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.
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