update samples from Release-167 as a part of 1.0.83 SDK release

update samples - test

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.76 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.83 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -13,7 +13,6 @@ dependencies:
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.80
-- pyarrow>=0.11.0
 - fbprophet==0.5
 - pytorch=1.1.0
 - cudatoolkit=9.0
@@ -30,7 +29,7 @@ dependencies:
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
-  - onnxruntime==0.4.0
+  - onnxruntime==1.0.0
   - 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

@@ -14,7 +14,6 @@ dependencies:
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<0.23.0
 - py-xgboost<=0.80
-- pyarrow>=0.11.0
 - fbprophet==0.5
 - pytorch=1.1.0
 - cudatoolkit=9.0
@@ -31,7 +30,7 @@ dependencies:
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
-  - onnxruntime==0.4.0
+  - onnxruntime==1.0.0
   - 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%
+  echo Upgrading existing conda environment %conda_env_name%
-  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
+  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
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   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
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   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

@@ -285,14 +285,16 @@
         "|**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",
+        "|**experiment_timeout_hours**| Maximum amount of time in hours 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",
         "|**featurization**| 'auto' / 'off'  Indicator for whether featurization step should be done automatically or not. Note: If the input data is sparse, featurization cannot be turned on.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
+        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -304,7 +306,7 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\" : 20,\n",
+        "    \"experiment_timeout_hours\" : 0.3,\n",
         "    \"enable_early_stopping\" : True,\n",
         "    \"iteration_timeout_minutes\": 5,\n",
         "    \"max_concurrent_iterations\": 4,\n",
@@ -324,6 +326,7 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
+        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -456,6 +459,72 @@
         "RunDetails(remote_run).show() "
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model's explanation\n",
+        "Retrieve the explanation from the best_run which includes explanations for engineered features and raw features. Make sure that the run for generating explanations for the best model is completed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait for the best model explanation run to complete\n",
+        "from azureml.train.automl.run import AutoMLRun\n",
+        "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
+        "print(model_explainability_run_id)\n",
+        "if model_explainability_run_id is not None:\n",
+        "    model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "    model_explainability_run.wait_for_completion()\n",
+        "\n",
+        "# Get the best run object\n",
+        "best_run, fitted_model = remote_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download engineered feature importance from artifact store\n",
+        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "exp_data = engineered_explanations.get_feature_importance_dict()\n",
+        "exp_data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download raw feature importance from artifact store\n",
+        "You can use ExplanationClient to download the raw feature explanations from the artifact store of the best_run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=True)\n",
+        "exp_data = engineered_explanations.get_feature_importance_dict()\n",
+        "exp_data"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -572,20 +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,
@@ -639,10 +694,10 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
-        "                                   entry_script = script_file_name,\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
-        "                                   conda_file = conda_env_file_name)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",

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

@@ -2,12 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - onnxruntime==0.4.0
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -213,7 +213,7 @@
         "    \"preprocess\": True,\n",
         "    \"enable_early_stopping\": True,\n",
         "    \"max_concurrent_iterations\": 2, # This is a limit for testing purpose, please increase it as per cluster size\n",
-        "    \"experiment_timeout_minutes\": 10, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
+        "    \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "}\n",
         "\n",
@@ -305,7 +305,7 @@
       "source": [
         "#### Explain model\n",
         "\n",
-        "Automated ML models can be explained and visualized using the SDK Explainability library. [Learn how to use the explainer](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb)."
+        "Automated ML models can be explained and visualized using the SDK Explainability library. "
       ]
     },
     {
@@ -334,17 +334,7 @@
       "metadata": {},
       "source": [
         "#### Print the properties of the model\n",
-        "The fitted_model is a python object and you can read the different properties of the object.\n",
+        "The fitted_model is a python object and you can read the different properties of the object.\n"
-        "See *Print the properties of the model* section in [this sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy\n",
-        "\n",
-        "To deploy the model into a web service endpoint, see _Deploy_ section in [this sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb)"
       ]
     },
     {

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

@@ -2,10 +2,3 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml

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

@@ -519,12 +519,12 @@
         "name": "anshirga"
       }
     ],
-    "datasets": [
-      "None"
-    ],
     "compute": [
       "AML Compute"
     ],
+    "datasets": [
+      "None"
+    ],
     "deployment": [
       "None"
     ],

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

@@ -2,9 +2,3 @@ name: auto-ml-classification-text-dnn
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-train
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - statsmodels

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

@@ -210,7 +210,24 @@
       "metadata": {},
       "source": [
         "## Data Ingestion Pipeline \n",
-        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You can replace this with your own dataset, or you can skip this pipeline if you already have a time-series based `TabularDataset`.\n",
+        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You can replace this with your own dataset, or you can skip this pipeline if you already have a time-series based `TabularDataset`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The name and target column of the Dataset to create \n",
+        "dataset = \"NOAA-Weather-DS4\"\n",
+        "target_column_name = \"temperature\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
         "\n",
         "### Upload Data Step\n",
         "The data ingestion pipeline has a single step with a script to query the latest weather data and upload it to the blob store. During the first run, the script will create and register a time-series based `TabularDataset` with the past one week of weather data. For each subsequent run, the script will create a partition in the blob store by querying NOAA for new weather data since the last modified time of the dataset (`dataset.data_changed_time`) and creating a data.csv file."
@@ -225,8 +242,6 @@
         "from azureml.pipeline.core import Pipeline, PipelineParameter\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "\n",
-        "# The name of the Dataset to create \n",
-        "dataset = \"NOAA-Weather-DS4\"\n",
         "ds_name = PipelineParameter(name=\"ds_name\", default_value=dataset)\n",
         "upload_data_step = PythonScriptStep(script_name=\"upload_weather_data.py\", \n",
         "                                         allow_reuse=False,\n",
@@ -272,7 +287,7 @@
         "## Training Pipeline\n",
         "### Prepare Training Data Step\n",
         "\n",
-        "Script to bring data into common X,y format. We need to set allow_reuse flag to False to allow the pipeline to run even when inputs don't change. We also need the name of the model to check the time the model was last trained."
+        "Script to check if new data is available since the model was last trained. If no new data is available, we cancel the remaining pipeline steps. We need to set allow_reuse flag to False to allow the pipeline to run even when inputs don't change. We also need the name of the model to check the time the model was last trained."
       ]
     },
     {
@@ -283,11 +298,8 @@
       "source": [
         "from azureml.pipeline.core import PipelineData\n",
         "\n",
-        "target_column = PipelineParameter(\"target_column\", default_value=\"y\")\n",
         "# The model name with which to register the trained model in the workspace.\n",
-        "model_name = PipelineParameter(\"model_name\", default_value=\"y\")\n",
+        "model_name = PipelineParameter(\"model_name\", default_value=\"noaaweatherds\")"
-        "output_x = PipelineData(\"output_x\", datastore=dstor)\n",
-        "output_y = PipelineData(\"output_y\", datastore=dstor)"
       ]
     },
     {
@@ -299,16 +311,23 @@
         "data_prep_step = PythonScriptStep(script_name=\"check_data.py\", \n",
         "                                         allow_reuse=False,\n",
         "                                         name=\"check_data\",\n",
-        "                                         arguments=[\"--target_column\", target_column,\n",
+        "                                         arguments=[\"--ds_name\", ds_name,\n",
-        "                                                       \"--output_x\", output_x,\n",
-        "                                                       \"--output_y\", output_y,\n",
-        "                                                       \"--ds_name\", ds_name,\n",
         "                                                    \"--model_name\", model_name],\n",
-        "                                         outputs=[output_x, output_y], \n",
         "                                         compute_target=compute_target, \n",
         "                                         runconfig=conda_run_config)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "train_ds = Dataset.get_by_name(ws, dataset)\n",
+        "train_ds = train_ds.drop_columns([\"partition_date\"])"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -324,11 +343,11 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.runtime import AutoMLStep\n",
+        "from azureml.train.automl import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 20,\n",
+        "    \"iteration_timeout_minutes\": 10,\n",
-        "    \"experiment_timeout_minutes\": 30,\n",
+        "    \"experiment_timeout_hours\": 0.2,\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
         "    \"preprocess\": True,\n",
@@ -342,8 +361,8 @@
         "                             debug_log = 'automl_errors.log',\n",
         "                             path = \".\",\n",
         "                             compute_target=compute_target,\n",
-        "                             run_configuration=conda_run_config,\n",
+        "                             training_data = train_ds,\n",
-        "                             data_script = \"get_data.py\",\n",
+        "                             label_column_name = target_column_name,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -359,7 +378,7 @@
         "metrics_output_name = 'metrics_output'\n",
         "best_model_output_name = 'best_model_output'\n",
         "\n",
-        "metirics_data = PipelineData(name='metrics_data',\n",
+        "metrics_data = PipelineData(name='metrics_data',\n",
         "                           datastore=dstor,\n",
         "                           pipeline_output_name=metrics_output_name,\n",
         "                           training_output=TrainingOutput(type='Metrics'))\n",
@@ -378,8 +397,7 @@
         "automl_step = AutoMLStep(\n",
         "    name='automl_module',\n",
         "    automl_config=automl_config,\n",
-        "    inputs=[output_x, output_y],\n",
+        "    outputs=[metrics_data, model_data],\n",
-        "    outputs=[metirics_data, model_data],\n",
         "    allow_reuse=False)"
       ]
     },
@@ -432,7 +450,7 @@
       "outputs": [],
       "source": [
         "training_pipeline_run = experiment.submit(training_pipeline, pipeline_parameters={\n",
-        "        \"target_column\": \"temperature\", \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
+        "        \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
       ]
     },
     {
@@ -441,7 +459,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "training_pipeline_run.wait_for_completion()"
+        "training_pipeline_run.wait_for_completion(show_output=False)"
       ]
     },
     {
@@ -475,7 +493,7 @@
       "source": [
         "from azureml.pipeline.core import Schedule\n",
         "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule\",\n",
-        "                           pipeline_parameters={\"target_column\": \"temperature\",\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
+        "                           pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
         "                           pipeline_id=published_pipeline.id, \n",
         "                           experiment_name=experiment_name, \n",
         "                           datastore=dstor,\n",

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

@@ -2,8 +2,3 @@ name: auto-ml-continuous-retraining
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-pipeline
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml

how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py

@@ -15,32 +15,16 @@ if type(run) == _OfflineRun:
 else:
     ws = run.experiment.workspace
 
-
+print("Check for new data.")
-def write_output(df, path):
-    os.makedirs(path, exist_ok=True)
-    print("%s created" % path)
-    df.to_csv(path + "/part-00000", index=False)
-
-
-print("Check for new data and prepare the data")
 
 parser = argparse.ArgumentParser("split")
-parser.add_argument("--target_column", type=str, help="input split features")
 parser.add_argument("--ds_name", help="input dataset name")
 parser.add_argument("--model_name", help="name of the deployed model")
-parser.add_argument("--output_x", type=str,
-                    help="output features")
-parser.add_argument("--output_y", type=str,
-                    help="output labels")
-
 
 args = parser.parse_args()
 
 print("Argument 1(ds_name): %s" % args.ds_name)
-print("Argument 2(target_column): %s" % args.target_column)
+print("Argument 2(model_name): %s" % args.model_name)
-print("Argument 3(model_name): %s" % args.model_name)
-print("Argument 4(output_x): %s" % args.output_x)
-print("Argument 5(output_y): %s" % args.output_y)
 
 # Get the latest registered model
 try:
@@ -54,22 +38,9 @@ except Exception as e:
 train_ds = Dataset.get_by_name(ws, args.ds_name)
 dataset_changed_time = train_ds.data_changed_time
 
