Multi-Tenant Machine Learning Platforms on Kubernetes

Question

Pulumi · Accepted Answer

To create a multi-tenant machine learning (ML) platform on Kubernetes, you'll need a few things in place:

1. A **Kubernetes Cluster** to serve as the foundation where all ML workloads will run.
2. **Namespace Isolation**, which is crucial for a multi-tenant setup to ensure that each tenant's data and workloads are isolated from others.
3. **Role-Based Access Control (RBAC)** configuration for fine-grained access control over Kubernetes resources.
4. **Machine Learning tools and platforms** like Kubeflow, Seldon, or custom ML solutions, deployed on the cluster and configured for multi-tenancy.
5. **Ingress Controllers and Services** to manage external access to the ML platforms.

I'll guide you through a program that sets up a basic multi-tenant capable Kubernetes cluster and deploys a generic ML workload. Please note that the actual machine learning tooling setup depends on the specific technologies you choose to use, like Kubeflow or others, which typically have their own installation and configuration processes.

Here's a Pulumi program in Python that creates a Google Kubernetes Engine (GKE) cluster, configures it for multi-tenancy with namespaces and RBAC, and prepares it for an ML workload:

```python
import pulumi
import pulumi_gcp as gcp

# Create a GKE cluster that will host the multi-tenant ML platform.
cluster = gcp.container.Cluster("ml-cluster",
    initial_node_count=3,
    node_config=gcp.container.ClusterNodeConfigArgs(
        machine_type="n1-standard-1",  # Choosing a machine type suitable for ML workloads.
        oauth_scopes=[
            "https://www.googleapis.com/auth/cloud-platform",
            "https://www.googleapis.com/auth/devstorage.read_only",
            "https://www.googleapis.com/auth/logging.write",
            "https://www.googleapis.com/auth/monitoring",
        ],
    ))

# Output the cluster name and endpoint, which can be used to interface with the cluster.
pulumi.export('cluster_name', cluster.name)
pulumi.export('cluster_endpoint', cluster.endpoint)

# Example of creating a namespace for a tenant. Repeat this for each tenant.
tenant_namespace = gcp.core.Namespace("tenant-namespace",
    metadata=gcp.meta.v1.ObjectMetaArgs(
        name="tenant-a"  # Use a unique name for each tenant's namespace.
    ))

# Create a role specifying permissions that will be granted to tenant users within their namespace.
tenant_role = gcp.rbac.v1.Role("tenant-role",
    metadata=gcp.meta.v1.ObjectMetaArgs(
        namespace=tenant_namespace.metadata["name"]  # The namespace to which this role is associated.
    ),
    rules=[gcp.rbac.v1.PolicyRuleArgs(
        api_groups=[""],  # Core API group.
        resources=["pods", "services", "deployments", "replicasets"],
        verbs=["get", "list", "watch", "create", "update", "patch", "delete"],
    )])

# Bind the role to tenant users, granting them the permissions defined in the role within their namespace.
tenant_role_binding = gcp.rbac.v1.RoleBinding("tenant-role-binding",
    metadata=gcp.meta.v1.ObjectMetaArgs(
        namespace=tenant_namespace.metadata["name"]
    ),
    subjects=[gcp.rbac.v1.SubjectArgs(
        kind="User",
        name="tenant-a-user",  # The user's identifier.
        api_group="rbac.authorization.k8s.io",
    )],
    role_ref=gcp.rbac.v1.RoleRefArgs(
        kind="Role",
        name=tenant_role.metadata["name"],
        api_group="rbac.authorization.k8s.io",
    ))

# Set up a basic service and deployment for demonstration purposes.
ml_service = gcp.core.Service("ml-service",
    metadata=gcp.meta.v1.ObjectMetaArgs(
        namespace=tenant_namespace.metadata["name"]
    ),
    spec=gcp.core.v1.ServiceSpecArgs(
        selector={"app": "ml-app"},
        ports=[gcp.core.v1.ServicePortArgs(
            port=80,
            target_port=pulumi.Input(8080)
        )]
    ))

ml_deployment = gcp.apps.v1.Deployment("ml-deployment",
    metadata=gcp.meta.v1.ObjectMetaArgs(
        namespace=tenant_namespace.metadata["name"]
    ),
    spec=gcp.apps.v1.DeploymentSpecArgs(
        replicas=1,
        selector=gcp.meta.v1.LabelSelectorArgs(
            match_labels={"app": "ml-app"}
        ),
        template=gcp.core.v1.PodTemplateSpecArgs(
            metadata=gcp.meta.v1.ObjectMetaArgs(
                labels={"app": "ml-app"}
            ),
            spec=gcp.core.v1.PodSpecArgs(
                containers=[
                    gcp.core.v1.ContainerArgs(
                        name="ml-container",
                        image="gcr.io/google_samples/ml-with-tensorflow:2.0",
                        ports=[gcp.core.v1.ContainerPortArgs(
                            container_port=8080
                        )]
                    )
                ]
            )
        )
    ))
```

In this program:

- The `gcp.container.Cluster` resource creates a new GKE cluster with the specified configuration suitable for ML workloads.
- The `gcp.core.Namespace` resource creates Kubernetes namespaces, which act as virtual clusters within the GKE cluster.
- The `gcp.rbac.v1.Role` and `gcp.rbac.v1.RoleBinding` resources are used to set up RBAC within the cluster, providing tenant users with the necessary permissions in their namespace.
- The `gcp.core.Service` and `gcp.apps.v1.Deployment` resources demonstrate how you could deploy a sample ML application to the cluster. In a real-world scenario, you would replace this with the deployment of your chosen ML platform or tools.

Remember, this is a foundational setup. Real-world multi-tenant ML platforms require careful planning around security, resource quotas, networking policies, and potentially more complex RBAC configurations to ensure proper isolation and governance.