Merge pull request #241653 from Blackmist/v1-v2-checkup

moving v1 file to v1 folder, fixing links

Author: v-dirichards

articles/machine-learning/concept-environments.md

@@ -106,7 +106,7 @@ Actual cached images in your workspace ACR will have names like `azureml/azureml
 >
 >   To update the package, specify a version number to force an image rebuild. An example of this would be changing `numpy` to `numpy==1.18.1`. New dependencies--including nested ones--will be installed, and they might break a previously working scenario.
 >
-> * Using an unpinned base image like `mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04` in your environment definition results in rebuilding the image every time the `latest` tag is updated. This helps the image receive the latest patches and system updates.zzs
+> * Using an unpinned base image like `mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04` in your environment definition results in rebuilding the image every time the `latest` tag is updated. This helps the image receive the latest patches and system updates.
 
 ### Image patching
 

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

@@ -134,7 +134,7 @@ Once you have the Visual Studio Code extension installed, use it to:
 
 * [Manage your Azure Machine Learning resources](how-to-manage-resources-vscode.md)
 * [Connect to an Azure Machine Learning compute instance](how-to-set-up-vs-code-remote.md)
-* [Run and debug experiments](how-to-debug-visual-studio-code.md)
+* [Debug online endpoints locally](how-to-debug-managed-online-endpoints-visual-studio-code.md)
 * [Deploy trained models](tutorial-train-deploy-image-classification-model-vscode.md).
 
 ## Azure Machine Learning compute instance

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

@@ -11,12 +11,13 @@ ms.author: lebaro
 author: lebaro-msft
 ms.reviewer: sgilley 
 ms.date: 04/10/2023
+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)
 
-In this article, you'll 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.
+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)]
 
@@ -33,7 +34,7 @@ There are two ways you can connect to a compute instance from Visual Studio Code
 
 ## Prerequisites
 
-Before you get started, you will need:
+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)]
@@ -50,7 +51,7 @@ Use one of these options to connect VS Code to your compute instance and workspa
 
 VS Code for the Web provides you with a **full-featured development environment** for building your machine learning projects, all from the browser and **without required installations or dependencies**. And by connecting your Azure Machine Learning compute instance, you get the rich and integrated development experience VS Code offers, enhanced by the power of Azure Machine Learning.
 
-Launch VS Code for the Web with one click from the Azure Machine Learning studio, and seamlessly continue your work.
+Launch VS Code for the Web with one select from the Azure Machine Learning studio, and seamlessly continue your work.
 
 Sign in to [Azure Machine Learning studio](https://ml.azure.com) and follow the steps to launch a VS Code (Web) browser tab, connected to your Azure Machine Learning compute instance.
 
@@ -60,9 +61,9 @@ You can create the connection from either the **Notebooks** or **Compute** secti
 
     1. Select the **Notebooks** tab.
     1. In the *Notebooks* tab, select the file you want to edit.
-    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 compute if it's stopped." lightbox="media/tutorial-azure-ml-in-a-day/start-compute.png":::
 
     1. Select **Editors > Edit in VS Code (Web)**.
 
@@ -78,9 +79,9 @@ You can create the connection from either the **Notebooks** or **Compute** secti
 
 # [Studio -> VS Code (Desktop)](#tab/vscode-desktop)
 
-This option will launch the VS Code desktop application, connected to your compute instance.
+This option launches the VS Code desktop application, connected to your compute instance.
 
-On the initial connection, you may be prompted to install the Azure Machine Learning Visual Studio Code extension if you do not already have it. For more information, see the [Azure Machine Learning Visual Studio Code Extension setup guide](how-to-setup-vs-code.md).
+On the initial connection, you may be prompted to install the Azure Machine Learning Visual Studio Code extension if you don't already have it. For more information, see the [Azure Machine Learning Visual Studio Code Extension setup guide](how-to-setup-vs-code.md).
 
