Merge pull request #279605 from sdgilley/sdg-freshness

freshness compute instance

Author: JamesJBarnett

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

@@ -7,10 +7,10 @@ ms.service: machine-learning
 ms.subservice: compute
 ms.custom: devx-track-azurecli
 ms.topic: how-to
-author: jesscioffi
-ms.author: jcioffi
+ms.author: vijetaj
+author: vijetajo
 ms.reviewer: sgilley
-ms.date: 05/03/2024
+ms.date: 06/10/2024
 ---
 
 # Create an Azure Machine Learning compute instance
@@ -40,11 +40,14 @@ Choose the tab for the environment you're using for other prerequisites.
 
 * To use the Python SDK, [set up your development environment with a workspace](how-to-configure-environment.md).  Once your environment is set up, attach to the workspace in your Python script:
 
-  [!INCLUDE [connect ws v2](includes/machine-learning-connect-ws-v2.md)]
+[!INCLUDE [connect ws v2](includes/machine-learning-connect-ws-v2.md)]
 
 # [Azure CLI](#tab/azure-cli)
 
-* To use the CLI, install the [Azure CLI extension for Machine Learning service (v2)](https://aka.ms/sdk-v2-install), [Azure Machine Learning Python SDK (v2)](https://aka.ms/sdk-v2-install), or the [Azure Machine Learning Visual Studio Code extension](how-to-setup-vs-code.md).
+* If you're working on a compute instance, the CLI is already installed.  If working on a different computer, install the [Azure CLI extension for Machine Learning service (v2)](https://aka.ms/sdk-v2-install).
+
+[!INCLUDE [set-up-cli](includes/set-up-cli.md)]
+
 
 # [Studio](#tab/azure-studio)
 
@@ -123,7 +126,6 @@ Where the file *create-instance.yml* is:
         * If you're using an __Azure Virtual Network__, specify the **Resource group**, **Virtual network**, and **Subnet** to create the compute instance inside an Azure Virtual Network. You can also select __No public IP__ 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.
 
         * If you're using an Azure Machine Learning __managed virtual network__, the compute instance is created inside the managed virtual network. You can also select __No public IP__ to prevent the creation of a public IP address. For more information, see [managed compute with a managed network](./how-to-managed-network-compute.md).
-    * Allow root access. (preview)
 
 1. Select **Applications** if you want to add custom applications to use on your compute instance, such as RStudio or Posit Workbench.  See [Add custom applications such as RStudio or Posit Workbench](#add-custom-applications-such-as-rstudio-or-posit-workbench).
 1. Select **Tags** if you want to add additional information to categorize the compute instance.
@@ -248,17 +250,6 @@ from azure.ai.ml.constants import TimeZone
 from azure.ai.ml import MLClient
 from azure.identity import DefaultAzureCredential
 
-# authenticate
-credential = DefaultAzureCredential()
-
-# Get a handle to the workspace
-ml_client = MLClient(
-    credential=credential,
-    subscription_id="<SUBSCRIPTION_ID>",
-    resource_group_name="<RESOURCE_GROUP>",
-    workspace_name="<AML_WORKSPACE_NAME>",
-)
-
 ci_minimal_name = "ci-name"
 ci_start_time = "2023-06-21T11:47:00" #specify your start time in the format yyyy-mm-ddThh:mm:ss
 
@@ -453,8 +444,7 @@ from azure.ai.ml import MLClient
 from azure.identity import ManagedIdentityCredential
 client_id = os.environ.get("DEFAULT_IDENTITY_CLIENT_ID", None)
 credential = ManagedIdentityCredential(client_id=client_id)
-ml_client = MLClient(credential, sub_id, rg_name, ws_name)
-data = ml_client.data.get(name=data_name, version="1")
+ml_client = MLClient(credential, subscription_id, resource_group, workspace)
 ```
 
 You can also use SDK V1:
@@ -464,7 +454,7 @@ from azureml.core.authentication import MsiAuthentication
 from azureml.core import Workspace
 client_id = os.environ.get("DEFAULT_IDENTITY_CLIENT_ID", None)
 auth = MsiAuthentication(identity_config={"client_id": client_id})
-workspace = Workspace.get("chrjia-eastus", auth=auth, subscription_id="381b38e9-9840-4719-a5a0-61d9585e1e91", resource_group="chrjia-rg", location="East US")
+workspace = Workspace.get("chrjia-eastus", auth=auth, subscription_id=subscription_id, resource_group=resource_group, location="East US")
 ```
 
