Merge pull request #187480 from sdgilley/sdg-v1v2

Adding v1/v2 information for CLI code

Author: LJSpiller

articles/machine-learning/azure-machine-learning-release-notes-cli-v2.md

@@ -13,6 +13,9 @@ ms.date: 11/03/2021
 
 # Azure Machine Learning CLI (v2) release notes
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
+
 In this article, learn about Azure Machine Learning CLI (v2) releases.
 
 __RSS feed__: Get notified when this page is updated by copying and pasting the following URL into your feed reader:

articles/machine-learning/concept-component.md

@@ -159,6 +159,8 @@ model = f"This is a dummy model with id: {str(uuid4())} generated at: {curtime}\
 
 ### Create a component using CLI (v2)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 After you define your component specification and Python script files, and [install CLI (v2) successfully](how-to-configure-cli.md) successfully, you can create the component in your workspaces using:
 
 ```azurecli

articles/machine-learning/concept-train-model-git-integration.md

@@ -184,6 +184,7 @@ run.properties['azureml.git.commit']
 ```
 
 ### Azure CLI
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
 
 The `az ml run` CLI command can be used to retrieve the properties from a run. For example, the following command returns the properties for the last run in the experiment named `train-on-amlcompute`:
 

articles/machine-learning/how-to-access-resources-from-endpoints-managed-identities.md

@@ -17,6 +17,8 @@ ms.custom: devplatv2
 
 # Access Azure resources from an online endpoint (preview) with a managed identity 
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 Learn how to access Azure resources from your scoring script with an online endpoint and either a system-assigned managed identity or a user-assigned managed identity. 
 
 Managed endpoints (preview) allow Azure Machine Learning to manage the burden of provisioning your compute resource and deploying your machine learning model. Typically your model needs to access Azure resources such as the Azure Container Registry or your blob storage for inferencing; with a managed identity you can access these resources without needing to manage credentials in your code. [Learn more about managed identities](../active-directory/managed-identities-azure-resources/overview.md).
@@ -29,7 +31,7 @@ This guide assumes you don't have a managed identity, a storage account or an on
 
 * To use Azure Machine Learning, you must have an Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/) today.
 
-* Install and configure the Azure CLI and ML extension. For more information, see [Install, set up, and use the 2.0 CLI (preview)](how-to-configure-cli.md).
+* Install and configure the Azure CLI and ML (v2) extension. For more information, see [Install, set up, and use the 2.0 CLI (preview)](how-to-configure-cli.md).
 
 * An Azure Resource group, in which you (or the service principal you use) need to have `User Access Administrator` and  `Contributor` access. You'll have such a resource group if you configured your ML extension per the above article.
 

articles/machine-learning/how-to-change-storage-access-key.md

@@ -15,7 +15,7 @@ ms.date: 10/21/2021
 ---
 
 # Regenerate storage account access keys
-
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
 
 Learn how to change the access keys for Azure Storage accounts used by Azure Machine Learning. Azure Machine Learning can use storage accounts to store data or trained models.
 
@@ -30,7 +30,7 @@ For security purposes, you may need to change the access keys for an Azure Stora
 
 * The [Azure Machine Learning SDK](/python/api/overview/azure/ml/install).
 
-* The [Azure Machine Learning CLI extension](reference-azure-machine-learning-cli.md).
+* The [Azure Machine Learning CLI extension v1](reference-azure-machine-learning-cli.md).
 
 > [!NOTE]
 > The code snippets in this document were tested with version 1.0.83 of the Python SDK.

articles/machine-learning/how-to-configure-cli.md

@@ -16,6 +16,8 @@ ms.custom: devx-track-azurecli, devplatv2
 
 # Install and set up the CLI (v2)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 The `ml` extension (preview) to the [Azure CLI](/cli/azure/) is the enhanced interface for Azure Machine Learning. It enables you to train and deploy models from the command line, with features that accelerate scaling data science up and out while tracking the model lifecycle.
 
