update samples from Release-169 as a part of 1.0.85 SDK release

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

README.md

@@ -20,8 +20,8 @@ If you want to...
  * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
  * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
  * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
- * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
- * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
+ * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
  * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
 
 ## Tutorials

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.79 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.85 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/README.md

@@ -9,7 +9,6 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
 * [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
-* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
 * [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
  

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

@@ -197,6 +197,17 @@ If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execut
 4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
 5) Check that you have access to the region using the Azure Portal.
 
+## import AutoMLConfig fails after upgrade from before 1.0.76 to 1.0.76 or later
+There were package changes in automated machine learning version 1.0.76, which require the previous version to be uninstalled before upgrading to the new version.
+If you have manually upgraded from a version of automated machine learning before 1.0.76 to 1.0.76 or later, you may get the error:
+`ImportError: cannot import name 'AutoMLConfig'`
+
+This can be resolved by running:
+`pip uninstall azureml-train-automl` and then 
+`pip install azureml-train-automl`
+
+The automl_setup.cmd script does this automatically.
+
 ## workspace.from_config fails
 If the call `ws = Workspace.from_config()` fails:
 1) Make sure that you have run the `configuration.ipynb` notebook successfully.

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

@@ -13,7 +13,6 @@ dependencies:
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.80
-- pyarrow>=0.11.0
 - fbprophet==0.5
 - pytorch=1.1.0
 - cudatoolkit=9.0
@@ -30,7 +29,7 @@ dependencies:
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
-  - onnxruntime==0.4.0
+  - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 
 channels:

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

@@ -14,7 +14,6 @@ dependencies:
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<0.23.0
 - py-xgboost<=0.80
-- pyarrow>=0.11.0
 - fbprophet==0.5
 - pytorch=1.1.0
 - cudatoolkit=9.0
@@ -31,7 +30,7 @@ dependencies:
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
-  - onnxruntime==0.4.0
+  - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
 
 channels:

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

@@ -92,6 +92,32 @@
         "from azureml.explain.model._internal.explanation_client import ExplanationClient"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Accessing the Azure ML workspace requires authentication with Azure.\n",
+        "\n",
+        "The default authentication is interactive authentication using the default tenant.  Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n",
+        "\n",
+        "If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
+        "\n",
+        "```\n",
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "\n",
+        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
+        "\n",
+        "```\n",
+        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
+        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -288,7 +314,7 @@
         "|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
         "| **whitelist_models** |  *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n",
         "|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
-        "|**experiment_timeout_minutes**| Maximum amount of time in minutes that all iterations combined can take before the experiment terminates.|\n",
+        "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
         "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
         "|**featurization**| 'auto' / 'off'  Indicator for whether featurization step should be done automatically or not. Note: If the input data is sparse, featurization cannot be turned on.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
@@ -306,7 +332,7 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\" : 20,\n",
+        "    \"experiment_timeout_hours\" : 0.3,\n",
         "    \"enable_early_stopping\" : True,\n",
         "    \"iteration_timeout_minutes\": 5,\n",
         "    \"max_concurrent_iterations\": 4,\n",
@@ -694,10 +720,10 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
-        "                                   entry_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",

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

@@ -2,12 +2,10 @@ name: auto-ml-classification-bank-marketing-all-features
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - onnxruntime==0.4.0
+  - interpret
+  - onnxruntime==1.0.0
   - azureml-explain-model
   - azureml-contrib-interpret

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

@@ -210,10 +210,9 @@
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'average_precision_score_weighted',\n",
-        "    \"preprocess\": True,\n",
         "    \"enable_early_stopping\": True,\n",
         "    \"max_concurrent_iterations\": 2, # This is a limit for testing purpose, please increase it as per cluster size\n",
-        "    \"experiment_timeout_minutes\": 10, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
+        "    \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "}\n",
         "\n",
@@ -305,7 +304,7 @@
       "source": [
         "#### Explain model\n",
         "\n",
-        "Automated ML models can be explained and visualized using the SDK Explainability library. [Learn how to use the explainer](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb)."
+        "Automated ML models can be explained and visualized using the SDK Explainability library. "
       ]
     },
     {
@@ -334,17 +333,7 @@
       "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"
       ]
     },
     {

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

@@ -2,10 +2,8 @@ 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
+  - interpret
+  - azureml-explain-model

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

@@ -275,7 +275,6 @@
         "automl_settings = {\n",
         "    \"experiment_timeout_minutes\": 20,\n",
         "    \"primary_metric\": 'accuracy',\n",
-        "    \"preprocess\": True,\n",
         "    \"max_concurrent_iterations\": 4, \n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"enable_dnn\": True,\n",
@@ -519,12 +518,12 @@
         "name": "anshirga"
       }
     ],
-    "datasets": [
-      "None"
-    ],
     "compute": [
       "AML Compute"
     ],
+    "datasets": [
+      "None"
+    ],
     "deployment": [
       "None"
     ],

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

@@ -3,8 +3,6 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-train-automl
-  - azureml-train
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - statsmodels
+  - azurmel-train

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

@@ -210,7 +210,24 @@
       "metadata": {},
       "source": [
         "## Data Ingestion Pipeline \n",
-        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You can replace this with your own dataset, or you can skip this pipeline if you already have a time-series based `TabularDataset`.\n",
+        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You can replace this with your own dataset, or you can skip this pipeline if you already have a time-series based `TabularDataset`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The name and target column of the Dataset to create \n",
+        "dataset = \"NOAA-Weather-DS4\"\n",
+        "target_column_name = \"temperature\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
         "\n",
         "### Upload Data Step\n",
         "The data ingestion pipeline has a single step with a script to query the latest weather data and upload it to the blob store. During the first run, the script will create and register a time-series based `TabularDataset` with the past one week of weather data. For each subsequent run, the script will create a partition in the blob store by querying NOAA for new weather data since the last modified time of the dataset (`dataset.data_changed_time`) and creating a data.csv file."
@@ -225,8 +242,6 @@
         "from azureml.pipeline.core import Pipeline, PipelineParameter\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "\n",
-        "# The name of the Dataset to create \n",
-        "dataset = \"NOAA-Weather-DS4\"\n",
         "ds_name = PipelineParameter(name=\"ds_name\", default_value=dataset)\n",
         "upload_data_step = PythonScriptStep(script_name=\"upload_weather_data.py\", \n",
         "                                         allow_reuse=False,\n",
@@ -272,7 +287,7 @@
         "## Training Pipeline\n",
         "### Prepare Training Data Step\n",
         "\n",
-        "Script to bring data into common X,y format. We need to set allow_reuse flag to False to allow the pipeline to run even when inputs don't change. We also need the name of the model to check the time the model was last trained."
+        "Script to check if new data is available since the model was last trained. If no new data is available, we cancel the remaining pipeline steps. We need to set allow_reuse flag to False to allow the pipeline to run even when inputs don't change. We also need the name of the model to check the time the model was last trained."
       ]
     },
     {
@@ -283,11 +298,8 @@
       "source": [
         "from azureml.pipeline.core import PipelineData\n",
         "\n",
-        "target_column = PipelineParameter(\"target_column\", default_value=\"y\")\n",
         "# The model name with which to register the trained model in the workspace.\n",
-        "model_name = PipelineParameter(\"model_name\", default_value=\"y\")\n",
-        "output_x = PipelineData(\"output_x\", datastore=dstor)\n",
-        "output_y = PipelineData(\"output_y\", datastore=dstor)"
+        "model_name = PipelineParameter(\"model_name\", default_value=\"noaaweatherds\")"
       ]
     },
     {
@@ -299,16 +311,23 @@
         "data_prep_step = PythonScriptStep(script_name=\"check_data.py\", \n",
         "                                         allow_reuse=False,\n",
         "                                         name=\"check_data\",\n",
-        "                                         arguments=[\"--target_column\", target_column,\n",
-        "                                                       \"--output_x\", output_x,\n",
-        "                                                       \"--output_y\", output_y,\n",
-        "                                                       \"--ds_name\", ds_name,\n",
-        "                                                       \"--model_name\", model_name],\n",
-        "                                         outputs=[output_x, output_y], \n",
+        "                                         arguments=[\"--ds_name\", ds_name,\n",
+        "                                                    \"--model_name\", model_name],\n",
         "                                         compute_target=compute_target, \n",
         "                                         runconfig=conda_run_config)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "train_ds = Dataset.get_by_name(ws, dataset)\n",
+        "train_ds = train_ds.drop_columns([\"partition_date\"])"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -324,14 +343,13 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.runtime import AutoMLStep\n",
+        "from azureml.train.automl import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 20,\n",
-        "    \"experiment_timeout_minutes\": 30,\n",
+        "    \"iteration_timeout_minutes\": 10,\n",
+        "    \"experiment_timeout_hours\": 0.2,\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
-        "    \"preprocess\": True,\n",
         "    \"max_concurrent_iterations\": 3,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",
@@ -342,8 +360,8 @@
         "                             debug_log = 'automl_errors.log',\n",
         "                             path = \".\",\n",
         "                             compute_target=compute_target,\n",
-        "                             run_configuration=conda_run_config,\n",
-        "                             data_script = \"get_data.py\",\n",
+        "                             training_data = train_ds,\n",
+        "                             label_column_name = target_column_name,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -359,7 +377,7 @@
         "metrics_output_name = 'metrics_output'\n",
         "best_model_output_name = 'best_model_output'\n",
         "\n",
-        "metirics_data = PipelineData(name='metrics_data',\n",
+        "metrics_data = PipelineData(name='metrics_data',\n",
         "                           datastore=dstor,\n",
         "                           pipeline_output_name=metrics_output_name,\n",
         "                           training_output=TrainingOutput(type='Metrics'))\n",
@@ -378,8 +396,7 @@
         "automl_step = AutoMLStep(\n",
         "    name='automl_module',\n",
         "    automl_config=automl_config,\n",
-        "    inputs=[output_x, output_y],\n",
-        "    outputs=[metirics_data, model_data],\n",
+        "    outputs=[metrics_data, model_data],\n",
         "    allow_reuse=False)"
       ]
     },
@@ -432,7 +449,7 @@
       "outputs": [],
       "source": [
         "training_pipeline_run = experiment.submit(training_pipeline, pipeline_parameters={\n",
-        "        \"target_column\": \"temperature\", \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
+        "        \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
       ]
     },
     {
@@ -475,7 +492,7 @@
       "source": [
         "from azureml.pipeline.core import Schedule\n",
         "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule\",\n",
-        "                           pipeline_parameters={\"target_column\": \"temperature\",\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
+        "                           pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
         "                           pipeline_id=published_pipeline.id, \n",
         "                           experiment_name=experiment_name, \n",
         "                           datastore=dstor,\n",

