HyperDriveStep\n",
         "HyperDriveStep can be used to run HyperDrive job as a step in pipeline.\n",
         "- **name:** Name of the step\n",
-        "- **hyperdrive_run_config:** A HyperDriveRunConfig that defines the configuration for this HyperDrive run\n",
+        "- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run\n",
         "- **estimator_entry_script_arguments:** List of command-line arguments for estimator entry script\n",
         "- **inputs:** List of input port bindings\n",
         "- **outputs:** List of output port bindings\n",
@@ -317,7 +304,7 @@
         "\n",
         "hd_step = HyperDriveStep(\n",
         "    name=\"hyperdrive_module\",\n",
-        "    hyperdrive_run_config=hd_config,\n",
+        "    hyperdrive_config=hd_config,\n",
         "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
         "    inputs=[data_folder],\n",
         "    metrics_output=metirics_data)"

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"
       ]
@@ -72,20 +79,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute_target import ComputeTargetException\n",
+        "aml_compute = ws.get_default_compute_target(\"CPU\")"
-        "\n",
-        "aml_compute_target = \"cpucluster\"\n",
-        "try:\n",
-        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
-        "    print(\"found existing compute target.\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"creating new compute target\")\n",
-        "    \n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
-        "                                                                min_nodes = 1, \n",
-        "                                                                max_nodes = 4)    \n",
-        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
-        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n"
       ]
     },
     {

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"
       ]
@@ -47,7 +54,7 @@
       "metadata": {},
       "source": [
         "### Compute Targets\n",
-        "#### Retrieve an already attached Azure Machine Learning Compute"
+        "#### Retrieve the default Azure Machine Learning Compute"
       ]
     },
     {
@@ -56,31 +63,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Run, Experiment, Datastore\n",
+        "aml_compute = ws.get_default_compute_target(\"CPU\")"
-        "\n",
-        "from azureml.widgets import RunDetails\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
-        "aml_compute_target = \"aml-compute\"\n",
-        "try:\n",
-        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
-        "    print(\"Found existing compute target: {}\".format(aml_compute_target))\n",
-        "except:\n",
-        "    print(\"Creating new compute target: {}\".format(aml_compute_target))\n",
-        "    \n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
-        "                                                                min_nodes = 1, \n",
-        "                                                                max_nodes = 4)    \n",
-        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
-        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)"
       ]
     },
     {
@@ -376,7 +359,7 @@
       "source": [
         "### Create a schedule for the pipeline using a Datastore\n",
         "This schedule will run when additions or modifications are made to Blobs in the Datastore.\n",
-        "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Changes made to subfolders in the container/path will not trigger the schedule.\n",
+        "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Note: the path_on_datastore will be under the container for the datastore, so the actual path monitored will be container/path_on_datastore. Changes made to subfolders in the container/path will not trigger the schedule.\n",
         "Note: Only Blob Datastores are supported."
       ]
     },
@@ -396,6 +379,7 @@
         "                           datastore=datastore,\n",
         "                           wait_for_provisioning=True,\n",
         "                           description=\"Schedule Run\")\n",
+        "                          #polling_interval=5, use polling_interval to specify how often to poll for blob additions/modifications. Default value is 5 minutes.\n",
         "                          #path_on_datastore=\"file/path\") use path_on_datastore to specify a specific folder to monitor for changes.\n",
         "\n",
         "# You may want to make sure that the schedule is provisioned properly\n",

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

@@ -0,0 +1,553 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "# How to Setup a PipelineEndpoint and Submit a Pipeline Using the PipelineEndpoint.\n",
+        "In this notebook, we will see how to setup a PipelineEndpoint and run specific pipeline version.\n",
+        "\n",
+        "PipelineEndpoint can be used to update a published pipeline while maintaining same endpoint.\n",
+        "PipelineEndpoint, provides a way to keep track of [PublishedPipelines](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.publishedpipeline) using versions. PipelineEndpoint uses endpoint with version information to trigger underlying published pipeline. Pipeline endpoints are uniquely named within a workspace.  \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prerequisites and AML Basics\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](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"
+      ]
+    },
+    {
+      "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": [
+        "### Notebook Overview\n",
+        "In this notebook, we provide an introduction to Azure machine learning PipelineEndpoints. It covers:\n",
+        "* [Create PipelineEndpoint](#Create-PipelineEndpoint), How to create PipelineEndpoint.\n",
+        "* [Retrieving PipelineEndpoint](#Retrieving-PipelineEndpoint), How to get specific PipelineEndpoint from worskpace by name/Id and get all [PipelineEndpoints](#Get-all-PipelineEndpoints-in-workspace) within workspace.\n",
+        "* [PipelineEndpoint Properties](#PipelineEndpoint-properties). How to get and set PipelineEndpoint properties, such as default version of PipelineEndpoint.\n",
+        "* [PipelineEndpoint Submission](#PipelineEndpoint-Submission). How to run a Pipeline using PipelineEndpoint."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Create PipelineEndpoint\n",
+        "Following are required input parameters to create PipelineEndpoint:\n",
+        "\n",
+        "* *workspace*: AML workspace.\n",
+        "* *name*: name of PipelineEndpoint, it is unique within workspace.\n",
+        "* *description*: description details for PipelineEndpoint.\n",
+        "* *pipeline*: A [Pipeline](#Steps-to-create-simple-Pipeline) or [PublishedPipeline](#Publish-Pipeline), to set default version of PipelineEndpoint.                                                       "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "####  Initialization, Steps to create a Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "aml_compute = ws.get_default_compute_target(\"CPU\")\n",
+        "\n",
+        "# source_directory\n",
+        "source_directory = '.'\n",
+        "# define a single step pipeline for demonstration purpose.\n",
+        "trainStep = PythonScriptStep(\n",
+        "    name=\"Training_Step\",\n",
+        "    script_name=\"train.py\", \n",
+        "    compute_target=aml_compute_target, \n",
+        "    source_directory=source_directory\n",
+        ")\n",
+        "print(\"TrainStep created\")\n",
+        "# build and validate Pipeline\n",
+        "pipeline = Pipeline(workspace=ws, steps=[trainStep])\n",
+        "print(\"Pipeline is built\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Publish Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "\n",
+        "timenow = datetime.now().strftime('%m-%d-%Y-%H-%M')\n",
+        "\n",
+        "pipeline_name = timenow + \"-Pipeline\"\n",
+        "print(pipeline_name)\n",
+        "\n",
+        "published_pipeline = pipeline.publish(\n",
+        "    name=pipeline_name, \n",
+        "    description=pipeline_name)\n",
+        "print(\"Newly published pipeline id: {}\".format(published_pipeline.id))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Publishing PipelineEndpoint\n",
+        "Create PipelineEndpoint with required parameters: workspace, name, description and pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineEndpoint\n",
+        "\n",
+        "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"PipelineEndpointTest\",\n",
+        "                                            pipeline=pipeline, description=\"Test description Notebook\")\n",
+        "pipeline_endpoint"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieving PipelineEndpoint\n",
+        "\n",
+        "PipelineEndpoint is uniquely defined by name and id within workspace. PipelineEndpoint in workspace can be retrived by Id or by name."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get PipelineEndpoint by Name\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n",
+        "pipeline_endpoint_by_name"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get PipelineEndpoint by Id\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#get the PipelineEndpoint Id\n",
+        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n",
+        "endpoint_id = pipeline_endpoint_by_name.id\n",
+        "\n",
+        "pipeline_endpoint_by_id = PipelineEndpoint.get(workspace=ws, id=endpoint_id)\n",
+        "pipeline_endpoint_by_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get all PipelineEndpoints in workspace\n",
+        "Returns all PipelineEndpoints within workspace"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "endpoint_list = PipelineEndpoint.get_all(workspace=ws, active_only=True)\n",
+        "endpoint_list"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### PipelineEndpoint properties"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Default Version of PipelineEndpoint\n",
+        "Default version of PipelineEndpoint starts from \"0\" and increments on addition of pipelines.\n",
+        "\n",
+        "##### Get the Default Version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "default_version = pipeline_endpoint_by_name.get_default_version()\n",
+        "default_version"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#####  Set default version \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name.set_default_version(\"0\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get the Published Pipeline corresponds to specific version of PipelineEndpoint"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline = pipeline_endpoint_by_name.get_pipeline(\"0\")\n",
+        "pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get default version Published Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline = pipeline_endpoint_by_name.get_pipeline()\n",
+        "pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Set Published Pipeline to default version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set Published Pipeline to PipelineEndpoint, if exists\n",
+        "pipeline_endpoint_by_name.set_default(published_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get all Versions in PipelineEndpoint\n",
+        "Returns list of published pipelines and its versions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "versions = pipeline_endpoint_by_name.get_all_versions()\n",
+        "\n",
+        "for ve in versions:\n",
+        "    print(ve.version)\n",
+        "    print(ve.pipeline.id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Get all Published Pipelines in PipelineEndpoint\n",
+        "Returns all active pipelines in PipelineEnpoint, if active_only flag is set to True."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipelines = pipeline_endpoint_by_name.get_all_pipelines(active_only=True)\n",
+        "pipelines"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Name property of PipelineEndpoint\n",
+        "PipelineEndpoint is uniquely identified by name"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Set Name PipelineEndpoint"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name.set_name(name=\"NewName\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Add Published Pipeline to PipelineEndpoint, \n",
+        "Adding published pipeline, if its not present in PipelineEndpoint."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name.add(published_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Add Published pipeline to PipelineEndpoint and set it to default version\n",
+        "Adding published pipeline to PipelineEndpoint if not present and set it to default"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name.add_default(published_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### PipelineEndpoint Submission\n",
+        "PipelineEndpoint triggers specific versioned pipeline or default pipeline by:\n",
+        "* Rest Endpoint \n",
+        "* Submit call."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Run Pipeline by endpoint property of PipelineEndpoint\n",
+        "Run specific pipeline using endpoint property of PipelineEndpoint and executing http post."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n",
+        "\n",
+        "# endpoint with id \n",
+        "rest_endpoint_id =  pipeline_endpoint_by_name.endpoint\n",
+        "\n",
+        "# for default version pipeline\n",
+        "rest_endpoint_id_without_version_with_id = rest_endpoint_id\n",
+        "\n",
+        "# for specific version pipeline just append version info\n",
+        "version=\"0\"\n",
+        "rest_endpoint_id_with_version = rest_endpoint_id_without_version_with_id+\"/\"+ version\n",
+        "print(rest_endpoint_id_with_version)\n",
+        "pipeline_endpoint_by_name"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# endpoint with name\n",
+        "rest_endpoint_name = rest_endpoint_id.split(\"Id\", 1)[0] + \"Name?name=\" + pipeline_endpoint_by_name.name\n",
+        "\n",
+        "# for default version pipeline\n",
+        "rest_endpoint_name_without_version = rest_endpoint_name\n",
+        "\n",
+        "# for specific version pipeline just append version info\n",
+        "version=\"0\"\n",
+        "rest_endpoint_name_with_version = rest_endpoint_name_without_version+\"&pipelineVersion=\"+ version\n",
+        "print(rest_endpoint_name_with_version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "import requests\n",
+        "\n",
+        "auth = InteractiveLoginAuthentication()\n",
+        "aad_token = auth.get_authentication_header()\n",
+        "\n",
+        "#endpoint = pipeline_endpoint_by_name.url\n",
+        "\n",
+        "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint_name_with_version))\n",
+        "\n",
+        "# specify the param when running the pipeline\n",
+        "response = requests.post(rest_endpoint_name_with_version, \n",
+        "                         headers=aad_token, \n",
+        "                         json={\"ExperimentName\": \"default_pipeline\",\n",
+        "                               \"RunSource\": \"SDK\",\n",
+        "                               \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})\n",
+        "\n",
+        "run_id = response.json()[\"Id\"]\n",
+        "print(run_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Run Pipeline by Submit call of PipelineEndpoint \n",
+        "Run specific pipeline using Submit api of PipelineEndpoint"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# submit pipeline with specific version\n",
+        "run_id = pipeline_endpoint_by_name.submit(\"TestPipelineEndpoint\", pipeline_version=\"0\")\n",
+        "print(run_id)\n",
+        "\n",
+        "# submit pipeline with default version\n",
+        "run_id = pipeline_endpoint_by_name.submit(\"TestPipelineEndpoint\")\n",
+        "print(run_id)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mameghwa"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/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"
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-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-databricks-as-compute-target.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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"
       ]
     },
     {
@@ -397,7 +404,9 @@
       "metadata": {},
       "source": [
         "### 1. Running the demo notebook already added to the Databricks workspace\n",
-        "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:"
+        "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:\n",
+        "\n",
+        "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.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-with-automated-machine-learning-step.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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",
@@ -123,9 +130,7 @@
       "metadata": {},
       "source": [
         "### Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process."
       ]
     },
     {
@@ -134,31 +139,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Choose a name for your cluster.\n",
+        "compute_target = ws.get_default_compute_target(\"CPU\")"
-        "amlcompute_cluster_name = \"cpucluster\"\n",
-        "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = 1, timeout_in_minutes = 10)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -237,7 +218,7 @@
         "    X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
         "    y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
         "\n",
-        "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
+        "    return { \"X\" : X_train.values, \"y\" : y_train.values.flatten() }\n"
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.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-with-data-dependency-steps.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",
         "### Azure Machine Learning and Pipeline SDK-specific Imports"
       ]
@@ -121,7 +128,7 @@
       "metadata": {},
       "source": [
         "#### Retrieve or create a Aml compute\n",
-        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Aml Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target."
+        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target."
       ]
     },
     {
@@ -130,22 +137,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute_target import ComputeTargetException\n",
+        "aml_compute = ws.get_default_compute_target(\"CPU\")\n"
-        "\n",
-        "aml_compute_target = \"aml-compute\"\n",
-        "try:\n",
-        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
-        "    print(\"found existing compute target.\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"creating new compute target\")\n",
-        "    \n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
-        "                                                                min_nodes = 1, \n",
-        "                                                                max_nodes = 4)    \n",
-        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
-        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "    \n",
-        "print(\"Aml Compute attached\")\n"
       ]
     },
     {
@@ -290,9 +282,6 @@
         "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
         "# specify CondaDependencies obj\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
       ]

