fix drift-on-aks failure, will be ready by next release

update samples from Release-163 as a part of 1.0.79 SDK release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.79 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/batch_inferencing/README.md

@@ -0,0 +1,127 @@
+# Azure Machine Learning Batch Inference
+
+Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.
+
+# Getting Started with Batch Inference Public Preview
+
+Batch inference public preview offers a platform in which to do large inference or generic parallel map-style operations. Below introduces the major steps to use this new functionality. For a quick try, please follow the prerequisites and simply run the sample notebooks provided in this directory.
+
+## Prerequisites
+
+### Python package installation
+Following the convention of most AzureML Public Preview features, Batch Inference SDK is currently available as a contrib package.
+
+If you're unfamiliar with creating a new Python environment, you may follow this example for [creating a conda environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local).  Batch Inference package can be installed through the following pip command.
+```
+pip install azureml-contrib-pipeline-steps
+```
+
+### Creation of Azure Machine Learning Workspace
+If you do not already have a Azure ML Workspace, please run the [configuration Notebook](../../configuration.ipynb).
+
+## Configure a Batch Inference job
+
+To run a Batch Inference job, you will need to gather some configuration data.
+
+1. **ParallelRunConfig**
+    - **entry_script**: the local file path to the scoring script. If source_directory is specified, use relative path, otherwise use any path accessible on machine.
+    - **error_threshold**: the number of record failures for TabularDataset and file failures for FileDataset that should be ignored during processing. If the aggregated error count (across all workers) goes above this value, then the job will be aborted. Set to -1 to ignore all failures during processing.
+    - **output_action**: one of the following values
+        - **"append_row"**: all values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
+        - **"summary_only"** – scoring script will handle the output by itself.  The script still needs to return one output row per successfully-processed input item. This is used for error threshold calculation (the actual value of the output row is ignored).
+    - **source_directory**: supporting files for scoring (optional)
+    - **compute_target**: only **AmlCompute** is supported currently
+    - **node_count**: number of compute nodes to use.
+    - **process_count_per_node**: number of processes per node (optional, default value is 1).
+    - **mini_batch_size**: the approximate amount of input data passed to each run() invocation.  For FileDataset input, this is number of files user script can process in one run() call. For TabularDataset input it is approximate size of data user script can process in one run() call. E.g. 1024, 1024KB, 10MB, 1GB (optional, default value 10 files for FileDataset and 1MB for TabularDataset.)
+    - **logging_level**: log verbosity. Values in increasing verbosity are: 'WARNING', 'INFO', 'DEBUG' (optional, default value is 'INFO').
+    - **run_invocation_timeout**: run method invocation timeout period in seconds (optional, default value is 60).
+    - **environment**: The environment definition. This field configures the Python environment. It can be configured to use an existing Python environment or to set up a temp environment for the experiment. The definition is also responsible for setting the required application dependencies.
+    - **description**: name given to batch service.
+
+2. **Scoring (entry) script**: entry point for execution, scoring script should contain two functions:
+    - **init()**: this function should be used for any costly or common preparation for subsequent inferences, e.g., deserializing and loading the model into a global object.
+    - **run(mini_batch)**: The method to be parallelized. Each invocation will have one minibatch.
+        - **mini_batch**: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
+        - **return value**: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.
+
+3. **Base image** (optional)
+    - if GPU is required, use DEFAULT_GPU_IMAGE as base image in environment. [Example GPU environment](./file-dataset-image-inference-mnist.ipynb#specify-the-environment-to-run-the-script)
+
+Example image pull:
+```python
+from azureml.core.runconfig import ContainerRegistry
+
+# use an image available in public Container Registry without authentication
+public_base_image = "mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda"
+
+# or use an image available in a private Container Registry
+base_image = "myregistry.azurecr.io/mycustomimage:1.0"
+base_image_registry = ContainerRegistry()
+base_image_registry.address = "myregistry.azurecr.io"
+base_image_registry.username = "username"
+base_image_registry.password = "password"
+```
+
+
+## Create a batch inference job
+
+**ParallelRunStep** is a newly added step in the azureml.contrib.pipeline.steps package. You will use it to add a step to create a batch inference job with your Azure machine learning pipeline. (Use batch inference without an Azure machine learning pipeline is not supported yet). ParallelRunStep has all the following parameters:
+  - **name**: this name will be used to register batch inference service, has the following naming restrictions: (unique, 3-32 chars and regex ^\[a-z\]([-a-z0-9]*[a-z0-9])?$)
+  - **models**: zero or more model names already registered in Azure Machine Learning model registry.
+  - **parallel_run_config**: ParallelRunConfig as defined above.
+  - **inputs**: one or more Dataset objects.
+  - **output**: this should be a PipelineData object encapsulating an Azure BLOB container path. 
+  - **arguments**: list of custom arguments passed to scoring script (optional)
+  - **allow_reuse**: optional, default value is True. If the inputs remain the same as a previous run, it will make the previous run results immediately available (skips re-computing the step).
+
+## Passing arguments from pipeline submission to script
+
+Many tasks require arguments to be passed from job submission to the distributed runs. Below is an example to pass such information.
+```
+# from script which creates pipeline job
+parallelrun_step = ParallelRunStep(
+  ...
+  arguments=["--model_name", "mosaic"]     # name of the model we want to use, in case we have more than one option
+)
+```
+```
+# from driver.py/score.py/task.py
+import argparse
+
+parser.add_argument('--model_name', dest="model_name")
+
+args, unknown_args = parser.parse_known_args()
+
+# to access values
+args.model_name # "mosaic"
+```
+
+## Submit a batch inference job
+
+You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline. To control node count using REST API/experiment, please use aml_node_count(special) pipeline parameter. A typical use case follows:
+
+```python
+pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
+pipeline_run = Experiment(ws, 'name_of_pipeline_run').submit(pipeline)
+```
+
+## Monitor your batch inference job
+
+A batch inference job can take a long time to finish. You can monitor your job's progress from Azure portal, using Azure ML widgets, view console output through SDK, or check out overview.txt in log/azureml directory.
+
+```python
+# view with widgets (will display GUI inside a browser)
+from azureml.widgets import RunDetails
+RunDetails(pipeline_run).show()
+
+# simple console output
+pipeline_run.wait_for_completion(show_output=True)
+```
+
+# Sample notebooks
+
+-  [file-dataset-image-inference-mnist.ipynb](./file-dataset-image-inference-mnist.ipynb) demonstrates how to run batch inference on an MNIST dataset.
+-  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset.
+
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/batch_inferencing/README.png) 

contrib/batch_inferencing/file-dataset-image-inference-mnist.ipynb

@@ -4,10 +4,17 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "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/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -29,8 +36,6 @@
         "- Register the pretrained MNIST model into the model registry. \n",
         "- Use the registered model to do batch inference on the images in the data blob container.\n",
         "\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)\n",
-        "\n",
         "## Prerequisites\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. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
@@ -476,7 +481,7 @@
         "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
         "df.columns = [\"Filename\", \"Prediction\"]\n",
         "print(\"Prediction has \", df.shape[0], \" rows\")\n",
-        "df.head(10)"
+        "df.head(10) "
       ]
     },
     {
@@ -485,7 +490,7 @@
       "source": [
         "## Cleanup Compute resources\n",
         "\n",
-        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
+        "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
       ]
     },
     {
@@ -514,6 +519,27 @@
         "name": "tracych"
       }
     ],
+    "friendly_name": "MNIST data inferencing using ParallelRunStep",
+    "exclude_from_index": false,
+    "index_order": 1,
+    "category": "Other notebooks",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "MNIST"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "None"
+    ],
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Digit identification",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",

contrib/batch_inferencing/file-dataset-image-inference-mnist.yml

@@ -3,5 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-pipeline-steps
-  - pandas
   - azureml-widgets

contrib/batch_inferencing/tabular-dataset-inference-iris.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/contrib/batch_inferencing/tabular-dataset-inference-iris.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -28,9 +35,7 @@
         "- Use the registered model to do batch inference on the CSV files in the data blob container.\n",
         "\n",
         "## Prerequisites\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. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
-        "\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/tabular-dataset-inference-iris.png)"
+        "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. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
       ]
     },
     {
@@ -98,7 +103,7 @@
         "    # 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",
+        "    # 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",
@@ -404,7 +409,7 @@
       "source": [
         "# GUI\n",
         "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
+        "RunDetails(pipeline_run).show() "
       ]
     },
     {
@@ -460,7 +465,7 @@
       "metadata": {},
       "source": [
         "## Cleanup compute resources\n",
-        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources."
+        "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources."
       ]
     },
     {
@@ -489,6 +494,27 @@
         "name": "tracych"
       }
     ],
+    "friendly_name": "IRIS data inferencing using ParallelRunStep",
+    "exclude_from_index": false,
+    "index_order": 1,
+    "category": "Other notebooks",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "IRIS"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "None"
+    ],
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Recognize flower type",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -505,8 +531,7 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.2"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00e2\u20ac\u00afLicensed under the MIT License."
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 2

contrib/batch_inferencing/tabular-dataset-inference-iris.yml

@@ -3,5 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-pipeline-steps
-  - pandas
   - azureml-widgets

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

@@ -27,10 +27,10 @@ dependencies:
   - azureml-explain-model
   - azureml-pipeline
   - azureml-contrib-interpret
-  - pandas_ml
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
+  - onnxruntime==0.4.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 
 channels:

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

@@ -28,10 +28,10 @@ dependencies:
   - azureml-explain-model
   - azureml-pipeline
   - azureml-contrib-interpret
-  - pandas_ml
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
+  - onnxruntime==0.4.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
 
 channels:

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

@@ -14,8 +14,9 @@ IF "%CONDA_EXE%"=="" GOTO CondaMissing
 call conda activate %conda_env_name% 2>nul:
 
 if not errorlevel 1 (
-  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
-  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
+  echo Upgrading existing conda environment %conda_env_name%
+  call pip uninstall azureml-train-automl -y -q
+  call conda env update --name %conda_env_name% --file %automl_env_file%
   if errorlevel 1 goto ErrorExit
 ) else (
   call conda env create -f %automl_env_file% -n %conda_env_name%

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

@@ -22,8 +22,9 @@ fi
 
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
-   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
+   pip uninstall azureml-train-automl -y -q
+   conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
    jupyter nbextension uninstall --user --py azureml.widgets
 else
    conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&

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

@@ -22,8 +22,9 @@ fi
 
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
-   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
+   pip uninstall azureml-train-automl -y -q
+   conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
    jupyter nbextension uninstall --user --py azureml.widgets
 else
    conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&

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

@@ -285,7 +285,8 @@
         "|**task**|classification or regression or forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**blacklist_models** or **whitelist_models** |*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
+        "|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
+        "| **whitelist_models** |  *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n",
         "|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
         "|**experiment_timeout_minutes**| Maximum amount of time in minutes that all iterations combined can take before the experiment terminates.|\n",
         "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
@@ -557,7 +558,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
+        "from azureml.automl.runtime.onnx_convert import OnnxConverter\n",
         "onnx_fl_path = \"./best_model.onnx\"\n",
         "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
       ]
@@ -566,17 +567,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Predict with the ONNX model, using onnxruntime package\n",
-        "#### Note: The code will install the onnxruntime==0.4.0 if not installed. Newer versions of the onnxruntime have compatibility issues."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "test_df = test_dataset.to_pandas_dataframe()"
+        "### Predict with the ONNX model, using onnxruntime package"
       ]
     },
     {
@@ -595,21 +586,8 @@
         "else:\n",
         "    python_version_compatible = False\n",
         "\n",
-        "onnxrt_present = False\n",
-        "try:\n",
-        "    import onnxruntime\n",
-        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
-        "    from onnxruntime import __version__ as ORT_VER\n",
-        "    if ORT_VER == '0.4.0':\n",
-        "        onnxrt_present = True\n",
-        "except ImportError:\n",
-        "    onnxrt_present = False\n",
-        "    \n",
-        "# Install the onnxruntime if the version 0.4.0 is not installed.\n",
-        "if not onnxrt_present:\n",
-        "    print(\"Installing the onnxruntime version 0.4.0.\")\n",
-        "    !{sys.executable} -m pip install --user --force-reinstall onnxruntime==0.4.0\n",
-        "    onnxrt_present = True\n",
+        "import onnxruntime\n",
+        "from azureml.automl.runtime.onnx_convert import OnnxInferenceHelper\n",
         "\n",
         "def get_onnx_res(run):\n",
         "    res_path = 'onnx_resource.json'\n",
@@ -618,7 +596,8 @@
         "        onnx_res = json.load(f)\n",
         "    return onnx_res\n",
         "\n",
-        "if onnxrt_present and python_version_compatible:    \n",
+        "if python_version_compatible:\n",
+        "    test_df = test_dataset.to_pandas_dataframe()\n",
         "    mdl_bytes = onnx_mdl.SerializeToString()\n",
         "    onnx_res = get_onnx_res(best_run)\n",
         "\n",
@@ -628,10 +607,7 @@
         "    print(pred_onnx)\n",
         "    print(pred_prob_onnx)\n",
         "else:\n",
-        "    if not python_version_compatible:\n",
-        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
-        "    if not onnxrt_present:\n",
-        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
+        "    print('Please use Python version 3.6 or 3.7 to run the inference helper.')"
       ]
     },
     {
@@ -665,20 +641,6 @@
         "best_run, fitted_model = remote_run.get_output()"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import shutil\n",
-        "\n",
-        "sript_folder = os.path.join(os.getcwd(), 'inference')\n",
-        "project_folder = '/inference'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,