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

@@ -3,7 +3,6 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-train-automl
-  - azureml-pipeline
   - azureml-widgets
   - matplotlib
-  - pandas_ml
+  - azureml-pipeline

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

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

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

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

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

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

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

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

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

@@ -5,8 +5,6 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-train-automl
-  - azureml-train
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - statsmodels
+  - azureml-train

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py

@@ -76,9 +76,12 @@ def get_result_df(remote_run):
 def run_inference(test_experiment, compute_target, script_folder, train_run,
                   test_dataset, lookback_dataset, max_horizon,
                   target_column_name, time_column_name, freq):
-    train_run.download_file('outputs/model.pkl', 'inference/model.pkl')
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml',
-                            'inference/condafile.yml')
+    model_base_name = 'model.pkl'
+    if 'model_data_location' in train_run.properties:
+        model_location = train_run.properties['model_data_location']
+        _, model_base_name = model_location.rsplit('/', 1)
+    train_run.download_file('outputs/{}'.format(model_base_name), 'inference/{}'.format(model_base_name))
+    train_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/condafile.yml')
 
     inference_env = Environment("myenv")
     inference_env.docker.enabled = True
@@ -91,7 +94,8 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--max_horizon': max_horizon,
                         '--target_column_name': target_column_name,
                         '--time_column_name': time_column_name,
-                        '--frequency': freq
+                        '--frequency': freq,
+                        '--model_path': model_base_name
                     },
                     inputs=[test_dataset.as_named_input('test_data'),
                             lookback_dataset.as_named_input('lookback_data')],

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py

@@ -232,6 +232,9 @@ parser.add_argument(
 parser.add_argument(
     '--frequency', type=str, dest='freq',
     help='Frequency of prediction')
+parser.add_argument(
+    '--model_path', type=str, dest='model_path',
+    default='model.pkl', help='Filename of model to be loaded')
 
 
 args = parser.parse_args()
@@ -239,6 +242,7 @@ max_horizon = args.max_horizon
 target_column_name = args.target_column_name
 time_column_name = args.time_column_name
 freq = args.freq
+model_path = args.model_path
 
 
 print('args passed are: ')
@@ -246,6 +250,7 @@ print(max_horizon)
 print(target_column_name)
 print(time_column_name)
 print(freq)
+print(model_path)
 
 run = Run.get_context()
 # get input dataset by name
@@ -267,7 +272,8 @@ X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name])
 y_lookback_df = lookback_dataset.with_timestamp_columns(
     None).keep_columns(columns=[target_column_name])
 
-fitted_model = joblib.load('model.pkl')
+fitted_model = joblib.load(model_path)
+
 
 if hasattr(fitted_model, 'get_lookback'):
     lookback = fitted_model.get_lookback()

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",

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

@@ -7,5 +7,3 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - statsmodels

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

@@ -253,7 +253,7 @@
       "source": [
         "# split into train based on time\n",
         "train = dataset.time_before(datetime(2017, 8, 8, 5), include_boundary=True)\n",
-        "train.to_pandas_dataframe().sort_values(time_column_name).tail(5).reset_index(drop=True)"
+        "train.to_pandas_dataframe().reset_index(drop=True).sort_values(time_column_name).tail(5)"
       ]
     },
     {
@@ -264,7 +264,7 @@
       "source": [
         "# split into test based on time\n",
         "test = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n",
-        "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
+        "test.to_pandas_dataframe().reset_index(drop=True).head(5)"
       ]
     },
     {
@@ -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",

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

@@ -2,11 +2,9 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - statsmodels
+  - interpret
   - azureml-explain-model
   - azureml-contrib-interpret

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

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

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

@@ -3,8 +3,6 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-train-automl
-  - azureml-pipeline
   - azureml-widgets
-  - pandas_ml
-  - statsmodels
   - matplotlib
+  - azureml-pipeline

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

@@ -30,11 +30,11 @@ def _get_configs(automlconfig: AutoMLConfig,
     groups = _get_groups(data, group_column_names)
     configs = {}
     for i, group in groups.iterrows():
-        single = data
+        single = data._dataflow
         group_name = "#####".join(str(x) for x in group.values)
         group_name = valid_chars.sub('', group_name)
         for key in group.index:
-            single = single._dataflow.filter(data._dataflow[key] == group[key])
+            single = single.filter(data._dataflow[key] == group[key])
         t_dataset = TabularDataset._create(single)
         group_conf = copy.deepcopy(automlconfig)
         group_conf.user_settings['training_data'] = t_dataset
@@ -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)
 

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

@@ -1,9 +1,9 @@
 import argparse
 import json
 
-from azureml.core import Run, Model, Workspace
-from azureml.core.conda_dependencies import CondaDependencies
+from azureml.core import Run, Model
 from azureml.core.model import InferenceConfig
+from azureml.core.environment import Environment
 from azureml.core.webservice import AciWebservice
 
 
@@ -39,6 +39,8 @@ print(model_list)
 run = Run.get_context()
 ws = run.experiment.workspace
 
+myenv = Environment.from_conda_specification(name="env", file_path=conda_env_file_name)
+
 deployment_config = AciWebservice.deploy_configuration(
     cpu_cores=1,
     memory_gb=2,
@@ -46,11 +48,7 @@ deployment_config = AciWebservice.deploy_configuration(
     description='grouping demo aci deployment'
 )
 
-inference_config = InferenceConfig(
-    entry_script=script_file_name,
-    runtime='python',
-    conda_file=conda_env_file_name
-)
+inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)
 
 models = []
 for model_name in model_list:

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",

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

@@ -6,6 +6,4 @@ dependencies:
   - azureml-sdk
   - azureml-train-automl
   - azureml-widgets
-  - pandas_ml
-  - statsmodels
   - matplotlib

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

@@ -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",
@@ -631,9 +631,7 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "# The request data frame needs to have y_query column which corresponds to query.\n",
         "X_query = X_test.copy()\n",
-        "X_query['y_query'] = np.NaN\n",
         "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",

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

@@ -7,5 +7,3 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - statsmodels

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

@@ -155,8 +155,7 @@
         "automl_settings = {\n",
         "    \"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 +259,7 @@
       "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"
       ]
     },
     {

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

@@ -2,10 +2,8 @@ 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
+  - interpret
+  - azureml-explain-model

