Merge pull request #179737 from Blackmist/11891786-verbatims

validating and fixing CLI 1.0 commands

Author: ShannonLeavitt

articles/machine-learning/how-to-deploy-and-where.md

@@ -6,7 +6,9 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.reviewer: larryfr
-ms.date: 04/21/2021
+ms.author: ssambare
+author: shivanissambare
+ms.date: 11/12/2021
 ms.topic: how-to
 ms.custom: devx-track-python, deploy, devx-track-azurecli, contperf-fy21q2, contperf-fy21q4, mktng-kw-nov2021
 adobe-target: true
@@ -16,8 +18,7 @@ adobe-target: true
 
 Learn how to deploy your machine learning or deep learning model as a web service in the Azure cloud.
 
-> [!TIP]
-> Managed online endpoints (preview) provide a way to deploy your trained model without your having to create and manage the underlying infrastructure. For more information, see [Deploy and score a machine learning model with a managed online endpoint (preview)](how-to-deploy-managed-online-endpoints.md).
+[!INCLUDE [endpoints-option](../../includes/machine-learning-endpoints-preview-note.md)]
 
 ## Workflow for deploying a model
 
@@ -28,26 +29,25 @@ The workflow is similar no matter where you deploy your model:
 1. Prepare an inference configuration.
 1. Deploy the model locally to ensure everything works.
 1. Choose a compute target.
-1. Re-deploy the model to the cloud.
+1. Deploy the model to the cloud.
 1. Test the resulting web service.
 
 For more information on the concepts involved in the machine learning deployment workflow, see [Manage, deploy, and monitor models with Azure Machine Learning](concept-model-management-and-deployment.md).
 
-[!INCLUDE [endpoints-option](../../includes/machine-learning-endpoints-preview-note.md)]
-
 ## Prerequisites
 
 # [Azure CLI](#tab/azcli)
 
+[!INCLUDE [cli10-only](../../includes/machine-learning-cli-version-1-only.md)]
+
 - An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
-- A model. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](https://aka.ms/azml-deploy-cloud).
-- The [Azure Command Line Interface (CLI) extension for the Machine Learning service](reference-azure-machine-learning-cli.md).
+- A model. The examples in this article use a pre-trained model.
 - A machine that can run Docker, such as a [compute instance](how-to-create-manage-compute-instance.md).
 
 # [Python](#tab/python)
 
 - An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
-- A model. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](https://aka.ms/azml-deploy-cloud).
+- A model. The examples in this article use a pre-trained model.
 - The [Azure Machine Learning software development kit (SDK) for Python](/python/api/overview/azure/ml/intro).
 - A machine that can run Docker, such as a [compute instance](how-to-create-manage-compute-instance.md).
 ---
@@ -56,16 +56,14 @@ For more information on the concepts involved in the machine learning deployment
 
 # [Azure CLI](#tab/azcli)
 
-Do
+To see the workspaces that you have access to, use the following commands:
 
 ```azurecli-interactive
 az login
-az account set -s <my subscription>
-az ml workspace list --resource-group=<my resource group>
+az account set -s <subscription>
+az ml workspace list --resource-group=<resource-group>
 ```
 