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

@@ -451,7 +451,7 @@
       "AML Compute"
     ],
     "datasets": [
-      "creditcard"
+      "Creditcard"
     ],
     "deployment": [
       "None"

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

@@ -522,6 +522,9 @@
     "datasets": [
       "None"
     ],
+    "compute": [
+      "AML Compute"
+    ],
     "deployment": [
       "None"
     ],

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

@@ -323,7 +323,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl import AutoMLStep, AutoMLConfig\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 20,\n",
@@ -440,7 +441,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "training_pipeline_run.wait_for_completion()"
+        "training_pipeline_run.wait_for_completion(show_output=False)"
       ]
     },
     {

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

@@ -301,7 +301,7 @@
       "source": [
         "### Setting forecaster maximum horizon \n",
         "\n",
-        "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 4 periods (i.e. 4 months). Notice that this is much shorter than the number of days in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand).  "
+        "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 12 periods (i.e. 12 months). Notice that this is much shorter than the number of months in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand).  "
       ]
     },
     {
@@ -363,7 +363,7 @@
         "                             label_column_name=target_column_name,\n",
         "                             validation_data=valid_dataset, \n",
         "                             verbosity=logging.INFO,\n",
-        "                             compute_target = compute_target,\n",
+        "                             compute_target=compute_target,\n",
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
         "                            **automl_settings)"

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

@@ -42,7 +42,7 @@
         "\n",
         "AutoML highlights here include built-in holiday featurization, accessing engineered feature names, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
         "\n",
-        "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "Notebook synopsis:\n",
         "1. Creating an Experiment in an existing Workspace\n",
@@ -161,7 +161,7 @@
       "source": [
         "## Data\n",
         "\n",
-        "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the bike share data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation."
+        "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace) is paired with the storage account, which contains the default data store. We will use it to upload the bike share data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation."
       ]
     },
     {
@@ -202,7 +202,7 @@
       "outputs": [],
       "source": [
         "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'dataset/bike-no.csv')]).with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
     },
     {
@@ -221,8 +221,8 @@
       "outputs": [],
       "source": [
         "# select data that occurs before a specified date\n",
-        "train = dataset.time_before(datetime(2012, 9, 1))\n",
-        "train.to_pandas_dataframe().tail(5)"
+        "train = dataset.time_before(datetime(2012, 8, 31), include_boundary=True)\n",
+        "train.to_pandas_dataframe().tail(5).reset_index(drop=True)"
       ]
     },
     {
@@ -231,8 +231,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test = dataset.time_after(datetime(2012, 8, 31))\n",
-        "test.to_pandas_dataframe().head(5)"
+        "test = dataset.time_after(datetime(2012, 9, 1), include_boundary=True)\n",
+        "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
     {
@@ -247,7 +247,7 @@
         "|-|-|\n",
         "|**task**|forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
-        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels.regression?view=azure-ml-py).|\n",
+        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
         "|**experiment_timeout_minutes**|Experimentation timeout in minutes.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
@@ -309,7 +309,7 @@
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
-        "                             enable_early_stopping = True,\n",
+        "                             enable_early_stopping=True,\n",
         "                             n_cross_validations=3, \n",
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
@@ -586,7 +586,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "BikeShare"
@@ -625,7 +625,7 @@
     "tags": [
       "Forecasting"
     ],
-    "task": "forecasting",
+    "task": "Forecasting",
     "version": 3
   },
   "nbformat": 4,

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

@@ -1,6 +1,6 @@
 import argparse
 import azureml.train.automl
-from azureml.automl.core._vendor.automl.client.core.runtime import forecasting_models
+from azureml.automl.runtime._vendor.automl.client.core.runtime import forecasting_models
 from azureml.core import Run
 from sklearn.externals import joblib
 import forecasting_helper
@@ -32,18 +32,17 @@ test_dataset = run.input_datasets['test_data']
 
 grain_column_names = []
 
-df = test_dataset.to_pandas_dataframe()
+df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
 
-X_test_df = test_dataset.drop_columns(columns=[target_column_name])
-y_test_df = test_dataset.with_timestamp_columns(
-    None).keep_columns(columns=[target_column_name])
+X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
+y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
 
 fitted_model = joblib.load('model.pkl')
 
 df_all = forecasting_helper.do_rolling_forecast(
     fitted_model,
-    X_test_df.to_pandas_dataframe(),
-    y_test_df.to_pandas_dataframe().values.T[0],
+    X_test_df,
+    y_test_df.values.T[0],
     target_column_name,
     time_column_name,
     max_horizon,

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

@@ -31,8 +31,8 @@
         "1. [Results](#Results)\n",
         "\n",
         "Advanced Forecasting\n",
-        "1. [Advanced Training](#Advanced Training)\n",
-        "1. [Advanced Results](#Advanced Results)"
+        "1. [Advanced Training](#advanced_training)\n",
+        "1. [Advanced Results](#advanced_results)"
       ]
     },
     {
@@ -211,7 +211,7 @@
       "outputs": [],
       "source": [
         "dataset = Dataset.Tabular.from_delimited_files(path = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/nyc_energy.csv\").with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
     },
     {
@@ -253,7 +253,7 @@
       "source": [
         "# split into train based on time\n",
         "train = dataset.time_before(datetime(2017, 8, 8, 5), include_boundary=True)\n",
-        "train.to_pandas_dataframe().sort_values(time_column_name).tail(5)"
+        "train.to_pandas_dataframe().sort_values(time_column_name).tail(5).reset_index(drop=True)"
       ]
     },
     {
@@ -263,8 +263,8 @@
       "outputs": [],
       "source": [
         "# split into test based on time\n",
-        "test = dataset.time_between(datetime(2017, 8, 8, 5), datetime(2017, 8, 10, 5))\n",
-        "test.to_pandas_dataframe().head(5)"
+        "test = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n",
+        "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
     {
@@ -301,7 +301,7 @@
         "|-|-|\n",
         "|**task**|forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels.regression?view=azure-ml-py).|\n",
+        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
         "|**experiment_timeout_minutes**|Maximum amount of time in minutes that the experiment take before it terminates.|\n",
         "|**training_data**|The training data to be used within the experiment.|\n",
         "|**label_column_name**|The name of the label column.|\n",
@@ -337,7 +337,7 @@
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
-        "                             enable_early_stopping = True,\n",
+        "                             enable_early_stopping=True,\n",
         "                             n_cross_validations=3,                             \n",
         "                             verbosity=logging.INFO,\n",
         "                            **automl_settings)"
@@ -454,7 +454,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_test = test.to_pandas_dataframe()\n",
+        "X_test = test.to_pandas_dataframe().reset_index(drop=True)\n",
         "y_test = X_test.pop(target_column_name).values"
       ]
     },
@@ -463,11 +463,7 @@
       "metadata": {},
       "source": [
         "### Forecast Function\n",
-        "For forecasting, we will use the forecast function instead of the predict function. There are two reasons for this.\n",
-        "\n",
-        "We need to pass the recent values of the target variable y, whereas the scikit-compatible predict function only takes the non-target variables 'test'. In our case, the test data immediately follows the training data, and we fill the target variable with NaN. The NaN serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the forecast origin - the last time when the value of the target is known.\n",
-        "\n",
-        "Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use."
+        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb)."
       ]
     },
     {
@@ -476,15 +472,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Replace ALL values in y by NaN.\n",
-        "# The forecast origin will be at the beginning of the first forecast period.\n",
-        "# (Which is the same time as the end of the last training period.)\n",
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.nan)\n",
         "# The featurized data, aligned to y, will also be returned.\n",
         "# This contains the assumptions that were made in the forecast\n",
         "# and helps align the forecast to the original data\n",
-        "y_predictions, X_trans = fitted_model.forecast(X_test, y_query)"
+        "y_predictions, X_trans = fitted_model.forecast(X_test)"
       ]
     },
     {
@@ -557,7 +548,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Advanced Training\n",
+        "## Advanced Training <a id=\"advanced_training\"></a>\n",
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
@@ -642,7 +633,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Advanced Results\n",
+        "## Advanced Results<a id=\"advanced_results\"></a>\n",
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
@@ -652,15 +643,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Replace ALL values in y by NaN.\n",
-        "# The forecast origin will be at the beginning of the first forecast period.\n",
-        "# (Which is the same time as the end of the last training period.)\n",
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.nan)\n",
         "# The featurized data, aligned to y, will also be returned.\n",
         "# This contains the assumptions that were made in the forecast\n",
         "# and helps align the forecast to the original data\n",
-        "y_predictions, X_trans = fitted_model_lags.forecast(X_test, y_query)"
+        "y_predictions, X_trans = fitted_model_lags.forecast(X_test)"
       ]
     },
     {
@@ -730,14 +716,7 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.8"
-    },
-    "star_tag": [
-      "featured"
-    ],
-    "tags": [
-      ""
-    ],
-    "task": "Forecasting"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -152,7 +152,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# upload data to your default datastore\n",
+        "# upload training and test data to your default datastore\n",
         "ds = ws.get_default_datastore()\n",
         "ds.upload(src_dir='./data', target_path='groupdata', overwrite=True, show_progress=True)"
       ]
@@ -178,7 +178,7 @@
         "\n",
         "#### Create or Attach existing AmlCompute\n",
         "\n",
-        "You will need to create a 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 for your automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."

how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py

@@ -8,10 +8,11 @@ from azureml.core import RunConfiguration
 from azureml.core.compute import ComputeTarget
 from azureml.core.conda_dependencies import CondaDependencies
 from azureml.core.dataset import Dataset
+from azureml.data import TabularDataset
 from azureml.pipeline.core import PipelineData, PipelineParameter, TrainingOutput, StepSequence
 from azureml.pipeline.steps import PythonScriptStep
 from azureml.train.automl import AutoMLConfig
-from azureml.train.automl import AutoMLStep
+from azureml.train.automl.runtime import AutoMLStep
 
 
 def _get_groups(data: Dataset, group_column_names: List[str]) -> pd.DataFrame:
@@ -33,9 +34,10 @@ def _get_configs(automlconfig: AutoMLConfig,
         group_name = "#####".join(str(x) for x in group.values)
         group_name = valid_chars.sub('', group_name)
         for key in group.index:
-            single = data._dataflow.filter(data._dataflow[key] == group[key])
+            single = single._dataflow.filter(data._dataflow[key] == group[key])
+        t_dataset = TabularDataset._create(single)
         group_conf = copy.deepcopy(automlconfig)
-        group_conf.user_settings['training_data'] = single
+        group_conf.user_settings['training_data'] = t_dataset
         group_conf.user_settings['label_column_name'] = target_column
         group_conf.user_settings['compute_target'] = compute_target
         configs[group_name] = group_conf
@@ -106,6 +108,13 @@ def build_pipeline_steps(automlconfig: AutoMLConfig,
 
     final_steps = steps
     if deploy:
+        # modify the conda dependencies to ensure we pick up correct
+        # versions of azureml-defaults and azureml-train-automl
+        cd = CondaDependencies.create(pip_packages=['azureml-defaults', 'azureml-train-automl'])
+        automl_deps = CondaDependencies(conda_dependencies_file_path='deploy/myenv.yml')
+        cd._merge_dependencies(automl_deps)
+        cd.save('deploy/myenv.yml')
+
         # add deployment step
         pp_group_column_names = PipelineParameter(
             "group_column_names",

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/deploy.py

@@ -1,10 +1,11 @@
 import argparse
-from azureml.core import Run, Model
-from azureml.core import Workspace
-from azureml.core.webservice import AciWebservice
-from azureml.core.model import InferenceConfig
 import json
 
+from azureml.core import Run, Model, Workspace
+from azureml.core.conda_dependencies import CondaDependencies
+from azureml.core.model import InferenceConfig
+from azureml.core.webservice import AciWebservice
+
 
 script_file_name = 'score.py'
 conda_env_file_name = 'myenv.yml'

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/myenv.yml

@@ -1,15 +1,11 @@
-name: project_environment
+name: automl_grouping_env
 dependencies:
   # The python interpreter version.
 