 > [!IMPORTANT]
 > In order to connect to your remote compute instance from Visual Studio Code, make sure that the account you're logged into in Azure Machine Learning studio is the same one you use in Visual Studio Code.
@@ -93,9 +94,9 @@ You can create the connection from either the **Notebooks** or **Compute** secti
 
     1. Select the **Notebooks** tab
     1. In the *Notebooks* tab, select the file you want to edit.
-    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 compute if it's stopped." lightbox="media/tutorial-azure-ml-in-a-day/start-compute.png":::
 
     1. Select **Edit in VS Code (Desktop)**.
 
@@ -115,7 +116,7 @@ You can create the connection from either the **Notebooks** or **Compute** secti
 
 # [From VS Code](#tab/extension)
 
-This option will connect your current VS Code session to a remote compute instance. In order to connect to your compute instance _from_ VS Code, you will need to install the Azure Machine Learning Visual Studio Code extension. For more information, see the [Azure Machine Learning Visual Studio Code Extension setup guide](how-to-setup-vs-code.md).
+This option connects your current VS Code session to a remote compute instance. In order to connect to your compute instance _from_ VS Code, you need to install the Azure Machine Learning Visual Studio Code extension. For more information, see the [Azure Machine Learning Visual Studio Code Extension setup guide](how-to-setup-vs-code.md).
 
 ### Azure Machine Learning Extension
 
@@ -135,13 +136,13 @@ This option will connect your current VS Code session to a remote compute instan
 
 ---
 
-If you pick one of the click-out experiences, a new VS Code window will be opened, and a connection attempt made to the remote compute instance. When attempting to make this connection, the following steps are taking place:
+If you pick one of the click-out experiences, a new VS Code window is opened, and a connection attempt made to the remote compute instance. When attempting to make this connection, the following steps are taking place:
 
 1. Authorization. Some checks are performed to make sure the user attempting to make a connection is authorized to use the compute instance.
 1. VS Code Remote Server is installed on the compute instance.
 1. A WebSocket connection is established for real-time interaction.
 
-Once the connection is established, it's persisted. A token is issued at the start of the session which gets refreshed automatically to maintain the connection with your compute instance.
+Once the connection is established, it's persisted. A token is issued at the start of the session, which gets refreshed automatically to maintain the connection with your compute instance.
 
 After you connect to your remote compute instance, use the editor to:
 

articles/machine-learning/how-to-manage-optimize-cost.md

@@ -12,8 +12,6 @@ ms.topic: how-to
 ms.date: 06/08/2021 
 ---
 
-[//]: # (needs PM review; ParallelJobStep or ParallelRunStep?)
-
 # Manage and optimize Azure Machine Learning costs
 
 Learn how to manage and optimize costs when training and deploying machine learning models to Azure Machine Learning.
@@ -99,15 +97,9 @@ Another way to save money on compute resources is Azure Reserved VM Instance. Wi
 
 Azure Machine Learning Compute supports reserved instances inherently. If you purchase a one-year or three-year reserved instance, we will automatically apply discount against your Azure Machine Learning managed compute.
 
-## Train locally
-
-When prototyping and running training jobs that are small enough to run on your local computer, consider training locally. Using the Python SDK, setting your compute target to `local` executes your script locally. 
-
-Visual Studio Code provides a full-featured environment for developing your machine learning applications. Using the Azure Machine Learning visual Visual Studio Code extension and Docker, you can run and debug locally. For more information, see [interactive debugging with Visual Studio Code](how-to-debug-visual-studio-code.md).
-
 ## Parallelize training
 
-One of the key methods of optimizing cost and performance is by parallelizing the workload with the help of a parallel run step in Azure Machine Learning. This step allows you to use many smaller nodes to execute the task in parallel, hence allowing you to scale horizontally. There is an overhead for parallelization. Depending on the workload and the degree of parallelism that can be achieved, this may or may not be an option. For further information, see the [ParallelRunStep](xref:azureml.contrib.pipeline.steps.ParallelRunStep) documentation.
+One of the key methods of optimizing cost and performance is by parallelizing the workload with the help of a parallel component in Azure Machine Learning. A parallel component allows you to use many smaller nodes to execute the task in parallel, hence allowing you to scale horizontally. There is an overhead for parallelization. Depending on the workload and the degree of parallelism that can be achieved, this may or may not be an option. For further information, see the [ParallelComponent](/python/api/azure-ai-ml/azure.ai.ml.entities.parallelcomponent) documentation.
 