 # [Azure CLI](#tab/azure-cli)

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

@@ -7,8 +7,8 @@ ms.service: machine-learning
 ms.subservice: compute
 ms.custom: devx-track-azurecli
 ms.topic: how-to
-author: jesscioffi
-ms.author: jcioffi
+ms.author: vijetaj
+author: vijetajo
 ms.reviewer: sgilley
 ms.date: 05/03/2024
 ---

articles/machine-learning/how-to-r-deploy-r-model.md

@@ -181,33 +181,11 @@ These steps assume you have an Azure Container Registry associated with your wor
 1. If you see custom environments, nothing more is needed.
 1. If you don't see any custom environments, create [an R environment](how-to-r-modify-script-for-production.md#create-an-environment), or any other custom environment.  (You *won't* use this environment for deployment, but you *will* use the container registry that is also created for you.)
 
-Once you have verified that you have at least one custom environment, use the following steps to build a container.
+Once you have verified that you have at least one custom environment, start a terminal and set up the CLI:
 
-1. Open a terminal window and sign in to Azure.  If you're doing this from an [Azure Machine Learning compute instance](quickstart-create-resources.md#create-a-compute-instance), use:
+[!INCLUDE [set-up-cli](includes/set-up-cli.md)]
 
-    ```azurecli
-    az login --identity
-    ```
-
-    If you're not on the compute instance, omit `--identity` and follow the prompt to open a browser window to authenticate.
-
-1. Make sure you have the most recent versions of the CLI and the `ml` extension:
-    
-    ```azurecli
-    az upgrade
-    ```
-
-1. If you have multiple Azure subscriptions, set the active subscription to the one you're using for your workspace. (You can skip this step if you only have access to a single subscription.)  Replace `<SUBSCRIPTION-NAME>` with your subscription name.  Also remove the brackets `<>`.
-
-    ```azurecli
-    az account set --subscription "<SUBSCRIPTION-NAME>"
-    ```
-
-1. Set the default workspace.  If you're doing this from a compute instance, you can use the following command as is.  If you're on any other computer, substitute your resource group and workspace name instead.  (You can find these values in [Azure Machine Learning studio](how-to-r-train-model.md#submit-the-job).)
-
-    ```azurecli
-    az configure --defaults group=$CI_RESOURCE_GROUP workspace=$CI_WORKSPACE
-    ```
+After you've set up the CLI, use the following steps to build a container.
 
 1. Make sure you are in your project directory.
 

articles/machine-learning/includes/set-up-cli.md

@@ -0,0 +1,36 @@
+---
+title: "Include file"
+description: "Include file"
+services: machine-learning
+author: sdgilley
+ms.service: machine-learning
+ms.author: sgilley
+ms.custom: "include file"
+ms.topic: "include"
+ms.date: 06/10/2024
+---
+
+1. Open a terminal window and sign in to Azure. If you're using an [Azure Machine Learning compute instance](../quickstart-create-resources.md#create-a-compute-instance), use:
+
+    ```azurecli
+    az login --identity
+    ```
+
+    If you're not on the compute instance, omit `--identity` and follow the prompt to open a browser window to authenticate.
+
+1. Make sure you have the most recent versions of the CLI and the `ml` extension:
+
+    ```azurecli
+    az upgrade
+    ```
+
+1. If you have multiple Azure subscriptions, set the active subscription to the one you're using for your workspace. (You can skip this step if you only have access to a single subscription.)  Replace `<YOUR_SUBSCRIPTION_NAME_OR_ID>` with either your subscription name or subscription ID. Also remove the brackets `<>`.
+
+    :::code language="azurecli" source="~/azureml-examples-main/cli/misc.sh" id="az_account_set":::
+
+
+1. Set the default workspace. If you're using a compute instance, you can keep the following command as is. If you're on any other computer, substitute your resource group and workspace name instead. (You can find these values in [Azure Machine Learning studio](../how-to-r-train-model.md#submit-the-job).)
+
+    ```azurecli
+    az configure --defaults group=$CI_RESOURCE_GROUP workspace=$CI_WORKSPACE
+    ```