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

@@ -206,9 +206,9 @@
         "|-|-|\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",
+        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|"
@@ -244,7 +244,7 @@
       "source": [
         "featurization_config = FeaturizationConfig()\n",
         "featurization_config.blocked_transformers = ['LabelEncoder']\n",
-        "#featurization_config.drop_columns = ['ERP', 'MMIN']\n",
+        "#featurization_config.drop_columns = ['MMIN']\n",
         "featurization_config.add_column_purpose('MYCT', 'Numeric')\n",
         "featurization_config.add_column_purpose('VendorName', 'CategoricalHash')\n",
         "#default strategy mean, add transformer param for for 3 columns\n",
@@ -262,7 +262,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_minutes\" : 10,\n",
+        "    \"experiment_timeout_hours\" : 0.2,\n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"n_cross_validations\": 5,\n",
@@ -558,7 +558,6 @@
         "\n",
         "# specify CondaDependencies obj\n",
         "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
-        "    conda_packages=['scikit-learn', 'numpy','py-xgboost<=0.80'],\n",
         "    pip_packages=azureml_pip_packages)"
       ]
     },
@@ -718,17 +717,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-explain-model', 'azureml-train-automl', 'azureml-defaults'\n",
-        "]\n",
-        " \n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas', 'numpy', 'py-xgboost<=0.80'],\n",
-        "                                 pip_packages=azureml_pip_packages,\n",
-        "                                 pin_sdk_version=True)\n",
+        "myenv = automl_run.get_environment().python.conda_dependencies\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",

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

@@ -2,12 +2,10 @@ 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
+  - interpret
+  - azureml-explain-model
   - azureml-explain-model
   - azureml-contrib-interpret

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

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

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

@@ -188,15 +188,18 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "automlconfig-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
-        "    \"preprocess\": True,\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_minutes\": 20, #for real scenarios we reccommend a timeout of at least one hour \n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",

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

@@ -5,5 +5,3 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - pandas_ml
-  - paramiko<2.5.0

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

@@ -56,7 +56,7 @@ CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]
     @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.
     @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.
     @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. 
-    @experiment_timeout_minutes INT = 60,            -- The maximum time in minutes for training all pipelines.
+    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.
     @n_cross_validations INT = 3,                    -- The number of cross validations.
     @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.
                                                      -- The list of possible models can be found at:
@@ -131,8 +131,8 @@ if __name__.startswith("sqlindb"):
 
     X_train = data_train
 
-    if experiment_timeout_minutes == 0:
-        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

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,

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

@@ -11,6 +11,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register Azure Databricks trained model and deploy it to ACI\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -161,9 +168,9 @@
       "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 +184,9 @@
         "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 +201,16 @@
         "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)"
@@ -255,6 +272,15 @@
         "myservice.delete()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploying to other types of computes\n",
+        "\n",
+        "In order to learn how to deploy to other types of compute targets, such as AKS, please take a look at the set of notebooks in the [deployment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment) folder."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -1,312 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This notebook uses image from ACI notebook for deploying to AKS."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Set auth to be used by workspace related APIs.\n",
-        "# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
-        "# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
-        "auth = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Register the model\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model_name = \"AdultCensus_runHistory_aks.mml\" # \n",
-        "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
-        "\n",
-        "print(\"copy model from dbfs to local\")\n",
-        "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
-        "dbutils.fs.cp(model_name, model_local, True)\n",
-        "\n",
-        "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
-        "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
-        "                       description = \"ADB trained model by Parashar\",\n",
-        "                       workspace = ws)\n",
-        "\n",
-        "print(mymodel.name, mymodel.description, mymodel.version)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#%%writefile score_sparkml.py\n",
-        "score_sparkml = \"\"\"\n",
-        " \n",
-        "import json\n",
-        " \n",
-        "def init():\n",
-        "    # One-time initialization of PySpark and predictive model\n",
-        "    import pyspark\n",
-        "    from azureml.core.model import Model\n",
-        "    from pyspark.ml import PipelineModel\n",
-        " \n",
-        "    global trainedModel\n",
-        "    global spark\n",
-        " \n",
-        "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
-        "    model_name = \"{model_name}\" #interpolated\n",
-        "    model_path = Model.get_model_path(model_name)\n",
-        "    trainedModel = PipelineModel.load(model_path)\n",
-        "    \n",
-        "def run(input_json):\n",
-        "    if isinstance(trainedModel, Exception):\n",
-        "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
-        "      \n",
-        "    try:\n",
-        "        sc = spark.sparkContext\n",
-        "        input_list = json.loads(input_json)\n",
-        "        input_rdd = sc.parallelize(input_list)\n",
-        "        input_df = spark.read.json(input_rdd)\n",
-        "    \n",
-        "        # Compute prediction\n",
-        "        prediction = trainedModel.transform(input_df)\n",
-        "        #result = prediction.first().prediction\n",
-        "        predictions = prediction.collect()\n",
-        " \n",
-        "        #Get each scored result\n",
-        "        preds = [str(x['prediction']) for x in predictions]\n",
-        "        result = \",\".join(preds)\n",
-        "        # you can return any data type as long as it is JSON-serializable\n",
-        "        return result.tolist()\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return result\n",
-        "    \n",
-        "\"\"\".format(model_name=model_name)\n",
-        " \n",
-        "exec(score_sparkml)\n",
-        " \n",
-        "with open(\"score_sparkml.py\", \"w\") as file:\n",
-        "    file.write(score_sparkml)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "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",
-        "\n",
-        "with open(\"mydeployenv.yml\",\"w\") as f:\n",
-        "    f.write(myacienv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#create AKS compute\n",
-        "#it may take 20-25 minutes to create a new cluster\n",
-        "\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "aks_name = 'ps-aks-demo2' \n",
-        "\n",
-        "try:\n",
-        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
-        "    print('Found existing cluster, use it.')\n",
-        "except ComputeTargetException:\n",
-        "    # Use the default configuration (can also provide parameters to customize)\n",
-        "    prov_config = AksCompute.provisioning_configuration()\n",
-        "    \n",
-        "    # Create the cluster\n",
-        "    aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)\n",
-        "\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#deploy to AKS\n",
-        "from azureml.core.webservice import AksWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "from azureml.core.model import InferenceConfig\n",
-        "\n",
-        "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)\n",
-        "\n",
-        "service_name = 'ps-aks-service'\n",
-        "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "inference_config = InferenceConfig(runtime = 'spark-py', \n",
-        "                                   entry_script ='score_sparkml.py',\n",
-        "                                   conda_file ='mydeployenv.yml')\n",
-        "\n",
-        "aks_service = Model.deploy(ws, service_name, [mymodel], inference_config, aks_config, aks_target)\n",
-        "aks_service.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.deployment_status"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#for using the Web HTTP API \n",
-        "print(aks_service.scoring_uri)\n",
-        "print(aks_service.get_keys())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "#get the some sample data\n",
-        "test_data_path = \"AdultCensusIncomeTest\"\n",
-        "test = spark.read.parquet(test_data_path).limit(5)\n",
-        "\n",
-        "test_json = json.dumps(test.toJSON().collect())\n",
-        "\n",
-        "print(test_json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
-        "aks_service.run(input_data=test_json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#comment to not delete the web service\n",
-        "aks_service.delete()\n",
-        "#model.delete()\n",
-        "aks_target.delete() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-existingimage-05.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "pasha"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    },
-    "name": "deploy-to-aks-existingimage-05",
-    "notebookId": 1030695628045968
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

