Merge pull request #208266 from Blackmist/cli-v1-deprecation

v-regandowner · web-flow · commit 7bd67136f607 · 2022-09-12T16:25:07.000-04:00
wording
diff --git a/articles/machine-learning/v1/how-to-access-data.md b/articles/machine-learning/v1/how-to-access-data.md
@@ -304,4 +304,4 @@ Azure Data Factory provides efficient and resilient data transfer with more than
 
 * [Create an Azure machine learning dataset](how-to-create-register-datasets.md)
 * [Train a model](how-to-set-up-training-targets.md)
-* [Deploy a model](how-to-deploy-and-where.md)
+* [Deploy a model](how-to-deploy-and-where.md)
diff --git a/articles/machine-learning/v1/how-to-consume-web-service.md b/articles/machine-learning/v1/how-to-consume-web-service.md
@@ -31,6 +31,8 @@ The general workflow for creating a client that uses a machine learning web serv
 > [!TIP]
 > The examples in this document are manually created without the use of OpenAPI (Swagger) specifications. If you've enabled an OpenAPI specification for your deployment, you can use tools such as [swagger-codegen](https://github.com/swagger-api/swagger-codegen) to create client libraries for your service.
 
+[!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 ## Connection information
 
 > [!NOTE]
diff --git a/articles/machine-learning/v1/how-to-create-attach-compute-cluster.md b/articles/machine-learning/v1/how-to-create-attach-compute-cluster.md
@@ -42,6 +42,8 @@ This article covers only the CLI v1 way to accomplish these tasks.  To see how t
 
 * The [Azure CLI extension for Machine Learning service (v1)](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](../how-to-setup-vs-code.md).
 
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 
 ## What is a compute cluster?
 
diff --git a/articles/machine-learning/v1/how-to-create-attach-kubernetes.md b/articles/machine-learning/v1/how-to-create-attach-kubernetes.md
@@ -27,7 +27,9 @@ Azure Machine Learning can deploy trained machine learning models to Azure Kuber
 
 - An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](../how-to-manage-workspace.md).
 
-- The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](../how-to-setup-vs-code.md).
+- The [Azure CLI extension (v1) for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](../how-to-setup-vs-code.md).
+
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
 
 - If you plan on using an Azure Virtual Network to secure communication between your Azure ML workspace and the AKS cluster, your workspace and its associated resources (storage, key vault, Azure Container Registry) must have private endpoints or service endpoints in the same VNET as AKS cluster's VNET. Please follow tutorial [create a secure workspace](../tutorial-create-secure-workspace.md) to add those private endpoints or service endpoints to your VNET.
 
@@ -234,7 +236,7 @@ For more information on creating an AKS cluster using the Azure CLI or portal, s
 
 * [Create an AKS cluster (CLI)](/cli/azure/aks?bc=/azure/bread/toc.json&toc=/azure/aks/TOC.json#az-aks-create)
 * [Create an AKS cluster (portal)](../../aks/learn/quick-kubernetes-deploy-portal.md)
-* [Create an AKS cluster (ARM Template on Azure Quickstart templates)](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.containerinstance/aks-azml-targetcompute)
+* [Create an AKS cluster (ARM Template on Azure Quickstart Templates)](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.containerinstance/aks-azml-targetcompute)
 
 The following example demonstrates how to attach an existing AKS cluster to your workspace:
 
diff --git a/articles/machine-learning/v1/how-to-create-manage-compute-instance.md b/articles/machine-learning/v1/how-to-create-manage-compute-instance.md
@@ -41,6 +41,8 @@ In this article, you learn how to:
 
 * The [Azure CLI extension for Machine Learning service (v1)](reference-azure-machine-learning-cli.md)
 
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 ## Create
 
 > [!IMPORTANT]
diff --git a/articles/machine-learning/v1/how-to-deploy-azure-container-instance.md b/articles/machine-learning/v1/how-to-deploy-azure-container-instance.md
@@ -37,6 +37,8 @@ For information on quota and region availability for ACI, see [Quotas and region
 
 - The [Azure CLI extension (v1) for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](../how-to-setup-vs-code.md).
 
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 - The __Python__ code snippets in this article assume that the following variables are set:
 
     * `ws` - Set to your workspace.
diff --git a/articles/machine-learning/v1/how-to-deploy-azure-kubernetes-service.md b/articles/machine-learning/v1/how-to-deploy-azure-kubernetes-service.md
@@ -45,6 +45,8 @@ When deploying to Azure Kubernetes Service, you deploy to an AKS cluster that is
 
 - The [Azure CLI extension (v1) for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](../how-to-setup-vs-code.md).
 