   # Currently Azure ML only supports 3.5.2 and later.
 
 - python=3.6.2
-
-- pip:
-  - azureml-defaults
-  - azureml-train-automl
-- numpy
-- scikit-learn
+- numpy>=1.16.0,<=1.16.2
+- scikit-learn>=0.19.0,<=0.20.3
 - conda-forge::fbprophet==0.5
 

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/score.py

@@ -44,7 +44,7 @@ def run(raw_data):
                 model_path = Model.get_model_path(cur_group)
                 model = joblib.load(model_path)
                 models[cur_group] = model
-            _, xtrans = models[cur_group].forecast(df_one, np.repeat(np.nan, len(df_one)))
+            _, xtrans = models[cur_group].forecast(df_one)
             dfs.append(xtrans)
         df_ret = pd.concat(dfs)
         df_ret.reset_index(drop=False, inplace=True)

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

@@ -377,9 +377,7 @@
         "\n",
         "![Forecasting after training](forecast_function_at_train.png)\n",
         "\n",
-        "The `X_test` and `y_query` below, taken together, form the **forecast request**. The two are interpreted as aligned - `y_query` could actally be a column in `X_test`. `NaN`s in `y_query` are the question marks. These will be filled with the forecasts.\n",
-        "\n",
-        "When the forecast period immediately follows the training period, the models retain the last few points of data. You can simply fill `y_query` filled with question marks - the model has the data for the lookback already.\n"
+        "We use `X_test` as a **forecast request** to generate the predictions."
       ]
     },
     {
@@ -408,8 +406,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_query = np.repeat(np.NaN, X_test.shape[0])\n",
-        "y_pred_no_gap, xy_nogap =  fitted_model.forecast(X_test, y_query)\n",
+        "y_pred_no_gap, xy_nogap =  fitted_model.forecast(X_test)\n",
         "\n",
         "# xy_nogap contains the predictions in the _automl_target_col column.\n",
         "# Those same numbers are output in y_pred_no_gap\n",
@@ -437,7 +434,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "quantiles =  fitted_model.forecast_quantiles(X_test, y_query)\n",
+        "quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "quantiles"
       ]
     },
@@ -448,7 +445,7 @@
         "#### Distribution forecasts\n",
         "\n",
         "Often the figure of interest is not just the point prediction, but the prediction at some quantile of the distribution. \n",
-        "This arises when the forecast is used to control some kind of inventory, for example of grocery items of virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts."
+        "This arises when the forecast is used to control some kind of inventory, for example of grocery items or virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts."
       ]
     },
     {
@@ -460,10 +457,10 @@
         "# specify which quantiles you would like \n",
         "fitted_model.quantiles = [0.01, 0.5, 0.95]\n",
         "# use forecast_quantiles function, not the forecast() one\n",
-        "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test, y_query)\n",
+        "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "\n",
         "# it all nicely aligns column-wise\n",
-        "pd.concat([X_test.reset_index(), pd.DataFrame({'query' : y_query}), y_pred_quantiles], axis=1)"
+        "pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
       ]
     },
     {
@@ -472,7 +469,7 @@
       "source": [
         "#### Destination-date forecast: \"just do something\"\n",
         "\n",
-        "In some scenarios, the X_test is not known. The forecast is likely to be weak, becaus it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
+        "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
       ]
     },
     {
@@ -539,9 +536,7 @@
       "outputs": [],
       "source": [
         "try: \n",
-        "    y_query = y_away.copy()\n",
-        "    y_query.fill(np.NaN)\n",
-        "    y_pred_away, xy_away = fitted_model.forecast(X_away, y_query)\n",
+        "    y_pred_away, xy_away = fitted_model.forecast(X_away)\n",
         "    xy_away\n",
         "except Exception as e:\n",
         "    print(e)"
@@ -551,7 +546,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "How should we read that eror message? The forecast origin is at the last time themodel saw an actual values of `y` (the target). That was at the end of the training data! Because the model received all `NaN` (and not an actual target value), it is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
+        "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
         "\n",
         "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
       ]
@@ -711,7 +706,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "None"
@@ -740,13 +735,13 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     },
     "tags": [
       "Forecasting",
       "Confidence Intervals"
     ],
-    "task": "forecasting"
+    "task": "Forecasting"
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -40,7 +40,7 @@
         "## Introduction\n",
         "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
         "\n",
-        "Make sure you have executed the [configuration notebook](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
       ]
@@ -325,9 +325,9 @@
         "\n",
         "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If a grain is not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
         "\n",
-        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up-to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
+        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
         "\n",
-        "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *X_valid* and *y_valid* parameters of AutoMLConfig.\n",
+        "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
         "\n",
         "Here is a summary of AutoMLConfig parameters used for training the OJ model:\n",
         "\n",
@@ -370,7 +370,7 @@
         "                             training_data=train_dataset,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
-        "                             enable_early_stopping = True,\n",
+        "                             enable_early_stopping=True,\n",
         "                             n_cross_validations=3,\n",
         "                             verbosity=logging.INFO,\n",
         "                             **time_series_settings)"
@@ -454,9 +454,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
-        "\n",
-        "We will first create a query `y_query`, which is aligned index-for-index to `X_test`. This is a vector of target values where each `NaN` serves the function of the question mark to be replaced by forecast. Passing definite values in the `y` argument allows the `forecast` function to make predictions on data that does not immediately follow the train data which contains `y`. In each grain, the last time point where the model sees a definite value of `y` is that grain's _forecast origin_."
+        "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data."
       ]
     },
     {
@@ -465,15 +463,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Replace ALL values in y by NaN.\n",
-        "# The forecast origin will be at the beginning of the first forecast period.\n",
-        "# (Which is the same time as the end of the last training period.)\n",
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.nan)\n",
         "# The featurized data, aligned to y, will also be returned.\n",
         "# This contains the assumptions that were made in the forecast\n",
         "# and helps align the forecast to the original data\n",
-        "y_predictions, X_trans = fitted_model.forecast(X_test, y_query)"
+        "y_predictions, X_trans = fitted_model.forecast(X_test)"
       ]
     },
     {
@@ -640,7 +633,7 @@
         "import json\n",
         "# The request data frame needs to have y_query column which corresponds to query.\n",
         "X_query = X_test.copy()\n",
-        "X_query['y_query'] = y_query\n",
+        "X_query['y_query'] = np.NaN\n",
         "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",
@@ -693,7 +686,7 @@
     "category": "tutorial",
     "celltoolbar": "Raw Cell Format",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "Orange Juice Sales"
@@ -722,8 +715,11 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     },
+    "tags": [
+      "None"
+    ],
     "task": "Forecasting"
   },
   "nbformat": 4,

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

@@ -634,7 +634,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
+        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
         "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
       ]
     },
@@ -653,11 +653,11 @@
       "outputs": [],
       "source": [
         "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
+        "from interpret_community.widget import ExplanationDashboard\n",
         "client = ExplanationClient.from_run(automl_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False)\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, explainer_setup_class.X_test_transform)"
+        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, datasetX=explainer_setup_class.X_test_transform)"
       ]
     },
     {
@@ -676,7 +676,7 @@
       "source": [
         "raw_explanations = client.download_model_explanation(raw=True)\n",
         "print(raw_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, explainer_setup_class.X_test_raw)"
+        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, datasetX=explainer_setup_class.X_test_raw)"
       ]
     },
     {

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

@@ -5,7 +5,8 @@ import os
 import pickle
 import azureml.train.automl
 import azureml.explain.model
-from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
+from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
+    automl_setup_model_explanations
 from sklearn.externals import joblib
 from azureml.core.model import Model
 

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

@@ -6,7 +6,8 @@ from azureml.core.run import Run
 from azureml.core.experiment import Experiment
 from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
-from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
+from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
+    automl_setup_model_explanations
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
 from automl.client.core.common.constants import MODEL_PATH

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

@@ -140,6 +140,9 @@
     "framework": [
       "Azure ML AutoML"
     ],
+    "tags": [
+      ""
+    ],
     "friendly_name": "Forecasting with automated ML SQL integration",
     "index_order": 1,
     "kernelspec": {
@@ -151,9 +154,6 @@
       "name": "sql",
       "version": ""
     },
-    "tags": [
-      ""
-    ],
     "task": "Forecasting"
   },
   "nbformat": 4,

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

@@ -560,6 +560,9 @@
     "framework": [
       "Azure ML AutoML"
     ],
+    "tags": [
+      ""
+    ],
     "friendly_name": "Setup automated ML SQL integration",
     "index_order": 1,
     "kernelspec": {
@@ -571,9 +574,6 @@
       "name": "sql",
       "version": ""
     },
-    "tags": [
-      ""
-    ],
     "task": "None"
   },
   "nbformat": 4,

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb

@@ -175,6 +175,7 @@
       "source": [
         "#deploy to ACI\n",
         "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "myaci_config = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
@@ -182,11 +183,19 @@
         "                                                  tags = {'name':'Databricks Azure ML ACI'}, \n",
         "                                                  description = 'This is for ADB and AML example.')\n",
         "\n",
+        "service_name = 'aciws'\n",
+        "\n",
+        "# Remove any existing service under the same name.\n",
+        "try:\n",
+        "    Webservice(ws, service_name).delete()\n",
+        "except WebserviceException:\n",
+        "    pass\n",
+        "\n",
         "inference_config = InferenceConfig(runtime= 'spark-py', \n",
         "                                   entry_script='score_sparkml.py',\n",
         "                                   conda_file='mydeployenv.yml')\n",
         "\n",
-        "myservice = Model.deploy(ws, 'aciws', [mymodel], inference_config, myaci_config)\n",
+        "myservice = Model.deploy(ws, service_name, [mymodel], inference_config, myaci_config)\n",
         "myservice.wait_for_deployment(show_output=True)"
       ]
     },
@@ -199,18 +208,6 @@
         "help(Webservice)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# List images by ws\n",
-        "\n",
-        "for i in ContainerImage.list(workspace = ws):\n",
-        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-05.ipynb

@@ -163,14 +163,19 @@
         "#it may take 20-25 minutes to create a new cluster\n",
         "\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Use the default configuration (can also provide parameters to customize)\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "aks_name = 'ps-aks-demo2' \n",
         "\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "try:\n",
+        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    # Use the default configuration (can also provide parameters to customize)\n",
+        "    prov_config = AksCompute.provisioning_configuration()\n",
+        "    \n",
+        "    # Create the cluster\n",
+        "    aks_target = ComputeTarget.create(workspace = ws, \n",
         "                                  name = aks_name, \n",
         "                                  provisioning_configuration = prov_config)\n",
         "\n",
@@ -188,15 +193,24 @@
       "source": [
         "#deploy to AKS\n",
         "from azureml.core.webservice import AksWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)\n",
         "\n",
+        "service_name = 'ps-aks-service'\n",
+        "\n",
+        "# Remove any existing service under the same name.\n",
+        "try:\n",
+        "    Webservice(ws, service_name).delete()\n",
+        "except WebserviceException:\n",
+        "    pass\n",
+        "\n",
         "inference_config = InferenceConfig(runtime = 'spark-py', \n",
         "                                   entry_script ='score_sparkml.py',\n",
         "                                   conda_file ='mydeployenv.yml')\n",
         "\n",
-        "aks_service = Model.deploy(ws, 'ps-aks-service', [mymodel], inference_config, aks_config, aks_target)\n",
+        "aks_service = Model.deploy(ws, service_name, [mymodel], inference_config, aks_config, aks_target)\n",
         "aks_service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -288,7 +302,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.8"
     },
     "name": "deploy-to-aks-existingimage-05",
     "notebookId": 1030695628045968

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

@@ -661,6 +661,7 @@
         "# this will take 10-15 minutes to finish\n",
         "\n",
         "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.model import Model\n",
         "import uuid\n",
@@ -677,6 +678,13 @@
         "\n",
         "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
         "service_name = \"myservice-{}\".format(guid)\n",
+        "\n",
+        "# Remove any existing service under the same name.\n",
+        "try:\n",
+        "    Webservice(ws, service_name).delete()\n",
+        "except WebserviceException:\n",
+        "    pass\n",
+        "\n",
         "print(\"Creating service with name: {}\".format(service_name))\n",
         "\n",
         "myservice = Model.deploy(ws, service_name, [model], inference_config, myaci_config)\n",
@@ -795,7 +803,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.5"
+      "version": "3.6.8"
     },
     "name": "auto-ml-classification-local-adb",
     "notebookId": 2733885892129020