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.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/pipeline-batch-scoring/pipeline-batch-scoring.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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. "
       ]
     },
     {
@@ -155,7 +162,7 @@
       "metadata": {},
       "source": [
         "### Create and attach Compute targets\n",
-        "Use the below code to create and attach Compute targets. "
+        "Use the below code to get the default Compute target. "
       ]
     },
     {
@@ -164,33 +171,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# choose a name for your cluster\n",
+        "cluster_type = os.environ.get(\"AML_CLUSTER_TYPE\", \"GPU\")\n",
-        "aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpucluster\")\n",
-        "cluster_min_nodes = os.environ.get(\"AML_COMPUTE_MIN_NODES\", 0)\n",
-        "cluster_max_nodes = os.environ.get(\"AML_COMPUTE_MAX_NODES\", 1)\n",
-        "vm_size = os.environ.get(\"AML_COMPUTE_SKU\", \"STANDARD_NC6\")\n",
         "\n",
-        "\n",
+        "compute_target = ws.get_default_compute_target(cluster_type)"
-        "if aml_compute_name in ws.compute_targets:\n",
-        "    compute_target = ws.compute_targets[aml_compute_name]\n",
-        "    if compute_target and type(compute_target) is AmlCompute:\n",
-        "        print('found compute target. just use it. ' + aml_compute_name)\n",
-        "else:\n",
-        "    print('creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size, # NC6 is GPU-enabled\n",
-        "                                                                vm_priority = 'lowpriority', # optional\n",
-        "                                                                min_nodes = cluster_min_nodes, \n",
-        "                                                                max_nodes = cluster_max_nodes)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, aml_compute_name, provisioning_config)\n",
-        "    \n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it will use the scale settings for the cluster\n",
-        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "    \n",
-        "     # For a more detailed view of current Azure Machine Learning Compute  status, use get_status()\n",
-        "    print(compute_target.get_status().serialize())"
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.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/pipeline-style-transfer/pipeline-style-transfer.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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. 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. "
       ]
     },
     {

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",

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

@@ -0,0 +1,260 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License.\n",
+        "\n",
+        "## Authentication in Azure Machine Learning\n",
+        "\n",
+        "This notebook shows you how to authenticate to your Azure ML Workspace using\n",
+        "\n",
+        " 1. Interactive Login Authentication\n",
+        " 2. Azure CLI Authentication\n",
+        " 3. Service Principal Authentication\n",
+        " \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",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Interactive Authentication\n",
+        "\n",
+        "Interactive authentication is the default mode when using Azure ML SDK.\n",
+        "\n",
+        "When you connect to your workspace using workspace.from_config, you will get an interactive login dialog."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Also, if you explicitly specify the subscription ID, resource group and resource group, you will get the dialog."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+        "               resource_group=\"my-ml-rg\",\n",
+        "               workspace_name=\"my-ml-workspace\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note the user you're authenticated as must have access to the subscription and resource group. If you receive an error\n",
+        "\n",
+        "```\n",
+        "AuthenticationException: You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription. All the subscriptions that you have access to = ...\n",
+        "```\n",
+        "\n",
+        "check that the you used correct login and entered the correct subscription ID."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In some cases, you may see a version of the error message containing text: ```All the subscriptions that you have access to = []```\n",
+        "\n",
+        "In such a case, you may have to specify the tenant ID of the Azure Active Directory you're using. An example would be accessing a subscription as a guest to a tenant that is not your default. You specify the tenant by explicitly instantiating _InteractiveLoginAuthentication_ with tenant ID as argument ([see instructions how to obtain tenant Id](#get-tenant-id))."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "\n",
+        "interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\")\n",
+        "\n",
+        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+        "               resource_group=\"my-ml-rg\",\n",
+        "               workspace_name=\"my-ml-workspace\",\n",
+        "               auth=interactive_auth)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Azure CLI Authentication\n",
+        "\n",
+        "If you have installed azure-cli package, and used ```az login``` command to log in to your Azure Subscription, you can use _AzureCliAuthentication_ class.\n",
+        "\n",
+        "Note that interactive authentication described above won't use existing Azure CLI auth tokens. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.authentication import AzureCliAuthentication\n",
+        "\n",
+        "cli_auth = AzureCliAuthentication()\n",
+        "\n",
+        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+        "               resource_group=\"my-ml-rg\",\n",
+        "               workspace_name=\"my-ml-workspace\",\n",
+        "               auth=cli_auth)\n",
+        "\n",
+        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Service Principal Authentication\n",
+        "\n",
+        "When setting up a machine learning workflow as an automated process, we recommend using Service Principal Authentication. This approach decouples the authentication from any specific user login, and allows managed access control.\n",
+        "\n",
+        "Note that you must have administrator privileges over the Azure subscription to complete these steps.\n",
+        "\n",
+        "The first step is to create a service principal. First, go to [Azure Portal](https://portal.azure.com), select **Azure Active Directory** and **App Registrations**. Then select **+New application registration**, give your service principal a name, for example _my-svc-principal_. You can leave application type as is, and specify a dummy value for Sign-on URL, such as _https://invalid_.\n",
+        "\n",
+        "Then click **Create**.\n",
+        "\n",
+        "![service principal creation]<img src=\"images/svc-pr-1.PNG\">"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The next step is to obtain the _Application ID_ (also called username) and create _password_ for the service principal.\n",
+        "\n",
+        "From the page for your newly created service principal, copy the _Application ID_. Then select **Settings** and **Keys**, write a description for your key, and select duration. Then click **Save**, and copy the _password_ to a secure location.\n",
+        "\n",
+        "![application id and password](images/svc-pr-2.PNG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id =\"get-tenant-id\"></a>\n",
+        "\n",
+        "Also, you need to obtain the tenant ID of your Azure subscription. Go back to **Azure Active Directory**, select **Properties** and copy _Directory ID_.\n",
+        "\n",
+        "![tenant id](images/svc-pr-3.PNG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, you need to give the service principal permissions to access your workspace. Navigate to **Resource Groups**, to the resource group for your Machine Learning Workspace. \n",
+        "\n",
+        "Then select **Access Control (IAM)** and **Add a role assignment**. For _Role_, specify which level of access you need to grant, for example _Contributor_. Start entering your service principal name and once it is found, select it, and click **Save**.\n",
+        "\n",
+        "![add role](images/svc-pr-4.PNG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you are ready to use the service principal authentication. For example, to connect to your Workspace, see code below and enter your own values for tenant ID, application ID, subscription ID, resource group and workspace.\n",
+        "\n",
+        "**We strongly recommended that you do not insert the secret password to code**. Instead, you can use environment variables to pass it to your code, for example through Azure Key Vault, or through secret build variables in Azure DevOps. For local testing, you can for example use following PowerShell command to set the environment variable.\n",
+        "\n",
+        "```\n",
+        "$env:AZUREML_PASSWORD = \"my-password\"\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
+        "\n",
+        "svc_pr_password = os.environ.get(\"AZUREML_PASSWORD\")\n",
+        "\n",
+        "svc_pr = ServicePrincipalAuthentication(\n",
+        "    tenant_id=\"my-tenant-id\",\n",
+        "    service_principal_id=\"my-application-id\",\n",
+        "    service_principal_password=svc_pr_password)\n",
+        "\n",
+        "\n",
+        "ws = Workspace(\n",
+        "    subscription_id=\"my-subscription-id\",\n",
+        "    resource_group=\"my-ml-rg\",\n",
+        "    workspace_name=\"my-ml-workspace\",\n",
+        "    auth=svc_pr\n",
+        "    )\n",
+        "\n",
+        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
+      ]
+    }
+  ],
+  "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/training-with-deep-learning/README.md

