az k8s-extension create lacks --yes parameter

Question

I am trying to attach an existing AKS cluster as an inference cluster for Azure ML workspace. Following MS instructions - https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2

az k8s-extension create --name <name> <name> --extension-type Microsoft.AzureML.Kubernetes --config enableTraining=True enableInference=True inferenceRouterServiceType=LoadBalancer allowInsecureConnections=True InferenceRouterHA=False --cluster-type managedClusters --cluster-name <name> --resource-group <RG name> --scope clusterThis is run from GitHub action and fails with error

Interactive authentication is needed. Please run:

az login

Error: Process completed with exit code 1.

Adding --yes flag in the end doesn't work either.

ERROR: unrecognized arguments: --yes

Saw a github issue on the same subject..... [didn't find the answer though]

https://github.com/Azure/azure-cli/issues/28766

Any pointers on how to implement this through GitHJub pipelines?

Able to attach the AKS inference cluster using Terraform. However, it gets attached with type "AksCompute", not "KubernetsCompute" [BTW, trying through Portal, we see types of "AksCompute" and "Kubernetes"]

Answer 1

Hi Suresh Bettadapur,

Thank you for your patience while we are reviewing your query!
As mentioned in the document, we support two ways to attach a Kubernetes cluster to Azure Machine Learning workspace, using Azure CLI or studio UI.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace?view=azureml-api-2&tabs=cli#how-to-attach-a-kubernetes-cluster-to-azure-machine-learning-workspace

Comparison of KubernetesCompute and legacy AksCompute targets

With the Azure Machine Learning CLI/Python SDK v1, you can deploy models on AKS by using the legacy AksCompute target. Both the KubernetesCompute and AksCompute targets support AKS integration, but the support approach is different.

The following table summarizes the key differences:

Capability	AksCompute (legacy)	KubernetesCompute
Use the CLI/SDK v1	Yes	No
Use the CLI/SDK v1	Yes	No
Use the CLI/SDK v2	No	Yes
Set up training	No	Yes
Apply real-time inference	Yes	Yes
Apply batch inference	No	Yes
Access new features for real-time inference	No new features development	Active roadmap available

In consideration of these differences, and the overall Machine Learning evolution to use the CLI/SDK v2, the recommended approach is to use Kubernetes compute target (KubernetesCompute) for AKS model deployment.

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2#comparison-of-kubernetescompute-and-legacy-akscompute-targets

As per my understanding, when using Terraform, the default attachment type for AKS clusters in Azure ML is AksCompute, not KubernetesCompute.

Hope this helps!

Let me know if you have any further queries!

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