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

@@ -116,7 +116,8 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "register model from file"
+          "register model from file",
+          "sample-model-register"
         ]
       },
       "outputs": [],
@@ -404,7 +405,7 @@
         "\n",
         " - To run a production-ready web service, see the [notebook on deployment to Azure Kubernetes Service](../production-deploy-to-aks/production-deploy-to-aks.ipynb).\n",
         " - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n",
-        " - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets.ipynb).\n",
+        " - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n",
         " - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n",
         " - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)."
       ]

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

@@ -96,7 +96,8 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "register model from file"
+          "register model from file",
+          "sample-model-register"
         ]
       },
       "outputs": [],

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

@@ -345,9 +345,11 @@
     ],
     "category": "tutorial",
     "compute": [
-      "local"
+      "Local"
+    ],
+    "datasets": [
+      "None"
     ],
-    "datasets": [],
     "deployment": [
       "Local"
     ],

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

@@ -0,0 +1,369 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploy models to Azure Kubernetes Service (AKS) using controlled roll out\n",
+        "This notebook will show you how to deploy mulitple AKS webservices with the same scoring endpoint and how to roll out your models in a controlled manner by configuring % of scoring traffic going to each webservice. If you are using a Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create an Azure ML Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check for latest version\n",
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Create a [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "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 a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n",
+        "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Model\n",
+        "\n",
+        "model = Model.register(workspace=ws,\n",
+        "                       model_name='sklearn_regression_model.pkl',                # Name of the registered model in your workspace.\n",
+        "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
+        "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
+        "                       model_framework_version='0.19.1',             # Version of scikit-learn used to create the model.\n",
+        "                       description='Ridge regression model to predict diabetes progression.',\n",
+        "                       tags={'area': 'diabetes', 'type': 'regression'})\n",
+        "\n",
+        "print('Name:', model.name)\n",
+        "print('Version:', model.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register an environment (for all models)\n",
+        "\n",
+        "If you control over how your model is run, or if it has special runtime requirements, you can specify your own environment and scoring method.\n",
+        "\n",
+        "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "environment=Environment('my-sklearn-environment')\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "    'azureml-defaults',\n",
+        "    'inference-schema[numpy-support]',\n",
+        "    'joblib',\n",
+        "    'numpy',\n",
+        "    'scikit-learn'\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with open('score.py') as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create the InferenceConfig\n",
+        "Create the inference configuration to reference your environment and entry script during deployment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script='score.py', \n",
+        "                                   source_directory='.',\n",
+        "                                   environment=environment)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Provision the AKS Cluster\n",
+        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AksCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "# Use the default configuration (can also provide parameters to customize)\n",
+        "prov_config = AksCompute.provisioning_configuration()\n",
+        "\n",
+        "aks_name = 'my-aks' \n",
+        "# Create the cluster\n",
+        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "                                  name = aks_name, \n",
+        "                                  provisioning_configuration = prov_config) \n",
+        "aks_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create an Endpoint and add a version (AKS service)\n",
+        "This creates a new endpoint and adds a version behind it. By default the first version added is the default version. You can specify the traffic percentile a version takes behind an endpoint. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# deploying the model and create a new endpoint\n",
+        "from azureml.core.webservice import AksEndpoint\n",
+        "# from azureml.core.compute import ComputeTarget\n",
+        "\n",
+        "#select a created compute\n",
+        "compute = ComputeTarget(ws, 'my-aks')\n",
+        "namespace_name=\"endpointnamespace\"\n",
+        "# define the endpoint name\n",
+        "endpoint_name = \"myendpoint1\"\n",
+        "# define the service name\n",
+        "version_name= \"versiona\"\n",
+        "\n",
+        "endpoint_deployment_config = AksEndpoint.deploy_configuration(tags = {'modelVersion':'firstversion', 'department':'finance'}, \n",
+        "                                                              description = \"my first version\", namespace = namespace_name, \n",
+        "                                                              version_name = version_name, traffic_percentile = 40)\n",
+        "\n",
+        "endpoint = Model.deploy(ws, endpoint_name, [model], inference_config, endpoint_deployment_config, compute)\n",
+        "endpoint.wait_for_deployment(True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "endpoint.get_logs()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Add another version of the service to an existing endpoint\n",
+        "This adds another version behind an existing endpoint. You can specify the traffic percentile the new version takes. If no traffic_percentile is specified then it defaults to 0. All the unspecified traffic percentile (in this example 50) across all versions goes to default version."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Adding a new version to an existing Endpoint.\n",
+        "version_name_add=\"versionb\" \n",
+        "\n",
+        "endpoint.create_version(version_name = version_name_add, inference_config=inference_config, models=[model], tags = {'modelVersion':'secondversion', 'department':'finance'}, \n",
+        "                        description = \"my second version\", traffic_percentile = 10)\n",
+        "endpoint.wait_for_deployment(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Update an existing version in an endpoint\n",
+        "There are two types of versions: control and treatment. An endpoint contains one or more treatment versions but only one control version. This categorization helps compare the different versions against the defined control version."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "endpoint.update_version(version_name=endpoint.versions[version_name_add].name, description=\"my second version update\", traffic_percentile=40, is_default=True, is_control_version_type=True)\n",
+        "endpoint.wait_for_deployment(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Test the web service using run method\n",
+        "Test the web sevice by passing in data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Scoring on endpoint\n",
+        "import json\n",
+        "test_sample = json.dumps({'data': [\n",
+        "    [1,2,3,4,5,6,7,8,9,10], \n",
+        "    [10,9,8,7,6,5,4,3,2,1]\n",
+        "]})\n",
+        "\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "prediction = endpoint.run(input_data=test_sample_encoded)\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Delete Resources"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# deleting a version in an endpoint\n",
+        "endpoint.delete_version(version_name=version_name)\n",
+        "endpoint.wait_for_deployment(True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# deleting an endpoint, this will delete all versions in the endpoint and the endpoint itself\n",
+        "endpoint.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "shipatel"
+      }
+    ],
+    "category": "deployment",
+    "compute": [
+      "None"
+    ],
+    "datasets": [
+      "Diabetes"
+    ],
+    "deployment": [
+      "Azure Kubernetes Service"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Scikit-learn"
+    ],
+    "friendly_name": "Deploy models to AKS using controlled roll out",
+    "index_order": 3,
+    "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"
+    },
+    "star_tag": [
+      "featured"
+    ],
+    "tags": [
+      "None"
+    ],
+    "task": "Deploy a model with Azure Machine Learning"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,4 @@
+name: deploy-aks-with-controlled-rollout
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/deploy-with-controlled-rollout/score.py

@@ -0,0 +1,28 @@
+import pickle
+import json
+import numpy
+from sklearn.externals import joblib
+from sklearn.linear_model import Ridge
+from azureml.core.model import Model
+
+
+def init():
+    global model
+    # note here "sklearn_regression_model.pkl" is the name of the model registered under
+    # this is a different behavior than before when the code is run locally, even though the code is the same.
+    model_path = Model.get_model_path('sklearn_regression_model.pkl')
+    # deserialize the model file back into a sklearn model
+    model = joblib.load(model_path)
+
+
+# note you can pass in multiple rows for scoring
+def run(raw_data):
+    try:
+        data = json.loads(raw_data)['data']
+        data = numpy.array(data)
+        result = model.predict(data)
+        # you can return any data type as long as it is JSON-serializable
+        return result.tolist()
+    except Exception as e:
+        error = str(e)
+        return error

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

@@ -431,7 +431,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "aks_service.update(enable_app_insights=False)"
+        "aks_service.update(enable_app_insights=False)\n",
+        "aks_service.wait_for_deployment(show_output = True)"
       ]
     },
     {

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

@@ -755,7 +755,7 @@
     ],
     "category": "deployment",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "Emotion FER"

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

@@ -763,7 +763,7 @@
     ],
     "category": "deployment",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "MNIST"

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

@@ -373,7 +373,7 @@
     ],
     "category": "deployment",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "ImageNet"

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

@@ -1,11 +1,14 @@
-## Using explain model APIs
+## Using AzureML Interpret APIs
 
 <a name="samples"></a>
-# Explain Model SDK Sample Notebooks
+# AzureML Interpret SDK Sample Notebooks
 
-Follow these sample notebooks to learn:
+You can run the interpret-community SDK to explain models locally without Azure.
+For notebooks on the local experience, please see:
+https://github.com/interpretml/interpret-community/tree/master/notebooks
 
-1. [Explain tabular data locally](tabular-data): Basic examples of explaining model trained on tabular data.
-2. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
-3. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
-4. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.
+Follow these sample notebooks to learn about the model interpretability integration with Azure:
+
+1. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
+2. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
+3. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.

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

@@ -669,7 +669,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -678,7 +678,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_model, x_test)"
+        "ExplanationDashboard(global_explanation, original_model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py

@@ -61,4 +61,4 @@ global_explanation = tabular_explainer.explain_global(X_test)
 # Uploading model explanation data for storage or visualization in webUX
 # The explanation can then be downloaded on any compute
 comment = 'Global explanation on regression model trained on boston dataset'
-client.upload_model_explanation(global_explanation, comment=comment)
+client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb

@@ -564,7 +564,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -573,7 +573,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(downloaded_global_explanation, model, x_test)"
+        "ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
       ]
     },
     {

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

@@ -290,7 +290,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -299,7 +299,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, clf, x_test)"
+        "ExplanationDashboard(global_explanation, clf, datasetX=x_test)"
       ]
     },
     {

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

@@ -355,7 +355,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -364,7 +364,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_svm_model, x_test)"
+        "ExplanationDashboard(global_explanation, original_svm_model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py

@@ -116,7 +116,7 @@ global_explanation = tabular_explainer.explain_global(x_test)
 
 # uploading model explanation data for storage or visualization
 comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
-client.upload_model_explanation(global_explanation, comment=comment)
+client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)
 
 # also create a lightweight explainer for scoring time
 scoring_explainer = LinearScoringExplainer(tabular_explainer)

how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb

@@ -1,509 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain binary classification model predictions with raw feature transformations\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses advanced many to one or many to many feature transformations.**_\n",
-        "\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Apply feature transformations\n",
-        "    1. Train a binary classification model\n",
-        "    1. Explain the model on raw features\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates creating explanations for a binary classification model, Titanic passenger data classification, that uses many to one and many to many feature transformations from raw data to engineered features. For the many to one transformation, we sum 2 features `age` and `fare`. For many to many transformations two features are computed: one that is product of `age` and `fare` and another that is square of this product. Our tabular data explainer is then used to get the explanation object with the flag `allow_all_transformations` passed. The object is then used to get raw feature importances.\n",
-        "\n",
-        "\n",
-        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Titanic passenger data classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Transform raw features to engineered features\n",
-        "2. Train a Logistic Regression model using Scikit-learn\n",
-        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "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",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Titanic passenger data"
-      ]
-    },
-    {
-      "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": [
-        "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",
-        "# Split data into train and test\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": [
-        "### Transform raw features"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# We add many to one and many to many transformations for illustration purposes.\n",
-        "# The support for raw feature explanations with many to one and many to many transformations are only supported \n",
-        "# When allow_all_transformations is set to True on explainer creation\n",
-        "from sklearn.preprocessing import FunctionTransformer\n",
-        "many_to_one_transformer = FunctionTransformer(lambda x: x.sum(axis=1).reshape(-1, 1))\n",
-        "many_to_many_transformer = FunctionTransformer(lambda x: np.hstack(\n",
-        "    (np.prod(x, axis=1).reshape(-1, 1), (np.prod(x, axis=1)**2).reshape(-1, 1))\n",
-        "))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.compose import ColumnTransformer\n",
-        "\n",
-        "transformations = ColumnTransformer([\n",
-        "    (\"age_fare_1\", Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ]), [\"age\", \"fare\"]),\n",
-        "    (\"age_fare_2\", many_to_one_transformer, [\"age\", \"fare\"]),\n",
-        "    (\"age_fare_3\", many_to_many_transformer, [\"age\", \"fare\"]),\n",
-        "    (\"embarked\", Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
-        "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
-        "])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\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",
-        "    ([\"age\", \"fare\"], many_to_one_transformer),\n",
-        "    ([\"age\", \"fare\"], many_to_many_transformer),\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'))])\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a Logistic Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\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": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "# When the last parameter allow_all_transformations is passed, we handle many to one and many to many transformations to \n",
-        "# generate approximations to raw feature importances. When this flag is passed, for transformations not recognized as one to \n",
-        "# many, we distribute feature importances evenly to raw features generating them.\n",
-        "# clf.steps[-1][1] returns the trained classification model\n",
-        "explainer = TabularExplainer(clf.steps[-1][1], \n",
-        "                             initialization_examples=x_train, \n",
-        "                             features=x_train.columns, \n",
-        "                             transformations=transformations, \n",
-        "                             allow_all_transformations=True)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=x_train.columns, \n",
-        "#                            transformations=transformations, \n",
-        "#                            allow_all_transformations=True)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "\n",
-        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
-        "#                          features=x_train.columns, \n",
-        "#                          transformations=transformations)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_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": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "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": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 1\n",
-        "local_explanation = 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",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. [Explain models with simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "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/tabular-data/advanced-feature-transformations-explain-local.yml

