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Deploy AzureML extension to your Kubernetes cluster

You will use Azure Arc k8s-extension CLI to deploy the extension on top of your Azure Arc-enabled Kubernetes cluster.

Login to Azure

az login
az account set --subscription <your-subscription-id>

Deploy AzureML extension for model training

az k8s-extension create --name amlarc-compute --extension-type Microsoft.AzureML.Kubernetes --configuration-settings enableTraining=True  --cluster-type connectedClusters --cluster-name <your-connected-cluster-name> --resource-group <resource-group> --scope cluster

Important: To enabled Azure Arc-enabled cluster for ML training, configuration setting enableTraining must be set to True. Running this command will create an Azure Service Bus and Azure Relay resource under the same resource group as the Arc cluster. These resources are used to communicate with the cluster and modifying them will break attached compute targets.

Following is the list of available configuration settings to be specified when you deploy AzureML extension for model training. Configuration settings can be specified by appending key/value pairs to --configuration-settings parameter in "az k8s-extension create" CLI command.

Configuration Setting Key Name Description
enableTraining True or False, default False. Must be set to True for AzureML extension deployment with Machine Learning model training support.
logAnalyticsWS True or False, default False. AzureML extension integrates with Azure LogAnalytics Workspace to provide log viewing and analysis capability through LogAalytics Workspace. This setting must be explicitly set to True if customer wants to leverage this capability. LogAnalytics Workspace cost may apply.
installNvidiaDevicePlugin True or False, default True. Nvidia Device Plugin is required for ML inference on Nvidia GPU hardware. By default, AzureML extension deployment will install Nvidia Device Plugin regardless Kubernetes cluster has GPU hardware or not. User can specify this configuration setting to False if Nvidia Device Plugin installation is not required (either it is installed already or there is no plan to use GPU for workload).```
openshift True or False, default False. If this is specified toTrue, it indicates user will deploy AzureML extension on OpenShift platform, the deployment process will automatically compile a policy package and load policy package on each node so AzureML services operation can function properly.

Verify your AzureML extension deployment

  1. Run the following command on Azure CLI:

    az k8s-extension show --name amlarc-compute --cluster-type connectedClusters --cluster-name <your-connected-cluster-name> --resource-group <resource-group>

  2. In the response, look for "extensionType": "amlarc-compute" and "installState": "Installed". Note it might show "installState": "Pending" for the first few minutes.

  3. If the state shows Installed, run the following command on your machine with the kubeconfig file pointed to your cluster to check that all pods under "azureml" namespace are in 'Running' state:

     kubectl get pods -n azureml