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",

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"
       ]
     },
     {

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

@@ -20,7 +20,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Register model and deploy as webservice\n",
+        "# Register model and deploy as webservice in ACI\n",
         "\n",
         "Following this notebook, you will:\n",
         "\n",
@@ -45,6 +45,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
+        "\n",
         "# Check core SDK version number.\n",
         "print('SDK version:', azureml.core.VERSION)"
       ]
@@ -70,6 +71,7 @@
       "source": [
         "from azureml.core import Workspace\n",
         "\n",
+        "\n",
         "ws = Workspace.from_config()\n",
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
@@ -91,6 +93,7 @@
       "source": [
         "from azureml.core import Dataset\n",
         "\n",
+        "\n",
         "datastore = ws.get_default_datastore()\n",
         "datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
         "                       target_path='sklearn_regression/',\n",
@@ -125,6 +128,7 @@
         "from azureml.core import Model\n",
         "from azureml.core.resource_configuration import ResourceConfiguration\n",
         "\n",
+        "\n",
         "model = Model.register(workspace=ws,\n",
         "                       model_name='my-sklearn-model',                # Name of the registered model in your workspace.\n",
         "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
@@ -159,6 +163,8 @@
         "\n",
         "The Azure Machine Learning service provides a default environment for supported model frameworks, including scikit-learn, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n",
         "\n",
+        "Even when you deploy your model to ACI with a default environment you can still customize the deploy configuration (i.e. the number of cores and amount of memory made available for the deployment) using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--). Look at the \"Use a custom environment\" section of this notebook for more information on deploy configuration.\n",
+        "\n",
         "**Note**: This step can take several minutes."
       ]
     },
@@ -171,6 +177,7 @@
         "from azureml.core import Webservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "\n",
+        "\n",
         "service_name = 'my-sklearn-service'\n",
         "\n",
         "# Remove any existing service under the same name.\n",
@@ -198,6 +205,7 @@
       "source": [
         "import json\n",
         "\n",
+        "\n",
         "input_payload = json.dumps({\n",
         "    'data': [\n",
         "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
@@ -231,9 +239,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Use a custom environment (for all models)\n",
+        "### Use a custom environment\n",
         "\n",
-        "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method.\n",
+        "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method. Custom environments can be used for any model you want to deploy.\n",
         "\n",
         "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
       ]
@@ -247,6 +255,7 @@
         "from azureml.core import Environment\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
+        "\n",
         "environment = Environment('my-sklearn-environment')\n",
         "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
@@ -278,7 +287,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-).\n",
+        "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). In this case we are also using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--) method to generate a custom deploy configuration.\n",
         "\n",
         "**Note**: This step can take several minutes."
       ]
@@ -288,15 +297,18 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "azuremlexception-remarks-sample"
+          "azuremlexception-remarks-sample",
+          "sample-aciwebservice-deploy-config"
         ]
       },
       "outputs": [],
       "source": [
         "from azureml.core import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import AciWebservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "\n",
+        "\n",
         "service_name = 'my-custom-env-service'\n",
         "\n",
         "# Remove any existing service under the same name.\n",
@@ -305,11 +317,14 @@
         "except WebserviceException:\n",
         "    pass\n",
         "\n",
-        "inference_config = InferenceConfig(entry_script='score.py',\n",
-        "                                   source_directory='.',\n",
-        "                                   environment=environment)\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
         "\n",
-        "service = Model.deploy(ws, service_name, [model], inference_config)\n",
+        "service = Model.deploy(workspace=ws,\n",
+        "                       name=service_name,\n",
+        "                       models=[model],\n",
+        "                       inference_config=inference_config,\n",
+        "                       deployment_config=aci_config)\n",
         "service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -328,6 +343,7 @@
       "source": [
         "import json\n",
         "\n",
+        "\n",
         "input_payload = json.dumps({\n",
         "    'data': [\n",
         "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",

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"
       ]
     },
     {

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)"
       ]
     },
     {

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()"
       ]
     }
   ],

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()"
       ]
     },
     {

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()"
       ]
     },
     {

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()"
       ]
     }
   ],

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()"
       ]
     }
   ],

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

@@ -318,7 +318,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-deploy-to-aks"
+        ]
+      },
       "outputs": [],
       "source": [
         "# Set the web service configuration (using default here)\n",
@@ -331,7 +335,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-deploy-to-aks"
+        ]
+      },
       "outputs": [],
       "source": [
         "%%time\n",

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

@@ -1,457 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register Model, Create Image and Deploy Service\n",
-        "\n",
-        "This example shows how to deploy a web service in step-by-step fashion:\n",
-        "\n",
-        " 1. Register model\n",
-        " 2. Query versions of models and select one to deploy\n",
-        " 3. Create Docker image\n",
-        " 4. Query versions of images\n",
-        " 5. Deploy the image as web service\n",
-        " \n",
-        "**IMPORTANT**:\n",
-        " * This notebook requires you to first complete [train-within-notebook](../../training/train-within-notebook/train-within-notebook.ipynb) example\n",
-        " \n",
-        "The train-within-notebook example taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
-        "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "import sklearn\n",
-        "\n",
-        "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
-        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "regression_models = Model.list(workspace=ws, tags=['area'])\n",
-        "for m in regression_models:\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can pick a specific model to deploy"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(model.name, model.description, model.version, sep = '\\t')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Docker Image"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import os\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "# note you can pass in multiple rows for scoring\n",
-        "def run(raw_data):\n",
-        "    try:\n",
-        "        data = json.loads(raw_data)['data']\n",
-        "        data = numpy.array(data)\n",
-        "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
-        "        return result.tolist()\n",
-        "    except Exception as e:\n",
-        "        error = str(e)\n",
-        "        return error"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that following command can take few minutes. \n",
-        "\n",
-        "You can add tags and descriptions to images. Also, an image can contain multiple models."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image",
-          "sample-image-create"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import Image, ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                 execution_script=\"score.py\",\n",
-        "                                 conda_file=\"myenv.yml\",\n",
-        "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                                 description = \"Image with ridge regression model\")\n",
-        "\n",
-        "image = Image.create(name = \"myimage1\",\n",
-        "                     # this is the model object. note you can pass in 0-n models via this list-type parameter\n",
-        "                     # in case you need to reference multiple models, or none at all, in your scoring script.\n",
-        "                     models = [model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use a custom Docker image\n",
-        "\n",
-        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
-        "\n",
-        "Only Supported for `ContainerImage`(from azureml.core.image) with `python` runtime.\n",
-        "```python\n",
-        "# use an image available in public Container Registry without authentication\n",
-        "image_config.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
-        "\n",
-        "# or, use an image available in a private Container Registry\n",
-        "image_config.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
-        "image_config.base_image_registry.address = \"myregistry.azurecr.io\"\n",
-        "image_config.base_image_registry.username = \"username\"\n",
-        "image_config.base_image_registry.password = \"password\"\n",
-        "\n",
-        "# or, use an image built during training.\n",
-        "image_config.base_image = run.properties[\"AzureML.DerivedImageName\"]\n",
-        "```\n",
-        "You can get the address of training image from the properties of a Run object. Only new runs submitted with azureml-sdk>=1.0.22 to AMLCompute targets will have the 'AzureML.DerivedImageName' property. Instructions on how to get a Run can be found in [manage-runs](../../training/manage-runs/manage-runs.ipynb). \n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "List images by tag and find out the detailed build log for debugging."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
-        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy image as web service on Azure Container Instance\n",
-        "\n",
-        "Note that the service creation can take few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci",
-          "sample-aciwebservice-deploy-config"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
-        "                                               description = 'Predict diabetes using regression model')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci",
-          "sample-aciwebservice-deploy-from-image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'my-aci-service-2'\n",
-        "print(aci_service_name)\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test web service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the web service with some dummy input data to get a prediction."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-        "\n",
-        "prediction = aci_service.run(input_data=test_sample)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete ACI to clean up"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "aci_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "aashishb"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -1,8 +0,0 @@
-name: register-model-create-image-deploy-service
-dependencies:
-- pip:
-  - azureml-sdk
-  - matplotlib
-  - tqdm
-  - scipy
-  - sklearn

how-to-use-azureml/deployment/spark/iris.model/metadata/part-00000

@@ -0,0 +1 @@
+{"class":"org.apache.spark.ml.classification.LogisticRegressionModel","timestamp":1570147252329,"sparkVersion":"2.4.0","uid":"LogisticRegression_5df3978caaf3","paramMap":{"regParam":0.01},"defaultParamMap":{"aggregationDepth":2,"threshold":0.5,"rawPredictionCol":"rawPrediction","featuresCol":"features","labelCol":"label","predictionCol":"prediction","family":"auto","regParam":0.0,"tol":1.0E-6,"probabilityCol":"probability","standardization":true,"elasticNetParam":0.0,"maxIter":100,"fitIntercept":true}}

