Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into updateHarmonyConnect

Author: siddomala

articles/machine-learning/how-to-create-manage-compute-instance.md

@@ -137,9 +137,10 @@ Where the file *create-instance.yml* is:
 
     * Enable SSH access.  Follow the [detailed SSH access instructions](#enable-ssh-access) below.
     * Enable virtual network. Specify the **Resource group**, **Virtual network**, and **Subnet** to create the compute instance inside an Azure Virtual Network (vnet). You can also select __No public IP__ (preview) to prevent the creation of a public IP address, which requires a private link workspace. You must also satisfy these [network requirements](./how-to-secure-training-vnet.md) for virtual network setup. 
-    * Assign the computer to another user. For more about assigning to other users, see [Create on behalf of](#create-on-behalf-of-preview).inel
+    * Assign the computer to another user. For more about assigning to other users, see [Create on behalf of](#create-on-behalf-of-preview)
     * Provision with a setup script (preview) - for more information about how to create and use a setup script, see [Customize the compute instance with a script](how-to-customize-compute-instance.md).
     * Add schedule (preview). Schedule times for the compute instance to automatically start and/or shutdown. See [schedule details](#schedule-automatic-start-and-stop-preview) below.
+    * Enable auto-stop (preview). Configure a compute instance to automatically shutdown if it is inactive. See [configure auto-stop](#configure-auto-stop-preview) for more details.
 
 
 
@@ -157,6 +158,58 @@ SSH access is disabled by default.  SSH access can't be changed after creation.
 
 ---
 
+## Configure auto-stop (preview)
+To avoid getting charged for a compute instance that is switched on but inactive, you can configure auto-stop. 
+
+A compute instance is considered inactive if the below conditions are met:
+* No active Jupyter Kernel sessions (this translates to no Notebooks usage via Jupyter, JupyterLab or Interactive notebooks)
+* No active Jupyter terminal sessions
+* No active AzureML runs or experiments
+* No SSH connections
+* No VS code connections; you must close your VS Code connection for your compute instance to be considered inactive. Sessions are auto-terminated if VS code detects no activity for 3 hours. 
+
+Note that activity on custom applications installed on the compute instance is not considered. There are also some basic bounds around inactivity time periods; CI must be inactive for a minimum of 15 mins and a maximum of 3 days.
+
+This setting can be configured during CI creation or for existing CIs via the following interfaces:
+* AzureML Studio
+    
+    :::image type="content" source="media/how-to-create-attach-studio/idle-shutdown-advanced-settings.jpg" alt-text="Screenshot of the Advanced Settings page for creating a compute instance":::
+    :::image type="content" source="media/how-to-create-attach-studio/idle-shutdown-update.jpg" alt-text="Screenshot of the compute instance details page showing how to update an existing compute instance with idle shutdown":::
+
+* REST API
+
+    Endpoint:
+    ```
+    POST https://management.azure.com/subscriptions/{SUB_ID}/resourceGroups/{RG_NAME}/providers/Microsoft.MachineLearningServices/workspaces/{WS_NAME}/computes/{CI_NAME}/updateIdleShutdownSetting?api-version=2021-07-01
+    ```
+    Body:
+    ```JSON
+    {
+        "idleTimeBeforeShutdown": "PT30M" // this must be a string in ISO 8601 format
+    }
+    ```
+
+* CLIv2 (YAML) -- only configurable during new CI creation
+
+    ```YAML
+    # Note that this is just a snippet for the idle shutdown property. Refer to the "Create" Azure CLI section for more information.
+    idle_time_before_shutdown_minutes: 30
+    ```
+
+* Python SDKv2 -- only configurable during new CI creation
+
+    ```Python
+    ComputeInstance(name=ci_basic_name, size="STANDARD_DS3_v2", idle_time_before_shutdown_minutes="30")
+    ```
+
+* ARM Templates -- only configurable during new CI creation
+    ```JSON
+    // Note that this is just a snippet for the idle shutdown property in an ARM template
+    {
+        "idleTimeBeforeShutdown":"PT30M" // this must be a string in ISO 8601 format
+    }
+    ```
+
 ## Create on behalf of (preview)
 
 As an administrator, you can create a compute instance on behalf of a data scientist and assign the instance to them with:

articles/machine-learning/how-to-deploy-custom-container.md

@@ -34,35 +34,12 @@ Custom container deployments can use web servers other than the default Python F
 
 ## Prerequisites
 
-* You must have an Azure resource group, in which you (or the service principal you use) need to have `Contributor` access. You'll have such a resource group if you configured your ML extension per the above article. 
+[!INCLUDE [cli & sdk](../../includes/machine-learning-cli-sdk-v2-prereqs.md)]
 
-* You must have an Azure Machine Learning workspace. You'll have such a workspace if you configured your ML extension per the above article.
+* You, or the service principal you use, must have `Contributor` access to the Azure Resource Group that contains your workspace. You'll have such a resource group if you configured your workspace using the quickstart article.
 
 * To deploy locally, you must have [Docker engine](https://docs.docker.com/engine/install/) running locally. This step is **highly recommended**. It will help you debug issues.
 