 ## Set data retention & deletion policies
 

articles/machine-learning/how-to-setup-vs-code.md

@@ -11,6 +11,7 @@ ms.subservice: core
 ms.date: 10/21/2021
 ms.topic: how-to
 ms.custom: devplatv2, event-tier1-build-2022, build-2023
+monikerRange: 'azureml-api-1 || azureml-api-2'
 ---
 
 # Set up Visual Studio Code desktop with the Azure Machine Learning extension (preview)
@@ -76,4 +77,4 @@ Alternatively, use the `> Azure ML: Set Default Workspace` command in the comman
 - [Manage your Azure Machine Learning resources](how-to-manage-resources-vscode.md)
 - [Develop on a remote compute instance locally](how-to-launch-vs-code-remote.md)
 - [Train an image classification model using the Visual Studio Code extension](tutorial-train-deploy-image-classification-model-vscode.md)
-- [Run and debug machine learning experiments locally](how-to-debug-visual-studio-code.md)
+- [Run and debug machine learning experiments locally (CLI v1)](./v1/how-to-debug-visual-studio-code.md)

articles/machine-learning/resource-curated-environments.md

@@ -10,11 +10,12 @@ ms.service: machine-learning
 ms.subservice: core
 ms.topic: reference
 ms.date: 10/21/2021
+monikerRange: 'azureml-api-1 || azureml-api-2'
 ---
 
 # Azure Machine Learning Curated Environments
 
-This article lists the curated environments with latest framework versions in Azure Machine Learning. Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They're backed by cached Docker images that use the latest version of the Azure Machine Learning SDK, reducing the run preparation cost and allowing for faster deployment time. Use these environments to quickly get started with various machine learning frameworks.
+This article lists the curated environments with latest framework versions in Azure Machine Learning. Curated environments are provided by Azure Machine Learning and are available in your workspace by default. The curated environments rely on cached Docker images that use the latest version of the Azure Machine Learning SDK. Using a curated environment can reduce the run preparation cost and allow for faster deployment time. Use these environments to quickly get started with various machine learning frameworks.
 
 > [!NOTE]
 > Use the [Python SDK](how-to-use-environments.md), [CLI](/cli/azure/ml/environment#az-ml-environment-list), or Azure Machine Learning [studio](how-to-manage-environments-in-studio.md) to get the full list of environments and their dependencies. For more information, see the [environments article](how-to-use-environments.md#use-a-curated-environment).
@@ -34,7 +35,7 @@ This article lists the curated environments with latest framework versions in Az
 
 ### Azure Container for PyTorch (ACPT)
 
-**Description**: Recommended environment for Deep Learning with PyTorch on Azure containing the Azure Machine Learning SDK with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, and NebulaML combined with optimizers like ORT Training, +DeepSpeed+MSCCL+ORT MoE, and checkpointing using NebulaML and more.
+**Description**: Recommended environment for Deep Learning with PyTorch on Azure. It contains the Azure Machine Learning SDK with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, and NebulaML. It also provides optimizers like ORT Training, +DeepSpeed+MSCCL+ORT MoE, and checkpointing using NebulaML and more.
 
 To learn more, see [Azure Container for PyTorch (ACPT)](resource-azure-container-for-pytorch.md).
 

articles/machine-learning/toc.yml

@@ -1329,7 +1329,7 @@
       items:
         - name: VS Code interactive debugging
           displayName: vscode,remote,debug,pipelines,deployments,ssh
-          href: how-to-debug-visual-studio-code.md
+          href: ./v1/how-to-debug-visual-studio-code.md
     # end v1
 - name: Reference
   items: