Kubernetes Ingress for Multi-Tenant Machine Learning Platforms

Question

Pulumi · Accepted Answer

Creating a Kubernetes Ingress resource is crucial for managing access to your multi-tenant machine learning (ML) platform. Ingress allows you to route external HTTP(S) traffic to different services within your Kubernetes cluster based on the request's host or path. This is particularly useful for a multi-tenant environment where you might have different services or namespaces dedicated to different users or tenant groups.

For this purpose, we'll use the `pulumi_kubernetes.networking.v1.Ingress` class from the Pulumi Kubernetes provider. This resource will create an Ingress object in your Kubernetes cluster, which you'll configure to route traffic to the appropriate backend services for your ML platform.

Here is the basic structure of the program in Python:

1. We'll import the required Pulumi modules.
2. We'll create a Kubernetes Provider if you are targeting a specific cluster not defined by your current context. This is optional and depends on your setup.
3. We'll define the Ingress resource with necessary specifications like hosts, paths, and backend services.

Let's begin with the program:

```python
import pulumi
import pulumi_kubernetes as k8s

# Assuming you have already set up the Kubeconfig file or context for Pulumi to connect to your Kubernetes cluster.
# If you need to target a specific cluster, you can set up a Kubernetes Provider as shown below.
# Replace 'kubeconfig-content' with the actual content of your kubeconfig file.

# Uncomment the lines below if you need to use a specific Kubernetes provider.
# kubeconfig = 'kubeconfig-content'
# k8s_provider = k8s.Provider('k8sprovider', kubeconfig=kubeconfig)

# The following Ingress resource definition assumes that you have services deployed corresponding 
# to different tenants in your machine learning platform. Replace 'service-name-tenant-a' and 
# 'service-name-tenant-b' with the names of your services, and match the hosts and paths to your needs.

ml_platform_ingress = k8s.networking.v1.Ingress(
    'ml-platform-ingress',
    metadata=k8s.meta.v1.ObjectMetaArgs(  # Metadata for the Ingress resource, like name and labels.
        name='ml-platform-ingress',
        namespace='ml-namespace',  # Set your namespace if different from default.
    ),
    spec=k8s.networking.v1.IngressSpecArgs(
        rules=[
            k8s.networking.v1.IngressRuleArgs(
                host='tenant-a.ml.example.com',  # DNS host for tenant A.
                http=k8s.networking.v1.HTTPIngressRuleValueArgs(
                    paths=[
                        k8s.networking.v1.HTTPIngressPathArgs(
                            path='/predict', # Endpoint for predictions.
                            path_type='ImplementationSpecific',
                            backend=k8s.networking.v1.IngressBackendArgs(
                                service=k8s.networking.v1.IngressServiceBackendArgs(
                                    name='service-name-tenant-a',  # Service handling tenant A requests.
                                    port=k8s.networking.v1.ServiceBackendPortArgs(
                                        number=80,
                                    ),
                                ),
                            ),
                        ),
                    ],
                ),
            ),
            k8s.networking.v1.IngressRuleArgs(
                host='tenant-b.ml.example.com',  # DNS host for tenant B.
                http=k8s.networking.v1.HTTPIngressRuleValueArgs(
                    paths=[
                        k8s.networking.v1.HTTPIngressPathArgs(
                            path='/predict', # Endpoint for predictions.
                            path_type='ImplementationSpecific',
                            backend=k8s.networking.v1.IngressBackendArgs(
                                service=k8s.networking.v1.IngressServiceBackendArgs(
                                    name='service-name-tenant-b',  # Service handling tenant B requests.
                                    port=k8s.networking.v1.ServiceBackendPortArgs(
                                        number=80,
                                    ),
                                ),
                            ),
                        ),
                    ],
                ),
            ),
        ],
        # Uncomment and configure TLS settings if you have TLS requirements for your ingress.
        # tls=[
        #     k8s.networking.v1.IngressTLSArgs(
        #         hosts=['tenant-a.ml.example.com', 'tenant-b.ml.example.com'],
        #         secret_name='tls-secret-name',  # Name of the secret containing TLS certificates
        #     ),
        # ],
    ),
    # Uncomment if your specific provider is being used.
    # opts=pulumi.ResourceOptions(provider=k8s_provider),
)

# Export the Ingress resource name
pulumi.export('ingress_name', ml_platform_ingress.metadata["name"])
```

In the code above, we define a Pulumi Kubernetes Ingress object. We start by assigning it a name (`ml-platform-ingress`) and a namespace (`ml-namespace`) where this will be deployed. If you have namespaces dedicated to each tenant, you may want to deploy separate Ingress resources into each namespace accordingly.

The `spec` describes the routing rules. We have two DNS hosts (for tenants A and B), each with a path (`/predict`) routing to their respective backend services at port 80. You should change these values to match the actual services and ports in your environment.

If your services require HTTPS, you'll use the `tls` field within `IngressSpecArgs` to define the TLS configuration for the Ingress, referencing a Kubernetes secret that contains the TLS certificate and private key for your domain.

In multi-tenant ML platforms, it's crucial to maintain isolation and secure access to tenant-specific resources. By using different hostnames or path prefixes in your Ingress rules, you're able to control traffic routing within your cluster, ensuring that each tenant's data and services are kept private.

Finally, we mark `ingress_name` for export, which allows you to retrieve the name of the Ingress resource.

Please adjust the service names, namespaces, hostnames, and paths to match your application's setup. If you need further customization, refer to the Pulumi Kubernetes documentation or the official Kubernetes Ingress documentation for more detailed options.