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]

articles/machine-learning/how-to-consume-web-service.md

@@ -76,6 +76,8 @@ There are a several ways to retrieve this information for deployed web services:
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 If you know the name of the deployed service, use the [az ml service show](/cli/azure/ml(v1)/service#az_ml_service_show) command:
 
 ```azurecli
@@ -158,6 +160,8 @@ print(token)
 
 If you have the [Azure CLI and the machine learning extension](reference-azure-machine-learning-cli.md), you can use the following command to get a token:
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 ```azurecli
 az ml service get-access-token -n <service-name>
 ```

articles/machine-learning/how-to-create-attach-compute-cluster.md

@@ -63,7 +63,6 @@ Compute clusters can run jobs securely in a [virtual network environment](how-to
 > [!TIP]
 > Clusters can generally scale up to 100 nodes as long as you have enough quota for the number of cores required. By default clusters are setup with inter-node communication enabled between the nodes of the cluster to support MPI jobs for example. However you can scale your clusters to 1000s of nodes by simply [raising a support ticket](https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade/newsupportrequest), and requesting to allow list your subscription, or workspace, or a specific cluster for disabling inter-node communication.
 
-
 ## Create
 
 **Time estimate**: Approximately 5 minutes.
@@ -93,6 +92,7 @@ You can also configure several advanced properties when you create Azure Machine
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
 
 ```azurecli-interactive
 az ml computetarget create amlcompute -n cpu --min-nodes 1 --max-nodes 1 -s STANDARD_D3_V2 --location westus2
@@ -125,6 +125,8 @@ compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 Set the `vm-priority`:
 
 ```azurecli-interactive
@@ -188,6 +190,9 @@ In the studio, choose **Low Priority** when you create a VM.
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
+
 * Create a new managed compute cluster with managed identity
 
   * User-assigned managed identity

articles/machine-learning/how-to-create-attach-kubernetes.md

@@ -193,6 +193,8 @@ For more information on the classes, methods, and parameters used in this exampl
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 ```azurecli
 az ml computetarget create aks -n myaks
 ```
@@ -256,6 +258,8 @@ For more information on the classes, methods, and parameters used in this exampl
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 To attach an existing cluster using the CLI, you need to get the resource ID of the existing cluster. To get this value, use the following command. Replace `myexistingcluster` with the name of your AKS cluster. Replace `myresourcegroup` with the resource group that contains the cluster:
 
 ```azurecli
@@ -369,6 +373,8 @@ aks_target.detach()
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 To detach the existing cluster to your workspace, use the following command. Replace `myaks` with the name that the AKS cluster is attached to your workspace as. Replace `myresourcegroup` with the resource group that contains your workspace. Replace `myworkspace` with your workspace name.
 
 ```azurecli
@@ -437,6 +443,8 @@ kubectl get secret/azuremlfessl -o yaml
 
 Many webservice failures in AKS can be debugged by connecting to the cluster using `kubectl`. You can get the `kubeconfig.json` for an AKS cluster by running
 
+[!INCLUDE [cli v1](../../includes/machine-learning-cli-v1.md)]
+
 ```azurecli-interactive
 az aks get-credentials -g <rg> -n <aks cluster name>
 ```

articles/machine-learning/how-to-create-component-pipelines-cli.md

@@ -16,6 +16,8 @@ ms.devlang: azurecli
 
 # Create and run machine learning pipelines using components with the Azure Machine Learning CLI (Preview)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 In this article, you learn how to create and run [machine learning pipelines](concept-ml-pipelines.md) by using the Azure CLI and Components (for more, see [What is an Azure Machine Learning component?](concept-component.md)). You can [create pipelines without using components](how-to-train-cli.md#build-a-training-pipeline), but components offer the greatest amount of flexibility and reuse. AzureML Pipelines may be defined in YAML and run from the CLI, authored in Python, or composed in AzureML Studio Designer with a drag-and-drop UI. This document focuses on the CLI.
 
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]