Multi-Tenant AI Platforms with Kubernetes Namespaces

Question

Pulumi · Accepted Answer

Creating a multi-tenant AI platform on Kubernetes involves segregating resources and workloads between different tenants in a way that maintains isolation but also allows for shared infrastructure utilization. To achieve this, Kubernetes namespaces are leveraged to create isolated environments for each tenant. Namespaces provide the scope for Kubernetes resources, allowing tenants to work within a segment of the cluster as if they had a private cluster without being aware of other tenants.

In Kubernetes, namespaces are used to divide cluster resources between multiple users. In the context of an AI platform, different teams or projects may require separate environments for their workloads, data, and configurations. Namespaces help in organizing the resources used by different projects and ensuring that resource names are unique within each project.

Here is a basic Pulumi program that sets up Kubernetes namespaces for different tenants in a multi-tenant AI platform:

1. The program will use the `pulumi_kubernetes` Python package to interact with Kubernetes.
2. It will define two namespaces, representing two separate tenants on the AI platform.

```python
import pulumi
import pulumi_kubernetes as kubernetes

# Create a Kubernetes namespace for Tenant A.
# This namespace will provide an isolated environment for Tenant A's resources.
tenant_a_namespace = kubernetes.core.v1.Namespace("tenant-a-namespace",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="tenant-a"  # The name of the namespace, which must be unique within the cluster.
    )
)

# Similarly, create a Kubernetes namespace for Tenant B.
tenant_b_namespace = kubernetes.core.v1.Namespace("tenant-b-namespace",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="tenant-b"  # The name of the namespace for Tenant B.
    )
)

# Export the names of the namespaces. These can be used to reference the namespaces in other Pulumi programs.
pulumi.export("tenant_a_namespace_name", tenant_a_namespace.metadata["name"])
pulumi.export("tenant_b_namespace_name", tenant_b_namespace.metadata["name"])
```

What this Pulumi program does:

- It imports the necessary Pulumi packages for interacting with Kubernetes objects.
- It defines two separate Kubernetes `Namespace` resources using the `pulumi_kubernetes.core.v1.Namespace` class. The `metadata` argument specifies the configuration for the namespace resource, including its name.
- Finally, it exports the names of each namespace. These exported variables can be used as references in other parts of your infrastructure provisioning or across other Pulumi stacks.

More about the resources we're using:

- **Namespace**: Namespaces are a way to divide cluster resources between multiple users. In the provided program, two namespaces are created to represent different tenants on your AI platform.
- **Meta.v1.ObjectMetaArgs**: This class specifies metadata for Kubernetes resources, including the name, which is required to be unique within a Kubernetes cluster.

Remember that once namespaces are established, you'll need additional Kubernetes resources like `Deployments`, `Services`, and `PersistentVolumeClaims` configured within each namespace for a complete AI platform setup. Each resource will have to specify the namespace it belongs to, ensuring isolation and organization within the cluster.