Merge pull request #281878 from sdgilley/sdg-vs-code-ga2

VS Code GA

Author: Jill Grant

articles/machine-learning/how-to-launch-vs-code-remote.md

@@ -1,5 +1,5 @@
 ---
-title: 'Launch Visual Studio Code integrated with Azure Machine Learning (preview)'
+title: 'Launch Visual Studio Code integrated with Azure Machine Learning'
 titleSuffix: Azure Machine Learning
 description: Connect to an Azure Machine Learning compute instance in Visual Studio Code to run interactive Jupyter Notebook and remote development workloads.
 services: machine-learning
@@ -10,17 +10,15 @@ ms.topic: how-to
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: lebaro
-ms.date: 04/10/2023
+ms.date: 08/05/2024
 monikerRange: 'azureml-api-1 || azureml-api-2'
 #Customer intent: As a data scientist, I want to connect to an Azure Machine Learning compute instance in Visual Studio Code to access my resources and run my code.
 ---
 
-# Launch Visual Studio Code integrated with Azure Machine Learning (preview)
+# Launch Visual Studio Code integrated with Azure Machine Learning
 
 In this article, you learn how to launch Visual Studio Code remotely connected to an Azure Machine Learning compute instance. Use VS Code as your integrated development environment (IDE) with the power of Azure Machine Learning resources. Use VS Code in the browser with VS Code for the Web, or use the VS Code desktop application.
 
-[!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
-
 There are two ways you can connect to a compute instance from Visual Studio Code. We recommend the first approach.
 
 1. **Use VS Code as your workspace's integrated development environment (IDE).** This option provides you with a **full-featured development environment** for building your machine learning projects.
@@ -39,10 +37,6 @@ Before you get started, you need:
 1. [!INCLUDE [workspace and compute instance](includes/prerequisite-workspace-compute-instance.md)]
 1. [!INCLUDE [sign in](includes/prereq-sign-in.md)]
 
-1. In the **Manage preview features** panel, scroll down and enable **Connect compute instances to Visual Studio Code for the Web**.
-
-    :::image type="content" source="media/how-to-launch-vs-code-remote/enable-web-preview.png" alt-text="Screenshot shows how to enable the VS Code for the web preview.":::
-
 ## Use VS Code as your workspace IDE
 
 Use one of these options to connect VS Code to your compute instance and workspace files.

articles/machine-learning/how-to-work-in-vs-code-remote.md

@@ -1,5 +1,5 @@
 ---
-title: 'Work in VS Code remotely connected to a compute instance (preview)'
+title: 'Work in VS Code remotely connected to a compute instance'
 titleSuffix: Azure Machine Learning
 description: Details for working with Jupyter notebooks and services from a VS Code remote connection to an Azure Machine Learning compute instance.
 services: machine-learning
@@ -10,18 +10,16 @@ ms.topic: how-to
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: lebaro
-ms.date: 04/17/2023
+ms.date: 08/05/2024
 #Customer intent: As a data scientist, I want to use Jupyter notebooks and tools while working from a VS Code remote connection to my Azure Machine Learning compute instance.
 ---
 
-# Work in VS Code remotely connected to a compute instance (preview)
+# Work in VS Code remotely connected to a compute instance
 
 In this article, learn specifics of working within a VS Code remote connection to an Azure Machine Learning compute instance. Use VS Code as your **full-featured integrated development environment (IDE)** with the power of Azure Machine Learning resources. You can work with a remote connection to your compute instance in the browser with VS Code for the Web, or the VS Code desktop application.
 
 * We recommend **VS Code for the Web**, as you can do all your machine learning work directly from the browser, and without any required installations or dependencies.
 
- [!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
-
 > [!IMPORTANT]
 > To connect to a compute instance behind a firewall, see [Configure inbound and outbound network traffic](how-to-access-azureml-behind-firewall.md#scenario-visual-studio-code).
 