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

@@ -0,0 +1,343 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register Spark Model and deploy as Webservice\n",
+        "\n",
+        "This example shows how to deploy a Webservice in step-by-step fashion:\n",
+        "\n",
+        " 1. Register Spark Model\n",
+        " 2. Deploy Spark Model as Webservice"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can add tags and descriptions to your Models. Note you need to have a `iris.model` file in the current directory. This model file is generated using [train in spark](../training/train-in-spark/train-in-spark.ipynb) notebook. The below call registers that file as a Model with the same name `iris.model` in the workspace.\n",
+        "\n",
+        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from file"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "model = Model.register(model_path=\"iris.model\",\n",
+        "                       model_name=\"iris.model\",\n",
+        "                       tags={'type': \"regression\"},\n",
+        "                       description=\"Logistic regression model to predict iris species\",\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Fetch Environment"
+      ]
+    },
+    {
+      "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.\n",
+        "\n",
+        "In this notebook, we will be using 'AzureML-PySpark-MmlSpark-0.15', a curated environment.\n",
+        "\n",
+        "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 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 is relative path to the source_directory path.\n",
+        "\n",
+        "Sample code for using a source directory:\n",
+        "\n",
+        "```python\n",
+        "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
+        "                                   entry_script=\"x/y/score.py\",\n",
+        "                                   environment=environment)\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",
+        " - entry_script = contains logic specific to initializing your model and running predictions\n",
+        " - environment = An environment object to use for the deployment. Doesn't have to be registered"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create image"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy Model as Webservice on Azure Container Instance\n",
+        "\n",
+        "Note that the service creation can take few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "azuremlexception-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
+        "\n",
+        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
+        "aci_service_name = 'aciservice1'\n",
+        "\n",
+        "try:\n",
+        "    # if you want to get existing service below is the command\n",
+        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
+        "    # we use aci_service_name to create azure aci\n",
+        "    service = Webservice(ws, name=aci_service_name)\n",
+        "    if service:\n",
+        "        service.delete()\n",
+        "except WebserviceException as e:\n",
+        "    print()\n",
+        "\n",
+        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
+        "\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Test web service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\n",
+        "\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Delete ACI to clean up"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Profiling\n",
+        "\n",
+        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
+        "\n",
+        "```python\n",
+        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
+        "profile.wait_for_profiling(True)\n",
+        "profiling_results = profile.get_results()\n",
+        "print(profiling_results)\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Packaging\n",
+        "\n",
+        "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
+        "\n",
+        "You must include an Environment object in your inference configuration to use `Model.package()`.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config)\n",
+        "package.wait_for_creation(show_output=True)  # Or show_output=False to hide the Docker build logs.\n",
+        "package.pull()\n",
+        "```\n",
+        "\n",
+        "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
+        "package.wait_for_creation(show_output=True)\n",
+        "package.save(\"./local_context_dir\")\n",
+        "```"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "aashishb"
+      }
+    ],
+    "category": "deployment",
+    "compute": [
+      "None"
+    ],
+    "datasets": [
+      "Iris"
+    ],
+    "deployment": [
+      "Azure Container Instance"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "PySpark"
+    ],
+    "friendly_name": "Register Spark model and deploy as webservice",
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.yml

@@ -0,0 +1,4 @@
+name: model-register-and-deploy-spark
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/spark/score.py

@@ -0,0 +1,37 @@
+import traceback
+from pyspark.ml.linalg import VectorUDT
+from azureml.core.model import Model
+from pyspark.ml.classification import LogisticRegressionModel
+from pyspark.sql.types import StructType, StructField
+from pyspark.sql.types import DoubleType
+from pyspark.sql import SQLContext
+from pyspark import SparkContext
+
+sc = SparkContext.getOrCreate()
+sqlContext = SQLContext(sc)
+spark = sqlContext.sparkSession
+
+input_schema = StructType([StructField("features", VectorUDT()), StructField("label", DoubleType())])
+reader = spark.read
+reader.schema(input_schema)
+
+
+def init():
+    global model
+    # note here "iris.model" is the name of the model registered under the workspace
+    # this call should return the path to the model.pkl file on the local disk.
+    model_path = Model.get_model_path('iris.model')
+    # Load the model file back into a LogisticRegression model
+    model = LogisticRegressionModel.load(model_path)
+
+
+def run(data):
+    try:
+        input_df = reader.json(sc.parallelize([data]))
+        result = model.transform(input_df)
+        # you can return any datatype as long as it is JSON-serializable
+        return result.collect()[0]['prediction']
+    except Exception as e:
+        traceback.print_exc()
+        error = str(e)
+        return error

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",

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

@@ -43,5 +43,7 @@ In this directory, there are two types of notebooks:
 1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score): This notebook demonstrates how to run a batch scoring job using Azure Machine Learning pipelines.
 2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans): This notebook demonstrates a multi-step pipeline that uses GPU compute. This sample also showcases how to use conda dependencies using runconfig when using Pipelines.
 3. [nyc-taxi-data-regression-model-building.ipynb](https://aka.ms/pl-nyctaxi-tutorial): This notebook is an AzureML Pipelines version of the previously published two part sample.
+4. [file-dataset-image-inference-mnist.ipynb](https://aka.ms/pl-pr-filedata): This notebook demonstrates how to use ParallelRunStep to process unstructured data (file dataset).
+5. [tabular-dataset-inference-iris.ipynb](https://aka.ms/pl-pr-tabulardata): This notebook demonstrates how to use ParallelRunStep to process structured data (tabular dataset).
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/README.png)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/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)"
       ]
     },
     {
@@ -620,14 +620,13 @@
       "outputs": [],
       "source": [
         "%%time\n",
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.model import Model, InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
-        "                                   entry_script = \"score.py\",\n",
-        "                                   conda_file = \"myenv.yml\")\n",
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"env\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",

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

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

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

@@ -230,7 +230,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "endpoint_list = PipelineEndpoint.get_all(workspace=ws, active_only=True)\n",
+        "endpoint_list = PipelineEndpoint.list(workspace=ws, active_only=True)\n",
         "endpoint_list"
       ]
     },
@@ -360,7 +360,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "versions = pipeline_endpoint_by_name.get_all_versions()\n",
+        "versions = pipeline_endpoint_by_name.list_versions()\n",
         "\n",
         "for ve in versions:\n",
         "    print(ve.version)\n",
@@ -381,7 +381,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "pipelines = pipeline_endpoint_by_name.get_all_pipelines(active_only=True)\n",
+        "pipelines = pipeline_endpoint_by_name.list_pipelines(active_only=True)\n",
         "pipelines"
       ]
     },

