Merge pull request #724 from Azure/release_update/Release-167

update samples from Release-167 as a part of 1.0.83 SDK release
2025-12-19 17:17:04 -05:00 · 2020-01-06 15:38:25 -05:00
parent 9b9d112719 c95f970dc8
commit ae1337fe70
61 changed files with 692 additions and 814 deletions
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -103,7 +103,7 @@
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"This notebook was created using version 1.0.81 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\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml
+++ b/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==1.0.0
-  - azureml-explain-model
-  - azureml-contrib-interpret
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
+++ b/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"
    ],
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -347,7 +347,7 @@
        "\n",
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\": 10,\n",
-        "    \"experiment_timeout_minutes\": 10,\n",
+        "    \"experiment_timeout_hours\": 0.2,\n",
        "    \"n_cross_validations\": 3,\n",
        "    \"primary_metric\": 'r2_score',\n",
        "    \"preprocess\": True,\n",
@@ -378,7 +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",
@@ -397,7 +397,7 @@
        "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=False)"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
+++ b/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",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -248,7 +248,7 @@
        "|**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-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",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -302,7 +302,7 @@
        "|**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-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",
@@ -578,7 +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",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb
+++ b/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",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py
@@ -71,7 +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'))
@@ -84,7 +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)

--- a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
+++ b/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",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -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",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
+++ b/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",
-        "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)"
+        "The fitted_model is a python object and you can read the different properties of the object.\n"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
+++ b/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",
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/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",
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLTrain.sql
+++ b/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:
-        experiment_timeout_minutes = None
+    if experiment_timeout_hours == 0:
+        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
--- a/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb
+++ b/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",
-        "        experiment_timeout_minutes = None\r\n",
+        "    if experiment_timeout_hours == 0:\r\n",
+        "        experiment_timeout_hours = None\r\n",
        "\r\n",
        "    if experiment_exit_score == 0:\r\n",
        "        experiment_exit_score = None\r\n",
@@ -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,
--- a/how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb
+++ b/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",
-        "                                   entry_script='score_sparkml.py',\n",
-        "                                   conda_file='mydeployenv.yml')\n",
+        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
+        "# we need to add extra packages to procured environment\n",
+        "# in order to deploy amended environment we need to rename it\n",
+        "myenv.name = 'myenv'\n",
+        "model_dependencies = CondaDependencies('myenv.yml')\n",
+        "for pip_dep in model_dependencies.pip_packages:\n",
+        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
+        "for conda_dep in model_dependencies.conda_packages:\n",
+        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
+        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
        "\n",
        "myservice = Model.deploy(ws, service_name, [mymodel], inference_config, myaci_config)\n",
        "myservice.wait_for_deployment(show_output=True)"
--- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
+++ b/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",
-        "                                   entry_script='score.py',\n",
-        "                                   conda_file='mydeployenv.yml')\n",
+        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
+        "# we need to add extra packages to procured environment\n",
+        "# in order to deploy amended environment we need to rename it\n",
+        "myenv.name = 'myenv'\n",
+        "model_dependencies = CondaDependencies('myenv.yml')\n",
+        "for pip_dep in model_dependencies.pip_packages:\n",
+        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
+        "for conda_dep in model_dependencies.conda_packages:\n",
+        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
+        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
        "\n",
        "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
        "service_name = \"myservice-{}\".format(guid)\n",
--- a/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb
+++ b/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",
-        "                                   extra_docker_file_steps=\"helloworld.txt\")\n",
+        "                                   environment=myenv)\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",
-        " - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
-        " - entry_script = contains logic specific to initializing your model and running predictions\n",
-        " - conda_file = manages conda and python package dependencies.\n",
-        " - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
+        " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
+        " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\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",
+        " - entry_script: contains logic specific to initializing your model and running predictions"
      ]
    },
    {
--- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
+++ b/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",
-        " - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
-        " - entry_script = contains logic specific to initializing your model and running predictions\n",
-        " - conda_file = manages conda and python package dependencies.\n",
-        " - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
+        " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
+        " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\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",
+        " - entry_script: contains logic specific to initializing your model and running predictions"
      ]
    },
    {
@@ -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",
-        "                                   extra_docker_file_steps=\"dockerstep/customDockerStep.txt\")"
+        "                                   environment=myenv)\n"
      ]
    },
    {
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
+++ b/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",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\")"
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
--- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
+++ b/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",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
@@ -373,7 +373,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#aci_service.delete()"
+        "aci_service.delete()"
      ]
    }
  ],
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
+++ b/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",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
@@ -724,7 +726,7 @@
      "source": [
        "# remember to delete your service after you are done using it!\n",
        "\n",
-        "# aci_service.delete()"
+        "aci_service.delete()"
      ]
    },
    {
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/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",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   extra_docker_file_steps = \"Dockerfile\",\n",
-        "                                   conda_file=\"myenv.yml\")"
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
@@ -733,7 +733,7 @@
      "source": [
        "# remember to delete your service after you are done using it!\n",
        "\n",
-        "# aci_service.delete()"
+        "aci_service.delete()"
      ]
    },
    {
--- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
+++ b/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",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
@@ -361,7 +362,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#aci_service.delete()"
+        "aci_service.delete()"
      ]
    }
  ],
--- a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
+++ b/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",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps = \"Dockerfile\")"
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
      ]
    },
    {
@@ -537,7 +537,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "#aci_service.delete()"
+        "aci_service.delete()"
      ]
    }
  ],
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
+++ b/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",
-        "                                   entry_script=\"score_local_explain.py\",\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps=\"dockerfile\")\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score_local_explain.py\", environment=myenv)\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",
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
+++ b/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)"
      ]
    },
    {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
+++ b/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)"
      ]
    },
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/digit_identification.py
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/digit_identification.py
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md
@@ -11,13 +11,13 @@ Batch inference public preview offers a platform in which to do large inference
 ### Python package installation
 Following the convention of most AzureML Public Preview features, Batch Inference SDK is currently available as a contrib package.

