1. Kubernetes Namespace Isolation for AI Projects with AppProject


    To create an isolated namespace for AI projects in Kubernetes, you will typically start by defining a Namespace resource within your Kubernetes cluster. This namespace will serve as a logical separation for your project-related resources, ensuring that the services, deployments, and other objects inside it are encapsulated away from other namespaces.

    With this separation in place, you can increase the isolation level by implementing Kubernetes RBAC (Role-Based Access Control) policies, Network Policies, Resource Quotas, and Limit Ranges within the namespace to ensure fine-grained access control and resource allocation.

    However, if you are interested in an additional layer of organization, segmentation, and governance, you might use a tool like ArgoCD, which introduces a concept called AppProject. An AppProject provides additional controls that limit what may be deployed and/or where within the cluster it may be deployed.

    In this example, we'll stick to the core Kubernetes concepts of creating an isolated namespace. We won't delve into AppProject since it's specific to ArgoCD and isn't represented in the standard Kubernetes API or the Pulumi Kubernetes package.

    Here’s a Pulumi program written in Python, which will create a Kubernetes namespace. This program assumes that you have configured Pulumi to use your Kubernetes cluster context, typically via a kubeconfig file.

    import pulumi import pulumi_kubernetes as kubernetes # Create a Kubernetes Namespace for the AI project ai_project_namespace = kubernetes.core.v1.Namespace("aiProjectNamespace", metadata=kubernetes.meta.v1.ObjectMetaArgs( name="ai-projects", # The name of the namespace labels={"type": "ai-projects"} # Custom labels can be applied ) ) pulumi.export('ai_project_namespace', ai_project_namespace.metadata["name"])

    This simple Pulumi program does the following:

    • Imports the necessary Pulumi libraries for Kubernetes.
    • Defines a new Kubernetes Namespace by creating an instance of kubernetes.core.v1.Namespace.
    • The namespace is named ai-projects and is also labeled with type: ai-projects. Labels are key/value pairs that are attached to objects and can be used to organize and to select subsets of objects.
    • Finally, the namespace name is exported as an output of this Pulumi program, which can be viewed after successful deployment.

    Once you run this Pulumi program with the appropriate access to a Kubernetes cluster, it will create a new namespace where you can deploy resources that will be part of your AI projects. The resources within this namespace will be isolated based on the boundaries of the Kubernetes Namespace entity.

    Remember to adhere to best practices for Kubernetes namespaces management, and configure additional isolation and permissions as needed for your specific use case.