1. Containerized Model Serving on Azure Kubernetes Service (AKS)


    Containerized model serving on Azure Kubernetes Service (AKS) involves deploying containerized machine learning models into a Kubernetes cluster hosted on Azure. This allows you to easily manage, scale, and update your machine learning models in a production environment. Here's how you can achieve this with Pulumi in Python:

    1. Provision an Azure Kubernetes Service (AKS) Cluster: This is the main environment where your containerized applications will run.
    2. Deploy Your Model as a Container: Once the AKS cluster is ready, you can deploy your machine learning models packaged as Docker containers onto the cluster.

    To begin, you'll be using the following main resources from Pulumi:

    • ManagedCluster: This resource represents an AKS cluster in Azure and is where you'll deploy your models.
    • ContainerRegistry: This is a managed Docker container registry service based on the open-source Docker Registry 2.0. You'll use this to store and manage your Docker container images.

    The following program demonstrates how to create these resources using Pulumi.

    Here's the step-by-step Pulumi program to create an AKS cluster and a container registry to achieve containerized model serving:

    import pulumi from pulumi_azure_native import containerservice as aks from pulumi_azure_native import resources from pulumi_azure_native import containerregistry as acr # Replace these variables with your own desired settings resource_group_name = 'model_serving_resource_group' aks_cluster_name = 'model-serving-aks' acr_name = 'modelservingacr' # Create an Azure Resource Group resource_group = resources.ResourceGroup(resource_group_name) # Create an Azure Container Registry to store container images container_registry = acr.Registry( acr_name, resource_group_name=resource_group.name, location=resource_group.location, sku=acr.SkuArgs(name='Basic') ) # Create an AKS cluster to run containerized models aks_cluster = aks.ManagedCluster( aks_cluster_name, resource_group_name=resource_group.name, location=resource_group.location, dns_prefix='modelserving', # Your DNS prefix here agent_pool_profiles=[aks.ManagedClusterAgentPoolProfileArgs( count=3, max_pods=110, mode='System', name='agentpool', node_labels={"purpose": "model-serving"}, os_type='Linux', vm_size='Standard_DS2_v2', )], identity=aks.ManagedClusterIdentityArgs( type='SystemAssigned', ), ) # Export the AKS cluster name and the ACI registry login server pulumi.export('aks_cluster_name', aks_cluster.name) pulumi.export('acr_login_server', container_registry.login_server)

    This program starts by importing necessary Pulumi libraries and specifying the Python version for the Pulumi program. Then, it defines the AKS cluster and Azure Container Registry configurations. Once the configurations are set up, the program provisions these Azure resources using their respective Pulumi resource classes. Finally, the program exports the AKS cluster name and the Azure Container Registry login server URL so they can be easily retrieved.

    After running this program with Pulumi, you will have an AKS cluster ready for deploying your containerized models and an Azure Container Registry where you can store and manage your Docker images.

    To deploy your containerized model onto the AKS cluster, you'll need a Docker image of your model, push it to the created Container Registry, and then create Kubernetes deployments and services pointing to the images in your Container Registry. For these steps, you will be using Kubernetes manifests or Helm charts, which can also be managed by Pulumi with the Kubernetes provider. However, these additional steps are beyond the scope of this initial setup.