-If you're unfamiliar with creating a new Python environment, you may follow this example for [creating a conda environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local).  Batch Inference package can be installed through the following pip command.
+If you're unfamiliar with creating a new Python environment, you may follow this example for [creating a conda environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local). Batch Inference package can be installed through the following pip command.
 ```
 pip install azureml-contrib-pipeline-steps
 ```

 ### Creation of Azure Machine Learning Workspace
-If you do not already have a Azure ML Workspace, please run the [configuration Notebook](../../configuration.ipynb).
+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)
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
+++ b/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": [
-      "Batch Inferencing",
-      "Pipeline"
-    ],
-    "task": "Digit identification",
+    "friendly_name": "MNIST data inferencing using ParallelRunStep",
+    "index_order": 1,
    "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
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.yml
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.yml
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
+++ b/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": [
-      "Batch Inferencing",
-      "Pipeline"
-    ],
-    "task": "Recognize flower type",
+    "friendly_name": "IRIS data inferencing using ParallelRunStep",
+    "index_order": 1,
    "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
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.yml
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.yml
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style_mpi.py
+++ b/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()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
@@ -16,13 +16,6 @@
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.png)"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -31,7 +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",
+        "```"
      ]
    },
    {
@@ -57,19 +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",
-        "\n",
-        "scripts_folder = \"scripts_folder\"\n",
-        "\n",
-        "if not os.path.isdir(scripts_folder):\n",
-        "    os.mkdir(scripts_folder)"
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
      ]
    },
    {
@@ -82,11 +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": {},
@@ -122,7 +212,7 @@
        "except ComputeTargetException:\n",
        "    print(\"creating new cluster\")\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
-        "                                                                    max_nodes = 3)\n",
+        "                                                                max_nodes = 3)\n",
        "\n",
        "    # create the cluster\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
@@ -145,8 +235,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import shutil\n",
-        "shutil.copy(\"neural_style_mpi.py\", scripts_folder)"
+        "scripts_folder = \"scripts\""
      ]
    },
    {
@@ -155,31 +244,11 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "%%writefile $scripts_folder/process_video.py\n",
-        "import argparse\n",
-        "import glob\n",
-        "import os\n",
-        "import subprocess\n",
+        "process_video_script_file = \"process_video.py\"\n",
        "\n",
-        "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
-        "parser.add_argument('--input_video', required=True)\n",
-        "parser.add_argument('--output_audio', required=True)\n",
-        "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",
-        "              )"
+        "# peek at contents\n",
+        "with open(os.path.join(scripts_folder, process_video_script_file)) as process_video_file:\n",
+        "    print(process_video_file.read())"
      ]
    },
    {
@@ -188,31 +257,11 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "%%writefile $scripts_folder/stitch_video.py\n",
-        "import argparse\n",
-        "import os\n",
-        "import subprocess\n",
+        "stitch_video_script_file = \"stitch_video.py\"\n",
        "\n",
-        "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
-        "parser.add_argument('--images_dir', required=True)\n",
-        "parser.add_argument('--input_audio', required=True)\n",
-        "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",
-        "              )"
+        "# peek at contents\n",
+        "with open(os.path.join(scripts_folder, stitch_video_script_file)) as stitch_video_file:\n",
+        "    print(stitch_video_file.read())"
      ]
    },
    {
@@ -233,15 +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()"
      ]
@@ -276,13 +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.base_image = \"pytorch/pytorch\"\n",
        "amlcompute_run_config.environment.spark.precache_packages = False"
      ]
@@ -294,9 +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)"
      ]
    },
    {
@@ -304,7 +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"
      ]
    },
    {
@@ -314,10 +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",
-        "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n",
-        "# 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)"
+        "# create a parameter for style (one of \"candy\", \"mosaic\") to transfer the images to\n",
+        "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")"
      ]
    },
    {
@@ -340,27 +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",
@@ -375,6 +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": {},
@@ -389,8 +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)"
      ]
    },
    {
@@ -406,10 +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": {},
@@ -459,24 +568,21 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "published_pipeline = pipeline_run.publish_pipeline(\n",
-        "    name=\"batch score style transfer\", description=\"style transfer\", version=\"1.0\")\n",
+        "pipeline_name = \"style-transfer-batch-inference\"\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",