-if dataset_changed_time > last_train_time:
+if not dataset_changed_time > last_train_time:
-    # New data is available since the model was last trained
-    print("Dataset was last updated on {0}. Retraining...".format(dataset_changed_time))
-    train_ds = train_ds.drop_columns(["partition_date"])
-    X_train = train_ds.drop_columns(
-        columns=[args.target_column]).to_pandas_dataframe()
-    y_train = train_ds.keep_columns(
-        columns=[args.target_column]).to_pandas_dataframe()
-
-    non_null = y_train[args.target_column].notnull()
-    y = y_train[non_null]
-    X = X_train[non_null]
-
-    if not (args.output_x is None and args.output_y is None):
-        write_output(X, args.output_x)
-        write_output(y, args.output_y)
-else:
     print("Cancelling run since there is no new data.")
     run.parent.cancel()
+else:
+    # New data is available since the model was last trained
+    print("Dataset was last updated on {0}. Retraining...".format(dataset_changed_time))

how-to-use-azureml/automated-machine-learning/continuous-retraining/get_data.py

@@ -1,15 +0,0 @@
-import os
-import pandas as pd
-
-
-def get_data():
-    print("In get_data")
-    print(os.environ['AZUREML_DATAREFERENCE_output_x'])
-    X_train = pd.read_csv(
-        os.environ['AZUREML_DATAREFERENCE_output_x'] + "/part-00000")
-    y_train = pd.read_csv(
-        os.environ['AZUREML_DATAREFERENCE_output_y'] + "/part-00000")
-
-    print(X_train.head(3))
-
-    return {"X": X_train.values, "y": y_train.values.flatten()}

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -58,7 +58,7 @@ except Exception as e:
     print(traceback.format_exc())
     print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
     register_dataset = True
-    end_time_last_slice = datetime.today() - relativedelta(weeks=1)
+    end_time_last_slice = datetime.today() - relativedelta(weeks=2)
 
 end_time = datetime.utcnow()
 train_df = get_noaa_data(end_time_last_slice, end_time)
@@ -80,10 +80,10 @@ if train_df.size > 0:
                        target_path=folder_name,
                        overwrite=True,
                        show_progress=True)
-
-    if register_dataset:
-        ds = Dataset.Tabular.from_delimited_files(dstor.path("{}/**/*.csv".format(
-            args.ds_name)), partition_format='/{partition_date:yyyy/MM/dd/hh/mm/ss}/data.csv')
-        ds.register(ws, name=args.ds_name)
 else:
     print("No new data since {0}.".format(end_time_last_slice))
+
+if register_dataset:
+    ds = Dataset.Tabular.from_delimited_files(dstor.path("{}/**/*.csv".format(
+        args.ds_name)), partition_format='/{partition_date:yyyy/MM/dd/HH/mm/ss}/data.csv')
+    ds.register(ws, name=args.ds_name)

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

@@ -358,7 +358,7 @@
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             experiment_timeout_minutes = 60,\n",
+        "                             experiment_timeout_hours = 1,\n",
         "                             training_data=train_dataset,\n",
         "                             label_column_name=target_column_name,\n",
         "                             validation_data=valid_dataset, \n",

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

@@ -1,12 +1,4 @@
 name: auto-ml-forecasting-beer-remote
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-train
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - statsmodels

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",
@@ -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 = dataset.time_before(datetime(2012, 8, 31), include_boundary=True)\n",
-        "train.to_pandas_dataframe().tail(5)"
+        "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 = dataset.time_after(datetime(2012, 9, 1), include_boundary=True)\n",
-        "test.to_pandas_dataframe().head(5)"
+        "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
     {
@@ -247,8 +247,8 @@
         "|-|-|\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",
+        "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
         "|**compute_target**|The remote compute for training.|\n",
@@ -260,7 +260,7 @@
         "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
         "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
         "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_minutes parameter value to get results."
+        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -305,7 +305,7 @@
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
         "                             blacklist_models = ['ExtremeRandomTrees'],                             \n",
-        "                             experiment_timeout_minutes=20,\n",
+        "                             experiment_timeout_hours=0.3,\n",
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",

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

@@ -1,11 +1,4 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - statsmodels

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

@@ -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])
+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(
+y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
-    None).keep_columns(columns=[target_column_name])
 
 fitted_model = joblib.load('model.pkl')
 
 df_all = forecasting_helper.do_rolling_forecast(
     fitted_model,
-    X_test_df.to_pandas_dataframe(),
+    X_test_df,
-    y_test_df.to_pandas_dataframe().values.T[0],
+    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 Training](#advanced_training)\n",
-        "1. [Advanced Results](#Advanced Results)"
+        "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 = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n",
-        "test.to_pandas_dataframe().head(5)"
+        "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
     {
@@ -301,8 +301,8 @@
         "|-|-|\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",
+        "|**experiment_timeout_hours**|Maximum amount of time in hours 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",
         "|**compute_target**|The remote compute for training.|\n",
@@ -316,7 +316,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_minutes parameter value to get results."
+        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -333,7 +333,7 @@
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
         "                             blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'],                             \n",
-        "                             experiment_timeout_minutes=20,\n",
+        "                             experiment_timeout_hours=0.3,\n",
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
@@ -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",
+        "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)."
-        "\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."
       ]
     },
     {
@@ -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."
       ]
     },
@@ -587,7 +578,7 @@
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
         "                             blacklist_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blacklisted for tutorial purposes, remove this for real use cases.                            \n",
-        "                             experiment_timeout_minutes=20,\n",
+        "                             experiment_timeout_hours=0.3,\n",
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
@@ -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)"
       ]
     },
     {

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

@@ -2,11 +2,3 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - statsmodels
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -251,7 +251,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\" : 5,\n",
-        "    \"experiment_timeout_minutes\" : 15,\n",
+        "    \"experiment_timeout_hours\" : 0.25,\n",
         "    \"primary_metric\" : 'normalized_mean_absolute_error',\n",
         "    \"time_column_name\": time_column_name,\n",
         "    \"grain_column_names\": grain_column_names,\n",

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

@@ -2,9 +2,3 @@ name: auto-ml-forecasting-grouping
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-pipeline
-  - azureml-widgets
-  - pandas_ml
-  - statsmodels
-  - matplotlib

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

@@ -8,6 +8,7 @@ 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
@@ -29,13 +30,14 @@ def _get_configs(automlconfig: AutoMLConfig,
     groups = _get_groups(data, group_column_names)
     configs = {}
     for i, group in groups.iterrows():
-        single = data
+        single = data._dataflow
         group_name = "#####".join(str(x) for x in group.values)
         group_name = valid_chars.sub('', group_name)
         for key in group.index:
-            single = single._dataflow.filter(data._dataflow[key] == group[key])
+            single = single.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
@@ -69,7 +71,7 @@ def build_pipeline_steps(automlconfig: AutoMLConfig,
     # create each automl step end-to-end (train, register)
     for group_name, conf in configs.items():
         # create automl metrics output
-        metirics_data = PipelineData(
+        metrics_data = PipelineData(
             name='metrics_data_{}'.format(group_name),
             pipeline_output_name=metrics_output_name.format(group_name),
             training_output=TrainingOutput(type='Metrics'))
@@ -82,7 +84,7 @@ def build_pipeline_steps(automlconfig: AutoMLConfig,
         automl_step = AutoMLStep(
             name='automl_{}'.format(group_name),
             automl_config=conf,
-            outputs=[metirics_data, model_data],
+            outputs=[metrics_data, model_data],
             allow_reuse=True)
         steps.append(automl_step)
 

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

@@ -335,7 +335,7 @@
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_forecasting_function.log',\n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             experiment_timeout_minutes=15,\n",
+        "                             experiment_timeout_hours=0.25,\n",
         "                             enable_early_stopping=True,\n",
         "                             training_data=train_data,\n",
         "                             compute_target=compute_target,\n",
@@ -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",
+        "We use `X_test` as a **forecast request** to generate the predictions."
-        "\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"
       ]
     },
     {
@@ -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)\n",
-        "y_pred_no_gap, xy_nogap =  fitted_model.forecast(X_test, y_query)\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"
       ]
     },
@@ -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)"
       ]
     },
     {
@@ -539,9 +536,7 @@
       "outputs": [],
       "source": [
         "try: \n",
-        "    y_query = y_away.copy()\n",
+        "    y_pred_away, xy_away = fitted_model.forecast(X_away)\n",
-        "    y_query.fill(np.NaN)\n",
-        "    y_pred_away, xy_away = fitted_model.forecast(X_away, y_query)\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 the model saw an actual value 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."
       ]
@@ -740,7 +735,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     },
     "tags": [
       "Forecasting",

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

@@ -1,11 +1,4 @@
 name: automl-forecasting-function
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - pandas_ml
-  - statsmodels
-  - matplotlib

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."
       ]
@@ -335,7 +335,7 @@
         "|-|-|\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",
-        "|**experiment_timeout_minutes**|Experimentation timeout in minutes.|\n",
+        "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
         "|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
@@ -366,7 +366,7 @@
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_oj_sales_errors.log',\n",
         "                             primary_metric='normalized_mean_absolute_error',\n",
-        "                             experiment_timeout_minutes=15,\n",
+        "                             experiment_timeout_hours=0.25,\n",
         "                             training_data=train_dataset,\n",
         "                             label_column_name=target_column_name,\n",
         "                             compute_target=compute_target,\n",
@@ -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",
+        "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",
-        "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_."
       ]
     },
     {
@@ -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",
@@ -705,9 +698,6 @@
     "framework": [
       "Azure ML AutoML"
     ],
-    "tags": [
-      "None"
-    ],
     "friendly_name": "Forecasting orange juice sales with deployment",
     "index_order": 1,
     "kernelspec": {
@@ -725,8 +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/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml

@@ -1,11 +1,4 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - statsmodels

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

@@ -156,7 +156,7 @@
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'average_precision_score_weighted',\n",
         "    \"preprocess\": True,\n",
-        "    \"experiment_timeout_minutes\": 10, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
+        "    \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "    \"enable_stack_ensemble\": False\n",
         "}\n",
@@ -260,17 +260,7 @@
       "metadata": {},
       "source": [
         "#### Print the properties of the model\n",
-        "The fitted_model is a python object and you can read the different properties of the object.\n",
+        "The fitted_model is a python object and you can read the different properties of the object.\n"
-        "See *Print the properties of the model* section in [this sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy\n",
-        "\n",
-        "To deploy the model into a web service endpoint, see _Deploy_ section in [this sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb)"
       ]
     },
     {

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

@@ -2,10 +2,3 @@ name: auto-ml-classification-credit-card-fraud-local
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml

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

@@ -206,7 +206,7 @@
         "|-|-|\n",
         "|**task**|classification, regression or forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**experiment_timeout_minutes**| Maximum amount of time in minutes that all iterations combined can take before the experiment terminates.|\n",
+        "|**experiment_timeout_hours**| Maximum amount of time in hours 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",
         "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Note: If the input data is sparse, featurization cannot be turned on.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
@@ -262,7 +262,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_minutes\" : 10,\n",
+        "    \"experiment_timeout_hours\" : 0.2,\n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"n_cross_validations\": 5,\n",
@@ -558,7 +558,6 @@
         "\n",
         "# specify CondaDependencies obj\n",
         "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
-        "    conda_packages=['scikit-learn', 'numpy','py-xgboost<=0.80'],\n",
         "    pip_packages=azureml_pip_packages)"
       ]
     },
@@ -718,17 +717,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "myenv = automl_run.get_environment().python.conda_dependencies\n",
-        "\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-explain-model', 'azureml-train-automl', 'azureml-defaults'\n",
-        "]\n",
-        " \n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas', 'numpy', 'py-xgboost<=0.80'],\n",
-        "                                 pip_packages=azureml_pip_packages,\n",
-        "                                 pin_sdk_version=True)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",