@@ -16,3 +16,6 @@ These examples show you:
 12. [Use TensorBoard to monitor training execution](tensorboard)
 
 Learn more about how to use `Estimator` class to  [train deep neural networks with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models).
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training-with-deep-learning/README.png)
+

how-to-use-azureml/training-with-deep-learning/distributed-chainer/distributed-chainer.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/training-with-deep-learning/distributed-chainer/distributed-chainer.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -22,7 +29,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
       ]
     },
     {
@@ -88,10 +95,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code gets the default compute cluster.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -102,24 +107,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
         "# use get_status() to get a detailed status for the current AmlCompute. \n",
         "print(compute_target.get_status().serialize())"
@@ -129,7 +117,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+        "The above code retrieves the default GPU compute. If you instead want to use default CPU compute, provide type=\"CPU\"."
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.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/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -23,7 +30,7 @@
       "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`)"
       ]
@@ -91,10 +98,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use default `AmlCompute` as the training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -105,24 +110,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
         "# use get_status() to get a detailed status for the current AmlCompute\n",
         "print(compute_target.get_status().serialize())"

how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.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/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -22,7 +29,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
         "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
       ]
     },
@@ -89,10 +96,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code uses the default compute in the workspace.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -103,24 +108,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
         "# use get_status() to get a detailed status for the current AmlCompute. \n",
         "print(compute_target.get_status().serialize())"
@@ -130,7 +118,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+        "The above code retrieves the default GPU compute. If you instead want to use default CPU compute, provide type=\"CPU\"."
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.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/training/manage-runs/manage-runs.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -23,7 +30,7 @@
       "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 (AML)\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`)\n",
         "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
@@ -91,10 +98,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you use default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -105,24 +110,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(compute_target.get_status().serialize())"
@@ -132,7 +120,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+        "The above code retrieves the default GPU compute. If you instead want to use default CPU compute, provide type=\"CPU\"."
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.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/training-with-deep-learning/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -23,7 +30,7 @@
       "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 (AML)\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`)\n",
         "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
@@ -91,10 +98,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you use default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -105,24 +110,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(compute_target.get_status().serialize())"

how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.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/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -26,7 +33,7 @@
       "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) notebook to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -43,22 +50,6 @@
         "print(\"SDK version:\", azureml.core.VERSION)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Install the Azure ML TensorBoard integration package if you haven't already."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip install azureml-tensorboard"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/dummy_train.py

@@ -1,14 +1,31 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
+import argparse
+
 print("*********************************************************")
 print("Hello Azure ML!")
 
+parser = argparse.ArgumentParser()
+parser.add_argument('--numbers-in-sequence', type=int, dest='num_in_sequence', default=10,
+                    help='number of fibonacci numbers in sequence')
+args = parser.parse_args()
+num = args.num_in_sequence
+
+
+def fibo(n):
+    if n < 2:
+        return n
+    else:
+        return fibo(n - 1) + fibo(n - 2)
+
+
 try:
     from azureml.core import Run
     run = Run.get_context()
     print("Log Fibonacci numbers.")
-    run.log_list('Fibonacci numbers', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
+    for i in range(0, num - 1):
+        run.log('Fibonacci numbers', fibo(i))
     run.complete()
 except:
     print("Warning: you need to install Azure ML SDK in order to log metrics.")