@@ -1,9 +0,0 @@
-name: advanced-feature-transformations-explain-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - sklearn-pandas
-  - ipywidgets

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

@@ -1,390 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain binary classification model predictions\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model predictions.**_\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Train a binary classification model\n",
-        "    1. Explain the model\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates how to explain a binary classification model predictions locally at training time without contacting any Azure services.\n",
-        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
-        "\n",
-        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Breast cancer diagnosis classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "explainer = TabularExplainer(model, \n",
-        "                             x_train, \n",
-        "                             features=breast_cancer_data.feature_names, \n",
-        "                             classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(model, \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=breast_cancer_data.feature_names, \n",
-        "#                            classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(model, \n",
-        "#                          features=breast_cancer_data.feature_names, \n",
-        "#                          classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_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",
-        "\n",
-        "# Note: PFIExplainer does not support per class explanations\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": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "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": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = 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",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "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/tabular-data/explain-binary-classification-local.yml

@@ -1,8 +0,0 @@
-name: explain-binary-classification-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - ipywidgets

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

@@ -1,388 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain multiclass classification model's predictions\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a multiclass classification model predictions.**_\n",
-        "\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Train a multiclass classification model\n",
-        "    1. Explain the model\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates how to explain a multiclass classification model predictions locally at training time without contacting any Azure services.\n",
-        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
-        "\n",
-        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Iris flower classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_iris\n",
-        "from sklearn import svm\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Iris flower dataset"
-      ]
-    },
-    {
-      "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": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "explainer = TabularExplainer(model, \n",
-        "                             x_train, \n",
-        "                             features=feature_names, \n",
-        "                             classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(model, \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=feature_names, \n",
-        "#                            classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(model, \n",
-        "#                          features=feature_names, \n",
-        "#                          classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_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",
-        "\n",
-        "# Note: PFIExplainer does not support per class explanations\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": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "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": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = 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",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "\n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)       \n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n",
-        "\u00e2\u20ac\u2039\n"
-      ]
-    }
-  ],
-  "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/tabular-data/explain-multiclass-classification-local.yml

@@ -1,8 +0,0 @@
-name: explain-multiclass-classification-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - ipywidgets

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

@@ -1,383 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain regression model predictions\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a regression model predictions.**_\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Train a regressor model\n",
-        "    1. Explain the model\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates how to explain regression model predictions locally at training time without contacting any Azure services.\n",
-        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
-        "\n",
-        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Boston Housing Price Prediction with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a GradientBoosting regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = reg.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "explainer = TabularExplainer(model, \n",
-        "                             x_train, \n",
-        "                             features = boston_data.feature_names)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(model, \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=boston_data.feature_names)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(model, \n",
-        "#                          features=boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values \n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# Feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "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": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# 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": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "local_explanation = explainer.explain_local(x_test[0,:])\n",
-        "\n",
-        "# E.g., Explain the first five data points in the test set\n",
-        "# local_explanation_group = explainer.explain_local(x_test[0:4,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted local feature importance information; reflects the original feature order\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
-        "\n",
-        "print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
-        "print('sorted local importance values: {}'.format(sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "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/tabular-data/explain-regression-local.yml

@@ -1,8 +0,0 @@
-name: explain-regression-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - ipywidgets

how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb

@@ -1,517 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain binary classification model predictions with raw feature transformations\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses one to one and one to many feature transformations.**_\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Apply feature transformations\n",
-        "    1. Train a binary classification model\n",
-        "    1. Explain the model on raw features\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next%20steps)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates creating explanations for a binary classification model, IBM employee attrition classification, that uses one to one and one to many feature transformations from raw data to engineered features. The one to many feature transformations include one hot encoding on categorical features. The one to one feature transformations apply standard scaling on numeric features. Our tabular data explainer is then used to get raw feature importances.\n",
-        "\n",
-        "\n",
-        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Transform raw features to engineered features\n",
-        "2. Train a SVC classification model using Scikit-learn\n",
-        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "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.svm import SVC\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the IBM employee attrition data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the IBM employee attrition dataset\n",
-        "outdirname = 'dataset.6.21.19'\n",
-        "try:\n",
-        "    from urllib import urlretrieve\n",
-        "except ImportError:\n",
-        "    from urllib.request import urlretrieve\n",
-        "import zipfile\n",
-        "zipfilename = outdirname + '.zip'\n",
-        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
-        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
-        "    unzip.extractall('.')\n",
-        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
-        "\n",
-        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
-        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
-        "# Dropping Employee Number since it is merely an identifier\n",
-        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
-        "\n",
-        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
-        "\n",
-        "# Since all values are 80\n",
-        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
-        "\n",
-        "# Converting target variables from string to numerical values\n",
-        "target_map = {'Yes': 1, 'No': 0}\n",
-        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
-        "target = attritionData[\"Attrition_numerical\"]\n",
-        "\n",
-        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
-      ]
-    },
-    {
-      "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(attritionXData, \n",
-        "                                                    target, \n",
-        "                                                    test_size = 0.2,\n",
-        "                                                    random_state=0,\n",
-        "                                                    stratify=target)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Creating dummy columns for each categorical feature\n",
-        "categorical = []\n",
-        "for col, value in attritionXData.iteritems():\n",
-        "    if value.dtype == 'object':\n",
-        "        categorical.append(col)\n",
-        "        \n",
-        "# Store the numerical columns in a list numerical\n",
-        "numerical = attritionXData.columns.difference(categorical)        "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Transform raw features"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.compose import ColumnTransformer\n",
-        "\n",
-        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
-        "numeric_transformer = Pipeline(steps=[\n",
-        "    ('imputer', SimpleImputer(strategy='median')),\n",
-        "    ('scaler', StandardScaler())])\n",
-        "\n",
-        "categorical_transformer = Pipeline(steps=[\n",
-        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
-        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
-        "\n",
-        "transformations = ColumnTransformer(\n",
-        "    transformers=[\n",
-        "        ('num', numeric_transformer, numerical),\n",
-        "        ('cat', categorical_transformer, categorical)])\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', SVC(C = 1.0, probability=True))])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\n",
-        "from sklearn_pandas import DataFrameMapper\n",
-        "\n",
-        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
-        "\n",
-        "\n",
-        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
-        "\n",
-        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
-        "\n",
-        "transformations = numeric_transformations + categorical_transformations\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', SVC(C = 1.0, probability=True))]) \n",
-        "\n",
-        "\n",
-        "\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a SVM classification 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": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "# clf.steps[-1][1] returns the trained classification model\n",
-        "explainer = TabularExplainer(clf.steps[-1][1], \n",
-        "                             initialization_examples=x_train, \n",
-        "                             features=attritionXData.columns, \n",
-        "                             classes=[\"Not leaving\", \"leaving\"], \n",
-        "                             transformations=transformations)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=attritionXData.columns, \n",
-        "#                            classes=[\"Not leaving\", \"leaving\"], \n",
-        "#                            transformations=transformations)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
-        "#                          features=x_train.columns, \n",
-        "#                          transformations=transformations,\n",
-        "#                          classes=[\"Not leaving\", \"leaving\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_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",
-        "\n",
-        "# Note: PFIExplainer does not support per class explanations\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": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "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": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 1\n",
-        "local_explanation = 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",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. [Explain models with advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "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/tabular-data/simple-feature-transformations-explain-local.yml

@@ -1,9 +0,0 @@
-name: simple-feature-transformations-explain-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - sklearn-pandas
-  - ipywidgets

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

@@ -34,7 +34,8 @@
         "| Azure Data Lake Storage Gen 1 | Yes | Yes |\n",
         "| Azure Data Lake Storage Gen 2 | Yes | Yes |\n",
         "| Azure SQL Database | Yes | Yes |\n",
-        "| Azure Database for PostgreSQL | Yes | No |"
+        "| Azure Database for PostgreSQL | Yes | Yes |",
+        "| Azure Database for MySQL | Yes | Yes |"
       ]
     },
     {
@@ -342,8 +343,8 @@
       "source": [
         "\n",
         "mysql_datastore_name=\"MySqlDatastore\"\n",
-        "server_name=os.getenv(\"MYSQL_SERVERNAME_62\", \"<my-server-name>\") # Name of PostgreSQL server \n",
-        "database_name=os.getenv(\"MYSQL_DATBASENAME_62\", \"<my-database-name>\") # Name of PostgreSQL database\n",
+        "server_name=os.getenv(\"MYSQL_SERVERNAME_62\", \"<my-server-name>\") # Name of MySQL server \n",
+        "database_name=os.getenv(\"MYSQL_DATBASENAME_62\", \"<my-database-name>\") # Name of MySQL database\n",
         "user_id=os.getenv(\"MYSQL_USERID_62\", \"<my-user-id>\") # user id\n",
         "user_password=os.getenv(\"MYSQL_USERPW_62\", \"<my-user-password>\") # user password\n",
         "\n",

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

@@ -23,9 +23,9 @@
         "# How to create Module, ModuleVersion, and use them in a pipeline with ModuleStep.\n",
         "In this notebook, we introduce the concept of versioned modules and how to use them in an Azure Machine Learning Pipeline.\n",
         "\n",
-        "The core idea behind introducing Module, ModuleVersion and ModuleStep is to allow the separation between a reusable executable components and their actual usage. These reusable software components (such as scripts or executables) can be used in different scenarios and by different users. This follows the same idea of separating software frameworks/libraries and their actual usage in applications. Module and ModuleVersion take the role of the reusable executable components where ModuleStep is there to link them to an actual usage.\n",
+        "The core idea behind introducing Module, ModuleVersion and ModuleStep is to allow the separation between reusable executable components and their actual usage. These reusable software components (such as scripts or executables) can be used in different scenarios and by different users. This follows the same idea of separating software frameworks/libraries and their actual usage in applications. Module and ModuleVersion take the role of the reusable executable components where ModuleStep is there to link them to an actual usage.\n",
         "\n",
-        "A module is an elaborated container of its versions, where each version is the actual computational unit. It is up to users to define the semantics of this hierarchical structure of container and versions. For example, they could be different versions for different use cases, development progress, etc.\n",
+        "A module is an elaborated container of its versions, where each version is the actual computational unit. It is up to users to define the semantics of this hierarchical structure of container and versions. For example, there could be different versions for different use cases, development progress, etc.\n",
         "\n",
         "Each ModuleVersion may have inputs, outputs and rely on parameters and its environment configuration to operate.\n",
         "\n",

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

@@ -382,10 +382,25 @@
         "                         headers=aad_token, \n",
         "                         json={\"ExperimentName\": \"My_Pipeline1\",\n",
         "                               \"RunSource\": \"SDK\",\n",
-        "                               \"ParameterAssignments\": {\"pipeline_arg\": 45}})\n",
-        "run_id = response.json()[\"Id\"]\n",
+        "                               \"ParameterAssignments\": {\"pipeline_arg\": 45}})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
         "\n",
-        "print(run_id)"
+        "run_id = response.json().get('Id')\n",
+        "print('Submitted pipeline run: ', run_id)"
       ]
     },
     {

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

@@ -494,10 +494,25 @@
         "                         headers=aad_token, \n",
         "                         json={\"ExperimentName\": \"default_pipeline\",\n",
         "                               \"RunSource\": \"SDK\",\n",
-        "                               \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})\n",
+        "                               \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
         "\n",
-        "run_id = response.json()[\"Id\"]\n",
-        "print(run_id)"
+        "run_id = response.json().get('Id')\n",
+        "print('Submitted pipeline run: ', run_id)"
       ]
     },
     {
@@ -522,6 +537,24 @@
         "run_id = pipeline_endpoint_by_name.submit(\"NewName\")\n",
         "print(run_id)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Experiment.Submit() to Submit Pipeline\n",
+        "Run specific pipeline using Experiment submit api"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "pipeline_run = Experiment(ws, name=\"submit_from_endpoint\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")"
+      ]
     }
   ],
   "metadata": {
@@ -560,7 +593,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.2"
+      "version": "3.6.7"
     },
     "order_index": 12,
     "tags": [