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 - The __Python__ code snippets in this article assume that the following variables are set:
 
     * `ws` - Set to your workspace.
diff --git a/articles/machine-learning/v1/how-to-deploy-profile-model.md b/articles/machine-learning/v1/how-to-deploy-profile-model.md
@@ -22,6 +22,8 @@ This article shows how to profile a machine learning to model to determine how m
 > [!IMPORTANT]
 > This article applies to CLI v1 and SDK v1.  This profiling technique is not available for v2 of either CLI or SDK.
 
+[!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 ## Prerequisites
 
 This article assumes you have trained and registered a model with Azure Machine Learning. See the [sample tutorial here](../how-to-train-scikit-learn.md) for an example of training and registering a scikit-learn model with Azure Machine Learning.
diff --git a/articles/machine-learning/v1/how-to-deploy-update-web-service.md b/articles/machine-learning/v1/how-to-deploy-update-web-service.md
@@ -32,6 +32,8 @@ In this article, you learn how to update a web service that was deployed with Az
                    workspace_name="myworkspace")
     ```
 
+[!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 ## Update web service
 
 To update a web service, use the `update` method. You can update the web service to use a new model, a new entry script, or new dependencies that can be specified in an inference configuration. For more information, see the documentation for [Webservice.update](/python/api/azureml-core/azureml.core.webservice.webservice.webservice#update--args-).
diff --git a/articles/machine-learning/v1/how-to-secure-inferencing-vnet.md b/articles/machine-learning/v1/how-to-secure-inferencing-vnet.md
@@ -55,6 +55,8 @@ In this article you learn how to secure the following inferencing resources in a
 
     For more information on Azure RBAC with networking, see the [Networking built-in roles](../../role-based-access-control/built-in-roles.md#networking)
 
+[!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 ## Limitations
 
 ### Azure Container Instances
diff --git a/articles/machine-learning/v1/how-to-secure-web-service.md b/articles/machine-learning/v1/how-to-secure-web-service.md
@@ -49,6 +49,8 @@ This is the general process to secure a web service:
 
 There are slight differences when you secure across [deployment targets](how-to-deploy-and-where.md).
 
+[!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 ## Get a domain name
 
 If you don't already own a domain name, purchase one from a *domain name registrar*. The process and price differ among registrars. The registrar provides tools to manage the domain name. You use these tools to map a fully qualified domain name (FQDN) (such as www\.contoso.com) to the IP address that hosts your web service.
diff --git a/articles/machine-learning/v1/how-to-track-monitor-analyze-runs.md b/articles/machine-learning/v1/how-to-track-monitor-analyze-runs.md
@@ -16,7 +16,6 @@ ms.custom: devx-track-python, devx-track-azurecli, cliv1, event-tier1-build-2022
 # Start, monitor, and track run history
 
 [!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
-
 [!INCLUDE [cli v1](../../../includes/machine-learning-cli-v1.md)]
 
 The [Azure Machine Learning SDK for Python v1](/python/api/overview/azure/ml/intro) and [Machine Learning CLI](reference-azure-machine-learning-cli.md) provide various methods to monitor, organize, and track your runs for training and experimentation. Your ML run history is an important part of an explainable and repeatable ML development process.
@@ -60,6 +59,7 @@ You'll need the following items:
 
 * The [Azure CLI](/cli/azure/) and [CLI extension for Azure Machine Learning](reference-azure-machine-learning-cli.md).
 
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
 
 ## Monitor run performance
 
diff --git a/articles/machine-learning/v1/how-to-troubleshoot-deployment.md b/articles/machine-learning/v1/how-to-troubleshoot-deployment.md
@@ -30,7 +30,9 @@ Learn how to troubleshoot and solve, or work around, common errors you may encou
 * An **Azure subscription**. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
 * The [Azure Machine Learning SDK](/python/api/overview/azure/ml/install).
 * The [Azure CLI](/cli/azure/install-azure-cli).
-* The [CLI extension for Azure Machine Learning](reference-azure-machine-learning-cli.md).
+* The [CLI extension v1 for Azure Machine Learning](reference-azure-machine-learning-cli.md).
+
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
 
 ## Steps for Docker deployment of machine learning models
 
diff --git a/articles/machine-learning/v1/how-to-use-managed-identities.md b/articles/machine-learning/v1/how-to-use-managed-identities.md
@@ -38,6 +38,9 @@ In this article, you'll learn how to use managed identities to:
 