-# [Azure CLI](#tab/cli)
-
-* Install and configure the Azure CLI and ML extension. For more information, see [Install, set up, and use the CLI (v2)](how-to-configure-cli.md). 
-
-* If you've not already set the defaults for Azure CLI, you should save your default settings. To avoid having to repeatedly pass in the values, run:
-
-   ```azurecli
-   az account set --subscription <subscription id>
-   az configure --defaults workspace=<azureml workspace name> group=<resource group>
-   ```
-
-# [Python SDK](#tab/python)
-
-* If you haven't installed Python SDK v2, please install with this command:
-
-  ```azurecli
-  pip install --pre azure-ai-ml
-  ```
-
-  For more information, see [Install the Azure Machine Learning SDK v2 for Python](/python/api/overview/azure/ml/installv2).
-
----
-
 ## Download source code
 
 To follow along with this tutorial, download the source code below.

articles/machine-learning/how-to-deploy-managed-online-endpoint-sdk-v2.md

@@ -26,10 +26,8 @@ In this article, you learn how to deploy your machine learning model to managed
 
 ## Prerequisites
 
-* 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.
-* The [Azure Machine Learning SDK v2 for Python](/python/api/overview/azure/ml/installv2).
-* You must have an Azure resource group, and you (or the service principal you use) must have Contributor access to it.
-* You must have an Azure Machine Learning workspace.
+[!INCLUDE [sdk](../../includes/machine-learning-sdk-v2-prereqs.md)]
+
 * To deploy locally, you must install [Docker Engine](https://docs.docker.com/engine/) on your local computer. We highly recommend this option, so it's easier to debug issues.
 
 ### Clone examples repository

articles/machine-learning/media/how-to-create-attach-studio/idle-shutdown-advanced-settings.jpg

@@ -436,7 +436,7 @@ For more information on creating a compute cluster and compute cluster, includin
 When Azure Container Registry is behind the virtual network, Azure Machine Learning can't use it to directly build Docker images (used for training and deployment). Instead, configure the workspace to use the compute cluster you created earlier. Use the following steps to create a compute cluster and configure the workspace to use it to build images:
 
 1. Navigate to [https://shell.azure.com/](https://shell.azure.com/) to open the Azure Cloud Shell.
-1. From the Cloud Shell, use the following command to install the 1.0 CLI for Azure Machine Learning:
+1. From the Cloud Shell, use the following command to install the 2.0 CLI for Azure Machine Learning:
 
     ```azurecli-interactive
     az extension add -n ml

articles/machine-learning/media/how-to-create-attach-studio/idle-shutdown-update.jpg

@@ -2,17 +2,17 @@
 author: blackmist
 ms.service: machine-learning
 ms.topic: include
-ms.date: 10/26/2021
+ms.date: 09/14/2022
 ms.author: larryfr
 ---
 
 Before following the steps in this article, make sure you have the following prerequisites:
 
 * 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/).
 
-* The [Azure CLI](/cli/azure/) and the `ml` extension to the Azure CLI. For more information, see [Install, set up, and use the CLI (v2) (preview)](../articles/machine-learning/how-to-configure-cli.md).
+* The [Azure CLI](/cli/azure/) and the `ml` extension to the Azure CLI. For more information, see [Install, set up, and use the CLI (v2)](../articles/machine-learning/how-to-configure-cli.md).
 
     > [!IMPORTANT]
     > The CLI examples in this article assume that you are using the Bash (or compatible) shell. For example, from a Linux system or [Windows Subsystem for Linux](/windows/wsl/about). 
  
-* An Azure Machine Learning workspace. If you don't have one, use the steps in the [Install, set up, and use the CLI (v2) (preview)](../articles/machine-learning/how-to-configure-cli.md) to create one.
+* An Azure Machine Learning workspace. If you don't have one, use the steps in the [Install, set up, and use the CLI (v2)](../articles/machine-learning/how-to-configure-cli.md) to create one.

articles/machine-learning/tutorial-create-secure-workspace.md

@@ -0,0 +1,26 @@
+---
+author: blackmist
+ms.service: machine-learning
+ms.topic: include
+ms.date: 09/14/2022
+ms.author: larryfr
+---
+
+Before following the steps in this article, make sure you have the following prerequisites:
+
+* 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/).
+
+* An Azure Machine Learning workspace. If you don't have one, use the steps in the [Quickstart: Create workspace resources](../articles/machine-learning/quickstart-create-resources.md) article to create one.
+
+* The [Azure CLI](/cli/azure/) and the `ml` extension to the Azure CLI. For more information, see [Install, set up, and use the CLI (v2)](../articles/machine-learning/how-to-configure-cli.md).
+
+    > [!IMPORTANT]
+    > The CLI examples in this article assume that you are using the Bash (or compatible) shell. For example, from a Linux system or [Windows Subsystem for Linux](/windows/wsl/about). 
+
+* To install the Python SDK v2 (preview), use the following command:
+
+    ```bash
+    pip install --pre azure-ai-ml
+    ```
+
+    For more information, see [Install the Python SDK v2 for Azure Machine Learning](https://aka.ms/sdk-v2-install) (preview).

includes/machine-learning-cli-prereqs.md

@@ -0,0 +1,21 @@
+---
+author: blackmist
+ms.service: machine-learning
+ms.topic: include
+ms.date: 09/14/2022
+ms.author: larryfr
+---
+
+Before following the steps in this article, make sure you have the following prerequisites:
+
+* 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/).
+
+* An Azure Machine Learning workspace. If you don't have one, use the steps in the [Quickstart: Create workspace resources](../articles/machine-learning/quickstart-create-resources.md) article to create one.
+
+* To install the Python SDK v2, use the following command:
+
+    ```bash
+    pip install --pre azure-ai-ml
+    ```
+
+    For more information, see [Install the Python SDK v2 for Azure Machine Learning](https://aka.ms/sdk-v2-install) (preview).