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

@@ -2,12 +2,3 @@ name: auto-ml-regression-hardware-performance-explanation-and-featurization
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -7,7 +7,7 @@ from azureml.core.experiment import Experiment
 from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
-    automl_setup_model_explanations
+    automl_setup_model_explanations, automl_check_model_if_explainable
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
 from automl.client.core.common.constants import MODEL_PATH
@@ -25,6 +25,11 @@ ws = run.experiment.workspace
 experiment = Experiment(ws, '<<experimnet_name>>')
 automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
+# Check if this AutoML model is explainable
+if not automl_check_model_if_explainable(automl_run):
+    raise Exception("Model explanations is currently not supported for " + automl_run.get_properties().get(
+        'run_algorithm'))
+
 # Download the best model from the artifact store
 automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
 

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

@@ -188,7 +188,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "automlconfig-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "automl_settings = {\n",
@@ -196,7 +200,7 @@
         "    \"primary_metric\": 'r2_score',\n",
         "    \"preprocess\": True,\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_minutes\": 20, #for real scenarios we reccommend a timeout of at least one hour \n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",

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

@@ -2,8 +2,3 @@ name: auto-ml-regression
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - paramiko<2.5.0

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

@@ -56,7 +56,7 @@ CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]
     @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.
     @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.
     @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. 
-    @experiment_timeout_minutes INT = 60,            -- The maximum time in minutes for training all pipelines.
+    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.
     @n_cross_validations INT = 3,                    -- The number of cross validations.
     @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.
                                                      -- The list of possible models can be found at:
@@ -131,8 +131,8 @@ if __name__.startswith("sqlindb"):
 
     X_train = data_train
 
-    if experiment_timeout_minutes == 0:
+    if experiment_timeout_hours == 0:
-        experiment_timeout_minutes = None
+        experiment_timeout_hours = None
 
     if experiment_exit_score == 0:
         experiment_exit_score = None
@@ -163,7 +163,7 @@ if __name__.startswith("sqlindb"):
                                  debug_log = log_file_name, 
                                  primary_metric = primary_metric, 
                                  iteration_timeout_minutes = iteration_timeout_minutes, 
-                                 experiment_timeout_minutes = experiment_timeout_minutes,
+                                 experiment_timeout_hours = experiment_timeout_hours,
                                  iterations = iterations, 
                                  n_cross_validations = n_cross_validations, 
                                  preprocess = preprocess,
@@ -204,7 +204,7 @@ if __name__.startswith("sqlindb"):
 				  @iterations INT, @task NVARCHAR(40),
 				  @experiment_name NVARCHAR(32),
 				  @iteration_timeout_minutes INT,
-				  @experiment_timeout_minutes INT,
+				  @experiment_timeout_hours FLOAT,
 				  @n_cross_validations INT,
 				  @blacklist_models NVARCHAR(MAX),
 				  @whitelist_models NVARCHAR(MAX),
@@ -223,7 +223,7 @@ if __name__.startswith("sqlindb"):
 	, @task = @task
 	, @experiment_name = @experiment_name
 	, @iteration_timeout_minutes = @iteration_timeout_minutes
-	, @experiment_timeout_minutes = @experiment_timeout_minutes
+	, @experiment_timeout_hours = @experiment_timeout_hours
 	, @n_cross_validations = @n_cross_validations
 	, @blacklist_models = @blacklist_models
 	, @whitelist_models = @whitelist_models

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

@@ -235,7 +235,7 @@
         "    @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.\r\n",
         "    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.\r\n",
         "    @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. \r\n",
-        "    @experiment_timeout_minutes INT = 60,            -- The maximum time in minutes for training all pipelines.\r\n",
+        "    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.\r\n",
         "    @n_cross_validations INT = 3,                    -- The number of cross validations.\r\n",
         "    @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.\r\n",
         "                                                     -- The list of possible models can be found at:\r\n",
@@ -307,8 +307,8 @@
         "\r\n",
         "    X_train = data_train\r\n",
         "\r\n",
-        "    if experiment_timeout_minutes == 0:\r\n",
+        "    if experiment_timeout_hours == 0:\r\n",
-        "        experiment_timeout_minutes = None\r\n",
+        "        experiment_timeout_hours = None\r\n",
         "\r\n",
         "    if experiment_exit_score == 0:\r\n",
         "        experiment_exit_score = None\r\n",
@@ -337,7 +337,7 @@
         "                                 debug_log = log_file_name, \r\n",
         "                                 primary_metric = primary_metric, \r\n",
         "                                 iteration_timeout_minutes = iteration_timeout_minutes, \r\n",
-        "                                 experiment_timeout_minutes = experiment_timeout_minutes,\r\n",
+        "                                 experiment_timeout_hours = experiment_timeout_hours,\r\n",
         "                                 iterations = iterations, \r\n",
         "                                 n_cross_validations = n_cross_validations, \r\n",
         "                                 preprocess = preprocess,\r\n",
@@ -378,7 +378,7 @@
         "\t\t\t\t  @iterations INT, @task NVARCHAR(40),\r\n",
         "\t\t\t\t  @experiment_name NVARCHAR(32),\r\n",
         "\t\t\t\t  @iteration_timeout_minutes INT,\r\n",
-        "\t\t\t\t  @experiment_timeout_minutes INT,\r\n",
+        "\t\t\t\t  @experiment_timeout_hours FLOAT,\r\n",
         "\t\t\t\t  @n_cross_validations INT,\r\n",
         "\t\t\t\t  @blacklist_models NVARCHAR(MAX),\r\n",
         "\t\t\t\t  @whitelist_models NVARCHAR(MAX),\r\n",
@@ -396,7 +396,7 @@
         "\t, @task = @task\r\n",
         "\t, @experiment_name = @experiment_name\r\n",
         "\t, @iteration_timeout_minutes = @iteration_timeout_minutes\r\n",
-        "\t, @experiment_timeout_minutes = @experiment_timeout_minutes\r\n",
+        "\t, @experiment_timeout_hours = @experiment_timeout_hours\r\n",
         "\t, @n_cross_validations = @n_cross_validations\r\n",
         "\t, @blacklist_models = @blacklist_models\r\n",
         "\t, @whitelist_models = @whitelist_models\r\n",
@@ -560,9 +560,6 @@
     "framework": [
       "Azure ML AutoML"
     ],
-    "tags": [
-      ""
-    ],
     "friendly_name": "Setup automated ML SQL integration",
     "index_order": 1,
     "kernelspec": {
@@ -574,6 +571,9 @@
       "name": "sql",
       "version": ""
     },
+    "tags": [
+      ""
+    ],
     "task": "None"
   },
   "nbformat": 4,

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

@@ -161,9 +161,9 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
+        "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) # showing how to add libs as an eg. - not needed for this model.\n",
         "\n",
-        "with open(\"mydeployenv.yml\",\"w\") as f:\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myacienv.serialize_to_string())"
       ]
     },
@@ -177,6 +177,9 @@
         "from azureml.core.webservice import AciWebservice, Webservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
         "\n",
         "myaci_config = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
         "                                                  memory_gb = 2, \n",
@@ -191,9 +194,16 @@
         "except WebserviceException:\n",
         "    pass\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= 'spark-py', \n",
+        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
-        "                                   entry_script='score_sparkml.py',\n",
+        "# we need to add extra packages to procured environment\n",
-        "                                   conda_file='mydeployenv.yml')\n",
+        "# in order to deploy amended environment we need to rename it\n",
+        "myenv.name = 'myenv'\n",
+        "model_dependencies = CondaDependencies('myenv.yml')\n",
+        "for pip_dep in model_dependencies.pip_packages:\n",
+        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
+        "for conda_dep in model_dependencies.conda_packages:\n",
+        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
+        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
         "\n",
         "myservice = Model.deploy(ws, service_name, [mymodel], inference_config, myaci_config)\n",
         "myservice.wait_for_deployment(show_output=True)"

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

@@ -640,7 +640,7 @@
         "\n",
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-defaults', 'azureml-sdk[automl]'])\n",
         "\n",
-        "conda_env_file_name = 'mydeployenv.yml'\n",
+        "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
       ]
     },
@@ -664,17 +664,27 @@
         "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
         "import uuid\n",
         "\n",
+        "\n",
         "myaci_config = AciWebservice.deploy_configuration(\n",
         "    cpu_cores = 2, \n",
         "    memory_gb = 2, \n",
         "    tags = {'name':'Databricks Azure ML ACI'}, \n",
         "    description = 'This is for ADB and AutoML example.')\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= 'spark-py', \n",
+        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
-        "                                   entry_script='score.py',\n",
+        "# we need to add extra packages to procured environment\n",
-        "                                   conda_file='mydeployenv.yml')\n",
+        "# in order to deploy amended environment we need to rename it\n",
+        "myenv.name = 'myenv'\n",
+        "model_dependencies = CondaDependencies('myenv.yml')\n",
+        "for pip_dep in model_dependencies.pip_packages:\n",
+        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
+        "for conda_dep in model_dependencies.conda_packages:\n",
+        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
+        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
         "\n",
         "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
         "service_name = \"myservice-{}\".format(guid)\n",

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

@@ -195,7 +195,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Only Environments that were created using azureml-defaults version 1.0.48 or later will work with this new handling however.\n",
+        "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Please note that your environment must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service.\n",
         "\n",
         "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
       ]
@@ -221,23 +221,30 @@
         "## Create Inference Configuration\n",
         "\n",
         "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n",
-        "Note: in that case, your entry_script, conda_file, and extra_docker_file_steps paths are relative paths to the source_directory path.\n",
+        "Note: in that case, environments's entry_script and file_path are relative paths to the source_directory path; myenv.docker.base_dockerfile is a string containing extra docker steps or contents of the docker file.\n",
         "\n",
         "Sample code for using a source directory:\n",
         "\n",
         "```python\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n",
+        "\n",
+        "# explicitly set base_image to None when setting base_dockerfile\n",
+        "myenv.docker.base_image = None\n",
+        "# add extra docker commends to execute\n",
+        "myenv.docker.base_dockerfile = \"FROM ubuntu\\n RUN echo \\\"hello\\\"\"\n",
+        "\n",
         "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-        "                                   runtime= \"python\", \n",
         "                                   entry_script=\"x/y/score.py\",\n",
-        "                                   conda_file=\"env/myenv.yml\", \n",
+        "                                   environment=myenv)\n",
-        "                                   extra_docker_file_steps=\"helloworld.txt\")\n",
         "```\n",
         "\n",
-        " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
+        " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
-        " - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
+        " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n",
-        " - entry_script = contains logic specific to initializing your model and running predictions\n",
+        " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
-        " - conda_file = manages conda and python package dependencies.\n",
+        " - entry_script: contains logic specific to initializing your model and running predictions"
-        " - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
       ]
     },
     {

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

@@ -405,7 +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

@@ -189,6 +189,15 @@
         "        return error"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency for your environemnt. This package contains the functionality needed to host the model as a web service."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -206,16 +215,6 @@
         "    - inference-schema[numpy-support]"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/dockerstep/customDockerStep.txt\n",
-        "RUN echo \"this is test\""
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -240,11 +239,10 @@
       "source": [
         "## Create Inference Configuration\n",
         "\n",
-        " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
+        " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
-        " - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
+        " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n",
-        " - entry_script = contains logic specific to initializing your model and running predictions\n",
+        " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
-        " - conda_file = manages conda and python package dependencies.\n",
+        " - entry_script: contains logic specific to initializing your model and running predictions"
-        " - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
       ]
     },
     {
@@ -253,13 +251,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.environment import Environment\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
+        "\n",
+        "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n",
+        "\n",
+        "# explicitly set base_image to None when setting base_dockerfile\n",
+        "myenv.docker.base_image = None\n",
+        "myenv.docker.base_dockerfile = \"RUN echo \\\"this is test\\\"\"\n",
+        "\n",
         "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-        "                                   runtime=\"python\", \n",
         "                                   entry_script=\"x/y/score.py\",\n",
-        "                                   conda_file=\"env/myenv.yml\", \n",
+        "                                   environment=myenv)\n"
-        "                                   extra_docker_file_steps=\"dockerstep/customDockerStep.txt\")"
       ]
     },
     {