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

@@ -366,8 +366,15 @@
         "\n",
         "rest_endpoint = published_pipeline.endpoint\n",
         "\n",
-        "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))\n",
-        "\n",
+        "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "# specify the param when running the pipeline\n",
         "response = requests.post(rest_endpoint, \n",
         "                         headers=aad_token, \n",
@@ -381,9 +388,24 @@
         "                               },\n",
         "                               \"ParameterAssignments\": {\"input_string\": \"sample_string3\"}\n",
         "                              }\n",
-        "                        )\n",
+        "                        )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
         "\n",
-        "run_id = response.json()[\"Id\"]\n",
+        "run_id = response.json().get('Id')\n",
         "print('Submitted pipeline run: ', run_id)"
       ]
     },

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

@@ -76,7 +76,7 @@
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"

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

@@ -822,7 +822,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "trainWithAutomlStep = AutoMLStep(\n",
         "    name='AutoML_Regression',\n",

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

@@ -15,6 +15,13 @@
         "![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": {},
+      "source": [
+        "**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -322,7 +329,6 @@
         "# Runconfig\n",
         "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
         "amlcompute_run_config.environment.docker.enabled = True\n",
-        "amlcompute_run_config.environment.docker.gpu_support = True\n",
         "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
         "amlcompute_run_config.environment.spark.precache_packages = False"
       ]
@@ -554,8 +560,25 @@
         "response = requests.post(rest_endpoint, \n",
         "                         headers=aad_token, \n",
         "                         json={\"ExperimentName\": \"batch_scoring\",\n",
-        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})\n",
-        "run_id = response.json()[\"Id\"]"
+        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
+        "\n",
+        "run_id = response.json().get('Id')\n",
+        "print('Submitted pipeline run: ', run_id)"
       ]
     },
     {

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

@@ -16,6 +16,13 @@
         "![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": {},
+      "source": [
+        "**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -276,7 +283,6 @@
         "# Runconfig\n",
         "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
         "amlcompute_run_config.environment.docker.enabled = True\n",
-        "amlcompute_run_config.environment.docker.gpu_support = True\n",
         "amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n",
         "amlcompute_run_config.environment.spark.precache_packages = False"
       ]
@@ -538,41 +544,59 @@
         "## Send request and monitor"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the pipeline using PipelineParameter values style='candy' and nodecount=2"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "# run the pipeline using PipelineParameter values style='candy' and nodecount=2\n",
         "response = requests.post(rest_endpoint, \n",
         "                         headers=aad_token,\n",
         "                         json={\"ExperimentName\": \"style_transfer\",\n",
-        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})                         \n",
-        "run_id = response.json()[\"Id\"]\n",
+        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
         "\n",
+        "run_id = response.json().get('Id')\n",
+        "print('Submitted pipeline run: ', run_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "from azureml.pipeline.core.run import PipelineRun\n",
         "published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
-        "\n",
         "RunDetails(published_pipeline_run_candy).show()"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
+      "cell_type": "markdown",
       "metadata": {},
-      "outputs": [],
       "source": [
-        "# run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3\n",
-        "response = requests.post(rest_endpoint, \n",
-        "                         headers=aad_token,\n",
-        "                         json={\"ExperimentName\": \"style_transfer\",\n",
-        "                               \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})    \n",
-        "run_id = response.json()[\"Id\"]\n",
-        "\n",
-        "published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
-        "\n",
-        "RunDetails(published_pipeline_run_rain).show()"
+        "Run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3"
       ]
     },
     {
@@ -581,15 +605,84 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# run the pipeline using PipelineParameter values style='udnie' and nodecount=4\n",
         "response = requests.post(rest_endpoint, \n",
         "                         headers=aad_token,\n",
         "                         json={\"ExperimentName\": \"style_transfer\",\n",
-        "                               \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 3}})   \n",
-        "run_id = response.json()[\"Id\"]\n",
+        "                               \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
         "\n",
+        "run_id = response.json().get('Id')\n",
+        "print('Submitted pipeline run: ', run_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
+        "RunDetails(published_pipeline_run_rain).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the pipeline using PipelineParameter values style='udnie' and nodecount=4"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "response = requests.post(rest_endpoint, \n",
+        "                         headers=aad_token,\n",
+        "                         json={\"ExperimentName\": \"style_transfer\",\n",
+        "                               \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 3}})\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    response.raise_for_status()\n",
+        "except Exception:    \n",
+        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
+        "                    'Response Code: {}\\n'\n",
+        "                    'Headers: {}\\n'\n",
+        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
+        "\n",
+        "run_id = response.json().get('Id')\n",
+        "print('Submitted pipeline run: ', run_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
-        "\n",
         "RunDetails(published_pipeline_run_udnie).show()"
       ]
     },

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

@@ -104,7 +104,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-interactiveloginauth-tenantid"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.authentication import InteractiveLoginAuthentication\n",
@@ -131,7 +135,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-azurecliauth"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.authentication import AzureCliAuthentication\n",
@@ -168,7 +176,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-msiauth"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.authentication import MsiAuthentication\n",
@@ -245,7 +257,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-serviceprincipalauth-tenantid"
+        ]
+      },
       "outputs": [],
       "source": [
         "import os\n",
@@ -300,7 +316,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-keyvault"
+        ]
+      },
       "outputs": [],
       "source": [
         "import os, uuid\n",

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

@@ -707,7 +707,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "dipeck"
+        "name": "swatig"
       }
     ],
     "category": "training",

how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py

@@ -166,7 +166,7 @@ def download_data():
     from zipfile import ZipFile
     # download data
     data_file = './fowl_data.zip'
-    download_url = 'https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip'
+    download_url = 'https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip'
     urllib.request.urlretrieve(download_url, filename=data_file)
 
     # extract files

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

@@ -174,7 +174,7 @@
       "metadata": {},
       "source": [
         "### Download training data\n",
-        "The dataset we will use (located on a public blob [here](https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
+        "The dataset we will use (located on a public blob [here](https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
       ]
     },
     {
@@ -698,7 +698,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "ninhu"
+        "name": "swatig"
       }
     ],
     "category": "training",

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

@@ -550,7 +550,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "dipeck"
+        "name": "swatig"
       }
     ],
     "category": "training",

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

@@ -1140,7 +1140,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "ninhu"
+        "name": "swatig"
       }
     ],
     "category": "training",

how-to-use-azureml/ml-frameworks/tensorflow/training/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb

@@ -517,7 +517,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.hyperdrive import *\n",
+        "from azureml.train.hyperdrive import RandomParameterSampling, choice, loguniform\n",
         "\n",
         "ps = RandomParameterSampling(\n",
         "    {\n",
@@ -562,6 +562,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.train.hyperdrive import TruncationSelectionPolicy\n",
         "policy = TruncationSelectionPolicy(evaluation_interval=2, truncation_percentage=25)"
       ]
     },
@@ -578,12 +579,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.train.hyperdrive import HyperDriveConfig, PrimaryMetricGoal\n",
         "htc = HyperDriveConfig(estimator=est, \n",
         "                       hyperparameter_sampling=ps, \n",
         "                       policy=policy, \n",
         "                       primary_metric_name='validation_acc', \n",
         "                       primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n",
-        "                       max_total_runs=20,\n",
+        "                       max_total_runs=15,\n",
         "                       max_concurrent_runs=4)"
       ]
     },
@@ -616,7 +618,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.widgets import RunDetails\n",
         "RunDetails(htr).show()"
       ]
     },
@@ -721,7 +722,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.widgets import RunDetails\n",
         "RunDetails(warm_start_htr).show()"
       ]
     },
@@ -820,7 +820,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.widgets import RunDetails\n",
         "RunDetails(resume_child_runs_htr).show()"
       ]
     },

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

@@ -0,0 +1,346 @@
+latitude,longitude,temperature,windAngle,windSpeed,elevation
+26.536,-81.755,17.8,10.0,2.1,9.0
+26.536,-81.755,16.7,360.0,1.5,9.0
+26.536,-81.755,16.1,350.0,1.5,9.0
+26.536,-81.755,15.0,0.0,0.0,9.0
+26.536,-81.755,14.4,350.0,1.5,9.0
+26.536,-81.755,0.0,0.0,0.0,9.0
+26.536,-81.755,13.9,360.0,2.1,9.0
+26.536,-81.755,13.3,350.0,1.5,9.0
+26.536,-81.755,13.3,10.0,2.1,9.0
+26.536,-81.755,13.3,360.0,1.5,9.0
+26.536,-81.755,13.3,0.0,0.0,9.0
+26.536,-81.755,12.2,0.0,0.0,9.0
+26.536,-81.755,11.7,0.0,0.0,9.0
+26.536,-81.755,14.4,0.0,0.0,9.0
+26.536,-81.755,17.2,10.0,2.6,9.0
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+-34.067,-56.238,17.5,30.0,3.1,68.0
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+-34.067,-56.238,20.6,30.0,3.1,68.0
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+-34.067,-56.238,23.0,210.0,3.6,68.0
+-34.067,-56.238,24.0,180.0,6.7,68.0
+-34.067,-56.238,24.5,210.0,7.2,68.0
+-34.067,-56.238,21.0,180.0,8.2,68.0
+-34.067,-56.238,20.0,180.0,6.7,68.0
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how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.0.74, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.0.79, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -542,7 +542,9 @@
     "compute": [
       "None"
     ],
-    "datasets": [],
+    "datasets": [
+      "None"
+    ],
     "deployment": [
       "None"
     ],

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb

@@ -1131,7 +1131,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "ninhu"
+        "name": "swatig"
       }
     ],
     "category": "training",

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

@@ -63,7 +63,6 @@
       "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')"
       ]
@@ -258,6 +257,16 @@
         "metrics = run.get_metrics()\n",
         "print(metrics)"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# register the generated model\n",
+        "model = run.register_model(model_name='iris.model', model_path='outputs/iris.model')\n"
+      ]
     }
   ],
   "metadata": {
@@ -297,7 +306,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.2"
     },
     "tags": [
       "None"

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

@@ -76,6 +76,8 @@ train, test = data.randomSplit([0.70, 0.30])
 lr = pyspark.ml.classification.LogisticRegression(regParam=reg)
 model = lr.fit(train)
 
+model.save(os.path.join("outputs", "iris.model"))
+
 # predict on the test set
 prediction = model.transform(test)
 print("Prediction")

how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb

@@ -685,7 +685,7 @@
     "framework": [
       "None"
     ],
-    "friendly_name": "",
+    "friendly_name": "Train and deploy a model using Python SDK",
     "index_order": 1,
     "kernelspec": {
       "display_name": "Python 3.6",

