Merge pull request #215129 from sdgilley/sdg-content-freshness

freshness & add links to quickstart

Author: JamesJBarnett

articles/machine-learning/concept-compute-instance.md

@@ -9,7 +9,8 @@ ms.custom: event-tier1-build-2022
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 09/22/2021
+ms.reviewer: sgilley
+ms.date: 10/19/2022
 #Customer intent: As a data scientist, I want to know what a compute instance is and how to use it for Azure Machine Learning.
 ---
 
@@ -89,7 +90,7 @@ Python packages are all installed in the **Python 3.8 - AzureML** environment. C
 
 ## Accessing files
 
-Notebooks and R scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your “User files” directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
+Notebooks and Python scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your “User files” directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
 
 The Azure file share account of your workspace is mounted as a drive on the compute instance. This drive is the default working directory for Jupyter, Jupyter Labs, and RStudio. This means that the notebooks and other files you create in Jupyter, JupyterLab, or RStudio are automatically stored on the file share and available to use in other compute instances as well.
 
@@ -101,17 +102,18 @@ Writing small files can be slower on network drives than writing to the compute
 
 Do not store training data on the notebooks file share. You can use the `/tmp` directory on the compute instance for your temporary data.  However, do not write very large files of data on the OS disk of the compute instance. OS disk on compute instance has 128 GB capacity. You can also store temporary training data on temporary disk mounted on /mnt. Temporary disk size is configurable based on the VM size chosen and can store larger amounts of data if a higher size VM is chosen. You can also mount [datastores and datasets](v1/concept-azure-machine-learning-architecture.md#datasets-and-datastores). Any software packages you install are saved on the OS disk of compute instance. Please note customer managed key encryption is currently not supported for OS disk. The OS disk for compute instance is encrypted with Microsoft-managed keys. 
 
-### Create
+## Create
+
+Follow the steps in the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md) to create a basic compute instance.  
+
+For more options, see [create a new compute instance](how-to-create-manage-compute-instance.md?tabs=azure-studio#create).
 
 As an administrator, you can **[create a compute instance for others in the workspace (preview)](how-to-create-manage-compute-instance.md#create-on-behalf-of-preview)**.
 
 You can also **[use a setup script (preview)](how-to-customize-compute-instance.md)** for an automated way to customize and configure the compute instance.
 
-To create a compute instance for yourself, use your workspace in Azure Machine Learning studio, [create a new compute instance](how-to-create-manage-compute-instance.md?tabs=azure-studio#create) from either the **Compute** section or in the **Notebooks** section when you are ready to run one of your notebooks.
-
-You can also create an instance
-* Directly from the [integrated notebooks experience](tutorial-train-deploy-notebook.md#azure)
-* In Azure portal
+Other ways to create a compute instance:
+* Directly from the integrated notebooks experience.
 * From Azure Resource Manager template. For an example template, see the [create an Azure Machine Learning compute instance template](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/machine-learning-compute-create-computeinstance).
 * With [Azure Machine Learning SDK](how-to-create-manage-compute-instance.md?tabs=python#create)
 * From the [CLI extension for Azure Machine Learning](how-to-create-manage-compute-instance.md?tabs=azure-cli#create)
@@ -123,7 +125,7 @@ Compute instance comes with P10 OS disk. Temp disk type depends on the VM size c
 
 ## Compute target
 
-Compute instances can be used as a [training compute target](concept-compute-target.md#train) similar to Azure Machine Learning [compute training clusters](how-to-create-attach-compute-cluster.md).  But a compute instance has only a single node, while a compute cluster can have more nodes.
+Compute instances can be used as a [training compute target](concept-compute-target.md#training-compute-targets) similar to Azure Machine Learning [compute training clusters](how-to-create-attach-compute-cluster.md).  But a compute instance has only a single node, while a compute cluster can have more nodes.
 