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

@@ -158,7 +158,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## 5. *Create myenv.yml file*"
+        "## 5. *Create myenv.yml file*\n",
+        "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -169,7 +170,8 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'],\n",
+        "                                 pip_packages=['azureml-defaults'])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -189,10 +191,11 @@
       "outputs": [],
       "source": [
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\")"
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
       ]
     },
     {

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

@@ -244,7 +244,7 @@
       "metadata": {},
       "source": [
         "### Setting up inference configuration\n",
-        "First we create a YAML file that specifies which dependencies we would like to see in our container."
+        "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -255,7 +255,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime==0.4.0\",\"azureml-core\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==0.4.0\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -275,11 +275,11 @@
       "outputs": [],
       "source": [
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
@@ -373,7 +373,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#aci_service.delete()"
+        "aci_service.delete()"
       ]
     }
   ],

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

@@ -319,7 +319,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Write Environment File"
+        "### Write Environment File\n",
+        "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -330,7 +331,8 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
+        "\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -350,11 +352,11 @@
       "outputs": [],
       "source": [
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
@@ -724,7 +726,7 @@
       "source": [
         "# remember to delete your service after you are done using it!\n",
         "\n",
-        "# aci_service.delete()"
+        "aci_service.delete()"
       ]
     },
     {

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

@@ -306,7 +306,7 @@
       "source": [
         "### Write Environment File\n",
         "\n",
-        "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine."
+        "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -317,7 +317,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -337,11 +337,11 @@
       "outputs": [],
       "source": [
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   extra_docker_file_steps = \"Dockerfile\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-        "                                   conda_file=\"myenv.yml\")"
       ]
     },
     {
@@ -733,7 +733,7 @@
       "source": [
         "# remember to delete your service after you are done using it!\n",
         "\n",
-        "# aci_service.delete()"
+        "aci_service.delete()"
       ]
     },
     {

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

@@ -241,7 +241,8 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
+        "\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -251,7 +252,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Create the inference configuration object"
+        "Create the inference configuration object. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -261,11 +262,11 @@
       "outputs": [],
       "source": [
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
@@ -361,7 +362,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#aci_service.delete()"
+        "aci_service.delete()"
       ]
     }
   ],

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

@@ -405,7 +405,7 @@
       "metadata": {},
       "source": [
         "### Create inference configuration\n",
-        "First we create a YAML file that specifies which dependencies we would like to see in our container."
+        "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -416,7 +416,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -436,11 +436,11 @@
       "outputs": [],
       "source": [
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
@@ -537,7 +537,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#aci_service.delete()"
+        "aci_service.delete()"
       ]
     }
   ],

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

@@ -308,7 +308,9 @@
       "source": [
         "## Deploy \n",
         "\n",
-        "Deploy Model and ScoringExplainer"
+        "Deploy Model and ScoringExplainer.\n",
+        "\n",
+        "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -319,7 +321,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
+        "# azureml-defaults is required to host the model as a web service.\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-interpret'\n",
@@ -338,16 +340,6 @@
         "    print(f.read())"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile dockerfile\n",
-        "RUN apt-get update && apt-get install -y g++ "
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -369,6 +361,8 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
+        "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",
@@ -376,10 +370,8 @@
         "                                                     \"method\" : \"local_explanation\"}, \n",
         "                                               description='Get local explanations for IBM Employee Attrition data')\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   entry_script=\"score_local_explain.py\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score_local_explain.py\", environment=myenv)\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps=\"dockerfile\")\n",
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",

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

@@ -246,7 +246,7 @@
       "metadata": {},
       "source": [
         "## Create TensorFlow estimator\n",
-        "Next, we construct an [TensorFlow](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) estimator object.\n",
+        "Next, we construct an [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) estimator object.\n",
         "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 -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker.\n",
         "\n",
         "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release.\n",
@@ -385,7 +385,7 @@
       "outputs": [],
       "source": [
         "metrics_output_name = 'metrics_output'\n",
-        "metirics_data = PipelineData(name='metrics_data',\n",
+        "metrics_data = PipelineData(name='metrics_data',\n",
         "                             datastore=ds,\n",
         "                             pipeline_output_name=metrics_output_name)\n",
         "\n",
@@ -395,7 +395,7 @@
         "    hyperdrive_config=hd_config,\n",
         "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
         "    inputs=[data_folder],\n",
-        "    metrics_output=metirics_data)"
+        "    metrics_output=metrics_data)"
       ]
     },
     {

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",
+        "                               \"ParameterAssignments\": {\"pipeline_arg\": 45}})"
-        "run_id = response.json()[\"Id\"]\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",
-        "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-schedule-for-a-published-pipeline.ipynb

@@ -180,7 +180,7 @@
         "# just get the published pipeline object that you have the ID for.\n",
         "\n",
         "# Get all published pipeline objects in the workspace\n",
-        "all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
+        "all_pub_pipelines = PublishedPipeline.list(ws)\n",
         "\n",
         "# We will iterate through the list of published pipelines and \n",
         "# use the last ID in the list for Schelue operations: \n",
@@ -244,7 +244,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "schedules = Schedule.get_all(ws, pipeline_id=pub_pipeline_id)\n",
+        "schedules = Schedule.list(ws, pipeline_id=pub_pipeline_id)\n",
         "\n",
         "# We will iterate through the list of schedules and \n",
         "# use the last recurrence schedule in the list for further operations: \n",
@@ -272,7 +272,7 @@
       "outputs": [],
       "source": [
         "# Use active_only=False to get all schedules including disabled schedules\n",
-        "schedules = Schedule.get_all(ws, active_only=True) \n",
+        "schedules = Schedule.list(ws, active_only=True) \n",
         "print(\"Your workspace has the following schedules set up:\")\n",
         "for schedule in schedules:\n",
         "    print(\"{} (Published pipeline: {}\".format(schedule.id, schedule.pipeline_id))"

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

@@ -230,7 +230,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "endpoint_list = PipelineEndpoint.get_all(workspace=ws, active_only=True)\n",
+        "endpoint_list = PipelineEndpoint.list(workspace=ws, active_only=True)\n",
         "endpoint_list"
       ]
     },
@@ -360,7 +360,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "versions = pipeline_endpoint_by_name.get_all_versions()\n",
+        "versions = pipeline_endpoint_by_name.list_versions()\n",
         "\n",
         "for ve in versions:\n",
         "    print(ve.version)\n",
@@ -381,7 +381,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "pipelines = pipeline_endpoint_by_name.get_all_pipelines(active_only=True)\n",
+        "pipelines = pipeline_endpoint_by_name.list_pipelines(active_only=True)\n",
         "pipelines"
       ]
     },
@@ -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",
+        "run_id = response.json().get('Id')\n",
-        "print(run_id)"
+        "print('Submitted pipeline run: ', run_id)"
       ]
     },
     {
@@ -578,7 +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",
+        "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))"
-        "\n",
+      ]
+    },
+    {
+      "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

@@ -285,7 +285,7 @@
         "metrics_output_name = 'metrics_output'\n",
         "best_model_output_name = 'best_model_output'\n",
         "\n",
-        "metirics_data = PipelineData(name='metrics_data',\n",
+        "metrics_data = PipelineData(name='metrics_data',\n",
         "                           datastore=ds,\n",
         "                           pipeline_output_name=metrics_output_name,\n",
         "                           training_output=TrainingOutput(type='Metrics'))\n",
@@ -311,7 +311,7 @@
         "automl_step = AutoMLStep(\n",
         "    name='automl_module',\n",
         "    automl_config=automl_config,\n",
-        "    outputs=[metirics_data, model_data],\n",
+        "    outputs=[metrics_data, model_data],\n",
         "    allow_reuse=True)"
       ]
     },

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

@@ -0,0 +1,436 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Azure Machine Learning Pipeline with NotebookRunnerStep\n",
+        "This notebook demonstrates the use of `NotebookRunnerStep`. It allows you to run a local notebook as a step in Azure Machine Learning Pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we showcase how you can run another notebook `notebook_runner/training_notebook.ipynb` as a step in Azure Machine Learning Pipeline.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Create or Attach existing AmlCompute to a workspace.\n",
+        "3. Configure NotebookRun using `NotebokRunConfig`.\n",
+        "5. Use NotebookRunnerStep.\n",
+        "6. Run the notebook on `AmlCompute` as a pipeline step consuming the output of a python script step.\n",
+        "\n",
+        "Advantages of running your notebook as a step in pipeline:\n",
+        "1. Run your notebook like a python script without converting into .py files, leveraging complete end to end experience of Azure Machine Learning Pipelines.\n",
+        "2. Use pipeline intermediate data to and from the notebook along with other steps in pipeline.\n",
+        "3. Parameterize your notebook with [Pipeline Parameters](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb).\n",
+        "\n",
+        "Try some more [quick start notebooks](https://github.com/microsoft/recommenders/tree/master/notebooks/00_quick_start) with `NotebookRunnerStep`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Azure Machine Learning and Pipeline SDK-specific imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "import azureml.core\n",
+        "\n",
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.data.data_reference import DataReference\n",
+        "from azureml.pipeline.core import PipelineData\n",
+        "from azureml.core.datastore import Datastore\n",
+        "\n",
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.contrib.notebook import NotebookRunConfig, AzureMLNotebookHandler\n",
+        "\n",
+        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "from azureml.contrib.notebook import NotebookRunnerStep\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Initialize Workspace\n",
+        "\n",
+        "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
+        "ws.set_default_datastore(\"workspaceblobstore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Upload data to datastore"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Datastore.get(ws, \"workspaceblobstore\").upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)\n",
+        "print(\"Upload call completed\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create an Azure ML experiment\n",
+        "Let's create an experiment named \"notebook-step-run-example\" and a folder to holding the notebook and other scripts. The script runs will be recorded under the experiment in Azure.\n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = 'notebook-step-run-example'\n",
+        "source_directory = 'notebook_runner'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach an AmlCompute cluster\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "    \n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 4)\n",
+        "\n",
+        "    # Create the cluster.\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "    \n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = 1, timeout_in_minutes = 10)\n",
+        "    \n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a new RunConfig object"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk'], pin_sdk_version=False)\n",
+        "conda_run_config.environment.python.conda_dependencies = cd\n",
+        "\n",
+        "print('run config is ready')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define input and outputs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "input_data = DataReference(\n",
+        "    datastore=Datastore.get(ws, \"workspaceblobstore\"),\n",
+        "    data_reference_name=\"blob_test_data\",\n",
+        "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
+        "\n",
+        "output_data = PipelineData(name=\"processed_data\",\n",
+        "                           datastore=Datastore.get(ws, \"workspaceblobstore\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create notebook run configuration and set parameters values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "handler = AzureMLNotebookHandler(timeout=600, progress_bar=False, log_output=True)\n",
+        "\n",
+        "cfg = NotebookRunConfig(source_directory=source_directory, notebook=\"training_notebook.ipynb\",\n",
+        "                        handler = handler,\n",
+        "                        parameters={\"arg1\": \"Machine Learning\"},\n",
+        "                        run_config=conda_run_config)\n",
+        "\n",
+        "print(\"Notebook Run Config is created.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define PythonScriptStep"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print('Source directory for the step is {}.'.format(os.path.realpath('./train')))\n",
+        "python_script_step = PythonScriptStep(\n",
+        "                         script_name=\"train.py\",\n",
+        "                         arguments=[\"--input_data\", input_data],\n",
+        "                         inputs=[input_data],\n",
+        "                         outputs=[output_data],\n",
+        "                         compute_target=compute_target, \n",
+        "                         source_directory=\"./train\",\n",
+        "                         allow_reuse=True)\n",
+        "print(\"python_script_step created\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define NotebookRunnerStep\n",
+        "\n",
+        "This step will consume intermediate output produced by `python_script_step` as an input.\n",
+        "\n",
+        "Optionally, a output of type `output_notebook_pipeline_data_name` can be added to the `NotebookRunnerStep` to redirect the `output_notebook` of notebook run to `NotebookRunnerStep`'s step output produced as `PipelineData` and can be further passed along the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineParameter, TrainingOutput\n",
+        "\n",
+        "output_from_notebook = PipelineData(name=\"notebook_processed_data\",\n",
+        "                                    datastore=Datastore.get(ws, \"workspaceblobstore\"))\n",
+        "\n",
+        "my_pipeline_param = PipelineParameter(name=\"pipeline_param\", default_value=\"my_param\")\n",
+        "\n",
+        "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n",
+        "notebook_runner_step = NotebookRunnerStep(name=\"training_notebook_step\",\n",
+        "                                          notebook_run_config=cfg,\n",
+        "                                          params={\"my_pipeline_param\": my_pipeline_param},\n",
+        "                                          inputs=[output_data],\n",
+        "                                          outputs=[output_from_notebook],\n",
+        "                                          allow_reuse=True,\n",
+        "                                          compute_target=compute_target,\n",
+        "                                          output_notebook_pipeline_data_name=\"notebook_result\")\n",
+        "\n",
+        "print(\"Notebook Runner Step is Created.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Build Pipeline\n",
+        "\n",
+        "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n",
+        "\n",
+        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/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/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/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": [
+        "pipeline1 = Pipeline(workspace=ws, steps=[notebook_runner_step])\n",
+        "\n",
+        "pipeline1.validate()\n",
+        "print(\"Pipeline validation complete\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run1 = experiment.submit(pipeline1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(pipeline_run1).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download output notebook\n",
+        "\n",
+        "`output_notebook` can be retrieved via pipeline step output if `output_notebook_pipeline_data_name` is provided to the `NotebookRunnerStep`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run1.wait_for_completion()\n",
+        " Retrieve the step runs by name `train.py`\n",
+        "train_step = pipeline_run1.find_step_run('training_notebook_step')\n",
+        "\n",
+        "if train_step:\n",
+        "    train_step_obj = train_step[0] # since we have only one step by name `training_notebook_step`\n",
+        "    train_step_obj.get_output_data('notebook_result').download(source_directory) # download the output to source_directory"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sanpil"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "Custom"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML"
+    ],
+    "friendly_name": "How to use run a notebook as a step in AML Pipelines",
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.3"
+    },
+    "order_index": 12,
+    "star_tag": [
+      "None"
+    ],
+    "tags": [
+      "None"
+    ],
+    "task": "Demonstrates the use of NotebookRunnerStep"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.yml