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

@@ -27,6 +27,7 @@
         "\n",
         "1. [Introduction](#Introduction)\n",
         "1. [Setup](#Setup)\n",
+        "1. [Use curated environment](#Use-curated-environment)\n",
         "1. [Create environment](#Create-environment)\n",
         "    1. Add Python packages\n",
         "    1. Specify environment variables\n",
@@ -36,6 +37,8 @@
         "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. [Estimators and environments](#Estimators-and-environments)    \n",
+        "1. [Using environments for inferencing](#Using-environments-for-inferencing)\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",
@@ -84,7 +87,57 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create environment\n",
+        "## Use curated environments\n",
+        "\n",
+        "Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They contain collections of Python packages and settings to help you get started different machine learning frameworks. \n",
+        "\n",
+        "  * The __AzureML-Minimal__ environment contains a minimal set of packages to enable run tracking and asset uploading. You can use it as a starting point for your own environment.\n",
+        "  * The __AzureML-Tutorial__ environment contains common data science packages, such as Scikit-Learn, Pandas and Matplotlib, and larger set of azureml-sdk packages.\n",
+        " \n",
+        "Curated environments are backed by cached Docker images, reducing the run preparation cost.\n",
+        " \n",
+        "You can get a curated environment using"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "curated_env = Environment.get(workspace=ws, name=\"AzureML-Minimal\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To list curated environments, use following code.\n",
+        "\n",
+        "**Note**: The name prefixes _AzureML_ and _Microsoft_ are reserved for curated environments. Do not use them for your own environments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "envs = Environment.list(workspace=ws)\n",
+        "\n",
+        "for env in envs:\n",
+        "    if env.startswith(\"AzureML\"):\n",
+        "        print(\"Name\",env)\n",
+        "        print(\"packages\", envs[env].python.conda_dependencies.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create your own 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",
@@ -96,10 +149,13 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "condadependencies-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
-        "from azureml.core import Environment\n",
         "from azureml.core.environment import CondaDependencies\n",
         "\n",
         "myenv = Environment(name=\"myenv\")\n",
@@ -117,7 +173,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "condadependencies-remarks-sample2"
+        ]
+      },
       "outputs": [],
       "source": [
         "conda_dep.add_pip_package(\"pillow==5.4.1\")\n",
@@ -185,6 +245,22 @@
         "run.wait_for_completion(show_output=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To audit the environment used by for a run, you can use ```get_environement```."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_environment()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -256,6 +332,48 @@
         "```\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Estimators and environments\n",
+        "\n",
+        "[Estimators](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models) are backed by environments that define the base images, Python packages and other settings for the training environment. \n",
+        "\n",
+        "For example, to see the environment behind PyTorch Estimator, you can create a dummy instance of the Estimator, and look at the ```run_config.environment``` property."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import PyTorch\n",
+        "\n",
+        "pt = PyTorch(source_directory=\".\", compute_target=\"local\")\n",
+        "pt.run_config.environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Using environments for inferencing\n",
+        "\n",
+        "You can re-use the training environment when you deploy your model as a web service, by specifying inferencing stack version, and adding then environment to ```InferenceConfig```.\n",
+        "\n",
+        "```\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "myenv.inferencing_stack_version = \"latest\"\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
+        "```\n",
+        "\n",
+        "See [Register Model and deploy as Webservice Notebook](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb) for an end-to-end example of web service deployment."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -299,7 +417,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can also specify whether to use GPU or shared volumes, and shm size."
+        "You can also specify shared volumes, and shm size."
       ]
     },
     {
@@ -308,7 +426,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myenv.docker.gpu_support\n",
         "myenv.docker.shared_volumes\n",
         "myenv.docker.shm_size"
       ]
@@ -336,7 +453,7 @@
         "\n",
         "Learn more about registering and deploying a model:\n",
         "\n",
-        "* [Model Register and Deploy](../../deploy-to-cloud/model-register-and-deploy.ipynb)"
+        "* [Register Model and deploy as Webservice](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb)"
       ]
     },
     {

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

@@ -10,7 +10,7 @@ With Azure Machine Learning datasets, you can:
 
 ## Learn how to use Azure Machine Learning datasets
 * [Create and register datasets](https://aka.ms/azureml/howto/createdatasets)
-* Use [Datasets in training](datasets-tutorial/train-with-datasets.ipynb)
+* Use [Datasets in training](datasets-tutorial/train-with-datasets/train-with-datasets.ipynb)
 * Use TabularDatasets in [automated machine learning training](https://aka.ms/automl-dataset)
 * Use FileDatasets in [image classification](https://aka.ms/filedataset-samplenotebook)
 * Use FileDatasets in [deep learning with hyperparameter tuning](https://aka.ms/filedataset-hyperdrive)

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

@@ -414,7 +414,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "NOAA"

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

@@ -0,0 +1,403 @@
+{
+  "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/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Introduction to labeled datasets\n",
+        "\n",
+        "Labeled datasets are output from Azure Machine Learning [labeling projects](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-labeling-projects). It captures the reference to the data (e.g. image files) and its labels. \n",
+        "\n",
+        "This tutorial introduces the capabilities of labeled datasets and how to use it in training.\n",
+        "\n",
+        "Learn how-to:\n",
+        "\n",
+        "> * Set up your development environment\n",
+        "> * Explore labeled datasets\n",
+        "> * Train a simple deep learning neural network on a remote cluster\n",
+        "\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 Azure Machine Learning [labeling projects](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-labeling-projects) and export the labels as an Azure Machine Learning dataset\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the latest version of azureml-sdk\n",
+        "    * install the latest version of azureml-contrib-dataset\n",
+        "    * install [PyTorch](https://pytorch.org/)\n",
+        "    * create a workspace and its configuration file (`config.json`)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up your development environment\n",
+        "\n",
+        "All the setup for your development work can be accomplished in a Python notebook.  Setup includes:\n",
+        "\n",
+        "* Importing Python packages\n",
+        "* Connecting to a workspace to enable communication between your local computer and remote resources\n",
+        "* Creating an experiment to track all your runs\n",
+        "* Creating a remote compute target to use for training\n",
+        "\n",
+        "### Import packages\n",
+        "\n",
+        "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import azureml.core\n",
+        "import azureml.contrib.dataset\n",
+        "from azureml.core import Dataset, Workspace, Experiment\n",
+        "from azureml.contrib.dataset import FileHandlingOption\n",
+        "\n",
+        "# check core SDK version number\n",
+        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)\n",
+        "print(\"Azure ML Contrib Version\", azureml.contrib.dataset.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Connect to workspace\n",
+        "\n",
+        "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# load workspace\n",
+        "workspace = Workspace.from_config()\n",
+        "print('Workspace name: ' + workspace.name, \n",
+        "      'Azure region: ' + workspace.location, \n",
+        "      'Subscription id: ' + workspace.subscription_id, \n",
+        "      'Resource group: ' + workspace.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create experiment and a directory\n",
+        "\n",
+        "Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# create an ML experiment\n",
+        "exp = Experiment(workspace=workspace, name='labeled-datasets')\n",
+        "\n",
+        "# create a directory\n",
+        "script_folder = './labeled-datasets'\n",
+        "os.makedirs(script_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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 will 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",
+        "\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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"openhack\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=workspace, 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(workspace, 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())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explore labeled datasets\n",
+        "\n",
+        "**Note**: How to create labeled datasets is not covered in this tutorial. To create labeled datasets, you can go through [labeling projects](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-labeling-projects) and export the output labels as Azure Machine Lerning datasets. \n",
+        "\n",
+        "`animal_labels` used in this tutorial section is the output from a labeling project, with the task type of \"Object Identification\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get animal_labels dataset from the workspace\n",
+        "animal_labels = Dataset.get_by_name(workspace, 'animal_labels')\n",
+        "animal_labels"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can load labeled datasets into pandas DataFrame. There are 3 file handling option that you can choose to load the data files referenced by the labeled datasets:\n",
+        "* Streaming: The default option to load data files.\n",
+        "* Download: Download your data files to a local path.\n",
+        "* Mount: Mount your data files to a mount point. Mount only works for Linux-based compute, including Azure Machine Learning notebook VM and Azure Machine Learning Compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "animal_pd = animal_labels.to_pandas_dataframe(file_handling_option=FileHandlingOption.DOWNLOAD, target_path='./download/', overwrite_download=True)\n",
+        "animal_pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.image as mpimg\n",
+        "\n",
+        "# read images from downloaded path\n",
+        "img = mpimg.imread(animal_pd.loc[0,'image_url'])\n",
+        "imgplot = plt.imshow(img)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also load labeled datasets into [torchvision datasets](https://pytorch.org/docs/stable/torchvision/datasets.html), so that you can leverage on the open source libraries provided by PyTorch for image transformation and training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from torchvision.transforms import functional as F\n",
+        "\n",
+        "# load animal_labels dataset into torchvision dataset\n",
+        "pytorch_dataset = animal_labels.to_torchvision()\n",
+        "img = pytorch_dataset[0][0]\n",
+        "print(type(img))\n",
+        "\n",
+        "# use methods from torchvision to transform the img into grayscale\n",
+        "pil_image = F.to_pil_image(img)\n",
+        "gray_image = F.to_grayscale(pil_image, num_output_channels=3)\n",
+        "\n",
+        "imgplot = plt.imshow(gray_image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train an image classification model\n",
+        "\n",
+        " `crack_labels` dataset used in this tutorial section is the output from a labeling project, with the task type of \"Image Classification Multi-class\". We will use this dataset to train an image classification model that classify whether an image has cracks or not."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get crack_labels dataset from the workspace\n",
+        "crack_labels = Dataset.get_by_name(workspace, 'crack_labels')\n",
+        "crack_labels"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure Estimator for training\n",
+        "\n",
+        "You can ask the system to build a conda environment based on your dependency specification. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "conda_env = Environment('conda-env')\n",
+        "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk',\n",
+        "                                                                             'azureml-contrib-dataset',\n",
+        "                                                                             'torch','torchvision',\n",
+        "                                                                             'azureml-dataprep[pandas]'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create a generic estimator for by specifying\n",
+        "\n",
+        "* The name of the estimator object, `est`\n",
+        "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
+        "* The training script name, train.py\n",
+        "* The input dataset for training\n",
+        "* The compute target. In this case you will use the AmlCompute you created\n",
+        "* The environment definition for the experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.estimator import Estimator\n",
+        "\n",
+        "est = Estimator(source_directory=script_folder, \n",
+        "                entry_script='train.py',\n",
+        "                inputs=[crack_labels.as_named_input('crack_labels')],\n",
+        "                compute_target=compute_target,\n",
+        "                environment_definition= conda_env)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job to run\n",
+        "\n",
+        "Submit the estimator to the Azure ML experiment to kick off the execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = exp.submit(est)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sihhu"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "Remote"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML"
+    ],
+    "friendly_name": "Introduction to labeled datasets",
+    "index_order": 1,
+    "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": "3.6.9"
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    },
+    "star_tag": [
+      "featured"
+    ],
+    "tags": [
+      "Dataset",
+      "label",
+      "Estimator"
+    ],
+    "task": "Train"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets/train.py

@@ -0,0 +1,106 @@
+import os
+import torchvision
+import torchvision.transforms as transforms
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+from azureml.core import Dataset, Run
+import azureml.contrib.dataset
+from azureml.contrib.dataset import FileHandlingOption, LabeledDatasetTask
+
+run = Run.get_context()
+
+# get input dataset by name
+labeled_dataset = run.input_datasets['crack_labels']
+pytorch_dataset = labeled_dataset.to_torchvision()
+
+
+indices = torch.randperm(len(pytorch_dataset)).tolist()
+dataset_train = torch.utils.data.Subset(pytorch_dataset, indices[:40])
+dataset_test = torch.utils.data.Subset(pytorch_dataset, indices[-10:])
+
+trainloader = torch.utils.data.DataLoader(dataset_train, batch_size=4,
+                                          shuffle=True, num_workers=0)
+
+testloader = torch.utils.data.DataLoader(dataset_test, batch_size=4,
+                                         shuffle=True, num_workers=0)
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 71 * 71, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(x.size(0), 16 * 71 * 71)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+net = Net()
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+
+for epoch in range(2):  # loop over the dataset multiple times
+
+    running_loss = 0.0
+    for i, data in enumerate(trainloader, 0):
+        # get the inputs; data is a list of [inputs, labels]
+        inputs, labels = data
+
+        # zero the parameter gradients
+        optimizer.zero_grad()
+
+        # forward + backward + optimize
+        outputs = net(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        # print statistics
+        running_loss += loss.item()
+        if i % 5 == 4:    # print every 5 mini-batches
+            print('[%d, %5d] loss: %.3f' %
+                  (epoch + 1, i + 1, running_loss / 5))
+            running_loss = 0.0
+
+print('Finished Training')
+classes = trainloader.dataset.dataset.labels
+PATH = './cifar_net.pth'
+torch.save(net.state_dict(), PATH)
+
+dataiter = iter(testloader)
+images, labels = dataiter.next()
+
+net = Net()
+net.load_state_dict(torch.load(PATH))
+
+outputs = net(images)
+
+_, predicted = torch.max(outputs, 1)
+
+correct = 0
+total = 0
+with torch.no_grad():
+    for data in testloader:
+        images, labels = data
+        outputs = net(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print('Accuracy of the network on the 10 test images: %d %%' % (100 * correct / total))
+pass