-        "\n",
-        "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",
-        "```"
+        "# Get published pipeline\n",
+        "This is another way to get the published pipeline."
      ]
    },
    {
@@ -487,25 +593,30 @@
      "source": [
        "from azureml.pipeline.core import PublishedPipeline\n",
        "\n",
-        "pipeline_id = published_pipeline.id # use your published pipeline id\n",
-        "published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
+        "# You could retrieve all pipelines that are published, or \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"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Get AAD token\n",
-        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
+        "# Run pipeline through REST calls for other styles\n",
+        "\n",
+        "# Get AAD token"
      ]
    },
    {
@@ -518,14 +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"
      ]
    },
    {
@@ -534,21 +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"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run the pipeline using PipelineParameter values style='candy' and nodecount=2"
+        "# Send request and monitor"
      ]
    },
    {
@@ -557,38 +662,16 @@
      "metadata": {},
      "outputs": [],
      "source": [
+        "experiment_name = 'styletransfer_parallel_candy'\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=aad_token,\n",
-        "                         json={\"ExperimentName\": \"style_transfer\",\n",
-        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "try:\n",
-        "    response.raise_for_status()\n",
-        "except Exception:    \n",
-        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
-        "                    'Response Code: {}\\n'\n",
-        "                    'Headers: {}\\n'\n",
-        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
+        "                         json={\"ExperimentName\": experiment_name,\n",
+        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"aml_node_count\": 2}})\n",
+        "run_id = response.json()[\"Id\"]\n",
        "\n",
-        "run_id = response.json().get('Id')\n",
-        "print('Submitted pipeline run: ', run_id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
        "from azureml.pipeline.core.run import PipelineRun\n",
-        "published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
+        "published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n",
+        "\n",
        "RunDetails(published_pipeline_run_candy).show()"
      ]
    },
@@ -596,7 +679,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "Run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3"
+        "# Download output from re-run"
      ]
    },
    {
@@ -605,10 +688,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "response = requests.post(rest_endpoint, \n",
-        "                         headers=aad_token,\n",
-        "                         json={\"ExperimentName\": \"style_transfer\",\n",
-        "                               \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})"
+        "published_pipeline_run_candy.wait_for_completion()"
      ]
    },
    {
@@ -617,111 +697,30 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "try:\n",
-        "    response.raise_for_status()\n",
-        "except Exception:    \n",
-        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
-        "                    'Response Code: {}\\n'\n",
-        "                    'Headers: {}\\n'\n",
-        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
-        "\n",
-        "run_id = response.json().get('Id')\n",
-        "print('Submitted pipeline run: ', run_id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
-        "RunDetails(published_pipeline_run_rain).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run the pipeline using PipelineParameter values style='udnie' and nodecount=4"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "response = requests.post(rest_endpoint, \n",
-        "                         headers=aad_token,\n",
-        "                         json={\"ExperimentName\": \"style_transfer\",\n",
-        "                               \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 3}})\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "try:\n",
-        "    response.raise_for_status()\n",
-        "except Exception:    \n",
-        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
-        "                    'Response Code: {}\\n'\n",
-        "                    'Headers: {}\\n'\n",
-        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
-        "\n",
-        "run_id = response.json().get('Id')\n",
-        "print('Submitted pipeline run: ', run_id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
-        "RunDetails(published_pipeline_run_udnie).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Download output from re-run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "published_pipeline_run_candy.wait_for_completion()\n",
-        "published_pipeline_run_rain.wait_for_completion()\n",
-        "published_pipeline_run_udnie.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")\n",
-        "download_video(published_pipeline_run_rain, target_dir=\"output_video_rain_princess\")\n",
-        "download_video(published_pipeline_run_udnie, target_dir=\"output_video_udnie\")"
+        "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")"
      ]
    }
  ],
  "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",
@@ -737,8 +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
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.yml
+++ b/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
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/scripts/process_video.py
+++ b/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)
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/scripts/stitch_video.py
+++ b/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)
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/scripts/transform.py
+++ b/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):
-    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)
+style_model = None