 A compute instance:
 
@@ -139,5 +141,5 @@ You can use compute instance as a local inferencing deployment target for test/d
 
 ## Next steps
 
-* [Create and manage a compute instance](how-to-create-manage-compute-instance.md)
+* [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md).
 * [Tutorial: Train your first ML model](tutorial-1st-experiment-sdk-train.md) shows how to use a compute instance with an integrated notebook.

articles/machine-learning/concept-compute-target.md

@@ -8,7 +8,8 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 10/21/2021
+ms.reviewer: sgilley
+ms.date: 10/19/2022
 ms.custom: ignite-fall-2021, event-tier1-build-2022, cliv2
 #Customer intent: As a data scientist, I want to understand what a compute target is and why I need it.
 ---
@@ -20,27 +21,26 @@ A *compute target* is a designated compute resource or environment where you run
 In a typical model development lifecycle, you might:
 
 1. Start by developing and experimenting on a small amount of data. At this stage, use your local environment, such as a local computer or cloud-based virtual machine (VM), as your compute target.
-1. Scale up to larger data, or do [distributed training](how-to-train-distributed-gpu.md) by using one of these [training compute targets](#train).
-1. After your model is ready, deploy it to a web hosting environment with one of these [deployment compute targets](#deploy).
+1. Scale up to larger data, or do [distributed training](how-to-train-distributed-gpu.md) by using one of these [training compute targets](#training-compute-targets).
+1. After your model is ready, deploy it to a web hosting environment with one of these [deployment compute targets](#compute-targets-for-inference).
 
 The compute resources you use for your compute targets are attached to a [workspace](concept-workspace.md). Compute resources other than the local machine are shared by users of the workspace.
 
-## <a name="train"></a> Training compute targets
+## Training compute targets
 
 Azure Machine Learning has varying support across different compute targets. A typical model development lifecycle starts with development or experimentation on a small amount of data. At this stage, use a local environment like your local computer or a cloud-based VM. As you scale up your training on larger datasets or perform [distributed training](how-to-train-distributed-gpu.md), use Azure Machine Learning compute to create a single- or multi-node cluster that autoscales each time you submit a job. You can also attach your own compute resource, although support for different scenarios might vary.
 
 [!INCLUDE [aml-compute-target-train](../../includes/aml-compute-target-train.md)]
 
 
-## <a name="deploy"></a> Compute targets for inference
+## Compute targets for inference
 
 When performing inference, Azure Machine Learning creates a Docker container that hosts the model and associated resources needed to use it. This container is then used in a compute target.
 
 [!INCLUDE [aml-deploy-target](../../includes/aml-compute-target-deploy.md)]
 
 Learn [where and how to deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md).
 
-<a name="amlcompute"></a>
 ## Azure Machine Learning compute (managed)
 
 A managed compute resource is created and managed by Azure Machine Learning. This compute is optimized for machine learning workloads. Azure Machine Learning compute clusters and [compute instances](concept-compute-instance.md) are the only managed computes.

articles/machine-learning/how-to-create-attach-compute-cluster.md

@@ -10,7 +10,7 @@ ms.custom: devx-track-azurecli, cliv2, sdkv1, event-tier1-build-2022
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: sgilley
-ms.date: 09/21/2022
+ms.date: 10/19/2022
 ---
 
 # Create an Azure Machine Learning compute cluster
@@ -77,6 +77,10 @@ The dedicated cores per region per VM family quota and total regional quota, whi
 [!INCLUDE [min-nodes-note](../../includes/machine-learning-min-nodes.md)]
 
 The compute autoscales down to zero nodes when it isn't used.   Dedicated VMs are created to run your jobs as needed.
+
+The fastest way to create a compute cluster is to follow the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md). 
+
+Or use the following examples to create a compute cluster with more options:
 
 # [Python SDK](#tab/python)
 

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

@@ -10,7 +10,7 @@ ms.topic: how-to
 author: swatig007
 ms.author: swatig
 ms.reviewer: sgilley
-ms.date: 09/21/2022
+ms.date: 10/19/2022
 ---
 