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

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

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

@@ -0,0 +1,433 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Azure Machine Learning Pipeline with NotebookRunnerStep\n",
+        "This notebook demonstrates the use of `NotebookRunnerStep`. It allows you to run a local notebook as a step in Azure Machine Learning Pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we showcase how you can run another notebook `notebook_runner/training_notebook.ipynb` as a step in Azure Machine Learning Pipeline.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Create or Attach existing AmlCompute to a workspace.\n",
+        "3. Configure NotebookRun using `NotebokRunConfig`.\n",
+        "5. Use NotebookRunnerStep.\n",
+        "6. Run the notebook on `AmlCompute` as a pipeline step consuming the output of a python script step.\n",
+        "\n",
+        "Advantages of running your notebook as a step in pipeline:\n",
+        "1. Run your notebook like a python script without converting into .py files, leveraging complete end to end experience of Azure Machine Learning Pipelines.\n",
+        "2. Use pipeline intermediate data to and from the notebook along with other steps in pipeline.\n",
+        "3. Parameterize your notebook with [Pipeline Parameters](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb).\n",
+        "\n",
+        "Try some more [quick start notebooks](https://github.com/microsoft/recommenders/tree/master/notebooks/00_quick_start) with `NotebookRunnerStep`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Azure Machine Learning and Pipeline SDK-specific imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "import azureml.core\n",
+        "\n",
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.data.data_reference import DataReference\n",
+        "from azureml.pipeline.core import PipelineData\n",
+        "from azureml.core.datastore import Datastore\n",
+        "\n",
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.contrib.notebook import NotebookRunConfig, AzureMLNotebookHandler\n",
+        "\n",
+        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "from azureml.contrib.notebook import NotebookRunnerStep\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Initialize Workspace\n",
+        "\n",
+        "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
+        "ws.set_default_datastore(\"workspaceblobstore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Upload data to datastore"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Datastore.get(ws, \"workspaceblobstore\").upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)\n",
+        "print(\"Upload call completed\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create an Azure ML experiment\n",
+        "Let's create an experiment named \"notebook-step-run-example\" and a folder to holding the notebook and other scripts. The script runs will be recorded under the experiment in Azure.\n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = 'notebook-step-run-example'\n",
+        "source_directory = 'notebook_runner'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach an AmlCompute cluster\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "    \n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 4)\n",
+        "\n",
+        "    # Create the cluster.\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "    \n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = 1, timeout_in_minutes = 10)\n",
+        "    \n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a new RunConfig object"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk'], pin_sdk_version=False)\n",
+        "conda_run_config.environment.python.conda_dependencies = cd\n",
+        "\n",
+        "print('run config is ready')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define input and outputs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "input_data = DataReference(\n",
+        "    datastore=Datastore.get(ws, \"workspaceblobstore\"),\n",
+        "    data_reference_name=\"blob_test_data\",\n",
+        "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
+        "\n",
+        "output_data = PipelineData(name=\"processed_data\",\n",
+        "                           datastore=Datastore.get(ws, \"workspaceblobstore\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create notebook run configuration and set parameters values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "handler = AzureMLNotebookHandler(timeout=600, progress_bar=False, log_output=True)\n",
+        "\n",
+        "cfg = NotebookRunConfig(source_directory=source_directory, notebook=\"training_notebook.ipynb\",\n",
+        "                        handler = handler,\n",
+        "                        parameters={\"arg1\": \"Machine Learning\"},\n",
+        "                        run_config=conda_run_config)\n",
+        "\n",
+        "print(\"Notebook Run Config is created.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define PythonScriptStep"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print('Source directory for the step is {}.'.format(os.path.realpath('./train')))\n",
+        "python_script_step = PythonScriptStep(\n",
+        "                         script_name=\"train.py\",\n",
+        "                         arguments=[\"--input_data\", input_data],\n",
+        "                         inputs=[input_data],\n",
+        "                         outputs=[output_data],\n",
+        "                         compute_target=compute_target, \n",
+        "                         source_directory=\"./train\",\n",
+        "                         allow_reuse=True)\n",
+        "print(\"python_script_step created\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define NotebookRunnerStep\n",
+        "\n",
+        "This step will consume intermediate output produced by `python_script_step` as an input.\n",
+        "\n",
+        "Optionally, a output of type `output_notebook_pipeline_data_name` can be added to the `NotebookRunnerStep` to redirect the `output_notebook` of notebook run to `NotebookRunnerStep`'s step output produced as `PipelineData` and can be further passed along the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineParameter, TrainingOutput\n",
+        "\n",
+        "output_from_notebook = PipelineData(name=\"notebook_processed_data\",\n",
+        "                                    datastore=Datastore.get(ws, \"workspaceblobstore\"))\n",
+        "\n",
+        "my_pipeline_param = PipelineParameter(name=\"pipeline_param\", default_value=\"my_param\")\n",
+        "\n",
+        "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n",
+        "notebook_runner_step = NotebookRunnerStep(name=\"training_notebook_step\",\n",
+        "                                          notebook_run_config=cfg,\n",
+        "                                          params={\"my_pipeline_param\": my_pipeline_param},\n",
+        "                                          inputs=[output_data],\n",
+        "                                          outputs=[output_from_notebook],\n",
+        "                                          allow_reuse=True,\n",
+        "                                          compute_target=compute_target,\n",
+        "                                          output_notebook_pipeline_data_name=\"notebook_result\")\n",
+        "\n",
+        "print(\"Notebook Runner Step is Created.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Build Pipeline\n",
+        "\n",
+        "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n",
+        "\n",
+        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit-experiment-name--pipeline-parameters-none--continue-on-step-failure-false--regenerate-outputs-false--parent-run-id-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline1 = Pipeline(workspace=ws, steps=[notebook_runner_step])\n",
+        "print(\"Pipeline creation complete\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run1 = experiment.submit(pipeline1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(pipeline_run1).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download output notebook\n",
+        "\n",
+        "`output_notebook` can be retrieved via pipeline step output if `output_notebook_pipeline_data_name` is provided to the `NotebookRunnerStep`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run1.wait_for_completion()\n",
+        "train_step = pipeline_run1.find_step_run('training_notebook_step') # Retrieve the step runs by name `train.py`\n",
+        "\n",
+        "if train_step:\n",
+        "    train_step_obj = train_step[0] # since we have only one step by name `training_notebook_step`\n",
+        "    train_step_obj.get_output_data('notebook_result').download(source_directory) # download the output to source_directory"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sanpil"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "Custom"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML"
+    ],
+    "friendly_name": "How to use run a notebook as a step in AML Pipelines",
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "order_index": 12,
+    "star_tag": [
+      "None"
+    ],
+    "tags": [
+      "None"
+    ],
+    "task": "Demonstrates the use of NotebookRunnerStep"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

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

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

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

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

@@ -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)

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

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

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

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

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

@@ -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

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

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

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

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

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

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

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

@@ -1,28 +1,17 @@
-# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
 import argparse
 import os
 import sys
 import re
-
+import json
+import traceback
 from PIL import Image
+
 import torch
 from torchvision import transforms
 
+from azureml.core.model import Model
 
-def load_image(filename, size=None, scale=None):
-    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):
@@ -125,60 +114,59 @@ class UpsampleConvLayer(torch.nn.Module):
         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

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",

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

@@ -561,10 +561,11 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"pytorch_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=\"pytorch_score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",

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

@@ -908,13 +908,16 @@
         "def init():\n",
         "    global X, output, sess\n",
         "    tf.reset_default_graph()\n",
-        "    model_root = Model.get_model_path('tf-dnn-mnist')\n",
-        "    saver = tf.train.import_meta_graph(os.path.join(model_root, 'mnist-tf.model.meta'))\n",
+        "    model_root = os.getenv('AZUREML_MODEL_DIR')\n",
+        "    # the name of the folder in which to look for tensorflow model files\n",
+        "    tf_model_folder = 'model'\n",
+        "    saver = tf.train.import_meta_graph(\n",
+        "        os.path.join(model_root, tf_model_folder, 'mnist-tf.model.meta'))\n",
         "    X = tf.get_default_graph().get_tensor_by_name(\"network/X:0\")\n",
         "    output = tf.get_default_graph().get_tensor_by_name(\"network/output/MatMul:0\")\n",
-        "    \n",
+        "\n",
         "    sess = tf.Session()\n",
-        "    saver.restore(sess, os.path.join(model_root, 'mnist-tf.model'))\n",
+        "    saver.restore(sess, os.path.join(model_root, tf_model_folder, 'mnist-tf.model'))\n",
         "\n",
         "def run(raw_data):\n",
         "    data = np.array(json.loads(raw_data)['data'])\n",
@@ -943,6 +946,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_conda_package('numpy')\n",
         "cd.add_tensorflow_conda_package()\n",
+        "cd.add_pip_package(\"azureml-defaults\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"
@@ -966,10 +970,11 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"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=\"score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",

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

@@ -0,0 +1,346 @@
+latitude,longitude,temperature,windAngle,windSpeed,elevation
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how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb

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

how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.0.79, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.0.85, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb

@@ -1,342 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/deploy-model/deploy-model.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy Model as Azure Machine Learning Web Service using MLflow\n",
-        "\n",
-        "This example shows you how to use mlflow together with Azure Machine Learning services for deploying a model as a web service. You'll learn how to:\n",
-        "\n",
-        " 1. Retrieve a previously trained scikit-learn model\n",
-        " 2. Create a Docker image from the model\n",
-        " 3. Deploy the model as a web service on Azure Container Instance\n",
-        " 4. Make a scoring request against the web service.\n",
-        "\n",
-        "## Prerequisites and Set-up\n",
-        "\n",
-        "This notebook requires you to first complete the [Use MLflow with Azure Machine Learning for Local Training Run](../train-local/train-local.ipnyb) or [Use MLflow with Azure Machine Learning for Remote Training Run](../train-remote/train-remote.ipnyb) notebook, so as to have an experiment run with uploaded model in your Azure Machine Learning Workspace.\n",
-        "\n",
-        "Also install following packages if you haven't already\n",
-        "\n",
-        "```\n",
-        "pip install azureml-mlflow pandas\n",
-        "```\n",
-        "\n",
-        "Then, import necessary packages:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import mlflow\n",
-        "import azureml.mlflow\n",
-        "import azureml.core\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Connect to workspace and set MLflow tracking URI\n",
-        "\n",
-        "Setting the tracking URI is required for retrieving the model and creating an image using the MLflow APIs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve model from previous run\n",
-        "\n",
-        "Let's retrieve the experiment from training notebook, and list the runs within that experiment."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = \"experiment-with-mlflow\"\n",
-        "exp = ws.experiments[experiment_name]\n",
-        "\n",
-        "runs = list(exp.get_runs())\n",
-        "runs"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then, let's select the most recent training run and find its ID. You also need to specify the path in run history where the model was saved. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "runid = runs[0].id\n",
-        "model_save_path = \"model\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Docker image\n",
-        "\n",
-        "To create a Docker image with Azure Machine Learning for Model Management, use ```mlflow.azureml.build_image``` method. Specify the model path, your workspace, run ID and other parameters.\n",
-        "\n",
-        "MLflow automatically recognizes the model framework as scikit-learn, and creates the scoring logic and includes library dependencies for you.\n",
-        "\n",
-        "Note that the image creation can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import mlflow.azureml\n",
-        "\n",
-        "azure_image, azure_model = mlflow.azureml.build_image(model_uri=\"runs:/{}/{}\".format(runid, model_save_path),\n",
-        "                                                      workspace=ws,\n",
-        "                                                      model_name='diabetes-sklearn-model',\n",
-        "                                                      image_name='diabetes-sklearn-image',\n",
-        "                                                      synchronous=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy web service\n",
-        "\n",
-        "Let's use Azure Machine Learning SDK to deploy the image as a web service. \n",
-        "\n",
-        "First, specify the deployment configuration. Azure Container Instance is a suitable choice for a quick dev-test deployment, while Azure Kubernetes Service is suitable for scalable production deployments.\n",
-        "\n",
-        "Then, deploy the image using Azure Machine Learning SDK's ```deploy_from_image``` method.\n",
-        "\n",
-        "Note that the deployment can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "\n",
-        "\n",
-        "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-        "                                                memory_gb=1, \n",
-        "                                                tags={\"method\" : \"sklearn\"}, \n",
-        "                                                description='Diabetes model',\n",
-        "                                                location='eastus2')\n",
-        "\n",
-        "\n",
-        "# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
-        "webservice = Webservice.deploy_from_image(\n",
-        "    image=azure_image, workspace=ws, name=\"diabetes-model-1\", deployment_config=aci_config)\n",
-        "\n",
-        "\n",
-        "webservice.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Make a scoring request\n",
-        "\n",
-        "Let's take the first few rows of test data and score them using the web service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "test_rows = [\n",
-        "    [0.01991321,  0.05068012,  0.10480869,  0.07007254, -0.03596778,\n",
-        "     -0.0266789 , -0.02499266, -0.00259226,  0.00371174,  0.04034337],\n",
-        "    [-0.01277963, -0.04464164,  0.06061839,  0.05285819,  0.04796534,\n",
-        "     0.02937467, -0.01762938,  0.03430886,  0.0702113 ,  0.00720652],\n",
-        "    [ 0.03807591,  0.05068012,  0.00888341,  0.04252958, -0.04284755,\n",
-        "     -0.02104223, -0.03971921, -0.00259226, -0.01811827,  0.00720652]]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "MLflow-based web service for scikit-learn model requires the data to be converted to Pandas DataFrame, and then serialized as JSON. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "import pandas as pd\n",
-        "\n",
-        "test_rows_as_json = pd.DataFrame(test_rows).to_json(orient=\"split\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's pass the conveted and serialized data to web service to get the predictions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "predictions = webservice.run(test_rows_as_json)\n",
-        "\n",
-        "print(predictions)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can use the web service's scoring URI to make a raw HTTP request"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "webservice.scoring_uri"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can diagnose the web service using ```get_logs``` method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "webservice.get_logs()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next Steps\n",
-        "\n",
-        "Learn about [model management and inference in Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "shipatel"
-      }
-    ],
-    "category": "deployment",
-    "compute": [
-      "None"
-    ],
-    "datasets": [
-      "Diabetes"
-    ],
-    "deployment": [
-      "Azure Container Instance"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Scikit-learn"
-    ],
-    "friendly_name": "Deploy a model as a web service using MLflow",
-    "index_order": 4,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "tags": [
-      "None"
-    ],
-    "task": "Use MLflow with AML"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.yml

@@ -1,8 +0,0 @@
-name: deploy-model
-dependencies:
-- scikit-learn
-- matplotlib
-- pip:
-  - azureml-sdk
-  - azureml-mlflow
-  - pandas

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/scripts/train.py

@@ -1,150 +0,0 @@
-# Copyright (c) 2017, PyTorch Team
-# All rights reserved
-# Licensed under BSD 3-Clause License.
-
-# This example is based on PyTorch MNIST example:
-# https://github.com/pytorch/examples/blob/master/mnist/main.py
-
-import mlflow
-import mlflow.pytorch
-from mlflow.utils.environment import _mlflow_conda_env
-import warnings
-import cloudpickle
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import torchvision
-from torchvision import datasets, transforms
-
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5, 1)
-        self.conv2 = nn.Conv2d(20, 50, 5, 1)
-        self.fc1 = nn.Linear(4 * 4 * 50, 500)
-        self.fc2 = nn.Linear(500, 10)
-
-    def forward(self, x):
-        # Added the view for reshaping score requests
-        x = x.view(-1, 1, 28, 28)
-        x = F.relu(self.conv1(x))
-        x = F.max_pool2d(x, 2, 2)
-        x = F.relu(self.conv2(x))
-        x = F.max_pool2d(x, 2, 2)
-        x = x.view(-1, 4 * 4 * 50)
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=1)
-
-
-def train(args, model, device, train_loader, optimizer, epoch):
-    model.train()
-    for batch_idx, (data, target) in enumerate(train_loader):
-        data, target = data.to(device), target.to(device)
-        optimizer.zero_grad()
-        output = model(data)
-        loss = F.nll_loss(output, target)
-        loss.backward()
-        optimizer.step()
-        if batch_idx % args.log_interval == 0:
-            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                epoch, batch_idx * len(data), len(train_loader.dataset),
-                100. * batch_idx / len(train_loader), loss.item()))
-            # Use MLflow logging
-            mlflow.log_metric("epoch_loss", loss.item())
-
-
-def test(args, model, device, test_loader):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for data, target in test_loader:
-            data, target = data.to(device), target.to(device)
-            output = model(data)
-            # sum up batch loss
-            test_loss += F.nll_loss(output, target, reduction="sum").item()
-            # get the index of the max log-probability
-            pred = output.argmax(dim=1, keepdim=True)
-            correct += pred.eq(target.view_as(pred)).sum().item()
-
-    test_loss /= len(test_loader.dataset)
-    print("\n")
-    print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
-        test_loss, correct, len(test_loader.dataset),
-        100. * correct / len(test_loader.dataset)))
-    # Use MLflow logging
-    mlflow.log_metric("average_loss", test_loss)
-
-
-class Args(object):
-    pass
-
-
-# Training settings
-args = Args()
-setattr(args, 'batch_size', 64)
-setattr(args, 'test_batch_size', 1000)
-setattr(args, 'epochs', 3)  # Higher number for better convergence
-setattr(args, 'lr', 0.01)
-setattr(args, 'momentum', 0.5)
-setattr(args, 'no_cuda', True)
-setattr(args, 'seed', 1)
-setattr(args, 'log_interval', 10)
-setattr(args, 'save_model', True)
-
-use_cuda = not args.no_cuda and torch.cuda.is_available()
-
-torch.manual_seed(args.seed)
-
-device = torch.device("cuda" if use_cuda else "cpu")
-
-kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
-train_loader = torch.utils.data.DataLoader(
-    datasets.MNIST('../data', train=True, download=True,
-                   transform=transforms.Compose([
-                       transforms.ToTensor(),
-                       transforms.Normalize((0.1307,), (0.3081,))
-                   ])),
-    batch_size=args.batch_size, shuffle=True, **kwargs)
-test_loader = torch.utils.data.DataLoader(
-    datasets.MNIST(
-        '../data',
-        train=False,
-        transform=transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize((0.1307,), (0.3081,))])),
-    batch_size=args.test_batch_size, shuffle=True, **kwargs)
-
-
-def driver():
-    warnings.filterwarnings("ignore")
-    # Dependencies for deploying the model
-    pytorch_index = "https://download.pytorch.org/whl/"
-    pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl"
-    deps = [
-        "cloudpickle=={}".format(cloudpickle.__version__),
-        pytorch_index + pytorch_version,
-        "torchvision=={}".format(torchvision.__version__),
-        "Pillow=={}".format("6.0.0")
-    ]
-    with mlflow.start_run() as run:
-        model = Net().to(device)
-        optimizer = optim.SGD(
-            model.parameters(),
-            lr=args.lr,
-            momentum=args.momentum)
-        for epoch in range(1, args.epochs + 1):
-            train(args, model, device, train_loader, optimizer, epoch)
-            test(args, model, device, test_loader)
-        # Log model to run history using MLflow
-        if args.save_model:
-            model_env = _mlflow_conda_env(additional_pip_deps=deps)
-            mlflow.pytorch.log_model(model, "model", conda_env=model_env)
-    return run
-
-
-if __name__ == "__main__":
-    driver()