@@ -82,6 +80,6 @@ While there are a few ways to connect and manage kernels in VS Code, connecting
 
 We recommend not trying to work on the same files in both applications at the same time as you may have conflicts you need to resolve. We'll save your current file in the studio before navigating to VS Code. You can execute many of the actions provided in the Azure Machine Learning studio in VS Code instead, using a YAML-first approach. You may find you prefer to do certain actions (for example, editing and debugging files) in VS Code, and other actions (for example, Creating a training job) in the Azure Machine Learning studio. You should find you can seamlessly navigate back and forth between the two.
 
-## Next steps
+## Next step
 
 For more information on managing Jupyter kernels in VS Code, see [Jupyter kernel management](https://code.visualstudio.com/docs/datascience/jupyter-kernel-management).

articles/machine-learning/includes/machine-learning-connect-ws-v2.md

@@ -14,7 +14,6 @@ Replace your Subscription ID, Resource Group name, and Workspace name in the fol
 1. Open the workspace you wish to use.
 1. Select your workspace name in the upper right Azure Machine Learning studio toolbar.
 1. Copy the value for workspace, resource group, and subscription ID into the code.  
-1. Copy one value, close the area and paste, then come back for the next one when you're pasting to a notebook inside studio.
 
 [!INCLUDE [sdk v2](./machine-learning-sdk-v2.md)]
 

articles/machine-learning/includes/prereq-set-kernel.md

@@ -1,6 +1,6 @@
 ---
-title: include file
-description: include file
+title: Include file
+description: Include file
 author: sdgilley
 ms.service: azure-machine-learning
 services: machine-learning
@@ -10,22 +10,25 @@ ms.author: sgilley
 ms.custom: include file
 ---
 
-## Set your kernel
+## Set your kernel and open in Visual Studio Code (VS Code)
 
 1. On the top bar above your opened notebook, create a compute instance if you don't already have one.
 
     :::image type="content" source="../media/tutorial-azure-ml-in-a-day/create-compute.png" alt-text="Screenshot shows how to create a compute instance." lightbox="../media/tutorial-azure-ml-in-a-day/create-compute.png":::
 
-1. If the compute instance is stopped, select **Start compute** and wait until it is running.
+1. If the compute instance is stopped, select **Start compute** and wait until it's running.
 
-    :::image type="content" source="../media/tutorial-azure-ml-in-a-day/start-compute.png" alt-text="Screenshot shows how to start compute if it is stopped." lightbox="../media/tutorial-azure-ml-in-a-day/start-compute.png":::
+    :::image type="content" source="../media/tutorial-azure-ml-in-a-day/start-compute.png" alt-text="Screenshot shows how to start a stopped compute instance." lightbox="../media/tutorial-azure-ml-in-a-day/start-compute.png":::
 
-1. Make sure that the kernel, found on the top right, is `Python 3.10 - SDK v2`.  If not, use the dropdown to select this kernel.
+1. Make sure that the kernel, found on the top right, is `Python 3.10 - SDK v2`. If not, use the dropdown to select this kernel.
 
     :::image type="content" source="../media/tutorial-azure-ml-in-a-day/set-kernel.png" alt-text="Screenshot shows how to set the kernel." lightbox="../media/tutorial-azure-ml-in-a-day/set-kernel.png":::
 
 1. If you see a banner that says you need to be authenticated, select **Authenticate**.
 
+1. You can run the notebook here, or open it in VS Code for a full integrated development environment (IDE) with the power of Azure Machine Learning resources. Select **Open in VS Code**, then select either the web or desktop option.  When launched this way, VS Code is attached to your compute instance, the kernel, and the workspace file system.
+
+    :::image type="content" source="../media/tutorial-azure-ml-in-a-day/open-vs-code.png" alt-text="Screenshot shows how to open the notebook in VS Code." lightbox="../media/tutorial-azure-ml-in-a-day/open-vs-code.png":::
+
 > [!Important]
-> The rest of this tutorial contains cells of the tutorial notebook.  Copy/paste them into your new notebook, or switch to the notebook now if you cloned it.
->
+> The rest of this tutorial contains cells of the tutorial notebook. Copy/paste them into your new notebook, or switch to the notebook now if you cloned it.