-to see the workspaces you have access to.
-
 # [Python](#tab/python)
 
 ```python
@@ -84,8 +82,8 @@ For more information on using the SDK to connect to a workspace, see the [Azure
 
 A typical situation for a deployed machine learning service is that you need the following components:
 
- + resources representing the specific model that you want deployed (for example: a pytorch model file)
- + code that you will be running in the service, that executes the model on a given input
++ Resources representing the specific model that you want deployed (for example: a pytorch model file).
++ Code that you will be running in the service, that executes the model on a given input.
 
 Azure Machine Learnings allows you to separate the deployment into two separate components, so that you can keep the same code, but merely update the model. We define the mechanism by which you upload a model _separately_ from your code as "registering the model".
 
@@ -97,33 +95,39 @@ The following examples demonstrate how to register a model.
 
 # [Azure CLI](#tab/azcli)
 
-### Register a model from a local file
+The following commands download a model and then register it with your Azure Machine Learning workspace:
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=register-model-from-local-file-code)]
+```azurecli-interactive
+wget https://aka.ms/bidaf-9-model -O model.onnx --show-progress
+az ml model register -n bidaf_onnx \
+    -p ./model.onnx \
+    -g <resource-group> \
+    -w <workspace-name>
+```
 
 Set `-p` to the path of a folder or a file that you want to register.
 
-For more information on `az ml model register`, consult the [reference documentation](/cli/azure/ext/azure-cli-ml/ml/model).
+For more information on `az ml model register`, see the [reference documentation](/cli/azure/ml(v1)/model).
 
 ### Register a model from an Azure ML training run
 
+If you need to register a model that was created previously through an Azure Machine Learning training job, you can specify the experiment, run, and path to the model:
+
 ```azurecli-interactive
 az ml model register -n bidaf_onnx --asset-path outputs/model.onnx --experiment-name myexperiment --run-id myrunid --tag area=qna
 ```
 
-[!INCLUDE [install extension](../../includes/machine-learning-service-install-extension.md)]
-
 The `--asset-path` parameter refers to the cloud location of the model. In this example, the path of a single file is used. To include multiple files in the model registration, set `--asset-path` to the path of a folder that contains the files.
 
-For more information on `az ml model register`, consult the [reference documentation](/cli/azure/ml/model).
+For more information on `az ml model register`, see the [reference documentation](/cli/azure/ml(v1)/model).
 
 # [Python](#tab/python)
 
 ### Register a model from a local file
 
 You can register a model by providing the local path of the model. You can provide the path of either a folder or a single file on your local machine.
 <!-- pyhton nb call -->
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=register-model-from-local-file-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=register-model-from-local-file-code)]
 
 
 To include multiple files in the model registration, set `model_path` to the path of a folder that contains the files.
@@ -195,7 +199,7 @@ Save this file with the name `dummyinferenceconfig.json`.
 
 The following example demonstrates how to create a minimal environment with no pip dependencies, using the dummy scoring script you defined above.
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=inference-configuration-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=inference-configuration-code)]
 
 For more information on environments, see [Create and manage environments for training and deployment](how-to-use-environments.md).
 
@@ -206,43 +210,69 @@ For more information on inference configuration, see the [InferenceConfig](/pyth
 
 ## Define a deployment configuration
 
-A deployment configuration specifies the amount of memory and cores to reserve for your webservice will require in order to run, as well as configuration details of the underlying webservice. For example, a deployment configuration lets you specify that your service needs 2 gigabytes of memory, 2 CPU cores, 1 GPU core, and that you want to enable autoscaling.
+A deployment configuration specifies the amount of memory and cores your webservice needs in order to run. It also provides configuration details of the underlying webservice. For example, a deployment configuration lets you specify that your service needs 2 gigabytes of memory, 2 CPU cores, 1 GPU core, and that you want to enable autoscaling.
 
 The options available for a deployment configuration differ depending on the compute target you choose. In a local deployment, all you can specify is which port your webservice will be served on.
 
 # [Azure CLI](#tab/azcli)
 
 [!INCLUDE [aml-local-deploy-config](../../includes/machine-learning-service-local-deploy-config.md)]
 
-For more information, see [this reference](./reference-azure-machine-learning-cli.md#deployment-configuration-schema).
+For more information, see the [deployment schema](./reference-azure-machine-learning-cli.md#deployment-configuration-schema).
 
 # [Python](#tab/python)
 
-To create a local deployment configuration, do the following:
+The following Python demonstrates how to create a local deployment configuration: 
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deployment-configuration-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deployment-configuration-code)]
 
 ---
 
 ## Deploy your machine learning model
 
 You are now ready to deploy your model. 
 
-[!INCLUDE [aml-deploy-service](../../includes/machine-learning-deploy-service.md)]
+# [Azure CLI](#tab/azcli)
+
+Replace `bidaf_onnx:1` with the name of your model and its version number.
+
+```azurecli-interactive
+az ml model deploy -n myservice \
+    -m bidaf_onnx:1 \
