If you already have an AKS cluster in your Azure subscription, and it is version 1.12.##, you can attach it to your workspace to use for deployments. The existing AKS cluster can be in a different Azure region than your workspace.
If you want to secure your AKS cluster using an Azure Virtual Network, you must create the virtual network first. For more information, see Secure Azure ML experimentation and inference jobs within an Azure Virtual Network
If you want to re-attach an AKS cluster, for example to to change SSL or other
cluster configuration settings, you must first remove the existing attachment
with detach_aks_compute().
Attaching a cluster will take approximately 5 minutes.
attach_aks_compute( workspace, resource_group, cluster_name, cluster_purpose = c("FastProd", "DevTest") )
| workspace | The |
|---|---|
| resource_group | A string of the resource group in which the AKS cluster is located. |
| cluster_name | A string of the name of the AKS cluster. |
| cluster_purpose | The targeted usage of the cluster. The possible values are "DevTest" or "FastProd". This is used to provision Azure Machine Learning components to ensure the desired level of fault-tolerance and QoS. If your cluster has less than 12 virtual CPUs, you will need to specify "DevTest" for this argument. We recommend that your cluster have at least 2 virtual CPUs for dev/test usage. |
The AksCompute object.
ws <- load_workspace_from_config()
compute_target <- attach_aks_compute(ws,
resource_group = 'myresourcegroup',
cluster_name = 'myakscluster')
If the cluster has less than 12 virtual CPUs, you will need to also specify the
cluster_purpose parameter in the attach_aks_compute() call: cluster_purpose = 'DevTest'.
detach_aks_compute()