1. Multi-Tenant AI Services with Kustomize Namespaces


    When you're building a multi-tenant AI service on Kubernetes, one of the approaches you can take to ensure isolation between different tenants is to use namespaces. Namespaces in Kubernetes are a way to divide cluster resources between multiple users. They are a sort of a "virtual cluster" within a cluster.

    In addition to namespaces, you mentioned using Kustomize, which is a tool for customizing Kubernetes configurations. Kustomize allows you to define template resources and then apply patch files and other customizations to create configuration variations for different environments or tenants.

    Here's a conceptual Pulumi program written in Python. The program will create multiple Kubernetes namespaces, each representing a distinct tenant. We will use the Kubernetes module from Pulumi's package for defining Kubernetes resources. While this example won't cover the Kustomize specifics, it will set up the namespace resources, which you can later customize with Kustomize overlays or patches if needed.

    Let’s walk through the steps this program takes:

    1. Import the necessary Pulumi modules.
    2. Define the list of tenant names that corresponds to the namespaces we want to create.
    3. Use a loop to create a namespace for each tenant.
    4. Export the names of the created namespaces for reference.

    Here’s the detailed Pulumi program:

    import pulumi import pulumi_kubernetes as kubernetes # Define the list of tenants. Each tenant will have a corresponding namespace. tenants = ["tenant-A", "tenant-B", "tenant-C"] # Initialize an empty list to hold our namespace objects. namespaces = [] # Loop over the list of tenants and create a namespace for each. for tenant in tenants: # Define the namespace using the tenant name. # The metadata.name property sets the name of the namespace. ns = kubernetes.core.v1.Namespace(f"{tenant}-namespace", metadata=kubernetes.meta.v1.ObjectMetaArgs( name=tenant.lower() )) # Append the created namespace to our list. namespaces.append(ns) # Export the names of the created namespaces. # This helps us to know which namespaces have been created and can be accessed using `pulumi stack output` pulumi.export("namespace_names", [ns.metadata.name for ns in namespaces])

    To execute this Pulumi program, you’d run it using the Pulumi CLI, after you’ve installed Pulumi and configured it for use with your Kubernetes cluster. Running pulumi up will apply the above configuration to the cluster.

    Additionally, after setting up the namespaces, you can use Kustomize to define resource templates and patches specific to each tenant. You can integrate Kustomize with Pulumi as part of your deployment scripts outside the Pulumi application's scope.

    Remember that this is just the namespace setup part. Actual AI services and configurations might need to be applied after creating your namespaces, and for that, you can use additional Pulumi resource definitions or Kustomize configurations as needed.