Cross-Namespace Ingress Configuration for ML Workflows

Introduction

In this solution, we will configure cross-namespace ingress for machine learning (ML) workflows using Pulumi in TypeScript. This setup will allow us to manage and expose services across different namespaces within a Kubernetes cluster, facilitating seamless communication and access. The key services involved in this configuration include Kubernetes for container orchestration, NGINX Ingress Controller for managing ingress resources, and Pulumi for infrastructure as code (IaC) management.

Step-by-Step Explanation

Step 1: Set Up Pulumi Project

First, we need to set up a new Pulumi project. This involves initializing a new Pulumi project and configuring the necessary dependencies.

Step 2: Configure Kubernetes Provider

Next, we will configure the Kubernetes provider in Pulumi. This will allow us to interact with our Kubernetes cluster and manage resources within it.

Step 3: Deploy NGINX Ingress Controller

We will deploy the NGINX Ingress Controller to manage ingress resources. This involves creating the necessary Kubernetes resources such as deployments, services, and config maps.

Step 4: Create Namespaces

We will create the namespaces required for our ML workflows. Each namespace will host different services and resources.

Step 5: Define Ingress Resources

We will define the ingress resources to manage traffic routing between services across different namespaces. This includes creating ingress rules and configuring the necessary annotations.

Step 6: Deploy ML Services

Finally, we will deploy the ML services within the respective namespaces. This involves creating deployments, services, and other necessary resources.

Key Points

• Pulumi: Used for managing infrastructure as code.
• Kubernetes: Container orchestration platform.
• NGINX Ingress Controller: Manages ingress resources and traffic routing.
• Namespaces: Isolate and manage resources within the Kubernetes cluster.
• Ingress Resources: Define rules for routing traffic between services.

Conclusion

By following this solution, we have successfully configured cross-namespace ingress for ML workflows using Pulumi in TypeScript. This setup allows for efficient management and exposure of services across different namespaces, facilitating seamless communication and access within the Kubernetes cluster. The key services involved, such as Pulumi, Kubernetes, and NGINX Ingress Controller, play a crucial role in achieving this configuration.

Full Code Example

import * as pulumi from "@pulumi/pulumi";
import * as k8s from "@pulumi/kubernetes";

// Create namespaces
const mlNamespace = new k8s.core.v1.Namespace("ml-namespace", {
    metadata: { name: "ml" }
});

const ingressNamespace = new k8s.core.v1.Namespace("ingress-namespace", {
    metadata: { name: "ingress-nginx" }
});

// Deploy NGINX Ingress Controller
const nginxIngressController = new k8s.helm.v3.Chart("nginx-ingress", {
    chart: "ingress-nginx",
    version: "4.0.6",
    fetchOpts: { repo: "https://kubernetes.github.io/ingress-nginx" },
    namespace: ingressNamespace.metadata.name,
    values: {
        controller: {
            service: {
                type: "LoadBalancer"
            }
        }
    }
});

// Create ML service deployment
const mlAppLabels = { app: "ml-app" };
const mlAppDeployment = new k8s.apps.v1.Deployment("ml-app-deployment", {
    metadata: { namespace: mlNamespace.metadata.name },
    spec: {
        selector: { matchLabels: mlAppLabels },
        replicas: 2,
        template: {
            metadata: { labels: mlAppLabels },
            spec: {
                containers: [{
                    name: "ml-app",
                    image: "ml-app-image:latest",
                    ports: [{ containerPort: 80 }]
                }]
            }
        }
    }
});

const mlAppService = new k8s.core.v1.Service("ml-app-service", {
    metadata: { namespace: mlNamespace.metadata.name },
    spec: {
        selector: mlAppLabels,
        ports: [{ port: 80, targetPort: 80 }]
    }
});

// Create Ingress resource
const mlAppIngress = new k8s.networking.v1.Ingress("ml-app-ingress", {
    metadata: {
        namespace: mlNamespace.metadata.name,
        annotations: {
            "nginx.ingress.kubernetes.io/rewrite-target": "/"
        }
    },
    spec: {
        rules: [{
            host: "ml-app.example.com",
            http: {
                paths: [{
                    path: "/",
                    pathType: "Prefix",
                    backend: {
                        service: {
                            name: mlAppService.metadata.name,
                            port: { number: 80 }
                        }
                    }
                }]
            }
        }]
    }
});

// Export the Ingress Controller URL
export const nginxIngressControllerUrl = nginxIngressController.getResource("v1/Service", "ingress-nginx/ingress-nginx-controller").status.loadBalancer.ingress[0].hostname;