@@ -0,0 +1,6 @@
+name: aml-pipelines-with-notebook-runner-step
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - azureml-contrib-notebook

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb

@@ -0,0 +1,106 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.png)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"In training_notebook.ipynb\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "parameters"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "# declaring parameters to override\n",
+        "\n",
+        "arg1 = \"Azure\"\n",
+        "processed_data = None\n",
+        "notebook_processed_data = None\n",
+        "my_pipeline_param = None"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Final parameter values\n",
+        "\n",
+        "print(\"arg1: %s\" % arg1)\n",
+        "print(\"input from previous step: %s\" % processed_data)\n",
+        "print(\"output from notebook: %s\" % notebook_processed_data)\n",
+        "print(\"pipeline_parameter: %s\" % my_pipeline_param)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if not (notebook_processed_data is None):\n",
+        "    os.makedirs(notebook_processed_data, exist_ok=True)\n",
+        "    print(\"%s created\" % notebook_processed_data)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sanpil"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -17,7 +17,7 @@ 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).
+If you do not already have a Azure ML Workspace, please run the [configuration Notebook](https://aka.ms/pl-config).
 
 ## Configure a Batch Inference job
 
@@ -124,4 +124,4 @@ pipeline_run.wait_for_completion(show_output=True)
 -  [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) 
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.png)

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

@@ -12,7 +12,7 @@
       "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)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.png)"
       ]
     },
     {
@@ -23,6 +23,11 @@
         "\n",
         "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
         "\n",
+        "> **Note**\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "```\n",
+        "pip install azureml-contrib-pipeline-steps\n",
+        "```\n",
         "> **Tip**\n",
         "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
         "\n",
@@ -519,9 +524,6 @@
         "name": "tracych"
       }
     ],
-    "friendly_name": "MNIST data inferencing using ParallelRunStep",
-    "exclude_from_index": false,
-    "index_order": 1,
     "category": "Other notebooks",
     "compute": [
       "AML Compute"
@@ -532,14 +534,12 @@
     "deployment": [
       "None"
     ],
+    "exclude_from_index": false,
     "framework": [
       "None"
     ],
-    "tags": [
+    "friendly_name": "MNIST data inferencing using ParallelRunStep",
-      "Batch Inferencing",
+    "index_order": 1,
-      "Pipeline"
-    ],
-    "task": "Digit identification",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -556,7 +556,12 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.9"
-    }
+    },
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Digit identification"
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -4,7 +4,7 @@
       "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."
       ]
     },
@@ -12,7 +12,7 @@
       "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)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.png)"
       ]
     },
     {
@@ -23,6 +23,11 @@
         "\n",
         "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
         "\n",
+        "> **Note**\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "```\n",
+        "pip install azureml-contrib-pipeline-steps\n",
+        "```\n",
         "> **Tip**\n",
         "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
         "\n",
@@ -494,9 +499,6 @@
         "name": "tracych"
       }
     ],
-    "friendly_name": "IRIS data inferencing using ParallelRunStep",
-    "exclude_from_index": false,
-    "index_order": 1,
     "category": "Other notebooks",
     "compute": [
       "AML Compute"
@@ -507,14 +509,12 @@
     "deployment": [
       "None"
     ],
+    "exclude_from_index": false,
     "framework": [
       "None"
     ],
-    "tags": [
+    "friendly_name": "IRIS data inferencing using ParallelRunStep",
-      "Batch Inferencing",
+    "index_order": 1,
-      "Pipeline"
-    ],
-    "task": "Recognize flower type",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -531,7 +531,12 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.2"
-    }
+    },
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Recognize flower type"
   },
   "nbformat": 4,
   "nbformat_minor": 2

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": {},
@@ -553,8 +560,25 @@
         "response = requests.post(rest_endpoint, \n",
         "                         headers=aad_token, \n",
         "                         json={\"ExperimentName\": \"batch_scoring\",\n",
-        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})\n",
+        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})"
-        "run_id = response.json()[\"Id\"]"
+      ]
+    },
+    {
+      "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/neural_style_mpi.py

@@ -1,207 +0,0 @@
-# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
-import argparse
-import os
-import sys
-import re
-
-from PIL import Image
-import torch
-from torchvision import transforms
-
-from mpi4py import MPI
-
-
-def load_image(filename, size=None, scale=None):
-    img = Image.open(filename)
-    if size is not None:
-        img = img.resize((size, size), Image.ANTIALIAS)
-    elif scale is not None:
-        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
-    return img
-
-
-def save_image(filename, data):
-    img = data.clone().clamp(0, 255).numpy()
-    img = img.transpose(1, 2, 0).astype("uint8")
-    img = Image.fromarray(img)
-    img.save(filename)
-
-
-class TransformerNet(torch.nn.Module):
-    def __init__(self):
-        super(TransformerNet, self).__init__()
-        # Initial convolution layers
-        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
-        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
-        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
-        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
-        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
-        self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
-        # Residual layers
-        self.res1 = ResidualBlock(128)
-        self.res2 = ResidualBlock(128)
-        self.res3 = ResidualBlock(128)
-        self.res4 = ResidualBlock(128)
-        self.res5 = ResidualBlock(128)
-        # Upsampling Layers
-        self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
-        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
-        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
-        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
-        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
-        # Non-linearities
-        self.relu = torch.nn.ReLU()
-
-    def forward(self, X):
-        y = self.relu(self.in1(self.conv1(X)))
-        y = self.relu(self.in2(self.conv2(y)))
-        y = self.relu(self.in3(self.conv3(y)))
-        y = self.res1(y)
-        y = self.res2(y)
-        y = self.res3(y)
-        y = self.res4(y)
-        y = self.res5(y)
-        y = self.relu(self.in4(self.deconv1(y)))
-        y = self.relu(self.in5(self.deconv2(y)))
-        y = self.deconv3(y)
-        return y
-
-
-class ConvLayer(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride):
-        super(ConvLayer, self).__init__()
-        reflection_padding = kernel_size // 2
-        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
-        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
-
-    def forward(self, x):
-        out = self.reflection_pad(x)
-        out = self.conv2d(out)
-        return out
-
-
-class ResidualBlock(torch.nn.Module):
-    """ResidualBlock
-    introduced in: https://arxiv.org/abs/1512.03385
-    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
-    """
-
-    def __init__(self, channels):
-        super(ResidualBlock, self).__init__()
-        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
-        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
-        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
-        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
-        self.relu = torch.nn.ReLU()
-
-    def forward(self, x):
-        residual = x
-        out = self.relu(self.in1(self.conv1(x)))
-        out = self.in2(self.conv2(out))
-        out = out + residual
-        return out
-
-
-class UpsampleConvLayer(torch.nn.Module):
-    """UpsampleConvLayer
-    Upsamples the input and then does a convolution. This method gives better results
-    compared to ConvTranspose2d.
-    ref: http://distill.pub/2016/deconv-checkerboard/
-    """
-
-    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
-        super(UpsampleConvLayer, self).__init__()
-        self.upsample = upsample
-        if upsample:
-            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
-        reflection_padding = kernel_size // 2
-        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
-        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
-
-    def forward(self, x):
-        x_in = x
-        if self.upsample:
-            x_in = self.upsample_layer(x_in)
-        out = self.reflection_pad(x_in)
-        out = self.conv2d(out)
-        return out
-
-
-def stylize(args, comm):
-
-    rank = comm.Get_rank()
-    size = comm.Get_size()
-
-    device = torch.device("cuda" if args.cuda else "cpu")
-    with torch.no_grad():
-        style_model = TransformerNet()
-        state_dict = torch.load(os.path.join(args.model_dir, args.style + ".pth"))
-        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
-        for k in list(state_dict.keys()):
-            if re.search(r'in\d+\.running_(mean|var)$', k):
-                del state_dict[k]
-        style_model.load_state_dict(state_dict)
-        style_model.to(device)
-
-        filenames = os.listdir(args.content_dir)
-        filenames = sorted(filenames)
-        partition_size = len(filenames) // size
-        partitioned_filenames = filenames[rank * partition_size: (rank + 1) * partition_size]
-        print("RANK {} - is processing {} images out of the total {}".format(rank, len(partitioned_filenames),
-                                                                             len(filenames)))
-
-        output_paths = []
-        for filename in partitioned_filenames:
-            # print("Processing {}".format(filename))
-            full_path = os.path.join(args.content_dir, filename)
-            content_image = load_image(full_path, scale=args.content_scale)
-            content_transform = transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Lambda(lambda x: x.mul(255))
-            ])
-            content_image = content_transform(content_image)
-            content_image = content_image.unsqueeze(0).to(device)
-
-            output = style_model(content_image).cpu()
-
-            output_path = os.path.join(args.output_dir, filename)
-            save_image(output_path, output[0])
-
-            output_paths.append(output_path)
-
-        print("RANK {} - number of pre-aggregated output files {}".format(rank, len(output_paths)))
-
-        output_paths_list = comm.gather(output_paths, root=0)
-
-        if rank == 0:
-            print("RANK {} - number of aggregated output files {}".format(rank, len(output_paths_list)))
-            print("RANK {} - end".format(rank))
-
-
-def main():
-    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
-
-    arg_parser.add_argument("--content-scale", type=float, default=None,
-                            help="factor for scaling down the content image")
-    arg_parser.add_argument("--model-dir", type=str, required=True,
-                            help="saved model to be used for stylizing the image.")
-    arg_parser.add_argument("--cuda", type=int, required=True,
-                            help="set it to 1 for running on GPU, 0 for CPU")
-    arg_parser.add_argument("--style", type=str, help="style name")
-    arg_parser.add_argument("--content-dir", type=str, required=True,
-                            help="directory holding the images")
-    arg_parser.add_argument("--output-dir", type=str, required=True,
-                            help="directory holding the output images")
-    args = arg_parser.parse_args()
-
-    comm = MPI.COMM_WORLD
-
-    if args.cuda and not torch.cuda.is_available():
-        print("ERROR: cuda is not available, try running on CPU")
-        sys.exit(1)
-    os.makedirs(args.output_dir, exist_ok=True)
-    stylize(args, comm)
-
-
-if __name__ == "__main__":
-    main()

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

@@ -24,7 +24,13 @@
         "Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n",
         "1. Split a video into images\n",
         "2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n",
-        "3. Stitch the image back into a video."
+        "3. Stitch the image back into a video.\n",
+        "\n",
+        "> **Note**\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "```\n",
+        "pip install azureml-contrib-pipeline-steps\n",
+        "```"
       ]
     },
     {
@@ -50,19 +56,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import os\n",
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "from azureml.core import Workspace, Experiment\n",
         "\n",
         "ws = Workspace.from_config()\n",
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-        "\n",
-        "scripts_folder = \"scripts_folder\"\n",
-        "\n",
-        "if not os.path.isdir(scripts_folder):\n",
-        "    os.mkdir(scripts_folder)"
       ]
     },
     {
@@ -75,11 +87,96 @@
         "from azureml.core.datastore import Datastore\n",
         "from azureml.data.data_reference import DataReference\n",
         "from azureml.pipeline.core import Pipeline, PipelineData\n",
-        "from azureml.pipeline.steps import PythonScriptStep, MpiStep\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
         "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
         "from azureml.core.compute_target import ComputeTargetException"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Download models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# create directory for model\n",
+        "model_dir = 'models'\n",
+        "if not os.path.isdir(model_dir):\n",
+        "    os.mkdir(model_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import urllib.request\n",
+        "\n",
+        "def download_model(model_name):\n",
+        "    # downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
+        "    url=\"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n",
+        "    local_path = os.path.join(model_dir, model_name)\n",
+        "    urllib.request.urlretrieve(url, local_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register all Models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "mosaic_model = None\n",
+        "candy_model = None\n",
+        "\n",
+        "models = Model.list(workspace=ws, tags=['scenario'])\n",
+        "for m in models:\n",
+        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)\n",
+        "    if m.name == 'mosaic' and mosaic_model is None:\n",
+        "        mosaic_model = m\n",
+        "    elif m.name == 'candy' and candy_model is None:\n",
+        "        candy_model = m\n",
+        "\n",
+        "if mosaic_model is None:\n",
+        "    print('Mosaic model does not exist, registering it')\n",
+        "    download_model('mosaic.pth')\n",
+        "    mosaic_model = Model.register(model_path = os.path.join(model_dir, \"mosaic.pth\"),\n",
+        "                       model_name = \"mosaic\",\n",
+        "                       tags = {'type': \"mosaic\", 'scenario': \"Style transfer using batch inference\"},\n",
+        "                       description = \"Style transfer - Mosaic\",\n",
+        "                       workspace = ws)\n",
+        "else:\n",
+        "    print('Reusing existing mosaic model')\n",
+        "    \n",
+        "\n",
+        "if candy_model is None:\n",
+        "    print('Candy model does not exist, registering it')\n",
+        "    download_model('candy.pth')\n",
+        "    candy_model = Model.register(model_path = os.path.join(model_dir, \"candy.pth\"),\n",
+        "                       model_name = \"candy\",\n",
+        "                       tags = {'type': \"candy\", 'scenario': \"Style transfer using batch inference\"},\n",
+        "                       description = \"Style transfer - Candy\",\n",
+        "                       workspace = ws)\n",
+        "else:\n",
+        "    print('Reusing existing candy model')"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -138,8 +235,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import shutil\n",
+        "scripts_folder = \"scripts\""
-        "shutil.copy(\"neural_style_mpi.py\", scripts_folder)"
       ]
     },
     {
@@ -148,31 +244,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile $scripts_folder/process_video.py\n",
+        "process_video_script_file = \"process_video.py\"\n",
-        "import argparse\n",
-        "import glob\n",
-        "import os\n",
-        "import subprocess\n",
         "\n",
-        "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
+        "# peek at contents\n",
-        "parser.add_argument('--input_video', required=True)\n",
+        "with open(os.path.join(scripts_folder, process_video_script_file)) as process_video_file:\n",
-        "parser.add_argument('--output_audio', required=True)\n",
+        "    print(process_video_file.read())"
-        "parser.add_argument('--output_images', required=True)\n",
-        "\n",
-        "args = parser.parse_args()\n",
-        "\n",
-        "os.makedirs(args.output_audio, exist_ok=True)\n",
-        "os.makedirs(args.output_images, exist_ok=True)\n",
-        "\n",
-        "subprocess.run(\"ffmpeg -i {} {}/video.aac\"\n",
-        "              .format(args.input_video, args.output_audio),\n",
-        "               shell=True, check=True\n",
-        "              )\n",
-        "\n",
-        "subprocess.run(\"ffmpeg -i {} {}/%05d_video.jpg -hide_banner\"\n",
-        "              .format(args.input_video, args.output_images),\n",
-        "               shell=True, check=True\n",
-        "              )"
       ]
     },
     {
@@ -181,31 +257,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile $scripts_folder/stitch_video.py\n",
+        "stitch_video_script_file = \"stitch_video.py\"\n",
-        "import argparse\n",
-        "import os\n",
-        "import subprocess\n",
         "\n",
-        "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
+        "# peek at contents\n",
-        "parser.add_argument('--images_dir', required=True)\n",
+        "with open(os.path.join(scripts_folder, stitch_video_script_file)) as stitch_video_file:\n",
-        "parser.add_argument('--input_audio', required=True)\n",
+        "    print(stitch_video_file.read())"
-        "parser.add_argument('--output_dir', required=True)\n",
-        "\n",
-        "args = parser.parse_args()\n",
-        "\n",
-        "os.makedirs(args.output_dir, exist_ok=True)\n",
-        "\n",
-        "subprocess.run(\"ffmpeg -framerate 30 -i {}/%05d_video.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p \"\n",
-        "               \"-y {}/video_without_audio.mp4\"\n",
-        "               .format(args.images_dir, args.output_dir),\n",
-        "               shell=True, check=True\n",
-        "              )\n",
-        "\n",
-        "subprocess.run(\"ffmpeg -i {}/video_without_audio.mp4 -i {}/video.aac -map 0:0 -map 1:0 -vcodec \"\n",
-        "               \"copy -acodec copy -y {}/video_with_audio.mp4\"\n",
-        "               .format(args.output_dir, args.input_audio, args.output_dir),\n",
-        "               shell=True, check=True\n",
-        "              )"
       ]
     },
     {
@@ -226,15 +282,6 @@
         "video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"sample-videos\",\n",
         "                                            account_name=account_name, overwrite=True)\n",
         "\n",
-        "# datastore for models\n",
-        "models_ds = Datastore.register_azure_blob_container(ws, \"models\", \"styletransfer\", \n",
-        "                                                        account_name=\"pipelinedata\", \n",
-        "                                                        overwrite=True)\n",
-        "                                                        \n",
-        "# downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
-        "models_dir = DataReference(data_reference_name=\"models\", datastore=models_ds, \n",
-        "                           path_on_datastore=\"saved_models\", mode=\"download\")\n",
-        "\n",
         "# the default blob store attached to a workspace\n",
         "default_datastore = ws.get_default_datastore()"
       ]
@@ -269,14 +316,8 @@
         "cd.add_channel(\"conda-forge\")\n",
         "cd.add_conda_package(\"ffmpeg\")\n",
         "\n",
-        "cd.add_channel(\"pytorch\")\n",
-        "cd.add_conda_package(\"pytorch\")\n",
-        "cd.add_conda_package(\"torchvision\")\n",
-        "\n",
         "# 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"
       ]
@@ -288,9 +329,13 @@
       "outputs": [],
       "source": [
         "ffmpeg_audio = PipelineData(name=\"ffmpeg_audio\", datastore=default_datastore)\n",
-        "ffmpeg_images = PipelineData(name=\"ffmpeg_images\", datastore=default_datastore)\n",
         "processed_images = PipelineData(name=\"processed_images\", datastore=default_datastore)\n",
-        "output_video = PipelineData(name=\"output_video\", datastore=default_datastore)"
+        "output_video = PipelineData(name=\"output_video\", datastore=default_datastore)\n",
+        "\n",
+        "ffmpeg_images_ds_name = \"ffmpeg_images_data\"\n",
+        "ffmpeg_images = PipelineData(name=\"ffmpeg_images\", datastore=default_datastore)\n",
+        "ffmpeg_images_file_dataset = ffmpeg_images.as_dataset()\n",
+        "ffmpeg_images_named_file_dataset = ffmpeg_images_file_dataset.as_named_input(ffmpeg_images_ds_name)"
       ]
     },
     {
@@ -298,7 +343,10 @@
       "metadata": {},
       "source": [
         "# Define tweakable parameters to pipeline\n",
-        "These parameters can be changed when the pipeline is published and rerun from a REST call"
+        "These parameters can be changed when the pipeline is published and rerun from a REST call.\n",
+        "As part of ParallelRunStep following 2 pipeline parameters will be created which can be used to override values.\n",
+        "    node_count\n",
+        "    process_count_per_node"
       ]
     },
     {
@@ -308,10 +356,8 @@
       "outputs": [],
       "source": [
         "from azureml.pipeline.core.graph import PipelineParameter\n",
-        "# create a parameter for style (one of \"candy\", \"mosaic\", \"rain_princess\", \"udnie\") to transfer the images to\n",
+        "# create a parameter for style (one of \"candy\", \"mosaic\") to transfer the images to\n",
-        "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n",
+        "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")"
-        "# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n",
-        "nodecount_param = PipelineParameter(name=\"nodecount\", default_value=1)"
       ]
     },
     {
@@ -334,27 +380,6 @@
         "    source_directory=scripts_folder\n",
         ")\n",
         "\n",
-        "# create a MPI step for distributing style transfer step across multiple nodes in AmlCompute \n",
-        "# using 'nodecount_param' PipelineParameter\n",
-        "distributed_style_transfer_step = MpiStep(\n",
-        "    name=\"mpi style transfer\",\n",
-        "    script_name=\"neural_style_mpi.py\",\n",
-        "    arguments=[\"--content-dir\", ffmpeg_images,\n",
-        "               \"--output-dir\", processed_images,\n",
-        "               \"--model-dir\", models_dir,\n",
-        "               \"--style\", style_param,\n",
-        "               \"--cuda\", 1\n",
-        "              ],\n",
-        "    compute_target=gpu_cluster,\n",
-        "    node_count=nodecount_param, \n",
-        "    process_count_per_node=1,\n",
-        "    inputs=[models_dir, ffmpeg_images],\n",
-        "    outputs=[processed_images],\n",
-        "    pip_packages=[\"mpi4py\", \"torch\", \"torchvision\"],\n",
-        "    use_gpu=True,\n",
-        "    source_directory=scripts_folder\n",
-        ")\n",
-        "\n",
         "stitch_video_step = PythonScriptStep(\n",
         "    name=\"stitch\",\n",
         "    script_name=\"stitch_video.py\",\n",
@@ -369,6 +394,76 @@
         ")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Create environment, parallel step run config and parallel run step"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
+        "\n",
+        "parallel_cd = CondaDependencies()\n",
+        "\n",
+        "parallel_cd.add_channel(\"pytorch\")\n",
+        "parallel_cd.add_conda_package(\"pytorch\")\n",
+        "parallel_cd.add_conda_package(\"torchvision\")\n",
+        "\n",
+        "styleenvironment = Environment(name=\"styleenvironment\")\n",
+        "styleenvironment.python.conda_dependencies=parallel_cd\n",
+        "styleenvironment.docker.base_image = DEFAULT_GPU_IMAGE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.pipeline.steps import ParallelRunConfig\n",
+        "\n",
+        "parallel_run_config = ParallelRunConfig(\n",
+        "                    environment=styleenvironment,\n",
+        "                    entry_script='transform.py',\n",
+        "                    output_action='summary_only',\n",
+        "                    mini_batch_size=\"1\",\n",
+        "                    error_threshold=1,\n",
+        "                    source_directory=scripts_folder,\n",
+        "                    compute_target=gpu_cluster, \n",
+        "                    node_count=3)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.pipeline.steps import ParallelRunStep\n",
+        "from datetime import datetime\n",
+        "\n",
+        "parallel_step_name = 'styletransfer-' + datetime.now().strftime('%Y%m%d%H%M')\n",
+        "\n",
+        "distributed_style_transfer_step = ParallelRunStep(\n",
+        "    name=parallel_step_name,\n",
+        "    inputs=[ffmpeg_images_named_file_dataset], # Input file share/blob container/file dataset\n",
+        "    output=processed_images,  # Output file share/blob container\n",
+        "    models=[mosaic_model, candy_model],\n",
+        "    tags = {'scenario': \"batch inference\", 'type': \"demo\"},\n",
+        "    properties = {'area': \"style transfer\"},\n",
+        "    arguments=[\"--style\", style_param],\n",
+        "    parallel_run_config=parallel_run_config,\n",
+        "    allow_reuse=True #[optional - default value True]\n",
+        ")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -383,8 +478,18 @@
       "outputs": [],
       "source": [
         "pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n",
+        "\n",
+        "pipeline.validate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n",
-        "pipeline_run = Experiment(ws, 'style_transfer').submit(pipeline, pipeline_parameters={\"style\": \"mosaic\", \"nodecount\": 3})"
+        "pipeline_run = Experiment(ws, 'styletransfer_parallel_mosaic').submit(pipeline)"
       ]
     },
     {
@@ -400,10 +505,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# Track pipeline run progress\n",
         "from azureml.widgets import RunDetails\n",
         "RunDetails(pipeline_run).show()"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run.wait_for_completion()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -453,24 +568,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "published_pipeline = pipeline_run.publish_pipeline(\n",
+        "pipeline_name = \"style-transfer-batch-inference\"\n",
-        "    name=\"batch score style transfer\", description=\"style transfer\", version=\"1.0\")\n",
+        "print(pipeline_name)\n",
         "\n",
-        "published_pipeline"
+        "published_pipeline = pipeline.publish(\n",
+        "    name=pipeline_name, \n",
+        "    description=pipeline_name)\n",
+        "print(\"Newly published pipeline id: {}\".format(published_pipeline.id))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Get published pipeline\n",
+        "# Get published pipeline\n",
-        "\n",
+        "This is another way to get the published pipeline."
-        "You can get the published pipeline using **pipeline id**.\n",
-        "\n",
-        "To get all the published pipelines for a given workspace(ws): \n",
-        "```css\n",
-        "all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
-        "```"
       ]
     },
     {
@@ -481,25 +593,30 @@
       "source": [
         "from azureml.pipeline.core import PublishedPipeline\n",
         "\n",
-        "pipeline_id = published_pipeline.id # use your published pipeline id\n",
+        "# You could retrieve all pipelines that are published, or \n",
-        "published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
+        "# just get the published pipeline object that you have the ID for.\n",
         "\n",
-        "published_pipeline"
+        "# Get all published pipeline objects in the workspace\n",
+        "all_pub_pipelines = PublishedPipeline.list(ws)\n",
+        "\n",
+        "# We will iterate through the list of published pipelines and \n",
+        "# use the last ID in the list for Schelue operations: \n",
+        "print(\"Published pipelines found in the workspace:\")\n",
+        "for pub_pipeline in all_pub_pipelines:\n",
+        "    print(\"Name:\", pub_pipeline.name,\"\\tDescription:\", pub_pipeline.description, \"\\tId:\", pub_pipeline.id, \"\\tStatus:\", pub_pipeline.status)\n",
+        "    if(pub_pipeline.name == pipeline_name):\n",
+        "        published_pipeline = pub_pipeline\n",
+        "\n",
+        "print(\"Published pipeline id: {}\".format(published_pipeline.id))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#  Re-run pipeline through REST calls for other styles"
+        "# Run pipeline through REST calls for other styles\n",
-      ]
+        "\n",
-    },
+        "# Get AAD token"
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Get AAD token\n",
-        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
       ]
     },
     {
@@ -512,14 +629,14 @@
         "import requests\n",
         "\n",
         "auth = InteractiveLoginAuthentication()\n",
-        "aad_token = auth.get_authentication_header()\n"
+        "aad_token = auth.get_authentication_header()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Get endpoint URL"
+        "# Get endpoint URL"
       ]
     },
     {
@@ -528,14 +645,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "rest_endpoint = published_pipeline.endpoint"
+        "rest_endpoint = published_pipeline.endpoint\n",
+        "print(\"Pipeline REST endpoing: {}\".format(rest_endpoint))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Send request and monitor"
+        "# Send request and monitor"
       ]
     },
     {
@@ -544,60 +662,24 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# run the pipeline using PipelineParameter values style='candy' and nodecount=2\n",
+        "experiment_name = 'styletransfer_parallel_candy'\n",
         "response = requests.post(rest_endpoint, \n",
         "                         headers=aad_token,\n",
-        "                         json={\"ExperimentName\": \"style_transfer\",\n",
+        "                         json={\"ExperimentName\": experiment_name,\n",
-        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})                         \n",
+        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"aml_node_count\": 2}})\n",
         "run_id = response.json()[\"Id\"]\n",
         "\n",
         "from azureml.pipeline.core.run import PipelineRun\n",
-        "published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
+        "published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n",
         "\n",
         "RunDetails(published_pipeline_run_candy).show()"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "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()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "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",
-        "\n",
-        "published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
-        "\n",
-        "RunDetails(published_pipeline_run_udnie).show()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Download output from re-run"
+        "# Download output from re-run"
       ]
     },
     {
@@ -606,9 +688,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "published_pipeline_run_candy.wait_for_completion()\n",
+        "published_pipeline_run_candy.wait_for_completion()"
-        "published_pipeline_run_rain.wait_for_completion()\n",
-        "published_pipeline_run_udnie.wait_for_completion()"
       ]
     },
     {
@@ -617,18 +697,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")\n",
+        "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")"
-        "download_video(published_pipeline_run_rain, target_dir=\"output_video_rain_princess\")\n",
-        "download_video(published_pipeline_run_udnie, target_dir=\"output_video_udnie\")"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "sanpil joringer asraniwa pansav tracych"
       }
     ],
+    "category": "Other notebooks",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": true,
+    "framework": [
+      "None"
+    ],
+    "friendly_name": "Style transfer using ParallelRunStep",
+    "index_order": 1,
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -644,8 +736,13 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
-    }
+    },
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Style transfer"
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -2,5 +2,6 @@ name: pipeline-style-transfer
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-contrib-pipeline-steps
   - azureml-widgets
   - requests

