How do I deploy MLFlow on Kubernetes?

In this guide, we will deploy MLFlow, an open-source platform for managing the end-to-end machine learning lifecycle, on a Kubernetes cluster using Pulumi. We will define the necessary Kubernetes resources such as a namespace, deployment, and service to run MLFlow.

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

// Define a namespace for MLFlow
const mlflowNamespace = new k8s.core.v1.Namespace("mlflow-namespace", {
    metadata: {
        name: "mlflow",
    },
});

// Create a deployment for MLFlow
const mlflowDeployment = new k8s.apps.v1.Deployment("mlflow-deployment", {
    metadata: {
        namespace: mlflowNamespace.metadata.name,
        name: "mlflow",
    },
    spec: {
        replicas: 1,
        selector: {
            matchLabels: {
                app: "mlflow",
            },
        },
        template: {
            metadata: {
                labels: {
                    app: "mlflow",
                },
            },
            spec: {
                containers: [
                    {
                        name: "mlflow",
                        image: "mlflow/mlflow:latest",
                        ports: [
                            {
                                containerPort: 5000,
                            },
                        ],
                        env: [
                            {
                                name: "BACKEND_STORE_URI",
                                value: "sqlite:///mlflow.db",
                            },
                            {
                                name: "ARTIFACT_ROOT",
                                value: "s3://my-mlflow-bucket/",
                            },
                        ],
                    },
                ],
            },
        },
    },
});

// Create a service to expose MLFlow
const mlflowService = new k8s.core.v1.Service("mlflow-service", {
    metadata: {
        namespace: mlflowNamespace.metadata.name,
        name: "mlflow",
    },
    spec: {
        type: "LoadBalancer",
        ports: [
            {
                port: 80,
                targetPort: 5000,
            },
        ],
        selector: {
            app: "mlflow",
        },
    },
});

// Export the service URL
export const mlflowUrl = mlflowService.status.loadBalancer.ingress[0].hostname;

Key Points

Namespace: We create a namespace mlflow to isolate MLFlow resources.
Deployment: We define a deployment for MLFlow with one replica, using the mlflow/mlflow:latest image. Environment variables are set for the backend store URI and artifact root.
Service: A LoadBalancer service is created to expose MLFlow on port 80, targeting port 5000 of the MLFlow container.
Export: The URL of the MLFlow service is exported for easy access.

Summary

We have successfully deployed MLFlow on a Kubernetes cluster using Pulumi. The deployment includes a namespace, a deployment for the MLFlow application, and a service to expose it. This setup allows you to manage your machine learning experiments effectively.

Deploy this code

Want to deploy this code? Sign up for a free Pulumi account to deploy in a few clicks.

New to Pulumi?

Want to deploy this code? Sign up with Pulumi to deploy in a few clicks.