 class TransformerNet(torch.nn.Module):
@@ -123,62 +112,61 @@ class UpsampleConvLayer(torch.nn.Module):
        out = self.reflection_pad(x_in)
        out = self.conv2d(out)
        return out
-    

-def stylize(args):
-    device = torch.device("cuda" if args.cuda else "cpu")
+
+def load_image(filename):
+    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:
-            print("Processing {}".format(filename))
-            full_path = os.path.join(args.content_dir, filename)
-            content_image = load_image(full_path, scale=args.content_scale)
+def run(mini_batch):
+
+    result = []
+    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)
-            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()
+    return result
--- a/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
+++ b/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",
-        "                                   entry_script=\"chainer_score.py\",\n",
-        "                                   conda_file=\"myenv.yml\")\n",
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"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",
--- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
+++ b/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 1.0.81, 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)"
      ]
    },
    {
--- a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb
+++ b/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",
--- a/index.md
+++ b/index.md
@@ -10,6 +10,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [Using Azure ML environments](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/using-environments/using-environments.ipynb) | Creating and registering environments | None | Local | None | None | None |
+
 | [Estimators in AML with hyperparameter tuning](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb) | Use the Estimator pattern in Azure Machine Learning SDK | None | AML Compute | None | None | None |


@@ -18,37 +19,69 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting |
+
 | [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None |
+
 | [Forecasting with automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.ipynb) | Forecasting | NYC Energy | Local | None | Azure ML AutoML |  |
+
 | [Setup automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb) | None | None | None | None | Azure ML AutoML |  |
+
 | [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | None | Local | Local | None | None |
+
 | :star:[Data drift on aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb) | Filtering | NOAA | Remote | AKS | Azure ML | Dataset, Timeseries, Drift |
+
 | [Train and deploy a model using Python SDK](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None |
+
 | :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift |
+
 | :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries |
+
 | :star:[Introduction to labeled datasets](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.ipynb) | Train |  | Remote | None | Azure ML | Dataset, label, Estimator |
+
 | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun |
+
 | :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun |
+
 | [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals |
+
 | [Automated ML run with basic edition features.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) | Classification | Bankmarketing | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
+
 | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML |
+
 | [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
+
 | [Use MLflow with Azure Machine Learning for training and deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb) | Use MLflow with Azure Machine Learning to train and deploy Pa yTorch image classifier model | MNIST | AML Compute | Azure Container Instance | PyTorch | None |
+
 | :star:[Azure Machine Learning Pipeline with DataTranferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) | Demonstrates the use of DataTranferStep | Custom | ADF | None | Azure ML | None |
+
 | [Getting Started with Azure Machine Learning Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) | Getting Started notebook for ANML Pipelines | Custom | AML Compute | None | Azure ML | None |
+
 | [Azure Machine Learning Pipeline with AzureBatchStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb) | Demonstrates the use of AzureBatchStep | Custom | Azure Batch | None | Azure ML | None |
+
 | [Azure Machine Learning Pipeline with EstimatorStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb) | Demonstrates the use of EstimatorStep | Custom | AML Compute | None | Azure ML | None |
+
 | :star:[How to use ModuleStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb) | Demonstrates the use of ModuleStep | Custom | AML Compute | None | Azure ML | None |
+
 | :star:[How to use Pipeline Drafts to create a Published Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb) | Demonstrates the use of Pipeline Drafts | Custom | AML Compute | None | Azure ML | None |
+
 | :star:[Azure Machine Learning Pipeline with HyperDriveStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb) | Demonstrates the use of HyperDriveStep | Custom | AML Compute | None | Azure ML | None |
+
 | :star:[How to Publish a Pipeline and Invoke the REST endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) | Demonstrates the use of Published Pipelines | Custom | AML Compute | None | Azure ML | None |
+
 | :star:[How to Setup a Schedule for a Published Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb) | Demonstrates the use of Schedules for Published Pipelines | Custom | AML Compute | None | Azure ML | None |
+
 | [How to setup a versioned Pipeline Endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb) | Demonstrates the use of PipelineEndpoint to run a specific version of the Published Pipeline | Custom | AML Compute | None | Azure ML | None |
+
 | :star:[How to use DataPath as a PipelineParameter](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb) | Demonstrates the use of DataPath as a PipelineParameter | Custom | AML Compute | None | Azure ML | None |
+
 | [How to use AdlaStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb) | Demonstrates the use of AdlaStep | Custom | Azure Data Lake Analytics | None | Azure ML | None |
+
 | :star:[How to use DatabricksStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb) | Demonstrates the use of DatabricksStep | Custom | Azure Databricks | None | Azure ML, Azure Databricks | None |
+
 | :star:[How to use AutoMLStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb) | Demonstrates the use of AutoMLStep | Custom | AML Compute | None | Automated Machine Learning | None |
+
 | :star:[Azure Machine Learning Pipelines with Data Dependency](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb) | Demonstrates how to construct a Pipeline with data dependency between steps | Custom | AML Compute | None | Azure ML | None |
+
 | [How to use run a notebook as a step in AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb) | Demonstrates the use of NotebookRunnerStep | Custom | AML Compute | None | Azure ML | None |