 # Create and manage an Azure Machine Learning compute instance
@@ -21,9 +21,9 @@ ms.date: 09/21/2022
 > * [v1](v1/how-to-create-manage-compute-instance.md)
 > * [v2 (current version)](how-to-create-manage-compute-instance.md)
 
-Learn how to create and manage a [compute instance](concept-compute-instance.md) in your Azure Machine Learning workspace.
+Learn how to create and manage a [compute instance](concept-compute-instance.md) in your Azure Machine Learning workspace. 
 
-Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#train).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
+Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#training-compute-targets).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
 In this article, you learn how to:
 
@@ -62,7 +62,9 @@ Creating a compute instance is a one time process for your workspace. You can re
 
 The dedicated cores per region per VM family quota and total regional quota, which applies to compute instance creation, is unified and shared with Azure Machine Learning training compute cluster quota. Stopping the compute instance doesn't release quota to ensure you'll be able to restart the compute instance. It isn't possible to change the virtual machine size of compute instance once it's created.
 
-The following example demonstrates how to create a compute instance:
+The fastest way to create a compute instance is to follow the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md). 
+
+Or use the following examples to create a compute instance with more options:
 
 # [Python SDK](#tab/python)
 
@@ -240,7 +242,6 @@ You can also create your own custom Azure policy. For example, if the below poli
 }
 ```
 
-
 ## 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-customize-compute-instance.md

@@ -22,7 +22,7 @@ ms.date: 05/04/2022
 
 Use a setup script for an automated way to customize and configure a compute instance at provisioning time. 
 
-Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#train) or for an [inference target](concept-compute-target.md#deploy).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
+Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#training-compute-targets) or for an [inference target](concept-compute-target.md#compute-targets-for-inference).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
 As an administrator, you can write a customization script to be used to provision all compute instances in the workspace according to your requirements.
 

articles/machine-learning/how-to-manage-resources-vscode.md

@@ -135,7 +135,7 @@ Alternatively, use the `> Azure ML: Create Dataset` command in the command palet
 1. Expand your workspace node.
 1. Expand the **Datasets** node.
 1. Right-click the dataset you want to:
-    - **View Dataset Properties**. Lets you view metadata associated with a specific dataset. If you have multiple version of a dataset, you can choose to only view the dataset properties of a specific version by expanding the dataset node and performing the same steps described in this section on the version of interest.
+    - **View Dataset Properties**. Lets you view metadata associated with a specific dataset. If you have multiple versions of a dataset, you can choose to only view the dataset properties of a specific version by expanding the dataset node and performing the same steps described in this section on the version of interest.
     - **Preview dataset**. View your dataset directly in the VS Code Data Viewer. Note that this option is only available for tabular datasets.
     - **Unregister dataset**. Removes a dataset and all versions of it from your workspace.
 
@@ -267,7 +267,7 @@ Alternatively, use the `Azure ML: Delete Compute instance` command in the comman
 
 ## Compute clusters
 
-For more information, see [training compute targets](concept-compute-target.md#train).
+For more information, see [training compute targets](concept-compute-target.md#training-compute-targets).
 
 ### Create compute cluster
 
@@ -300,7 +300,7 @@ Alternatively, use the `> Azure ML: Remove Compute` command in the command palet
 
 ## Inference Clusters
 
-For more information, see [compute targets for inference](concept-compute-target.md#deploy).
+For more information, see [compute targets for inference](concept-compute-target.md#compute-targets-for-inference).
 
 ### Manage inference clusters
 

articles/machine-learning/v1/concept-azure-machine-learning-architecture.md

@@ -58,7 +58,7 @@ Azure Machine Learning introduces two fully managed cloud-based virtual machines
 
 * **Compute clusters**: Compute clusters are a cluster of VMs with multi-node scaling capabilities. Compute clusters are better suited for compute targets for large jobs and production.  The cluster scales up automatically when a job is submitted.  Use as a training compute target or for dev/test deployment.
 
