1. Scaling AI Workloads with Azure Kubernetes Service


    To scale AI workloads with Azure Kubernetes Service (AKS), we'll need to set up an AKS cluster that can handle the demands of AI applications, which typically require significant computational resources. AKS allows you to easily deploy a managed Kubernetes cluster on Azure and is an ideal platform for running AI workloads because it easily scales as your computational needs grow.

    Below I've outlined code to create an AKS cluster using Pulumi's Azure Native provider. The AKS cluster is defined with a managed identity, and a default node pool, which is a group of nodes (virtual machines) where the Kubernetes pods (containers) get deployed. I’ve added an example with GPU-enabled virtual machines which are often required for AI workloads due to their accelerated computing capabilities.

    High Level Steps:

    1. Define the Managed Cluster: The Pulumi resource ManagedCluster is used to create and manage an AKS cluster in Azure. We’ll specify the resource group, AKS cluster properties like DNS prefix, Kubernetes version, and node pool properties such as VM size and count.

    2. Setting up Node Pool for AI Workloads: Azure offers various machine types; for AI workloads, you may want to consider using GPU-enabled VMs like the Standard_NC6.

    3. Deploy the Resources: Run pulumi up to deploy your resources to Azure.

    Python Program:

    import pulumi import pulumi_azure_native.containerservice as containerservice import pulumi_azure_native.resources as resources # Create an Azure Resource Group resource_group = resources.ResourceGroup("aiResourceGroup") # Create an AKS cluster with a single node pool managed_cluster = containerservice.ManagedCluster( "aiCluster", resource_group_name=resource_group.name, dns_prefix="aikubernetes", agent_pool_profiles=[{ "count": 1, "max_pods": 110, "mode": "System", "name": "agentpool", "node_labels": {}, "os_disk_size_gb": 30, "os_type": "Linux", "vm_size": "Standard_NC6", # GPU enabled VM # Choose appropriate VM size and count according to your workload. # Note: GPU VM sizes are more expensive but provide necessary compute for AI workloads. # For non-GPU, general purpose VMs, consider using "Standard_DS2_v2" }], identity={ "type": "SystemAssigned", }, # Specify Kubernetes version as needed kubernetes_version="1.19.7", ) # Export the Kubernetes cluster name pulumi.export("cluster_name", managed_cluster.name) # Export the Resource Group name pulumi.export("resource_group_name", resource_group.name) # Export the kubeconfig kubeconfig = pulumi.Output.all(resource_group.name, managed_cluster.name).apply( lambda args: containerservice.list_managed_cluster_user_credentials( resource_group_name=args[0], resource_name=args[1], ) ).apply(lambda creds: creds.kubeconfigs[0].value.apply(lambda enc: enc.decode("utf-8"))) pulumi.export("kubeconfig", kubeconfig)

    How to Use the Code:

    1. Install Pulumi on your local system if you haven't already.

    2. Set up the Azure CLI and log in to your account with az login.

    3. Create a new directory for your Pulumi project.

    4. Run pulumi new azure-python inside the new directory.

    5. Replace the generated __main__.py file with the above code.

    6. Run pulumi up to deploy the AKS cluster.

    7. Once deployed, Pulumi outputs the AKS cluster name, resource group name, and Kubernetes configuration (kubeconfig) to access your cluster.

    Effect of the Code:

    • The code will spin up resources in Azure, which will incur costs depending on the AKS cluster configuration.
    • The VM size "Standard_NC6" is a GPU-enabled virtual machine which has a higher cost but necessary for AI workloads that require heavy computation.

    Additional Information:

    • Always check for the latest supported Kubernetes version in Azure AKS before deploying.
    • Scale up node count or VM sizes according to your workload needs. More nodes or higher VM sizes will result in increased cost.
    • You may attach additional node pools for different types of workloads within the same AKS cluster.

    Using Pulumi with Azure Kubernetes Service provides programmatic infrastructure management. This can be beneficial for scaling AI workloads as it allows you to define infrastructure in code, making it versionable, repeatable, and predictable.