+    --overwrite \
+    --ic dummyinferenceconfig.json \
+    --dc deploymentconfig.json \
+    -g <resource-group> \
+    -w <workspace-name>
+```
+
+# [Python](#tab/python)
+
 
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-code)]
+
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-print-logs)]
+
+For more information, see the documentation for [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).
+
+---
 
 ## Call into your model
 
 Let's check that your echo model deployed successfully. You should be able to do a simple liveness request, as well as a scoring request:
 
 # [Azure CLI](#tab/azcli)
-<!-- cli nb call -->
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=call-into-model-code)]
+```azurecli-interactive
+curl -v http://localhost:32267
+curl -v -X POST -H "content-type:application/json" \
+    -d '{"query": "What color is the fox", "context": "The quick brown fox jumped over the lazy dog."}' \
+    http://localhost:32267/score
+```
 
 # [Python](#tab/python)
 <!-- python nb call -->
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-into-model-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-into-model-code)]
 
 ---
 
@@ -282,19 +312,25 @@ For more information, see the documentation for [LocalWebservice](/python/api/az
 
 Deploy your service again:
 
----
-
 # [Azure CLI](#tab/azcli)
 
 Replace `bidaf_onnx:1` with the name of your model and its version number.
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=re-deploy-model-code)]
+```azurecli-interactive
+az ml model deploy -n myservice \
+    -m bidaf_onnx:1 \
+    --overwrite \
+    --ic inferenceconfig.json \
+    --dc deploymentconfig.json \
+    -g <resource-group> \
+    -w <workspace-name>
+```
 
 # [Python](#tab/python)
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-code)]
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-print-logs)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-print-logs)]
 
 For more information, see the documentation for [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).
 
@@ -303,23 +339,23 @@ Then ensure you can send a post request to the service:
 
 # [Azure CLI](#tab/azcli)
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=send-post-request-code)]
+```azurecli-interactive
+curl -v -X POST -H "content-type:application/json" \
+    -d '{"query": "What color is the fox", "context": "The quick brown fox jumped over the lazy dog."}' \
+    http://localhost:32267/score
+```
 
 # [Python](#tab/python)
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=send-post-request-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=send-post-request-code)]
 
 ---
 
 ## Choose a compute target
 
-Refer to the below diagram when choosing a compute target.
-
-[![How to choose a compute target](./media/how-to-deploy-and-where/how-to-choose-target.png)](././media/how-to-deploy-and-where/how-to-choose-target.png#lightbox)
-
 [!INCLUDE [aml-deploy-target](../../includes/aml-compute-target-deploy.md)]
 
-## Re-deploy to cloud
+## Deploy to cloud
 
 Once you've confirmed your service works locally and chosen a remote compute target, you are ready to deploy to the cloud. 
 
@@ -337,7 +373,7 @@ For more information, see [this reference](./reference-azure-machine-learning-cl
 
 # [Python](#tab/python)
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-on-cloud-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-on-cloud-code)]
 
 ---
 
@@ -348,16 +384,30 @@ Deploy your service again:
 
 Replace `bidaf_onnx:1` with the name of your model and its version number.
 
+```azurecli-interactive
+az ml model deploy -n myservice \
+    -m bidaf_onnx:1 \
+    --overwrite \
+    --ic inferenceconfig.json \
+    --dc re-deploymentconfig.json \
+    -g <resource-group> \
+    -w <workspace-name>
+```
 
+To view the service logs, use the following command:
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=deploy-model-on-cloud-code)]
+```azurecli-interactive
+az ml service get-logs -n myservice \
+    -g <resource-group> \
+    -w <workspace-name>
+```
 
 # [Python](#tab/python)
 
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-code)]
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-print-logs)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-print-logs)]
 
 For more information, see the documentation for [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).
 
@@ -368,9 +418,9 @@ For more information, see the documentation for [Model.deploy()](/python/api/azu
 
 When you deploy remotely, you may have key authentication enabled. The example below shows how to get your service key with Python in order to make an inference request.
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-web-service-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-web-service-code)]
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-webservice-print-logs)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-webservice-print-logs)]
 
 
 
@@ -406,9 +456,13 @@ The following table describes the different service states:
 # [Azure CLI](#tab/azcli)
 
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=delete-resource-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=delete-resource-code)]
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=delete-your-resource-code)]
+```azurecli-interactive
+az ml service delete -n myservice
+az ml service delete -n myaciservice
+az ml model delete --model-id=<MODEL_ID>
+```
 
 To delete a deployed webservice, use `az ml service delete <name of webservice>`.
 
@@ -418,7 +472,7 @@ Read more about [deleting a webservice](/cli/azure/ml(v1)/computetarget/create#a
 
 # [Python](#tab/python)
 
-[!notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=delete-resource-code)]
+[!Notebook-python[] (~/azureml-examples-main/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=delete-resource-code)]
 
 To delete a deployed web service, use `service.delete()`.
 To delete a registered model, use `model.delete()`.