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py

@@ -0,0 +1,35 @@
+import os
+
+
+def convert(imgf, labelf, outf, n):
+    f = open(imgf, "rb")
+    l = open(labelf, "rb")
+    o = open(outf, "w")
+
+    f.read(16)
+    l.read(8)
+    images = []
+
+    for i in range(n):
+        image = [ord(l.read(1))]
+        for j in range(28 * 28):
+            image.append(ord(f.read(1)))
+        images.append(image)
+
+    for image in images:
+        o.write(",".join(str(pix) for pix in image) + "\n")
+    f.close()
+    o.close()
+    l.close()
+
+
+mounted_input_path = os.environ['fashion_ds']
+mounted_output_path = os.environ['AZUREML_DATAREFERENCE_prepared_fashion_ds']
+os.makedirs(mounted_output_path, exist_ok=True)
+
+convert(os.path.join(mounted_input_path, 'train-images-idx3-ubyte'),
+        os.path.join(mounted_input_path, 'train-labels-idx1-ubyte'),
+        os.path.join(mounted_output_path, 'mnist_train.csv'), 60000)
+convert(os.path.join(mounted_input_path, 't10k-images-idx3-ubyte'),
+        os.path.join(mounted_input_path, 't10k-labels-idx1-ubyte'),
+        os.path.join(mounted_output_path, 'mnist_test.csv'), 10000)

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py

@@ -0,0 +1,120 @@
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers.normalization import BatchNormalization
+from keras.utils import to_categorical
+from keras.callbacks import Callback
+
+import numpy as np
+import pandas as pd
+import os
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from azureml.core import Run
+
+# dataset object from the run
+run = Run.get_context()
+dataset = run.input_datasets['prepared_fashion_ds']
+
+# split dataset into train and test set
+(train_dataset, test_dataset) = dataset.random_split(percentage=0.8, seed=111)
+
+# load dataset into pandas dataframe
+data_train = train_dataset.to_pandas_dataframe()
+data_test = test_dataset.to_pandas_dataframe()
+
+img_rows, img_cols = 28, 28
+input_shape = (img_rows, img_cols, 1)
+
+X = np.array(data_train.iloc[:, 1:])
+y = to_categorical(np.array(data_train.iloc[:, 0]))
+
+# here we split validation data to optimiza classifier during training
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=13)
+
+# test data
+X_test = np.array(data_test.iloc[:, 1:])
+y_test = to_categorical(np.array(data_test.iloc[:, 0]))
+
+
+X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255
+X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255
+X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1).astype('float32') / 255
+
+batch_size = 256
+num_classes = 10
+epochs = 10
+
+# construct neuron network
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 kernel_initializer='he_normal',
+                 input_shape=input_shape))
+model.add(MaxPooling2D((2, 2)))
+model.add(Dropout(0.25))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Conv2D(128, (3, 3), activation='relu'))
+model.add(Dropout(0.4))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.3))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adam(),
+              metrics=['accuracy'])
+
+# start an Azure ML run
+run = Run.get_context()
+
+
+class LogRunMetrics(Callback):
+    # callback at the end of every epoch
+    def on_epoch_end(self, epoch, log):
+        # log a value repeated which creates a list
+        run.log('Loss', log['loss'])
+        run.log('Accuracy', log['accuracy'])
+
+
+history = model.fit(X_train, y_train,
+                    batch_size=batch_size,
+                    epochs=epochs,
+                    verbose=1,
+                    validation_data=(X_val, y_val),
+                    callbacks=[LogRunMetrics()])
+
+score = model.evaluate(X_test, y_test, verbose=0)
+
+# log a single value
+run.log("Final test loss", score[0])
+print('Test loss:', score[0])
+
+run.log('Final test accuracy', score[1])
+print('Test accuracy:', score[1])
+
+plt.figure(figsize=(6, 3))
+plt.title('Fashion MNIST with Keras ({} epochs)'.format(epochs), fontsize=14)
+plt.plot(history.history['accuracy'], 'b-', label='Accuracy', lw=4, alpha=0.5)
+plt.plot(history.history['loss'], 'r--', label='Loss', lw=4, alpha=0.5)
+plt.legend(fontsize=12)
+plt.grid(True)
+
+# log an image
+run.log_image('Loss v.s. Accuracy', plot=plt)
+
+# create a ./outputs/model folder in the compute target
+# files saved in the "./outputs" folder are automatically uploaded into run history
+os.makedirs('./outputs/model', exist_ok=True)
+
+# serialize NN architecture to JSON
+model_json = model.to_json()
+# save model JSON
+with open('./outputs/model/model.json', 'w') as f:
+    f.write(model_json)
+# save model weights
+model.save_weights('./outputs/model/model.h5')
+print("model saved in ./outputs/model folder")

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb

@@ -0,0 +1,488 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License [2017] Zalando SE, https://tech.zalando.com"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Build a simple ML pipeline for image classification\n",
+        "\n",
+        "## Introduction\n",
+        "This tutorial shows how to train a simple deep neural network using the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and Keras on Azure Machine Learning. Fashion-MNIST is a dataset of Zalando's article images\u00e2\u20ac\u201dconsisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.\n",
+        "\n",
+        "Learn how to:\n",
+        "\n",
+        "> * Set up your development environment\n",
+        "> * Create the Fashion MNIST dataset\n",
+        "> * Create a machine learning pipeline to train a simple deep learning neural network on a remote cluster\n",
+        "> * Retrieve input datasets from the experiment and register the output model with datasets\n",
+        "\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",
+        "* 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 latest version of AzureML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up your development environment\n",
+        "\n",
+        "All the setup for your development work can be accomplished in a Python notebook.  Setup includes:\n",
+        "\n",
+        "* Importing Python packages\n",
+        "* Connecting to a workspace to enable communication between your local computer and remote resources\n",
+        "* Creating an experiment to track all your runs\n",
+        "* Creating a remote compute target to use for training\n",
+        "\n",
+        "### Import packages\n",
+        "\n",
+        "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Dataset, Datastore, ComputeTarget, RunConfiguration, Experiment\n",
+        "from azureml.core.runconfig import CondaDependencies\n",
+        "from azureml.pipeline.steps import PythonScriptStep, EstimatorStep\n",
+        "from azureml.pipeline.core import Pipeline, PipelineData\n",
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "# check core SDK version number\n",
+        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Connect to workspace\n",
+        "\n",
+        "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# load workspace\n",
+        "workspace = Workspace.from_config()\n",
+        "print('Workspace name: ' + workspace.name, \n",
+        "      'Azure region: ' + workspace.location, \n",
+        "      'Subscription id: ' + workspace.subscription_id, \n",
+        "      'Resource group: ' + workspace.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create experiment and a directory\n",
+        "\n",
+        "Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# create an ML experiment\n",
+        "exp = Experiment(workspace=workspace, name='keras-mnist-fashion')\n",
+        "\n",
+        "# create a directory\n",
+        "script_folder = './keras-mnist-fashion'\n",
+        "os.makedirs(script_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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",
+        "\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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"your-cluster-name\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=workspace, 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(workspace, 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())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create the Fashion MNIST dataset\n",
+        "\n",
+        "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. \n",
+        "\n",
+        "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create a dataset from it. We will now upload the [Fashion MNIST](./keras-mnist-fashion) 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 = ['keras-mnist-fashion/t10k-images-idx3-ubyte', 'keras-mnist-fashion/t10k-labels-idx1-ubyte',\n",
+        "                               'keras-mnist-fashion/train-images-idx3-ubyte','keras-mnist-fashion/train-labels-idx1-ubyte'],\n",
+        "                       target_path = 'mnist-fashion',\n",
+        "                       overwrite = True,\n",
+        "                       show_progress = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then we will create an unregistered FileDataset pointing to the path in the datastore. You can also create a dataset from multiple paths. [Learn More](https://aka.ms/azureml/howto/createdatasets) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fashion_ds = Dataset.File.from_files([(datastore, 'mnist-fashion')])\n",
+        "\n",
+        "# list the files referenced by fashion_ds\n",
+        "fashion_ds.to_path()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build 2-step ML pipeline\n",
+        "\n",
+        "The [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages. [Learn More](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines)\n",
+        "\n",
+        "\n",
+        "### Step 1: data preparation\n",
+        "\n",
+        "In step one, we will load the image and labels from Fashion MNIST dataset into mnist_train.csv and mnist_test.csv\n",
+        "\n",
+        "Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. Both mnist_train.csv and mnist_test.csv contain 785 columns. The first column consists of the class labels, which represent the article of clothing. The rest of the columns contain the pixel-values of the associated image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# set up the compute environment to install required packages\n",
+        "conda = CondaDependencies.create(\n",
+        "        pip_packages=['azureml-sdk','azureml-dataprep[fuse,pandas]'],\n",
+        "        pin_sdk_version=False)\n",
+        "\n",
+        "conda.set_pip_option('--pre')\n",
+        "\n",
+        "run_config = RunConfiguration()\n",
+        "run_config.environment.python.conda_dependencies = conda"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Intermediate data (or output of a step) is represented by a `PipelineData` object. preprared_fashion_ds is produced as the output of step 1, and used as the input of step 2. PipelineData introduces a data dependency between steps, and creates an implicit execution order in the pipeline. You can register a `PipelineData` as a dataset and version the output data automatically. [Learn More](https://docs.microsoft.com/azure/machine-learning/service/how-to-version-track-datasets#version-a-pipeline-output-dataset) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# define output data\n",
+        "prepared_fashion_ds = PipelineData('prepared_fashion_ds', datastore=datastore).as_dataset()\n",
+        "\n",
+        "# register output data as dataset\n",
+        "prepared_fashion_ds = prepared_fashion_ds.register(name='prepared_fashion_ds', create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "prep_step = PythonScriptStep(name='prepare step',\n",
+        "                             script_name=\"prepare.py\",\n",
+        "                             # mount fashion_ds dataset to the compute_target\n",
+        "                             inputs=[fashion_ds.as_named_input('fashion_ds').as_mount()],\n",
+        "                             outputs=[prepared_fashion_ds],\n",
+        "                             source_directory=script_folder,\n",
+        "                             compute_target=compute_target,\n",
+        "                             runconfig=run_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Step 2: train CNN with Keras\n",
+        "\n",
+        "Next, we construct an `azureml.train.dnn.TensorFlow` estimator object. The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed.\n",
+        "\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 Tensorflow Estimator in a Pipeline. It takes a dataset as the input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# set up training step with Tensorflow estimator\n",
+        "est = TensorFlow(entry_script='train.py',\n",
+        "                 source_directory=script_folder,                 \n",
+        "                 pip_packages = ['azureml-sdk','keras','numpy','scikit-learn', 'matplotlib'],\n",
+        "                 compute_target=compute_target)\n",
+        "\n",
+        "est_step = EstimatorStep(name='train step',\n",
+        "                         estimator=est,\n",
+        "                         estimator_entry_script_arguments=[],\n",
+        "                         # parse prepared_fashion_ds into TabularDataset and use it as the input\n",
+        "                         inputs=[prepared_fashion_ds.parse_delimited_files()],\n",
+        "                         compute_target=compute_target)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Build the pipeline\n",
+        "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py).\n",
+        "\n",
+        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/python/api/azureml-core/azureml.core.experiment(class)?view=azure-ml-py#submit-config--tags-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# build pipeline & run experiment\n",
+        "pipeline = Pipeline(workspace, steps=[prep_step, est_step])\n",
+        "run = exp.submit(pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor the PipelineRun"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "inputHidden": false,
+        "outputHidden": false
+      },
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.find_step_run('train step')[0].get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register the input dataset and the output model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Azure Machine Learning dataset makes it easy to trace how your data is used in ML. [Learn More](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-version-track-datasets#track-datasets-in-experiments)<br>\n",
+        "For each Machine Learning experiment, you can easily trace the datasets used as the input through `Run` object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get input datasets\n",
+        "prep_step = run.find_step_run('prepare step')[0]\n",
+        "inputs = prep_step.get_details()['inputDatasets']\n",
+        "input_dataset = inputs[0]['dataset']\n",
+        "\n",
+        "# list the files referenced by input_dataset\n",
+        "input_dataset.to_path()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the input Fashion MNIST dataset with the workspace so that you can reuse it in other experiments or share it with your colleagues who have access to your workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fashion_ds = input_dataset.register(workspace = workspace,\n",
+        "                                    name = 'fashion_ds',\n",
+        "                                    description = 'image and label files from fashion mnist',\n",
+        "                                    create_new_version = True)\n",
+        "fashion_ds"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the output model with dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.find_step_run('train step')[0].register_model(model_name = 'keras-model', model_path = 'outputs/model/', \n",
+        "                                                  datasets =[('train test data',fashion_ds)])"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sihhu"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "Remote"
+    ],
+    "datasets": [
+      "Fashion MNIST"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML"
+    ],
+    "friendly_name": "Datasets with ML Pipeline",
+    "index_order": 1,
+    "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": "3.6.9"
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    },
+    "star_tag": [
+      "featured"
+    ],
+    "tags": [
+      "Dataset",
+      "Pipeline",
+      "Estimator",
+      "ScriptRun"
+    ],
+    "task": "Train"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb

@@ -522,7 +522,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "NOAA"