Create an Azure Machine Learning compute cluster with CLI v1

APPLIES TO: Azure CLI ml extension v1 Python SDK azureml v1

Learn how to create and manage a compute cluster in your Azure Machine Learning workspace.

You can use Azure Machine Learning compute cluster to distribute a training or batch inference process across a cluster of CPU or GPU compute nodes in the cloud. For more information on the VM sizes that include GPUs, see GPU-optimized virtual machine sizes.

In this article, learn how to:

• Create a compute cluster
• Lower your compute cluster cost
• Set up a managed identity for the cluster

Prerequisites

• An Azure Machine Learning workspace. For more information, see Create an Azure Machine Learning workspace.

• The Azure CLI extension for Machine Learning service (v1), Azure Machine Learning Python SDK, or the Azure Machine Learning Visual Studio Code extension.

  Important

  Some of the Azure CLI commands in this article use the azure-cli-ml, or v1, extension for Azure Machine Learning. Support for the v1 extension will end on September 30, 2025. You will be able to install and use the v1 extension until that date.

  We recommend that you transition to the ml, or v2, extension before September 30, 2025. For more information on the v2 extension, see Azure ML CLI extension and Python SDK v2.

• If using the Python SDK, set up your development environment with a workspace. Once your environment is set up, attach to the workspace in your Python script:

  APPLIES TO: Python SDK azureml v1

  from azureml.core import Workspace
  
  ws = Workspace.from_config() 
  

What is a compute cluster?

Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute cluster is a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network. Compute cluster supports no public IP deployment as well in virtual network. The compute executes in a containerized environment and packages your model dependencies in a Docker container.

Compute clusters can run jobs securely in a virtual network environment, without requiring enterprises to open up SSH ports. The job executes in a containerized environment and packages your model dependencies in a Docker container.

Limitations

• Compute clusters can be created in a different region and VNet than your workspace. However, this functionality is only available using the SDK v2, CLI v2, or studio. For more information, see the v2 version of secure training environments.

• We currently support only creation (and not updating) of clusters through ARM templates. For updating compute, we recommend using the SDK, Azure CLI or UX for now.

• Azure Machine Learning Compute has default limits, such as the number of cores that can be allocated. For more information, see Manage and request quotas for Azure resources.

• Azure allows you to place locks on resources, so that they cannot be deleted or are read only. Do not apply resource locks to the resource group that contains your workspace. Applying a lock to the resource group that contains your workspace will prevent scaling operations for Azure Machine Learning compute clusters. For more information on locking resources, see Lock resources to prevent unexpected changes.

Create

Time estimate: Approximately 5 minutes.

Azure Machine Learning Compute can be reused across runs. The compute can be shared with other users in the workspace and is retained between runs, automatically scaling nodes up or down based on the number of runs submitted, and the max_nodes set on your cluster. The min_nodes setting controls the minimum nodes available.

The dedicated cores per region per VM family quota and total regional quota, which applies to compute cluster creation, is unified and shared with Azure Machine Learning training compute instance quota.

The compute autoscales down to zero nodes when it isn't used. Dedicated VMs are created to run your jobs as needed.

from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription.  See the [AmlCompute class](https://learn.microsoft.com/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute) for details.

> [!WARNING]
> When setting the `location` parameter, if it is a different region than your workspace or datastores you may see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.

# [Azure CLI](#tab/azure-cli)

[!INCLUDE [cli v1](../includes/machine-learning-cli-v1.md)]

```azurecli
az ml computetarget create amlcompute -n cpu --min-nodes 1 --max-nodes 1 -s STANDARD_D3_V2 --location chinanorth2

Lower your compute cluster cost

You may also choose to use low-priority VMs to run some or all of your workloads. These VMs do not have guaranteed availability and may be preempted while in use. You will have to restart a preempted job.

compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
                                                            vm_priority='lowpriority',
                                                            max_nodes=4)

az ml computetarget create amlcompute --name lowpriocluster --vm-size Standard_NC6 --max-nodes 5 --vm-priority lowpriority

Set up managed identity

Azure Machine Learning compute clusters also support managed identities to authenticate access to Azure resources without including credentials in your code. There are two types of managed identities:

• A system-assigned managed identity is enabled directly on the Azure Machine Learning compute cluster and compute instance. The life cycle of a system-assigned identity is directly tied to the compute cluster or instance. If the compute cluster or instance is deleted, Azure automatically cleans up the credentials and the identity in Microsoft Entra ID.
• A user-assigned managed identity is a standalone Azure resource provided through Azure Managed Identity service. You can assign a user-assigned managed identity to multiple resources, and it persists for as long as you want. This managed identity needs to be created beforehand and then passed as the identity_id as a required parameter.

Managed identity usage

The default managed identity is the system-assigned managed identity or the first user-assigned managed identity.

During a run there are two applications of an identity:

1. The system uses an identity to set up the user's storage mounts, container registry, and datastores.

   • In this case, the system will use the default-managed identity.

2. The user applies an identity to access resources from within the code for a submitted run

   • In this case, provide the client_id corresponding to the managed identity you want to use to retrieve a credential.
   • Alternatively, get the user-assigned identity's client ID through the DEFAULT_IDENTITY_CLIENT_ID environment variable.

   For example, to retrieve a token for a datastore with the default-managed identity:

   client_id = os.environ.get('DEFAULT_IDENTITY_CLIENT_ID')
   credential = ManagedIdentityCredential(client_id=client_id)
   token = credential.get_token('https://storage.azure.com/')
   

Troubleshooting

There is a chance that some users who created their Azure Machine Learning workspace from the Azure portal before the GA release might not be able to create AmlCompute in that workspace. You can either raise a support request against the service or create a new workspace through the portal or the SDK to unblock yourself immediately.

Stuck at resizing

If your Azure Machine Learning compute cluster appears stuck at resizing (0 -> 0) for the node state, this may be caused by Azure resource locks.

Azure allows you to place locks on resources, so that they cannot be deleted or are read only. Locking a resource can lead to unexpected results. Some operations that don't seem to modify the resource actually require actions that are blocked by the lock.

With Azure Machine Learning, applying a delete lock to the resource group for your workspace will prevent scaling operations for Azure ML compute clusters. To work around this problem we recommend removing the lock from resource group and instead applying it to individual items in the group.

These resources are used to communicate with, and perform operations such as scaling on, the compute cluster. Removing the resource lock from these resources should allow autoscaling for your compute clusters.

For more information on resource locking, see Lock resources to prevent unexpected changes.

Next steps

Use your compute cluster to:

• Submit a training run
• Run batch inference.