how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.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/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -25,7 +32,7 @@
         "\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`)"
       ]
@@ -106,18 +113,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you use default `AmlCompute` as your training compute resource."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
-        "1. create the configuration (this step is local and only takes a second)\n",
-        "2. create the cluster (this step will take about **20 seconds**)\n",
-        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
       ]
     },
     {
@@ -126,25 +123,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "cpu_cluster = ws.get_default_compute_target(\"CPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"cpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it uses the scale settings for the cluster\n",
-        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(cpu_cluster.get_status().serialize())"
@@ -154,7 +133,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpucluster' of type `AmlCompute`."
+        "Now that you have retrieved the compute target, let's see what the workspace's `compute_targets` property returns."
       ]
     },
     {
@@ -177,7 +156,7 @@
       },
       "source": [
         "## Use a simple script\n",
-        "We have already created a simple \"hello world\" script. This is the script that we will submit through the estimator pattern. It prints a hello-world message, and if Azure ML SDK is installed, it will also logs an array of values ([Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number))."
+        "We have already created a simple \"hello world\" script. This is the script that we will submit through the estimator pattern. It prints a hello-world message, and if Azure ML SDK is installed, it will also logs an array of values ([Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number)). The script takes as input the number of Fibonacci numbers in the sequence to log."
       ]
     },
     {
@@ -228,7 +207,10 @@
       "outputs": [],
       "source": [
         "# use a conda environment, don't use Docker, on local computer\n",
-        "est = Estimator(source_directory='.', compute_target='local', entry_script='dummy_train.py', use_docker=False)\n",
+        "script_params = {\n",
+        "    '--numbers-in-sequence': 10\n",
+        "}\n",
+        "est = Estimator(source_directory='.', script_params=script_params, compute_target='local', entry_script='dummy_train.py', use_docker=False)\n",
         "run = exp.submit(est)\n",
         "RunDetails(run).show()"
       ]
@@ -247,7 +229,10 @@
       "outputs": [],
       "source": [
         "# use a conda environment on default Docker image in an AmlCompute cluster\n",
-        "est = Estimator(source_directory='.', compute_target=cpu_cluster, entry_script='dummy_train.py', use_docker=True)\n",
+        "script_params = {\n",
+        "    '--numbers-in-sequence': 10\n",
+        "}\n",
+        "est = Estimator(source_directory='.', script_params=script_params, compute_target=cpu_cluster, entry_script='dummy_train.py', use_docker=True)\n",
         "run = exp.submit(est)\n",
         "RunDetails(run).show()"
       ]
@@ -266,7 +251,11 @@
       "outputs": [],
       "source": [
         "# add a conda package\n",
+        "script_params = {\n",
+        "    '--numbers-in-sequence': 10\n",
+        "}\n",
         "est = Estimator(source_directory='.', \n",
+        "                script_params=script_params, \n",
         "                compute_target='local', \n",
         "                entry_script='dummy_train.py', \n",
         "                use_docker=False, \n",
@@ -306,7 +295,12 @@
         "user_managed_dependencies = True\n",
         "\n",
         "# submit to a local Docker container. if you don't have Docker engine running locally, you can set compute_target to cpu_cluster.\n",
-        "est = Estimator(source_directory='.', compute_target='local', \n",
+        "script_params = {\n",
+        "    '--numbers-in-sequence': 10\n",
+        "}\n",
+        "est = Estimator(source_directory='.', \n",
+        "                script_params=script_params, \n",
+        "                compute_target='local', \n",
         "                entry_script='dummy_train.py',\n",
         "                custom_docker_image=image_name,\n",
         "                # uncomment below line to use your private ACR\n",
@@ -325,6 +319,130 @@
         "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Intelligent hyperparameter tuning\n",
+        "\n",
+        "The simple \"hello world\" script above lets the user fix the value of a parameter for the number of Fibonacci numbers in the sequence to log. Similarly, when training models, you can fix values of parameters of the training algorithm itself. E.g. the learning rate, the number of layers, the number of nodes in each layer in a neural network, etc. These adjustable parameters that govern the training process are referred to as the hyperparameters of the model. The goal of hyperparameter tuning is to search across various hyperparameter configurations and find the configuration that results in the best performance.\n",
+        "\n",
+        "\n",
+        "To demonstrate how Azure Machine Learning can help you automate the process of hyperarameter tuning, we will launch multiple runs with different values for numbers in the sequence. First let's define the parameter space using random sampling."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive import choice\n",
+        "\n",
+        "ps = RandomParameterSampling(\n",
+        "    {\n",
+        "        '--numbers-in-sequence': choice(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20)\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we will create a new estimator without the above numbers-in-sequence parameter since that will be passed in later. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "est = Estimator(source_directory='.', script_params={}, compute_target=cpu_cluster, entry_script='dummy_train.py', use_docker=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we will look at training metrics and early termination policies. When training a model, users are interested in logging and optimizing certain metrics of the model e.g. maximize the accuracy of the model, or minimize loss. This metric is logged by the training script for each run. In our simple script above, we are logging Fibonacci numbers in a sequence. But a training script could just as easily log other metrics like accuracy or loss, which can be used to evaluate the performance of a given training run.\n",
+        "\n",
+        "The intelligent hyperparameter tuning capability in Azure Machine Learning automatically terminates poorly performing runs using an early termination policy. Early termination reduces wastage of compute resources and instead uses these resources for exploring other hyperparameter configurations. In this example, we use the BanditPolicy. This basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML will terminate the training run. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we are ready to configure a run configuration object for hyperparameter tuning. We need to call out the primary metric that we want the experiment to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script and we specify that we are looking to maximize this value. Next, we control the resource budget for the experiment by setting the maximum total number of training runs to 10. We also set the maximum number of training runs to run concurrently at 4, which is the same as the number of nodes in our computer cluster."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hdc = HyperDriveConfig(estimator=est, \n",
+        "                          hyperparameter_sampling=ps, \n",
+        "                          policy=policy, \n",
+        "                          primary_metric_name='Fibonacci numbers', \n",
+        "                          primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n",
+        "                          max_total_runs=10,\n",
+        "                          max_concurrent_runs=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, let's launch the hyperparameter tuning job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hdr = exp.submit(config=hdc)\n",
+        "RunDetails(hdr).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When all the runs complete, we can find the run with the best performance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = hdr.get_best_run_by_primary_metric()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can register the model from the best run and use it to deploy a web service that can be used for Inferencing. Details on how how you can do this can be found in the sample folders for the ohter types of estimators.\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.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/training-with-deep-learning/tensorboard/tensorboard.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -27,7 +34,7 @@
       "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) notebook to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -44,22 +51,6 @@
         "print(\"SDK version:\", azureml.core.VERSION)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Install the Azure ML TensorBoard package."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip install azureml-tensorboard"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.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/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -23,7 +30,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
       ]
     },
     {
@@ -88,10 +95,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
@@ -102,24 +107,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(compute_target.get_status().serialize())"
@@ -129,7 +117,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+        "The above code retrieves the default GPU compute. If you instead want to use default CPU compute, provide type=\"CPU\"."
       ]
     },
     {
@@ -330,7 +318,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.hyperdrive.runconfig import HyperDriveRunConfig\n",
+        "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
         "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
         "from azureml.train.hyperdrive.policy import BanditPolicy\n",
         "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
@@ -343,7 +331,7 @@
         "    }\n",
         ")\n",
         "\n",
-        "hyperdrive_run_config = HyperDriveRunConfig(estimator=estimator,\n",
+        "hyperdrive_config = HyperDriveConfig(estimator=estimator,\n",
         "                                     hyperparameter_sampling=param_sampling, \n",
         "                                     primary_metric_name='Accuracy',\n",
         "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
@@ -365,7 +353,7 @@
       "outputs": [],
       "source": [
         "# start the HyperDrive run\n",
-        "hyperdrive_run = experiment.submit(hyperdrive_run_config)"
+        "hyperdrive_run = experiment.submit(hyperdrive_config)"
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.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/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -26,7 +33,7 @@
         "\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`)\n",
         "* For local scoring test, you will also need to have `tensorflow` and `keras` installed in the current Jupyter kernel."
@@ -232,18 +239,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you use default `AmlCompute` as your training compute resource."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
-        "1. create the configuration (this step is local and only takes a second)\n",
-        "2. create the cluster (this step will take about **20 seconds**)\n",
-        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
       ]
     },
     {
@@ -252,26 +249,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it uses the scale settings for the cluster\n",
-        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(compute_target.get_status().serialize())"
@@ -281,7 +259,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named \"gpucluster\" of type `AmlCompute`."
+        "Now that you have retrtieved the compute target, let's see what the workspace's `compute_targets` property returns."
       ]
     },
     {
@@ -664,7 +642,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveRunConfig, PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
         "from azureml.train.hyperdrive import choice, loguniform\n",
         "\n",
         "ps = RandomParameterSampling(\n",
@@ -727,7 +705,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "hdc = HyperDriveRunConfig(estimator=est, \n",
+        "hdc = HyperDriveConfig(estimator=est, \n",
         "                       hyperparameter_sampling=ps, \n",
         "                       policy=policy, \n",
         "                       primary_metric_name='Accuracy', \n",

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.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-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.png)"
+         ] 
     },
     {
       "cell_type": "markdown",
@@ -25,7 +32,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.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/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -26,7 +33,7 @@
         "\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`)"
       ]
@@ -254,18 +261,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create or Attach existing AmlCompute\n",
+        "## Get default AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+        "You can create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you use default `AmlCompute` as your training compute resource."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
-        "1. create the configuration (this step is local and only takes a second)\n",
-        "2. create the cluster (this step will take about **20 seconds**)\n",
-        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
       ]
     },
     {
@@ -274,26 +271,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "compute_target = ws.get_default_compute_target(type=\"GPU\")\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it uses the scale settings for the cluster\n",
-        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(compute_target.get_status().serialize())"
@@ -303,7 +281,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpucluster' of type `AmlCompute`."
+        "Now that you have retrieved the compute target, let's see what the workspace's `compute_targets` property returns."
       ]
     },
     {
@@ -684,7 +662,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveRunConfig, PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
         "from azureml.train.hyperdrive import choice, loguniform\n",
         "\n",
         "ps = RandomParameterSampling(\n",
@@ -746,7 +724,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "htc = HyperDriveRunConfig(estimator=est, \n",
+        "htc = HyperDriveConfig(estimator=est, \n",
         "                       hyperparameter_sampling=ps, \n",
         "                       policy=policy, \n",
         "                       primary_metric_name='validation_acc', \n",

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

@@ -8,3 +8,5 @@ Follow these sample notebooks to learn:
 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.
+
+ ![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

@@ -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/training/logging-api/logging-api.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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",
@@ -93,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.0.23, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.0.39, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -264,9 +271,11 @@
       "metadata": {},
       "source": [
         "### Logging vectors\n",
-        "Vectors are good for recording information such as loss curves.  You can log a vector by create a list of numbers and call ``log_list()`` and supply a name and the list, or by repeatedly logging a value using the same name.\n",
+        "Vectors are good for recording information such as loss curves. You can log a vector by creating a list of numbers, calling ``log_list()`` and supplying a name and the list, or by repeatedly logging a value using the same name.\n",
         "\n",
-        "Vectors are presented in Run Details as a chart, and are directly comparable in experiment reports when placed in a chart.  **Note:** vectors logged into the run are expected to be relatively small.  Logging very large vectors into Azure ML can result in reduced performance.  If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded."
+        "Vectors are presented in Run Details as a chart, and are directly comparable in experiment reports when placed in a chart. \n",
+        "\n",
+        "**Note:** vectors logged into the run are expected to be relatively small. Logging very large vectors into Azure ML can result in reduced performance. If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded."
       ]
     },
     {
@@ -297,7 +306,9 @@
         "* Create a dictionary of lists where each list represents a column in the table and call ``log_table()``\n",
         "* Repeatedly call ``log_row()`` providing the same table name with a consistent set of named args as the column values\n",
         "\n",
-        "Tables are presented in Run Details as a chart using the first two columns of the table  **Note:** tables logged into the run are expected to be relatively small.  Logging very large tables into Azure ML can result in reduced performance.  If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded."
+        "Tables are presented in Run Details as a chart using the first two columns of the table  \n",
+        "\n",
+        "**Note:** tables logged into the run are expected to be relatively small.  Logging very large tables into Azure ML can result in reduced performance.  If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded."
       ]
     },
     {
@@ -358,7 +369,7 @@
       "source": [
         "### Uploading files\n",
         "\n",
-        "Any files that are placed in the ``.\\outputs`` directory are automatically uploaded when the run is completed.  These files are also visible in the *Outputs* tab of the Run Details page.  Files can also be uploaded explicitly and stored as artifacts along with the run record.\n"
+        "Files can also be uploaded explicitly and stored as artifacts along with the run record. These files are also visible in the *Outputs* tab of the Run Details page.\n"
       ]
     },
     {
@@ -367,9 +378,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile .\\outputs\\myfile.txt\n",
+        "file_name = 'outputs/myfile.txt'\n",
         "\n",
-        "This is an output file that will be automatically uploaded."
+        "with open(file_name, \"w\") as f:\n",
+        "    f.write('This is an output file that will be uploaded.\\n')\n",
+        "\n",
+        "# Upload the file explicitly into artifacts \n",
+        "run.upload_file(name = file_name, path_or_stream = file_name)"
       ]
     },
     {
@@ -497,7 +512,7 @@
         "## Next steps\n",
         "To experiment more with logging and to understand how metrics can be visualized, go back to the *Start a run* section, try changing the category and scale_factor values and going through the notebook several times.  Play with the KPI, charting, and column selection options on the experiment's Run History reports page to see how the various metrics can be combined and visualized.\n",
         "\n",
-        "After learning about all of the logging options, go to the [train on remote vm](..\\train_on_remote_vm\\train_on_remote_vm.ipnyb) notebook and experiment with logging from remote compute contexts."
+        "After learning about all of the logging options, go to the [train on remote vm](..\\train-on-remote-vm\\train-on-remote-vm.ipynb) notebook and experiment with logging from remote compute contexts."
       ]
     }
   ],

how-to-use-azureml/training/manage-runs/manage-runs.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/training/manage-runs/manage-runs.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace. 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."
       ]