-For more information about training compute targets, see [Training compute targets](../concept-compute-target.md#train).  For more information about deployment compute targets, see [Deployment targets](../concept-compute-target.md#deploy).
+For more information about training compute targets, see [Training compute targets](../concept-compute-target.md#training-compute-targets).  For more information about deployment compute targets, see [Deployment targets](../concept-compute-target.md#compute-targets-for-inference).
 
 ## Datasets and datastores
 

articles/machine-learning/v1/how-to-configure-auto-train-v1.md

@@ -141,7 +141,7 @@ Next determine where the model will be trained. An automated ML training experim
 
  * **Choose a local compute**: If your scenario is about initial explorations or demos using small data and short trains (i.e. seconds or a couple of minutes per child run), training on your local computer might be a better choice.  There is no setup time, the infrastructure resources (your PC or VM) are directly available. See [this notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb) for a local compute example. 
 
- * **Choose a remote ML compute cluster**: If you are training with larger datasets like in production training creating models which need longer trains, remote compute will provide much better end-to-end time performance because `AutoML` will parallelize trains across the cluster's nodes. On a remote compute, the start-up time for the internal infrastructure will add around 1.5 minutes per child run, plus additional minutes for the cluster infrastructure if the VMs are not yet up and running.[Azure Machine Learning Managed Compute](../concept-compute-target.md#amlcompute) is a managed service that enables the ability to train machine learning models on clusters of Azure virtual machines. Compute instance is also supported as a compute target.
+ * **Choose a remote ML compute cluster**: If you are training with larger datasets like in production training creating models which need longer trains, remote compute will provide much better end-to-end time performance because `AutoML` will parallelize trains across the cluster's nodes. On a remote compute, the start-up time for the internal infrastructure will add around 1.5 minutes per child run, plus additional minutes for the cluster infrastructure if the VMs are not yet up and running.[Azure Machine Learning Managed Compute](../concept-compute-target.md#azure-machine-learning-compute-managed) is a managed service that enables the ability to train machine learning models on clusters of Azure virtual machines. Compute instance is also supported as a compute target.
 
  * An **Azure Databricks cluster** in your Azure subscription. You can find more details in [Set up an Azure Databricks cluster for automated ML](../how-to-configure-databricks-automl-environment.md). See this [GitHub site](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-databricks) for examples of notebooks with Azure Databricks.
 

articles/machine-learning/v1/how-to-create-machine-learning-pipelines.md

@@ -109,7 +109,7 @@ output_data_dataset = output_data1.register_on_complete(name = 'prepared_output_
 ## Set up a compute target
 
 
-In Azure Machine Learning, the term __compute__ (or __compute target__) refers to the machines or clusters that do the computational steps in your machine learning pipeline.   See [compute targets for model training](../concept-compute-target.md#train) for a full list of compute targets and [Create compute targets](../how-to-create-attach-compute-studio.md) for how to create and attach them to your workspace.   The process for creating and or attaching a compute target is the same whether you're training a model or running a pipeline step. After you create and attach your compute target, use the `ComputeTarget` object in your [pipeline step](#steps).
+In Azure Machine Learning, the term __compute__ (or __compute target__) refers to the machines or clusters that do the computational steps in your machine learning pipeline.   See [compute targets for model training](../concept-compute-target.md#training-compute-targets) for a full list of compute targets and [Create compute targets](../how-to-create-attach-compute-studio.md) for how to create and attach them to your workspace.   The process for creating and or attaching a compute target is the same whether you're training a model or running a pipeline step. After you create and attach your compute target, use the `ComputeTarget` object in your [pipeline step](#steps).
 
 > [!IMPORTANT]
 > Performing management operations on compute targets isn't supported from inside remote jobs. Since machine learning pipelines are submitted as a remote job, do not use management operations on compute targets from inside the pipeline.