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb

@@ -1,501 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-deploy-pytorch.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier\n",
-        "\n",
-        "This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a PyTorch model to classify MNIST digit images, and then deploy the model  as a web service. You'll learn how to:\n",
-        "\n",
-        " 1. Set up MLflow tracking URI so as to use Azure ML\n",
-        " 2. Create experiment\n",
-        " 3. Instrument your model with MLflow tracking\n",
-        " 4. Train a PyTorch model locally\n",
-        " 5. Train a model on GPU compute on Azure\n",
-        " 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
-        " 7. Create a Docker image from the trained model\n",
-        " 8. Deploy the model as a web service on Azure Container Instance\n",
-        " 9. Call the model to make predictions\n",
-        " \n",
-        "### Pre-requisites\n",
-        " \n",
-        "Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
-        "\n",
-        "Also, install mlflow-azureml package using ```pip install mlflow-azureml```. Note that mlflow-azureml installs mlflow package itself as a dependency, if you haven't done so previously.\n",
-        "\n",
-        "### Set-up\n",
-        "\n",
-        "Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import sys, os\n",
-        "import mlflow\n",
-        "import mlflow.azureml\n",
-        "import mlflow.sklearn\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)\n",
-        "print(\"MLflow version:\", mlflow.version.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "ws.get_details()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Set tracking URI\n",
-        "\n",
-        "Set the MLFlow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLFlow APIs will go to Azure ML services and will be tracked under your Workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Experiment\n",
-        "\n",
-        "In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = \"pytorch-with-mlflow\"\n",
-        "mlflow.set_experiment(experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train model locally while logging metrics and artifacts\n",
-        "\n",
-        "The ```scripts/train.py``` program contains the code to load the image dataset, and train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
-        "\n",
-        "Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, ```mlflow.log_metric``` functions are used to track the convergence of the neural network training iterations. Finally ```mlflow.pytorch.save_model``` is used to save the trained model in framework-aware manner.\n",
-        "\n",
-        "Let's add the program to search path, import it as a module, and then invoke the driver function. Note that the training can take few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lib_path = os.path.abspath(\"scripts\")\n",
-        "sys.path.append(lib_path)\n",
-        "\n",
-        "import train"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = train.driver()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can view the metrics of the run at Azure Portal"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(azureml.mlflow.get_portal_url(run))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train model on GPU compute on Azure\n",
-        "\n",
-        "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
-        "\n",
-        "Create a PyTorch estimator to specify the training configuration: script, compute as well as additional packages needed. To enable MLflow tracking, include ```azureml-mlflow``` as pip package. The low-level specifications for the training run are encapsulated in the estimator instance."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.dnn import PyTorch\n",
-        "\n",
-        "pt = PyTorch(source_directory=\"./scripts\", \n",
-        "             entry_script = \"train.py\", \n",
-        "             compute_target = \"gpu-cluster\", \n",
-        "             node_count = 1, \n",
-        "             process_count_per_node = 1, \n",
-        "             use_gpu=True,\n",
-        "             pip_packages = [\"azureml-mlflow\", \"Pillow==6.0.0\"])\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get a reference to the experiment you created previously, but this time, as Azure Machine Learning experiment object.\n",
-        "\n",
-        "Then, use ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as cached image is used."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "exp = Experiment(ws, experiment_name)\n",
-        "run = exp.submit(pt)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can monitor the run and its metrics on Azure Portal."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Also, you can wait for run to complete."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy model as web service\n",
-        "\n",
-        "To deploy a web service, first create a Docker image, and then deploy that Docker image on inferencing compute.\n",
-        "\n",
-        "The ```mlflow.azureml.build_image``` function builds a Docker image from saved PyTorch model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specififies package dependencies for you."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.get_file_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then build a docker image using *runs:/<run.id>/model* as the model_uri path.\n",
-        "\n",
-        "Note that the image building can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_path = \"model\"\n",
-        "\n",
-        "\n",
-        "azure_image, azure_model = mlflow.azureml.build_image(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
-        "                                                      workspace=ws,\n",
-        "                                                      model_name='pytorch_mnist',\n",
-        "                                                      image_name='pytorch-mnist-img',\n",
-        "                                                      synchronous=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then, deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
-        "\n",
-        "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
-        "\n",
-        "Note that the service deployment can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "\n",
-        "aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
-        "                                                memory_gb=5, \n",
-        "                                                tags={\"data\": \"MNIST\",  \"method\" : \"pytorch\"}, \n",
-        "                                                description=\"Predict using webservice\")\n",
-        "\n",
-        "\n",
-        "# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
-        "webservice = Webservice.deploy_from_image(\n",
-        "    image=azure_image, workspace=ws, name=\"pytorch-mnist-1\", deployment_config=aci_config)\n",
-        "\n",
-        "\n",
-        "webservice.wait_for_deployment()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once the deployment has completed you can check the scoring URI of the web service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Make predictions using web service\n",
-        "\n",
-        "To make the web service, create a test data set as normalized PyTorch tensors. \n",
-        "\n",
-        "Then, let's define a utility function that takes a random image and converts it into format and shape suitable for as input to PyTorch inferencing end-point. The conversion is done by: \n",
-        "\n",
-        " 1. Select a random (image, label) tuple\n",
-        " 2. Take the image and converting the tensor to NumPy array \n",
-        " 3. Reshape array into 1 x 1 x N array\n",
-        "    * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
-        "    * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
-        " 4. Convert the NumPy array to list to make it into a built-in type.\n",
-        " 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from torchvision import datasets, transforms\n",
-        "import random\n",
-        "import numpy as np\n",
-        "\n",
-        "test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
-        "                       transforms.ToTensor(),\n",
-        "                       transforms.Normalize((0.1307,), (0.3081,))]))\n",
-        "\n",
-        "\n",
-        "def get_random_image():\n",
-        "    image_idx = random.randint(0,len(test_data))\n",
-        "    image_as_tensor = test_data[image_idx][0]\n",
-        "    return {\"data\": elem for elem in image_as_tensor.numpy().reshape(1,1,-1).tolist()}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then, invoke the web service using a random test image. Convert the dictionary containing the image to JSON string before passing it to web service.\n",
-        "\n",
-        "The response contains the raw scores for each label, with greater value indicating higher probability. Sort the labels and select the one with greatest score to get the prediction. Let's also plot the image sent to web service for comparison purposes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%matplotlib inline\n",
-        "\n",
-        "import json\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "test_image = get_random_image()\n",
-        "\n",
-        "response = webservice.run(json.dumps(test_image))\n",
-        "\n",
-        "response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
-        "\n",
-        "\n",
-        "print(\"Predicted label:\", response[0][0])\n",
-        "plt.imshow(np.array(test_image[\"data\"]).reshape(28,28), cmap = \"gray\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also call the web service using a raw POST method against the web service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import requests\n",
-        "\n",
-        "response = requests.post(url=webservice.scoring_uri, data=json.dumps(test_image),headers={\"Content-type\": \"application/json\"})\n",
-        "print(response.text)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "shipatel"
-      }
-    ],
-    "category": "tutorial",
-    "celltoolbar": "Edit Metadata",
-    "compute": [
-      "AML Compute"
-    ],
-    "datasets": [
-      "MNIST"
-    ],
-    "deployment": [
-      "Azure Container Instance"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "PyTorch"
-    ],
-    "friendly_name": "Use MLflow with Azure Machine Learning for training and deployment",
-    "index_order": 6,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.3"
-    },
-    "name": "mlflow-sparksummit-pytorch",
-    "notebookId": 2495374963457641,
-    "tags": [
-      "None"
-    ],
-    "task": "Use MLflow with Azure Machine Learning to train and deploy Pa yTorch image classifier model"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.yml

@@ -1,8 +0,0 @@
-name: train-and-deploy-pytorch
-dependencies:
-- matplotlib
-- pip:
-  - azureml-sdk
-  - azureml-mlflow
-  - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
-  - https://download.pytorch.org/whl/cpu/torchvision-0.3.0-cp35-cp35m-win_amd64.whl

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

@@ -925,6 +925,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_tensorflow_conda_package()\n",
         "cd.add_conda_package('keras==2.2.5')\n",
+        "cd.add_pip_package(\"azureml-defaults\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"
@@ -947,10 +948,11 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"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=\"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",