Attach a Kubernetes cluster to AzureML workspace

Once AzureML extension is deployed on AKS or Arc Kubernetes cluster, you can attach the Kubernetes cluster to AzureML workspace and create compute targets for ML professionals to use.

Some key considerations when attaching Kubernetes cluster to AzureML workspace:

  • If you need to access Azure resource seamlessly from your training script, you can specify a managed identity for Kubernetes compute target during attach operation.
  • If you plan to have different compute target for different project/team, you can specify Kubernetes namespace for the compute target to isolate workload among different teams/projects.
  • For the same Kubernetes cluster, you can attach it to the same workspace multiple times and create multiple compute targets for different project/team/workload.
  • For the same Kubernetes cluster, you can also attach it to multiple workspaces, and the multiple workspaces can share the same Kubernetes cluster.


Azure Machine Learning workspace defaults to having a system-assigned managed identity to access Azure ML resources. The steps are completed if the system assigned default setting is on.

Otherwise, if a user-assigned managed identity is specified in Azure Machine Learning workspace creation, the following role assignments need to be granted to the managed identity manually before attaching the compute.

Azure resource name Role to be assigned Description
Azure Relay Azure Relay Owner Only applicable for Arc-enabled Kubernetes cluster. Azure Relay isn't created for AKS cluster without Arc connected.
Azure Arc-enabled Kubernetes or AKS Reader Applicable for both Arc-enabled Kubernetes cluster and AKS cluster.

Azure Relay resource is created during the extension deployment under the same Resource Group as the Arc-enabled Kubernetes cluster.

APPLIES TO: Azure CLI ml extension v2 (current)

The following commands show how to attach an AKS and Azure Arc-enabled Kubernetes cluster, and use it as a compute target with managed identity enabled.

AKS cluster

az ml compute attach --resource-group <resource-group-name> --workspace-name <workspace-name> --type Kubernetes --name k8s-compute --resource-id "/subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.ContainerService/managedclusters/<cluster-name>" --identity-type SystemAssigned --namespace <Kubernetes namespace to run AzureML workloads> --no-wait

Arc Kubernetes cluster

az ml compute attach --resource-group <resource-group-name> --workspace-name <workspace-name> --type Kubernetes --name amlarc-compute --resource-id "/subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.Kubernetes/connectedClusters/<cluster-name>" --user-assigned-identities "subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<identity-name>" --no-wait

Set the --type argument to Kubernetes. Use the identity_type argument to enable SystemAssigned or UserAssigned managed identities.


--user-assigned-identities is only required for UserAssigned managed identities. Although you can provide a list of comma-separated user managed identities, only the first one is used when you attach your cluster.

Compute attach won't create the Kubernetes namespace automatically or validate whether the kubernetes namespace existed. You need to verify that the specified namespace exists in your cluster, otherwise, any AzureML workloads submitted to this compute will fail.

Next steps