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/scripts/process_video.py

@@ -0,0 +1,22 @@
+import argparse
+import glob
+import os
+import subprocess
+
+parser = argparse.ArgumentParser(description="Process input video")
+parser.add_argument('--input_video', required=True)
+parser.add_argument('--output_audio', required=True)
+parser.add_argument('--output_images', required=True)
+
+args = parser.parse_args()
+
+os.makedirs(args.output_audio, exist_ok=True)
+os.makedirs(args.output_images, exist_ok=True)
+
+subprocess.run("ffmpeg -i {} {}/video.aac".format(args.input_video, args.output_audio),
+               shell=True,
+               check=True)
+
+subprocess.run("ffmpeg -i {} {}/%05d_video.jpg -hide_banner".format(args.input_video, args.output_images),
+               shell=True,
+               check=True)

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/scripts/stitch_video.py

@@ -0,0 +1,22 @@
+import argparse
+import os
+import subprocess
+
+parser = argparse.ArgumentParser(description="Process input video")
+parser.add_argument('--images_dir', required=True)
+parser.add_argument('--input_audio', required=True)
+parser.add_argument('--output_dir', required=True)
+
+args = parser.parse_args()
+
+os.makedirs(args.output_dir, exist_ok=True)
+
+subprocess.run("ffmpeg -framerate 30 -i {}/%05d_video.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p "
+               "-y {}/video_without_audio.mp4"
+               .format(args.images_dir, args.output_dir),
+               shell=True, check=True)
+
+subprocess.run("ffmpeg -i {}/video_without_audio.mp4 -i {}/video.aac -map 0:0 -map 1:0 -vcodec "
+               "copy -acodec copy -y {}/video_with_audio.mp4"
+               .format(args.output_dir, args.input_audio, args.output_dir),
+               shell=True, check=True)

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/scripts/transform.py

@@ -1,28 +1,17 @@
-# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
 import argparse
 import os
 import sys
 import re
-
+import json
+import traceback
 from PIL import Image
+
 import torch
 from torchvision import transforms
 
+from azureml.core.model import Model
 
-def load_image(filename, size=None, scale=None):
+style_model = None
-    img = Image.open(filename)
-    if size is not None:
-        img = img.resize((size, size), Image.ANTIALIAS)
-    elif scale is not None:
-        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
-    return img
-
-
-def save_image(filename, data):
-    img = data.clone().clamp(0, 255).numpy()
-    img = img.transpose(1, 2, 0).astype("uint8")
-    img = Image.fromarray(img)
-    img.save(filename)
 
 
 class TransformerNet(torch.nn.Module):
@@ -125,60 +114,59 @@ class UpsampleConvLayer(torch.nn.Module):
         return out
 
 
-def stylize(args):
+def load_image(filename):
-    device = torch.device("cuda" if args.cuda else "cpu")
+    img = Image.open(filename)
+    return img
+
+
+def save_image(filename, data):
+    img = data.clone().clamp(0, 255).numpy()
+    img = img.transpose(1, 2, 0).astype("uint8")
+    img = Image.fromarray(img)
+    img.save(filename)
+
+
+def init():
+    global output_path, args
+    global style_model, device
+    output_path = os.environ['AZUREML_BI_OUTPUT_PATH']
+    print(f'output path: {output_path}')
+    print(f'Cuda available? {torch.cuda.is_available()}')
+
+    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
+    arg_parser.add_argument("--style", type=str, help="style name")
+    args, unknown_args = arg_parser.parse_known_args()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     with torch.no_grad():
         style_model = TransformerNet()
-        state_dict = torch.load(os.path.join(args.model_dir, args.style+".pth"))
+        model_path = Model.get_model_path(args.style)
+        state_dict = torch.load(os.path.join(model_path))
         # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
         for k in list(state_dict.keys()):
             if re.search(r'in\d+\.running_(mean|var)$', k):
                 del state_dict[k]
         style_model.load_state_dict(state_dict)
         style_model.to(device)
+    print(f'Model loaded successfully. Path: {model_path}')
 