how-to-use-azureml/training/train-in-spark/train-in-spark.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/training/train-in-spark/train-in-spark.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace."
       ]
     },
     {
@@ -110,7 +117,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**Note** You can use Docker-based execution to run the Spark job in local computer or a remote VM. Please see the `train-in-remote-vm` notebook for example on how to configure and run in Docker mode in a VM. Make sure you choose a Docker image that has Spark installed, such as `azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE`."
+        "**Note** You can use Docker-based execution to run the Spark job in local computer or a remote VM. Please see the `train-in-remote-vm` notebook for example on how to configure and run in Docker mode in a VM. Make sure you choose a Docker image that has Spark installed, such as `microsoft/mmlspark:0.12`."
       ]
     },
     {

how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.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/training/train-on-amlcompute/train-on-amlcompute.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace."
       ]
     },
     {
@@ -161,9 +168,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Provision as a run based compute target\n",
+        "### Get the default compute target\n",
         "\n",
-        "You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
+        "In this case, we use the default `AmlCompute`target from the workspace."
       ]
     },
     {
@@ -179,12 +186,10 @@
         "# create a new runconfig object\n",
         "run_config = RunConfiguration()\n",
         "\n",
-        "# signal that you want to use AmlCompute to execute script.\n",
+        "default_compute_target = ws.get_default_compute_target(type=\"CPU\")\n",
-        "run_config.target = \"amlcompute\"\n",
         "\n",
-        "# AmlCompute will be created in the same region as workspace\n",
+        "# signal that you want to use AmlCompute to execute script.\n",
-        "# Set vm size for AmlCompute\n",
+        "run_config.target = default_compute_target.name\n",
-        "run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
         "\n",
         "# enable Docker \n",
         "run_config.environment.docker.enabled = True\n",
@@ -195,9 +200,6 @@
         "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
         "# specify CondaDependencies obj\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
         "\n",

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

@@ -13,23 +13,63 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# 02. Train locally\n",
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-local/train-on-local.png)"
-        "* Create or load workspace.\n",
-        "* Create scripts locally.\n",
-        "* Create `train.py` in a folder, along with a `my.lib` file.\n",
-        "* Configure & execute a local run in a user-managed Python environment.\n",
-        "* Configure & execute a local run in a system-managed Python environment.\n",
-        "* Configure & execute a local run in a Docker environment.\n",
-        "* Query run metrics to find the best model\n",
-        "* Register model for operationalization."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Prerequisites\n",
+        "# 02. Train locally\n",
-        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "_**Train a model locally: Directly on your machine and within a Docker container**_\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "## Table of contents\n",
+        "1. [Introduction](#intro)\n",
+        "1. [Pre-requisites](#pre-reqs)\n",
+        "1. [Initialize Workspace](#init)\n",
+        "1. [Create An Experiment](#exp)\n",
+        "1. [View training and auxiliary scripts](#view)\n",
+        "1. [Configure & Run](#config-run)\n",
+        "    1. User-managed environment\n",
+        "        1. Set the environment up\n",
+        "        1. Submit the script to run in the user-managed environment\n",
+        "        1. Get run history details\n",
+        "    1. System-managed environment\n",
+        "        1. Set the environment up\n",
+        "        1. Submit the script to run in the system-managed environment\n",
+        "        1. Get run history details\n",
+        "    1. Docker-based execution\n",
+        "        1. Set the environment up\n",
+        "        1. Submit the script to run in the system-managed environment\n",
+        "        1. Get run history details\n",
+        "        1. Use a custom Docker image\n",
+        "1. [Query run metrics](#query)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## 1. Introduction <a id='intro'></a>\n",
+        "\n",
+        "In this notebook, we will learn how to:\n",
+        "\n",
+        "* Connect to our AML workspace\n",
+        "* Create or load a workspace\n",
+        "* Configure & execute a local run in:\n",
+        "    - a user-managed Python environment\n",
+        "    - a system-managed Python environment\n",
+        "    - a Docker environment\n",
+        "* Query run metrics to find the best model trained in the run\n",
+        "* Register that model for operationalization"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Pre-requisites <a id='pre-reqs'></a>\n",
+        "In this notebook, we assume that you have set your Azure Machine Learning workspace. If you have not, make sure you go through the [configuration notebook](../../../configuration.ipynb) first. In the end, you should have configuration file that contains the subscription ID, resource group and name of your workspace."
       ]
     },
     {
@@ -48,9 +88,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Initialize Workspace\n",
+        "## 3. Initialize Workspace <a id='init'></a>\n",
         "\n",
-        "Initialize a workspace object from persisted configuration."
+        "Initialize your workspace object from configuration file"
       ]
     },
     {
@@ -69,8 +109,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create An Experiment\n",
+        "## 4. Create An Experiment <a id='exp'></a>\n",
-        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+        "An experiment is a logical container in an Azure ML Workspace. It contains a series of trials called `Runs`. As such, it hosts run records such as run metrics, logs, and other output artifacts from your experiments."
       ]
     },
     {
@@ -88,9 +128,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## View `train.py`\n",
+        "## 5. View training and auxiliary scripts <a id='view'></a>\n",
         "\n",
-        "`train.py` is already created for you."
+        "For convenience, we already created the training (`train.py`) script and supportive libraries (`mylib.py`) for you. Take a few minutes to examine both files."
       ]
     },
     {
@@ -103,13 +143,6 @@
         "    print(f.read())"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note `train.py` also references a `mylib.py` file."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -124,9 +157,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Configure & Run\n",
+        "## 6. Configure & Run <a id='config-run'></a>\n",
-        "### User-managed environment\n",
+        "### 6.A User-managed environment\n",
-        "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
+        "\n",
+        "#### 6.A.a Set the environment up\n",
+        "When using a user-managed environment, you are responsible for ensuring that all the necessary packages are available in the Python environment you choose to run the script in."
       ]
     },
     {
@@ -150,8 +185,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Submit script to run in the user-managed environment\n",
+        "#### 6.A.b Submit the script to run in the user-managed environment\n",
-        "Note whole script folder is submitted for execution, including the `mylib.py` file."
+        "Whatever the way you manage your environment, you need to use the `ScriptRunConfig` class. It allows you to further configure your run by pointing to the `train.py` script and to the working directory, which also contains the `mylib.py` file. These inputs indeed provide the commands to execute in the run. Once the run is configured, you submit it to your experiment."
       ]
     },
     {
@@ -170,7 +205,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Get run history details"
+        "#### 6.A.c Get run history details\n",
+        "\n",
+        "While all calculations were run on your machine (cf. below), by using a `run` you also captured the results of your calculations into your run and experiment. You can then see them on the Azure portal, through the link displayed as output of the following cell.\n",
+        "\n",
+        "**Note**: The recording of the computation results into your run was made possible by the `run.log()` commands in the `train.py` file."
       ]
     },
     {
@@ -193,7 +232,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Block to wait till run finishes."
+        "Block any execution to wait until the run finishes."
       ]
     },
     {
@@ -209,8 +248,25 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### System-managed environment\n",
+        "**Note:** All these calculations were run on your local machine, in the conda environment you defined above. You can find the results in:\n",
-        "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
+        "- `~/.azureml/envs/azureml_xxxx` for the conda environment you just created\n",
+        "- `~/AppData/Local/Temp/azureml_runs/train-on-local_xxxx` for the machine learning models you trained (this path may differ depending on the platform you use). This folder also contains\n",
+        "  - Logs (under azureml_logs/)\n",
+        "  - Output pickled files (under outputs/)\n",
+        "  - The configuration files (credentials, local and docker image setups)\n",
+        "  - The train.py and mylib.py scripts\n",
+        "  - The current notebook\n",
+        "\n",
+        "Take a few minutes to examine the output of the cell above. It shows the content of some of the log files, and extra information on the conda environment used."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 6.B System-managed environment\n",
+        "#### 6.B.a Set the environment up\n",
+        "Now, instead of managing the setup of the environment yourself, you can ask the system to build a new conda environment for you. The environment is built once, and will be reused in subsequent executions as long as the conda dependencies remain unchanged."
       ]
     },
     {
@@ -224,7 +280,6 @@
         "run_config_system_managed = RunConfiguration()\n",
         "\n",
         "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
-        "run_config_system_managed.auto_prepare_environment = True\n",
         "\n",
         "# Specify conda dependencies with scikit-learn\n",
         "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
@@ -235,8 +290,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Submit script to run in the system-managed environment\n",
+        "#### 6.B.b Submit the script to run in the system-managed environment\n",
-        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time,  this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
+        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 minutes.\n",
+        "\n",
+        "The commands used to execute the run are then the same as the ones you used above."
       ]
     },
     {
@@ -253,7 +310,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Get run history details"
+        "#### 6.B.c Get run history details"
       ]
     },
     {
@@ -265,13 +322,6 @@
         "run"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Block and wait till run finishes."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -285,12 +335,34 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Docker-based execution\n",
+        "### 6.C Docker-based execution\n",
-        "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
+        "In this section, you will train the same models, but you will do so in a Docker container, on your local machine. For this, you then need to have the Docker engine installed locally. If you don't have it yet, please follow the instructions below.\n",
         "\n",
-        "NOTE: The GPU base image must be used on Microsoft Azure Services only such as ACI, AML Compute, Azure VMs, and AKS.\n",
+        "#### How to install Docker\n",
         "\n",
-        "You can also ask the system to pull down a Docker image and execute your scripts in it."
+        "- [Linux](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n",
+        "- [MacOs](https://docs.docker.com/docker-for-mac/install/)\n",
+        "- [Windows](https://docs.docker.com/docker-for-windows/install/)\n",
+        "\n",
+        "    In case of issues, troubleshooting documentation can be found [here](https://docs.docker.com/docker-for-windows/troubleshoot/#running-docker-for-windows-in-nested-virtualization-scenarios). Additionally, you can follow the steps below, if Virtualization is not enabled on your machine:\n",
+        "    - Go to Task Manager > Performance\n",
+        "    - Check that Virtualization is enabled\n",
+        "    - If it is not, go to `Start > Settings > Update and security > Recovery > Advanced Startup - Restart now > Troubleshoot > Advanced options > UEFI firmware settings - restart`\n",
+        "    - In the BIOS, go to `Advanced > System options > Click the \"Virtualization Technology (VTx)\" only > Save > Exit > Save all changes` -- This will restart the machine\n",
+        "\n",
+        "**Notes**: \n",
+        "- If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
+        "- If you use a GPU base image, it needs to be used on Microsoft Azure Services such as ACI, AML Compute, Azure VMs, or AKS.\n",
+        "\n",
+        "You can also ask the system to pull down a Docker image and execute your scripts in it.\n",
+        "\n",
+        "#### 6.C.a Set the environment up\n",
+        "\n",
+        "In the cell below, you will configure your run to execute in a Docker container. It will:\n",
+        "- run on a CPU\n",
+        "- contain a conda environment in which the scikit-learn library will be installed.\n",
+        "\n",
+        "As before, you will finish configuring your run by pointing to the `train.py` and `mylib.py` files."
       ]
     },
     {
@@ -301,11 +373,10 @@
       "source": [
         "run_config_docker = RunConfiguration()\n",
         "run_config_docker.environment.python.user_managed_dependencies = False\n",
-        "run_config_docker.auto_prepare_environment = True\n",
         "run_config_docker.environment.docker.enabled = True\n",
         "\n",
         "# use the default CPU-based Docker image from Azure ML\n",
-        "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE  # Reference Docker image\n",
         "\n",
         "# Specify conda dependencies with scikit-learn\n",
         "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
@@ -318,8 +389,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Submit script to run in the system-managed environment\n",
+        "####  6.C.b Submit the script to run in the system-managed environment\n",
-        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 minutes. But this conda environment is reused so long as you don't change the conda dependencies."
+        "\n",
+        "The run is now configured and ready to be executed in a Docker container. If you are running this for the first time, the Docker container will get created, as well as the conda environment inside it. This will take several minutes. Once all this is generated, however, this conda environment will be reused as long as you don't change the conda dependencies."
       ]
     },
     {
@@ -338,7 +410,34 @@
         "    else:\n",
         "        run = exp.submit(src)\n",
         "else:\n",
-        "    print(\"Docker engine not installed.\")"
+        "    print(\"Docker engine is not installed.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Potential issue on Windows and how to solve it\n",
+        "\n",
+        "If you are using a Windows machine, the creation of the Docker image may fail, and you may see the following error message\n",
+        "`docker: Error response from daemon: Drive has not been shared. Failed to launch docker container. Check that docker is running and that C:\\ on Windows and /tmp elsewhere is shared.`\n",
+        "\n",
+        "This is because the process above tries to create a linux-based, i.e. non-windows-based, Docker image. To fix this, you can:\n",
+        "- Open the Docker user interface\n",
+        "- Navigate to Settings > Shared drives\n",
+        "- Select C (or both C and D, if you have one)\n",
+        "- Apply\n",
+        "\n",
+        "When this is done, you can try and re-run the command above.\n",
+        "\n",
+        "<img src=\"./docker_settings.png\" width=\"500\" align=\"left\">"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 6.C.c Get run history details"
       ]
     },
     {
@@ -364,22 +463,51 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Use a custom Docker image\n",
+        "The results obtained here should be the same as those obtained before. However, take a look at the \"Execution summary\" section in the output of the cell above. Look for \"docker\". There, you should see the \"enabled\" field set to True. Compare this to the 2 prior runs (\"enabled\" was then set to False)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 6.C.d Use a custom Docker image\n",
         "\n",
-        "You can also specify a custom Docker image if you don't want to use the default image provided by Azure ML.\n",
+        "You can also specify a custom Docker image, if you don't want to use the default image provided by Azure ML.\n",
         "\n",
+        "You can either pull an image directly from Anaconda:\n",
         "```python\n",
-        "# use an image available in Docker Hub without authentication\n",
+        "# Use an image available in Docker Hub without authentication\n",
         "run_config_docker.environment.docker.base_image = \"continuumio/miniconda3\"\n",
+        "```\n",
         "\n",
-        "# or, use an image available in a private Azure Container Registry\n",
+        "Or one of the images you may already have created:\n",
+        "```python\n",
+        "# or, use an image available in your private Azure Container Registry\n",
         "run_config_docker.environment.docker.base_image = \"mycustomimage:1.0\"\n",
         "run_config_docker.environment.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
         "run_config_docker.environment.docker.base_image_registry.username = \"username\"\n",
         "run_config_docker.environment.docker.base_image_registry.password = \"password\"\n",
         "```\n",
         "\n",
-        "When you are using a custom Docker image, you might already have your environment setup properly in a Python environment in the Docker image. In that case, you can skip specifying conda dependencies, and just use `user_managed_dependencies` option instead:\n",
+        "##### Where to find my Docker image name and registry credentials\n",
+        "    If you do not know what the name of your Docker image or container registry is, or if you don't know how to access the username and password needed above, proceed as follows:\n",
+        "    - Docker image name:\n",
+        "        - In the portal, under your resource group, click on your current workspace\n",
+        "        - Click on Experiments\n",
+        "        - Click on Images\n",
+        "        - Click on the image of your choice\n",
+        "        - Copy the \"ID\" string\n",
+        "        - In this notebook, replace \"mycustomimage:1/0\" with that ID string\n",
+        "    - Username and password:\n",
+        "        - In the portal, under your resource group, click on the container registry associated with your workspace\n",
+        "            - If you have several and don't know which one you need, click on your workspace, go to Overview and click on the \"Registry\" name on the upper right of the screen\n",
+        "        - There, go to \"Access keys\"\n",
+        "        - Copy the username and one of the passwords\n",
+        "        - In this notebook, replace \"username\" and \"password\" by these values\n",
+        "\n",
+        "In any case, you will need to use the lines above in place of the line marked as `# Reference Docker image` in section 6.C.a. \n",
+        "\n",
+        "When you are using your custom Docker image, you might already have your Python environment properly set up. In that case, you can skip specifying conda dependencies, and just use the `user_managed_dependencies` option instead:\n",
         "```python\n",
         "run_config_docker.environment.python.user_managed_dependencies = True\n",
         "# path to the Python environment in the custom Docker image\n",
@@ -391,7 +519,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Query run metrics"
+        "## 7. Query run metrics <a id='query'></a>\n",
+        "\n",
+        "Once your run has completed, you can now extract the metrics you captured by using the `get_metrics` method. As shown in the `train.py` file, these metrics are \"alpha\" and \"mse\"."
       ]
     },
     {
@@ -405,7 +535,7 @@
       },
       "outputs": [],
       "source": [
-        "# get all metris logged in the run\n",
+        "# Get all metris logged in the run\n",
         "run.get_metrics()\n",
         "metrics = run.get_metrics()"
       ]
@@ -476,7 +606,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We know the model `ridge_0.40.pkl` is the best performing model from the earlier queries. So let's register it with the workspace."
+        "From the results obtained above, `ridge_0.40.pkl` is the best performing model. You can now register that particular model with the workspace. Once you have done so, go back to the portal and click on \"Models\". You should see it there."
       ]
     },
     {
@@ -485,7 +615,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# supply a model name, and the full path to the serialized model file.\n",
+        "# Supply a model name, and the full path to the serialized model file.\n",
         "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
       ]
     },
@@ -495,14 +625,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(model.name, model.version, model.url)"
+        "print(\"Registered model:\\n --> Name: {}\\n --> Version: {}\\n --> URL: {}\".format(model.name, model.version, model.url))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now you can deploy this model following the example in the 01 notebook."
+        "You can now deploy your model by following [this example](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb)."
       ]
     }
   ],

