how-to-create-attach-compute-studio.md

title	titleSuffix	description	services	author	ms.author	ms.reviewer	ms.service	ms.subservice	ms.date	ms.topic	ms.custom
Manage training & deploy computes (studio)	Azure Machine Learning	Use studio to manage training and deployment compute resources (compute targets) for machine learning.	machine-learning	vijetajo	vijetaj	sgilley	machine-learning	compute	03/04/2024	how-to	build-2023

Manage compute resources for model training and deployment in studio

In this article, learn how to manage the compute resources you use for model training and deployment in Azure Machine studio.

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 today
• An Azure Machine Learning workspace

What's a compute target?

With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.

You can also use serverless compute as a compute target. There's nothing for you to manage when you use serverless compute.

View compute targets

To see all compute targets for your workspace, use the following steps:

1. Navigate to Azure Machine Learning studio.

2. Under Manage, select Compute.

3. Select tabs at the top to show each type of compute target.

   :::image type="content" source="media/how-to-create-attach-studio/compute-targets.png" alt-text="Screenshot of view list of compute targets." lightbox="media/how-to-create-attach-studio/compute-targets.png":::

[!INCLUDE retiring vms]

Compute instance and clusters

You can create compute instances and compute clusters in your workspace, using the Azure Machine Learning SDK, CLI, or studio:

• Compute instance
• Compute cluster

In addition, you can use the VS Code extension to create compute instances and compute clusters in your workspace.

Kubernetes clusters

For information on configuring and attaching a Kubernetes cluster to your workspace, see Configure Kubernetes cluster for Azure Machine Learning.

Other compute targets

To use VMs created outside the Azure Machine Learning workspace, you must first attach them to your workspace. Attaching the compute resource makes it available to your workspace.

1. Navigate to Azure Machine Learning studio.

2. Under Manage, select Compute.

3. In the tabs at the top, select Attached compute to attach a compute target for training.

4. Select +New, then select the type of compute to attach. Not all compute types can be attached from Azure Machine Learning studio.

5. Fill out the form and provide values for the required properties.

   [!NOTE] Microsoft recommends that you use SSH keys, which are more secure than passwords. Passwords are vulnerable to brute force attacks. SSH keys rely on cryptographic signatures. For information on how to create SSH keys for use with Azure Virtual Machines, see the following documents:

   • Create and use SSH keys on Linux or macOS
   • Create and use SSH keys on Windows

6. Select Attach.

To detach your compute use the following steps:

1. In Azure Machine Learning studio, select Compute, Attached compute, and the compute you wish to remove.
2. Use the Detach link to detach your compute.

Connect with SSH access

[!INCLUDE ssh-access]

Next steps

• Use the compute resource to submit a training run.
• Learn how to efficiently tune hyperparameters to build better models.
• Once you have a trained model, learn how and where to deploy models.
• Use Azure Machine Learning with Azure Virtual Networks