Kubernetes Multi-Tenant Environments for AI Model Development

Question

Pulumi · Accepted Answer

To create a Kubernetes multi-tenant environment suitable for AI model development, you would typically require a Kubernetes cluster and a way to isolate and manage resources for different tenants. This can be achieved using namespaces to provide logical separation between tenants, along with resource quotas and network policies to control resource consumption and network traffic.

In a multi-tenant environment, you want to ensure that tenants cannot access each other's resources, data, or interfere with their operations. As such, beyond just setting up a Kubernetes cluster, you also want to apply configurations that define these boundaries explicitly.

Here's a Pulumi program that sets up a Google Kubernetes Engine (GKE) cluster with a couple of namespaces, each representing a tenant environment. The namespaces have resource quotas to manage the limits on resource usage and network policies to control the communication between pods.

This will be done using the `google-native` provider, which is the Google Cloud provider that exposes the full set of APIs provided by the services.

### Detailed Explanation
- **Cluster Creation**: We start by creating a Kubernetes cluster using `google_native.container.v1.Cluster` which allows us to define configurations for a GKE cluster. We specify parameters such as the node type, disk size, and the number of nodes.
  
- **Namespace Creation**: For isolation of resources, we create namespaces, each representing a tenant in our multi-tenant architecture.
  
- **Resource Quotas**: Within each namespace, we define resource quotas using Kubernetes' own `ResourceQuota` object. This ensures that each tenant can only use a specified amount of resources.

- **Network Policies**: We apply network policies to restrict the network access amongst the pods across different namespaces, enhancing security and preventing unwanted access.

Now let's construct the actual pulumi program in Python:

```python
import pulumi
import pulumi_google_native as google_native
from pulumi_kubernetes.core.v1 import Namespace, ResourceQuota, NetworkPolicy
from pulumi_kubernetes.networking.v1 import NetworkPolicyPeer, NetworkPolicyEgressRule, NetworkPolicyIngressRule

# Specify your project ID and compute zone
project_id = 'your-gcp-project-id'
compute_zone = 'your-gcp-zone'

# Create a new GKE cluster
cluster = google_native.container.v1.Cluster(
    "ai-model-dev-cluster",
    auto_repair=True,
    initial_node_count=3,
    node_version="latest",
    name="ai-model-dev-cluster",
    location=compute_zone,
    project=project_id,
    resource_labels={
        "environment": "development",
    },
    node_config={
        "oauth_scopes": [
            "https://www.googleapis.com/auth/logging.write",
            "https://www.googleapis.com/auth/monitoring",
        ],
        "disk_size_gb": 100,
        "machine_type": "n1-standard-1",
    },
)

# Kubernetes provider to interact with the newly created cluster
k8s_provider = pulumi_kubernetes.Provider(
    "k8s-provider",
    kubeconfig=cluster.name.apply(lambda name: google_native.container.v1.get_cluster(
        name=name,
        location=compute_zone,
        project=project_id,
    ).then(lambda cluster: cluster.endpoint)),
)

# A function to create a namespace with a resource quota and network policy
def create_tenant_environment(tenant_name):
    # Create a namespace for the tenant
    ns = Namespace(
        tenant_name,
        metadata={"name": tenant_name},
        opts=pulumi.ResourceOptions(provider=k8s_provider),
    )
    
    # Set a resource quota for the tenant
    ResourceQuota(
        f"{tenant_name}-quota",
        metadata={"namespace": ns.metadata["name"]},
        spec={
            "hard": {
                "cpu": "20",
                "memory": "100Gi",
                "pods": "10",
            },
        },
        opts=pulumi.ResourceOptions(provider=k8s_provider),
    )
    
    # Create a default-deny network policy for the tenant