how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.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/training/train-on-remote-vm/train-on-remote-vm.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -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, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
       ]
     },
     {

how-to-use-azureml/training/train-within-notebook/train-within-notebook.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/training/train-within-notebook/train-within-notebook.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -36,7 +43,7 @@
         "    1. Deploy your webservice\n",
         "    1. Test your webservice\n",
         "    1. Clean up\n",
-        "1. [Next Steps](#Next%20Steps)\n",
+        "1. [Next Steps](#nextsteps)\n",
         "\n",
         "---\n",
         "\n",
@@ -57,7 +64,7 @@
         "---\n",
         "\n",
         "## Setup\n",
-        "Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.  From the configuration, the important sections are the workspace configuration and ACI regristration.\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. From the configuration, the important sections are the workspace configuration and ACI regristration.\n",
         "\n",
         "We will also need the following libraries install to our conda environment.  If these are not installed, use the following command to do so and restart the notebook.\n",
         "```shell\n",
@@ -170,7 +177,12 @@
         "run.log('mse', mean_squared_error(data['test']['y'], preds))\n",
         "\n",
         "# Save the model to the outputs directory for capture\n",
-        "joblib.dump(value=regression_model, filename='outputs/model.pkl')\n",
+        "model_file_name = 'outputs/model.pkl'\n",
+        "\n",
+        "joblib.dump(value = regression_model, filename = model_file_name)\n",
+        "\n",
+        "# upload the model file explicitly into artifacts \n",
+        "run.upload_file(name = model_file_name, path_or_stream = model_file_name)\n",
         "\n",
         "# Complete the run\n",
         "run.complete()"
@@ -214,8 +226,6 @@
         "import numpy as np\n",
         "from tqdm import tqdm\n",
         "\n",
-        "model_name = \"model.pkl\"\n",
-        "\n",
         "# list of numbers from 0 to 1.0 with a 0.05 interval\n",
         "alphas = np.arange(0.0, 1.0, 0.05)\n",
         "\n",
@@ -414,7 +424,7 @@
         "### Describe your target compute\n",
         "In addition to the container, we also need to describe the type of compute we want to allocate for our webservice.  In in this example we are using an [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/) which is a good choice for quick and cost-effective dev/test deployment scenarios.  ACI instances require the number of cores you want to run and memory you need.  Tags and descriptions are available for you to identify the instances in AML when viewing the Compute tab in the AML Portal.\n",
         "\n",
-        "For production workloads, it is better to use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Try [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n"
+        "For production workloads, it is better to use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Try [this notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n"
       ]
     },
     {
@@ -510,6 +520,9 @@
       "outputs": [],
       "source": [
         "import json\n",
+        "\n",
+        "service = ws.webservices['my-aci-svc']\n",
+        "\n",
         "# scrape the first row from the test set.\n",
         "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
         "\n",
@@ -643,7 +656,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "---\n",
+        "<a id='nextsteps'></a>\n",
         "## Next Steps"
       ]
     },
@@ -661,13 +674,6 @@
         "If you want to deploy models to a production cluster try the [production-deploy-to-aks](../../deployment/production-deploy-to-aks\n",
         ") notebook."
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {
@@ -691,7 +697,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.5"
     }
   },
   "nbformat": 4,

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 +1,371 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/using-environments/using-environments.png)"
+      ]
+    },
+    {
+      "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",
+        "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.\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/dataprep/README.md

@@ -15,7 +15,7 @@ You will find in this repo:
 - [How-To Guide Notebooks](how-to-guides) for more in-depth sample code at feature level.
 