 - An Azure Machine Learning workspace. For more information, see [Create workspace resources](../quickstart-create-resources.md).
 - The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md)
+
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 - The [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro).
 - To assign roles, the login for your Azure subscription must have the [Managed Identity Operator](../../role-based-access-control/built-in-roles.md#managed-identity-operator) role, or other role that grants the required actions (such as __Owner__).
 - You must be familiar with creating and working with [Managed Identities](../../active-directory/managed-identities-azure-resources/overview.md).
diff --git a/articles/machine-learning/v1/how-to-use-mlflow.md b/articles/machine-learning/v1/how-to-use-mlflow.md
@@ -40,7 +40,10 @@ See [MLflow and Azure Machine Learning](../concept-mlflow.md) for all supported
 * [Create an Azure Machine Learning Workspace](../quickstart-create-resources.md).
     * See which [access permissions you need to perform your MLflow operations with your workspace](../how-to-assign-roles.md#mlflow-operations).
 
-* Install and [set up Azure Machine Learning CLI](reference-azure-machine-learning-cli.md) and make sure you install the ml extension.
+* Install and [set up Azure Machine Learning CLI (v1)](reference-azure-machine-learning-cli.md) and make sure you install the ml extension.
+
+    [!INCLUDE [cli v1 deprecation](../../../includes/machine-learning-cli-v1-deprecation.md)]
+
 * Install and set up [Azure Machine Learning SDK for Python](introduction.md#sdk-v1).
 
 ## Track runs from your local machine or remote compute
diff --git a/articles/machine-learning/v1/reference-pipeline-yaml.md b/articles/machine-learning/v1/reference-pipeline-yaml.md
@@ -26,6 +26,8 @@ ms.custom: devx-track-python, devx-track-azurecli, cliv1
 > The YAML syntax detailed in this document is based on the JSON schema for the v1 version of the ML CLI extension. This syntax is guaranteed only to work with the ML CLI v1 extension.
 > Switch to the [v2 (current version)](../reference-yaml-job-pipeline.md) for the syntax for ML CLI v2.
 
+[!INCLUDE [cli v1 only](../../../includes/machine-learning-cli-version-1-only.md)]
+
 Define your machine learning pipelines in [YAML](https://yaml.org/). When using the machine learning extension for the [Azure CLI **v1**](reference-azure-machine-learning-cli.md)., many of the pipeline-related commands expect a YAML file that defines the pipeline.
 
 The following table lists what is and is not currently supported when defining a pipeline in YAML for use with CLI v1:
diff --git a/includes/machine-learning-cli-v1-deprecation.md b/includes/machine-learning-cli-v1-deprecation.md
@@ -0,0 +1,12 @@
+---
+author: Blackmist
+ms.service: machine-learning
+ms.topic: include
+ms.date: 09/06/2022
+ms.author: larryfr
+---
+
+> [!IMPORTANT]
+> Some of the Azure CLI commands in this article use the `azure-cli-ml`, or v1, extension for Azure Machine Learning. Support for the v1 extension will end on September 30, 2025. You will be able to install and use the v1 extension until that date.
+>
+> We recommend that you transition to the `ml`, or v2, extension before September 30, 2025. For more information on the v2 extension, see [Azure ML CLI extension and Python SDK v2](../articles/machine-learning/concept-v2.md).
diff --git a/includes/machine-learning-cli-version-1-only.md b/includes/machine-learning-cli-version-1-only.md
@@ -2,9 +2,11 @@
 author: Blackmist
 ms.service: machine-learning
 ms.topic: include
-ms.date: 04/06/2022
+ms.date: 09/06/2022
 ms.author: larryfr
 ---
 
 > [!IMPORTANT]
-> The Azure CLI commands in this article __require__ the `azure-cli-ml`, or v1, extension for Azure Machine Learning. We recommend you select **v2 (current)** for the enhanced v2 CLI using the `ml` extension. For more information, see [Machine Learning CLI (v1)](../articles/machine-learning/v1/introduction.md).
+> The Azure CLI commands in this article __require__ the `azure-cli-ml`, or v1, extension for Azure Machine Learning. Support for the v1 extension will end on September 30, 2025. You will be able to install and use the v1 extension until that date.
+>
+> We recommend that you transition to the `ml`, or v2, extension before September 30, 2025. For more information on the v2 extension, see [Azure ML CLI extension and Python SDK v2](../articles/machine-learning/concept-v2.md).