@@ -57,25 +90,45 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [Train a model with hyperparameter tuning](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb) | Train a Convolutional Neural Network (CNN) | MNIST | AML Compute | Azure Container Instance | Chainer | None |
+
 | [Distributed Training with Chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.ipynb) | Use the Chainer estimator to perform distributed training | MNIST | AML Compute | None | Chainer | None |
+
 | [Training with hyperparameter tuning using PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) | Train an image classification model using transfer learning with the PyTorch estimator | ImageNet | AML Compute | Azure Container Instance | PyTorch | None |
+
 | [Distributed PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb) | Train a model using the distributed training via Horovod | MNIST | AML Compute | None | PyTorch | None |
+
 | [Distributed training with PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.ipynb) | Train a model using distributed training via Nccl/Gloo | MNIST | AML Compute | None | PyTorch | None |
+
 | [Training and hyperparameter tuning with Scikit-learn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) | Train a support vector machine (SVM) to perform classification | Iris | AML Compute | None | Scikit-learn | None |
+
 | [Training and hyperparameter tuning using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None |
+
 | [Distributed training using TensorFlow with Horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb) | Use the TensorFlow estimator to train a word2vec model | None | AML Compute | None | TensorFlow | None |
+
 | [Distributed TensorFlow with parameter server](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb) | Use the TensorFlow estimator to train a model using distributed training | MNIST | AML Compute | None | TensorFlow | None |
+
 | [Hyperparameter tuning and warm start using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None |
+
 | [Resuming a model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) | Resume a model in TensorFlow from a previously submitted run | MNIST | AML Compute | None | TensorFlow | None |
+
 | [Training in Spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) | Submiting a run on a spark cluster | None | HDI cluster | None | PySpark | None |
+
 | [Train on Azure Machine Learning Compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) | Submit a run on Azure Machine Learning Compute. | Diabetes | AML Compute | None | None | None |
+
 | [Train on local compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-local/train-on-local.ipynb) | Train a model locally | Diabetes | Local | None | None | None |
+
 | [Train in a remote Linux virtual machine](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) | Configure and execute a run | Diabetes | Data Science Virtual Machine | None | None | None |
+
 | [Using Tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) | Export the run history as Tensorboard logs | None | None | None | TensorFlow | None |
+
 | [Train a DNN using hyperparameter tuning and deploying with Keras](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb) | Create a multi-class classifier | MNIST | AML Compute | Azure Container Instance | TensorFlow | None |
+
 | [Managing your training runs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb) | Monitor and complete runs | None | Local | None | None | None |
+
 | [Tensorboard integration with run history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb) | Run a TensorFlow job and view its Tensorboard output live | None | Local, DSVM, AML Compute | None | TensorFlow | None |
+
 | [Use MLflow with AML for a local training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb) | Use MLflow tracking APIs together with Azure Machine Learning for storing your metrics and artifacts | Diabetes | Local | None | None | None |
+
 | [Use MLflow with AML for a remote training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb) | Use MLflow tracking APIs together with AML for storing your metrics and artifacts | Diabetes | AML Compute | None | None | None |