 ## Installation
-Here are the [SDK installation steps](https://docs.microsoft.com/python/api/overview/azure/dataprep/intro?view=azure-dataprep-py#install).
+Here are the [SDK installation steps](https://aka.ms/aml-data-prep-installation).
 
 ## Documentation 
 Here is more information on how to use the new Data Prep SDK:
@@ -31,17 +31,40 @@ If you have any questions or feedback, send us an email at: [askamldataprep@micr
 
 ## Release Notes
 
-### 2019-04-08 (version 1.1.1)
+### 2019-04-17 (version 1.1.2)
+
+Note: Data Prep Python SDK will no longer install `numpy` and `pandas` packages. See [updated installation instructions](https://aka.ms/aml-data-prep-installation).
 
 New features
-- You can read multiple Datastore/DataPath/DataReference sources using read_* transforms.
+- You can now use the Pivot transform.
+  - How-to guide: [Pivot notebook](https://aka.ms/aml-data-prep-pivot-nb)
+- You can now use regular expressions in native functions.
+  - Examples:
+    - `dflow.filter(dprep.RegEx('pattern').is_match(dflow['column_name']))`
+    - `dflow.assert_value('column_name', dprep.RegEx('pattern').is_match(dprep.value))`
+- You can now use `to_upper` and `to_lower` functions in expression language.
+- You can now see the number of unique values of each column in a data profile.
+- For some of the commonly used reader steps, you can now pass in the `infer_column_types` argument. If it is set to `True`, Data Prep will attempt to detect and automatically convert column types.
+  - `inference_arguments` is now deprecated.
+- You can now call `Dataflow.shape`.
+
+Bug fixes and improvements
+- `keep_columns` now accepts an additional optional argument `validate_column_exists`, which checks if the result of `keep_columns` will contain any columns.
+- All reader steps (which read from a file) now accept an additional optional argument `verify_exists`.
+- Improved performance of reading from pandas dataframe and getting data profiles.
+- Fixed a bug where slicing a single step from a Dataflow failed with a single index.
+
+### 2019-04-08 (version 1.1.1)
+
+New features
+- You can read multiple Datastore/DataPath/DataReference sources using read_* transforms.
 - You can perform the following operations on columns to create a new column: division, floor, modulo, power, length.
 - Data Prep is now part of the Azure ML diagnostics suite and will log diagnostic information by default.
   - To turn this off, set this environment variable to true: DISABLE_DPREP_LOGGER
 
 Bug fixes and improvements
 - Improved code documentation for commonly used classes and functions.
-- Fixed a bug in auto_read_file that failed to read Excel files.
+- Fixed a bug in auto_read_file that failed to read Excel files.
 - Added option to overwrite the folder in read_pandas_dataframe.
 - Improved performance of dotnetcore2 dependency installation, and added support for Fedora 27/28 and Ubuntu 1804.
 - Improved the performance of reading from Azure Blobs.

how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb

@@ -99,6 +99,46 @@
         "dflow_length.head(5)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### `to_upper()`\n",
+        "Using the to_upper() expression, add a new numeric column \"Upper Case\", which contains the length of the string in \"Primary Type\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dflow_to_upper = dflow.add_column(new_column_name='Upper Case',\n",
+        "                                prior_column='Primary Type',\n",
+        "                                expression=dflow['Primary Type'].to_upper())\n",
+        "dflow_to_upper.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### `to_lower()`\n",
+        "Using the to_lower() expression, add a new numeric column \"Lower Case\", which contains the length of the string in \"Primary Type\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dflow_to_lower = dflow.add_column(new_column_name='Lower Case',\n",
+        "                                prior_column='Primary Type',\n",
+        "                                expression=dflow['Primary Type'].to_lower())\n",
+        "dflow_to_lower.head(5)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb

@@ -110,7 +110,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "When reading delimited files, you can let the underlying runtime infer the parsing parameters (e.g. separator, encoding, whether to use headers, etc.) simply by not providing them. In this case, you can read a file by specifying only its location, then retrieve the first 10 rows to evaluate the result."
+        "When reading delimited files, the only required parameter is `path`. Other parameters (e.g. separator, encoding, whether to use headers, etc.) are available to modify default behavior.In this case, you can read a file by specifying only its location, then retrieve the first 5 rows to evaluate the result."
       ]
     },
     {
@@ -144,7 +144,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now the data set contains the correct headers and the extraneous row has been skipped by read_csv. Next, look at the data types of the columns."
+        "Now the data set contains the correct headers and the extraneous row has been skipped by `read_csv`. Next, look at the data types of the columns."
       ]
     },
     {
@@ -160,8 +160,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Unfortunately, all of the columns came back as strings. This is because, by default, Data Prep will not change the type of the data. Since the data source is a text file, all values are kept as strings. In this case, however, numeric columns should be parsed as numbers. To do this, set the `inference_arguments` parameter to a new instance of the `InferenceArguments` class, which will trigger type inference to be performed.\n",
+        "Unfortunately, all of the columns came back as strings. This is because, by default, Data Prep will not change the type of the data. Since the data source is a text file, all values are kept as strings. In this case, however, numeric columns should be parsed as numbers. To do this, set the `infer_column_types` parameter to `True`, which will trigger type inference to be performed.\n"
-        "Note that setting inference arguments at this step also requires you to choose a strategy for dealing with ambiguous dates. The example below shows the month before day option."
       ]
     },
     {
@@ -172,7 +171,7 @@
       "source": [
         "dflow_inferred_types = dprep.read_csv(path='../data/crime_duplicate_headers.csv',\n",
         "                          skip_rows=1,\n",
-        "                          inference_arguments=dprep.InferenceArguments(day_first=False))\n",
+        "                          infer_column_types=True)\n",
         "dflow_inferred_types.dtypes"
       ]
     },

how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.ipynb

@@ -192,7 +192,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
+      "version": "3.6.4"
     }
   },
   "nbformat": 4,