-        filenames = os.listdir(args.content_dir)
 
-        for filename in filenames:
+def run(mini_batch):
-            print("Processing {}".format(filename))
+
-            full_path = os.path.join(args.content_dir, filename)
+    result = []
-            content_image = load_image(full_path, scale=args.content_scale)
+    for image_file_path in mini_batch:
+        img = load_image(image_file_path)
+
+        with torch.no_grad():
             content_transform = transforms.Compose([
                 transforms.ToTensor(),
                 transforms.Lambda(lambda x: x.mul(255))
             ])
-            content_image = content_transform(content_image)
+            content_image = content_transform(img)
             content_image = content_image.unsqueeze(0).to(device)
 
             output = style_model(content_image).cpu()
+            output_file_path = os.path.join(output_path, os.path.basename(image_file_path))
+            save_image(output_file_path, output[0])
+            result.append(output_file_path)
 
-            output_path = os.path.join(args.output_dir, filename)
+    return result
-            save_image(output_path, output[0])
-
-def main():
-    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
-
-    arg_parser.add_argument("--content-scale", type=float, default=None,
-                                 help="factor for scaling down the content image")
-    arg_parser.add_argument("--model-dir", type=str, required=True,
-                                 help="saved model to be used for stylizing the image.")
-    arg_parser.add_argument("--cuda", type=int, required=True,
-                                 help="set it to 1 for running on GPU, 0 for CPU")
-    arg_parser.add_argument("--style", type=str,
-                                 help="style name")
-
-    arg_parser.add_argument("--content-dir", type=str, required=True,
-            help="directory holding the images")
-    arg_parser.add_argument("--output-dir", type=str, required=True,
-            help="directory holding the output images")
-    args = arg_parser.parse_args()
-
-    if args.cuda and not torch.cuda.is_available():
-        print("ERROR: cuda is not available, try running on CPU")
-        sys.exit(1)
-    os.makedirs(args.output_dir, exist_ok=True)
-    stylize(args)
-
-
-if __name__ == "__main__":
-    main()

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

@@ -507,7 +507,7 @@
       "metadata": {},
       "source": [
         "### Create myenv.yml\n",
-        "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda packages `numpy` and `chainer`."
+        "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda packages `numpy` and `chainer`. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
       ]
     },
     {
@@ -521,6 +521,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_conda_package('numpy')\n",
         "cd.add_conda_package('chainer')\n",
+        "cd.add_pip_package(\"azureml-defaults\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"
@@ -544,10 +545,11 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"chainer_score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"chainer_score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n",
         "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
@@ -707,7 +709,7 @@
   "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`"
       ]
     },
     {
@@ -561,10 +561,11 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"pytorch_score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"pytorch_score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",
@@ -698,7 +699,7 @@
   "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

@@ -908,13 +908,16 @@
         "def init():\n",
         "    global X, output, sess\n",
         "    tf.reset_default_graph()\n",
-        "    model_root = Model.get_model_path('tf-dnn-mnist')\n",
+        "    model_root = os.getenv('AZUREML_MODEL_DIR')\n",
-        "    saver = tf.train.import_meta_graph(os.path.join(model_root, 'mnist-tf.model.meta'))\n",
+        "    # the name of the folder in which to look for tensorflow model files\n",
+        "    tf_model_folder = 'model'\n",
+        "    saver = tf.train.import_meta_graph(\n",
+        "        os.path.join(model_root, tf_model_folder, 'mnist-tf.model.meta'))\n",
         "    X = tf.get_default_graph().get_tensor_by_name(\"network/X:0\")\n",
         "    output = tf.get_default_graph().get_tensor_by_name(\"network/output/MatMul:0\")\n",
-        "    \n",
+        "\n",
         "    sess = tf.Session()\n",
-        "    saver.restore(sess, os.path.join(model_root, 'mnist-tf.model'))\n",
+        "    saver.restore(sess, os.path.join(model_root, tf_model_folder, 'mnist-tf.model'))\n",
         "\n",
         "def run(raw_data):\n",
         "    data = np.array(json.loads(raw_data)['data'])\n",
@@ -943,6 +946,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_conda_package('numpy')\n",
         "cd.add_tensorflow_conda_package()\n",
+        "cd.add_pip_package(\"azureml-defaults\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"
@@ -966,10 +970,11 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",
@@ -1140,7 +1145,7 @@
   "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
+26.536,-81.755,20.0,20.0,2.6,9.0
+26.536,-81.755,22.2,10.0,3.6,9.0
+26.536,-81.755,23.3,30.0,4.6,9.0
+26.536,-81.755,23.3,330.0,2.6,9.0
+26.536,-81.755,24.4,0.0,0.0,9.0
+26.536,-81.755,25.0,360.0,3.1,9.0
+26.536,-81.755,24.4,20.0,4.1,9.0
+26.536,-81.755,23.3,10.0,2.6,9.0
+26.536,-81.755,21.1,30.0,2.1,9.0
+26.536,-81.755,18.3,0.0,0.0,9.0
+26.536,-81.755,17.2,30.0,2.1,9.0
+26.536,-81.755,15.6,60.0,2.6,9.0
+26.536,-81.755,15.6,0.0,0.0,9.0
+26.536,-81.755,13.9,60.0,2.6,9.0
+26.536,-81.755,12.8,70.0,2.6,9.0
+26.536,-81.755,0.0,0.0,0.0,9.0
+26.536,-81.755,11.7,70.0,2.1,9.0
+26.536,-81.755,12.2,20.0,2.1,9.0
+26.536,-81.755,11.7,30.0,1.5,9.0
+26.536,-81.755,11.1,40.0,2.1,9.0
+26.536,-81.755,12.2,40.0,2.6,9.0
+26.536,-81.755,12.2,30.0,2.6,9.0
+26.536,-81.755,12.2,0.0,0.0,9.0
+26.536,-81.755,15.0,30.0,6.2,9.0
+26.536,-81.755,17.2,50.0,3.6,9.0
+26.536,-81.755,20.6,60.0,5.1,9.0
+26.536,-81.755,22.8,50.0,4.6,9.0
+26.536,-81.755,24.4,80.0,6.2,9.0
+26.536,-81.755,25.0,100.0,5.7,9.0
+26.536,-81.755,25.6,60.0,3.1,9.0
+26.536,-81.755,25.6,80.0,4.6,9.0
+26.536,-81.755,25.0,90.0,5.1,9.0
+26.536,-81.755,24.4,80.0,5.1,9.0
+26.536,-81.755,21.1,60.0,2.6,9.0
+26.536,-81.755,19.4,70.0,3.6,9.0
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+26.536,-81.755,16.1,70.0,2.6,9.0
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+26.536,-81.755,26.1,120.0,3.1,9.0
+26.536,-81.755,26.7,0.0,2.6,9.0
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+26.536,-81.755,22.8,310.0,2.1,9.0
+26.536,-81.755,23.3,330.0,2.1,9.0
+-34.067,-56.238,17.5,30.0,3.1,68.0
+-34.067,-56.238,21.2,30.0,5.7,68.0
+-34.067,-56.238,24.5,30.0,3.1,68.0
+-34.067,-56.238,27.5,330.0,3.6,68.0
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+-34.067,-56.238,28.6,50.0,3.1,68.0
+-34.067,-56.238,30.6,50.0,4.1,68.0
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how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb

@@ -92,7 +92,7 @@
         "dstore = ws.get_default_datastore()\n",
         "\n",
         "# upload weather data\n",
-        "dstore.upload('training-dataset', 'drift-on-aks-data', overwrite=True, show_progress=False)"
+        "dstore.upload('dataset', 'drift-on-aks-data', overwrite=True, show_progress=False)"
       ]
     },
     {
@@ -229,7 +229,7 @@
       "source": [
         "## Run recent weather data through the webservice \n",
         "\n",
-        "The below cells take the past 2 days of weather data, filter and transform using the same processes as the training dataset, and runs the data through the service."
+        "The below cells take the weather data of Florida from 2019-11-20 to 2019-11-12, filter and transform using the same processes as the training dataset, and runs the data through the service."
       ]
     },
     {
@@ -238,16 +238,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from datetime import datetime, timedelta\n",
+        "# create dataset \n",
-        "from azureml.opendatasets import NoaaIsdWeather\n",
+        "tset = Dataset.Tabular.from_delimited_files(dstore.path('drift-on-aks-data/testing.csv'))\n",
         "\n",
-        "start = datetime.today() - timedelta(days=2)\n",
+        "df = tset.to_pandas_dataframe().fillna(0)\n",
-        "end = datetime.today()\n",
-        "\n",
-        "isd = NoaaIsdWeather(start, end)\n",
-        "\n",
-        "df = isd.to_pandas_dataframe().fillna(0)\n",
-        "df = df[df['stationName'].str.contains('FLORIDA', regex=True, na=False)]\n",
         "\n",
         "X_features = ['latitude', 'longitude', 'temperature', 'windAngle', 'windSpeed']\n",
         "y_features = ['elevation']\n",
@@ -264,9 +258,9 @@
       "source": [
         "import json\n",
         "\n",
-        "today_data = json.dumps({'data': X.values.tolist()})\n",
+        "data = json.dumps({'data': X.values.tolist()})\n",
         "\n",
-        "data_encoded = bytes(today_data, encoding='utf8')\n",
+        "data_encoded = bytes(data, encoding='utf8')\n",
         "prediction = service.run(input_data=data_encoded)\n",
         "print(prediction)"
       ]
@@ -342,6 +336,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from datetime import datetime, timedelta\n",
         "from azureml.datadrift import DataDriftDetector, AlertConfiguration\n",
         "\n",
         "services = [service_name]\n",

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 1.0.76, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.0.83, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

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

@@ -925,6 +925,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_tensorflow_conda_package()\n",
         "cd.add_conda_package('keras==2.2.5')\n",
+        "cd.add_pip_package(\"azureml-defaults\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"
@@ -947,10 +948,11 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "\n",
-        "                                   entry_script=\"score.py\",\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   conda_file=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n",
         "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
@@ -1131,7 +1133,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "ninhu"
+        "name": "swatig"
       }
     ],
     "category": "training",

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

@@ -149,7 +149,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "condadependencies-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.environment import CondaDependencies\n",
@@ -169,7 +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",

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

@@ -206,7 +206,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "datadrift-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.datadrift import DataDriftDetector, AlertConfiguration\n",
@@ -290,7 +294,9 @@
       "outputs": [],
       "source": [
         "# backfill for one month\n",
-        "backfill = monitor.backfill(datetime(2019, 9, 1), datetime(2019, 10, 1))\n",
+        "backfill_start_date = datetime(2019, 9, 1)\n",
+        "backfill_end_date = datetime(2019, 10, 1)\n",
+        "backfill = monitor.backfill(backfill_start_date, backfill_end_date)\n",
         "backfill"
       ]
     },
@@ -353,7 +359,7 @@
       "outputs": [],
       "source": [
         "# plot the results from Python SDK \n",
-        "monitor.show()"
+        "monitor.show(backfill_start_date, backfill_end_date)"
       ]
     },
     {
@@ -371,7 +377,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "link = 'https://ml.azure.com/data/monitor/{}?wsid=/subscriptions/{}/resourcegroups/{}/workspaces/{}'.format(monitor.name, ws.subscription_id, ws.resource_group, ws.name)\n",
+        "link = 'https://ml.azure.com/data/monitor/{}?wsid=/subscriptions/{}/resourcegroups/{}/workspaces/{}&startDate={}&endDate={}'.format(monitor.name, ws.subscription_id, ws.resource_group, ws.name, backfill_start_date.strftime('%Y-%m-%d'), backfill_end_date .strftime('%Y-%m-%d'))\n",
         "print(link)"
       ]
     },

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)