@@ -86,12 +139,19 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
+
 | [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
+
 | :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
+
 | :star:[Deploy models to AKS using controlled roll out](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Kubernetes Service | Scikit-learn | None |
+
 | [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
+
 | [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
+
 | [Deploy a model as a web service using MLflow](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb) | Use MLflow with AML | Diabetes | None | Azure Container Instance | Scikit-learn | None |
+
 | :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter |


@@ -100,48 +160,90 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None | AML Compute | None | None | None |
+
 | [Automated ML Grouping with Pipeline.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb) | Use AzureML Pipeline to trigger multiple Automated ML runs. | Orange Juice Sales | AML Compute | Azure Container Instance | Scikit-learn, Pytorch | AutomatedML |
+
 | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) |  |  |  |  |  |  |
+
 | [lightgbm-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/gbdt/lightgbm/lightgbm-example.ipynb) |  |  |  |  |  |  |
+
 | [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) |  |  |  |  |  |  |
+
 | [auto-ml-continuous-retraining](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) |  |  |  |  |  |  |
+
 | [auto-ml-forecasting-beer-remote](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb) |  |  |  |  |  |  |
+
 | [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) |  |  |  |  |  |  |
+
 | [auto-ml-regression](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb) |  |  |  |  |  |  |
+
 | [build-model-run-history-03](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb) |  |  |  |  |  |  |
+
 | [deploy-to-aci-04](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb) |  |  |  |  |  |  |
+
 | [deploy-to-aks-05](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-05.ipynb) |  |  |  |  |  |  |
+
 | [ingest-data-02](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb) |  |  |  |  |  |  |
+
 | [installation-and-configuration-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb) |  |  |  |  |  |  |
+
 | [automl-databricks-local-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb) |  |  |  |  |  |  |
+
 | [automl-databricks-local-with-deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb) |  |  |  |  |  |  |
+
 | [aml-pipelines-use-databricks-as-compute-target](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb) |  |  |  |  |  |  |
+
 | [accelerated-models-object-detection](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb) |  |  |  |  |  |  |
+
 | [accelerated-models-quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb) |  |  |  |  |  |  |
+
 | [accelerated-models-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb) |  |  |  |  |  |  |
+
 | [multi-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
+
 | [register-model-deploy-local-advanced](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb) |  |  |  |  |  |  |
+
 | [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) |  |  |  |  |  |  |
+
 | [onnx-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
+
 | [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) |  |  |  |  |  |  |
+
 | [register-model-create-image-deploy-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb) |  |  |  |  |  |  |
+
 | [tensorflow-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/tensorflow/tensorflow-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
+
 | [explain-model-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb) |  |  |  |  |  |  |
+
 | [save-retrieve-explanations-run-history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb) |  |  |  |  |  |  |
+
 | [train-explain-model-locally-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb) |  |  |  |  |  |  |
+
 | [train-explain-model-on-amlcompute-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb) |  |  |  |  |  |  |
+
 | [training_notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb) |  |  |  |  |  |  |
+
 | [nyc-taxi-data-regression-model-building](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) |  |  |  |  |  |  |
+
 | [pipeline-batch-scoring](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb) |  |  |  |  |  |  |
-| [pipeline-style-transfer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb) |  |  |  |  |  |  |
+
 | [authentication-in-azureml](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) |  |  |  |  |  |  |
+
 | [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
+
 | [distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb) |  |  |  |  |  |  |
+
 | [notebook_example](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/notebook_example.ipynb) |  |  |  |  |  |  |
+
 | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master//setup-environment/configuration.ipynb) |  |  |  |  |  |  |
+
 | [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part1-training.ipynb) |  |  |  |  |  |  |
+
 | [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part2-deploy.ipynb) |  |  |  |  |  |  |
+
 | [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automated-ml.ipynb) |  |  |  |  |  |  |
+
 | [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-1st-experiment-sdk-train.ipynb) |  |  |  |  |  |  |
+
 | [tutorial-pipeline-batch-scoring-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-pipeline-batch-scoring-classification.ipynb) |  |  |  |  |  |  |

--- a/setup-environment/configuration.ipynb
+++ b/setup-environment/configuration.ipynb
@@ -102,7 +102,7 @@
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"This notebook was created using version 1.0.81 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\")"
      ]
    },