how-to-use-azureml/work-with-data/datasets/README.md

@@ -0,0 +1,155 @@
+# Azure Machine Learning Datasets (preview)
+
+Azure Machine Learning Datasets (preview) make it easier to access and work with your data. Datasets manage data in various scenarios such as model training and pipeline creation. Using the Azure Machine Learning SDK, you can access underlying storage, explore and prepare data, manage the life cycle of different Dataset definitions, and compare between Datasets used in training and in production.
+
+## Create and Register Datasets
+
+It's easy to create Datasets from either local files, or Azure Datastores.
+
+```Python
+from azureml.core.workspace import Workspace
+from azureml.core.datastore import Datastore
+from azureml.core.dataset import Dataset
+
+datastore_name = 'your datastore name'
+
+# get existing workspace
+workspace = Workspace.from_config()
+
+# get Datastore from the workspace
+dstore = Datastore.get(workspace, datastore_name)
+
+# create an in-memory Dataset on your local machine
+dataset = Dataset.from_delimited_files(dstore.path('data/src/crime.csv'))
+```
+
+To consume Datasets across various scenarios in Azure Machine Learning service such as automated machine learning, model training and pipeline creation, you need to register the Datasets with your workspace. By doing so, you can also share and reuse the Datasets within your organization.
+
+```Python
+dataset = dataset.register(workspace = workspace,
+                           name = 'dataset_crime',
+                           description = 'Training data'
+                           )
+```
+
+## Sampling
+
+Sampling can be particular useful with Datasets that are too large to efficiently analyze in full. It enables data scientists to work with a manageable amount of data to build and train machine learning models. At this time, the [`sample()`](https://docs.microsoft.com//python/api/azureml-core/azureml.core.dataset(class)?view=azure-ml-py#sample-sample-strategy--arguments-) method from the Dataset class supports Top N, Simple Random, and Stratified sampling strategies.
+
+After sampling, you can convert your sampled Dataset to pandas DataFrame for training. By using  the native [`sample()`](https://docs.microsoft.com//python/api/azureml-core/azureml.core.dataset(class)?view=azure-ml-py#sample-sample-strategy--arguments-) method from the Dataset class, you will load the sampled data on the fly instead of loading full data into memory.
+
+### Top N sample
+
+For Top N sampling, the first n records of your Dataset are your sample. This is helpful if you are just trying  to get an idea of what your data records look like or to see what fields are in your data.
+
+```Python
+top_n_sample_dataset = dataset.sample('top_n', {'n': 5})
+top_n_sample_dataset.to_pandas_dataframe()
+```
+
+### Simple random sample
+
+In Simple Random sampling, every member of the data population has an equal chance of being selected as a part of the sample. In the `simple_random` sample strategy, the records from your Dataset are selected based on the probability specified and returns a modified Dataset. The seed parameter is optional.
+
+```Python
+simple_random_sample_dataset = dataset.sample('simple_random', {'probability':0.3, 'seed': seed})
+simple_random_sample_dataset.to_pandas_dataframe()
+```
+
+### Stratified sample
+
+Stratified samples ensure that certain groups of a population are represented in the sample. In the `stratified` sample strategy, the population is divided into strata, or subgroups, based on similarities, and records are randomly selected from each strata according to the strata weights indicated by the `fractions` parameter.
+
+In the following example, we group each record by the specified columns, and include said record based on the strata X weight information in `fractions`. If a strata is not specified or the record cannot be grouped, the default weight to sample is 0.
+
+```Python
+# take 50% of records with `Primary Type` as `THEFT` and 20% of records with `Primary Type` as `DECEPTIVE PRACTICE` into sample Dataset
+fractions = {}
+fractions[('THEFT',)] = 0.5
+fractions[('DECEPTIVE PRACTICE',)] = 0.2
+
+sample_dataset = dataset.sample('stratified', {'columns': ['Primary Type'], 'fractions': fractions, 'seed': seed})
+
+sample_dataset.to_pandas_dataframe()
+```
+
+## Explore with summary statistics
+
+ Detect anomalies, missing values, or error counts with the [`get_profile()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-profile-arguments-none--generate-if-not-exist-true--workspace-none--compute-target-none-) method. This function gets the profile and summary statistics of your data, which in turn helps determine the necessary data preparation operations to apply.
+
+```Python
+# get pre-calculated profile
+# if there is no precalculated profile available or the precalculated profile is not up-to-date, this method will generate a new profile of the Dataset
+dataset.get_profile()
+```
+
+||Type|Min|Max|Count|Missing Count|Not Missing Count|Percent missing|Error Count|Empty count|0.1% Quantile|1% Quantile|5% Quantile|25% Quantile|50% Quantile|75% Quantile|95% Quantile|99% Quantile|99.9% Quantile|Mean|Standard Deviation|Variance|Skewness|Kurtosis
+-|----|---|---|-----|-------------|-----------------|---------------|-----------|-----------|-------------|-----------|-----------|------------|------------|------------|------------|------------|--------------|----|------------------|--------|--------|--------
+ID|FieldType.INTEGER|1.04986e+07|1.05351e+07|10.0|0.0|10.0|0.0|0.0|0.0|1.04986e+07|1.04992e+07|1.04986e+07|1.05166e+07|1.05209e+07|1.05259e+07|1.05351e+07|1.05351e+07|1.05351e+07|1.05195e+07|12302.7|1.51358e+08|-0.495701|-1.02814
+Case Number|FieldType.STRING|HZ239907|HZ278872|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Date|FieldType.DATE|2016-04-04 23:56:00+00:00|2016-04-15 17:00:00+00:00|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Block|FieldType.STRING|004XX S KILBOURN AVE|113XX S PRAIRIE AVE|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+IUCR|FieldType.INTEGER|810|1154|10.0|0.0|10.0|0.0|0.0|0.0|810|850|810|890|1136|1153|1154|1154|1154|1058.5|137.285|18847.2|-0.785501|-1.3543
+Primary Type|FieldType.STRING|DECEPTIVE PRACTICE|THEFT|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Description|FieldType.STRING|BOGUS CHECK|OVER $500|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Location Description|FieldType.STRING||SCHOOL, PUBLIC, BUILDING|10.0|0.0|10.0|0.0|0.0|1.0||||||||||||||
+Arrest|FieldType.BOOLEAN|False|False|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Domestic|FieldType.BOOLEAN|False|False|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Beat|FieldType.INTEGER|531|2433|10.0|0.0|10.0|0.0|0.0|0.0|531|531|531|614|1318.5|1911|2433|2433|2433|1371.1|692.094|478994|0.105418|-1.60684
+District|FieldType.INTEGER|5|24|10.0|0.0|10.0|0.0|0.0|0.0|5|5|5|6|13|19|24|24|24|13.5|6.94822|48.2778|0.0930109|-1.62325
+Ward|FieldType.INTEGER|1|48|10.0|0.0|10.0|0.0|0.0|0.0|1|5|1|9|22.5|40|48|48|48|24.5|16.2635|264.5|0.173723|-1.51271
+Community Area|FieldType.INTEGER|4|77|10.0|0.0|10.0|0.0|0.0|0.0|4|8.5|4|24|37.5|71|77|77|77|41.2|26.6366|709.511|0.112157|-1.73379
+FBI Code|FieldType.INTEGER|6|11|10.0|0.0|10.0|0.0|0.0|0.0|6|6|6|6|11|11|11|11|11|9.4|2.36643|5.6|-0.702685|-1.59582
+X Coordinate|FieldType.INTEGER|1.16309e+06|1.18336e+06|10.0|7.0|3.0|0.7|0.0|0.0|1.16309e+06|1.16309e+06|1.16309e+06|1.16401e+06|1.16678e+06|1.17921e+06|1.18336e+06|1.18336e+06|1.18336e+06|1.17108e+06|10793.5|1.165e+08|0.335126|-2.33333
+Y Coordinate|FieldType.INTEGER|1.8315e+06|1.908e+06|10.0|7.0|3.0|0.7|0.0|0.0|1.8315e+06|1.8315e+06|1.8315e+06|1.83614e+06|1.85005e+06|1.89352e+06|1.908e+06|1.908e+06|1.908e+06|1.86319e+06|39905.2|1.59243e+09|0.293465|-2.33333
+Year|FieldType.INTEGER|2016|2016|10.0|0.0|10.0|0.0|0.0|0.0|2016|2016|2016|2016|2016|2016|2016|2016|2016|2016|0|0|NaN|NaN
+Updated On|FieldType.DATE|2016-05-11 15:48:00+00:00|2016-05-27 15:45:00+00:00|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
+Latitude|FieldType.DECIMAL|41.6928|41.9032|10.0|7.0|3.0|0.7|0.0|0.0|41.6928|41.6928|41.6928|41.7057|41.7441|41.8634|41.9032|41.9032|41.9032|41.78|0.109695|0.012033|0.292478|-2.33333
+Longitude|FieldType.DECIMAL|-87.6764|-87.6043|10.0|7.0|3.0|0.7|0.0|0.0|-87.6764|-87.6764|-87.6764|-87.6734|-87.6645|-87.6194|-87.6043|-87.6043|-87.6043|-87.6484|0.0386264|0.001492|0.344429|-2.33333
+Location|FieldType.STRING||(41.903206037, -87.676361925)|10.0|0.0|10.0|0.0|0.0|7.0||||||||||||||
+
+
+## Training with Dataset
+
+Now that you have registered your Dataset, you can call up the Dataset and convert it to pandas DataFrame or Spark DataFrame easily in your train.py script.
+
+```Python
+# Sample train.py script
+import azureml.core
+import pandas as pd
+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
+
+# Access Dataset registered with the workspace by name
+dataset_name = 'training_data'
+dataset = Dataset.get(workspace=workspace, name=dataset_name)
+
+ds_def = dataset.get_definition()
+dataset_val, dataset_train = ds_def.random_split(percentage=0.3)
+y_df = dataset_train.keep_columns(['HasDetections']).to_pandas_dataframe()
+x_df = dataset_train.drop_columns(['HasDetections']).to_pandas_dataframe()
+y_val = dataset_val.keep_columns(['HasDetections']).to_pandas_dataframe()
+x_val = dataset_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)))
+```
+
+For an end-to-end tutorial, you may refer to [Dataset tutorial](datasets-tutorial.ipynb). You will learn how to:
+- Explore and prepare data for training the model.
+- Register the Dataset in your workspace for easy access in training.
+- Take snapshots of data to ensure models can be trained with the same data every time.
+- Use registered Dataset in your training script.
+- Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts.

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
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
+133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
+134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
+135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
+136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
+137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
+138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
+139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
+140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
+141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
+147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
+148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
+149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
+150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
+151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
+152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
+153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
+154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
+155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
+156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
+157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
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+822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
+823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
+824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
+825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
+826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
+827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
+828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
+829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
+830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
+831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
+832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
+833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
+834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
+835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
+836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
+837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
+838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
+839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
+840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
+841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
+842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
+843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
+844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
+845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
+846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
+847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
+848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
+849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
+850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
+851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
+852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
+853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
+865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
+866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
+867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

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)))

tutorials/README.md

@@ -6,7 +6,7 @@ two sets of tutorial articles for:
  * [Image classification using MNIST dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml)
  * [Regression using NYC Taxi dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep)
 
-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, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 
 ### Image classification
 

tutorials/img-classification-part1-training.ipynb

@@ -126,9 +126,7 @@
       "metadata": {},
       "source": [
         "### Create or Attach existing compute resource\n",
-        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you use default Azure Machine Learning Compute as your training environment."
-        "\n",
-        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
       ]
     },
     {
@@ -142,38 +140,10 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
         "import os\n",
         "\n",
-        "# choose a name for your cluster\n",
+        "cluster_type = os.environ.get(\"AML_COMPUTE_CLUSTER_TYPE\", \"CPU\")\n",
-        "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpucluster\")\n",
+        "compute_target = ws.get_default_compute_target(cluster_type)"
-        "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n",
-        "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n",
-        "\n",
-        "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
-        "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n",
-        "\n",
-        "\n",
-        "if compute_name in ws.compute_targets:\n",
-        "    compute_target = ws.compute_targets[compute_name]\n",
-        "    if compute_target and type(compute_target) is AmlCompute:\n",
-        "        print('found compute target. just use it. ' + compute_name)\n",
-        "else:\n",
-        "    print('creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n",
-        "                                                                min_nodes = compute_min_nodes, \n",
-        "                                                                max_nodes = compute_max_nodes)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
-        "    \n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it will use the scale settings for the cluster\n",
-        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()\n",
-        "    print(compute_target.get_status().serialize())"
       ]
     },
     {