Automated Machine Learning Pipelines on Kubernetes with Ray

Question

Pulumi · Accepted Answer

Machine Learning (ML) pipelines are a sequence of data processing and model training steps. Automating these pipelines allows for scalable and efficient machine learning workflows. Ray is an open-source project that provides a simple, universal API for building distributed applications, and it's well-suited for ML tasks due to its easy-to-use computational primitives that fit within a Pythonic programming model.

To set up an automated ML pipeline on Kubernetes with Ray, you'll need:

A Kubernetes cluster to deploy and manage your workloads on. This could be a cloud-based service like Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), or Azure Kubernetes Service (AKS), depending on the cloud provider you are using.
Ray deployed within your Kubernetes cluster. Ray can be installed on a Kubernetes cluster as a set of containers.
An ML workflow defined in Python code, with tasks and data processing steps orchestrated by Ray.

The Pulumi program below will focus on step #2, deploying Ray to a Kubernetes cluster. Pulumi provides the pulumi_kubernetes library which we'll use to deploy Ray to the cluster. I am assuming that you have already set up a Kubernetes cluster and configured Pulumi to work with your Kubernetes configuration.

The program below does not include the actual ML pipeline code but provides a base on which to deploy such applications using Ray.

import pulumi
from pulumi_kubernetes.apps.v1 import Deployment

# Define the Ray head node deployment for the Kubernetes cluster
ray_head_node = Deployment(
    "ray-head-node",
    spec={
        "selector": {
            "matchLabels": {
                "component": "ray-head"
            }
        },
        "replicas": 1,
        "template": {
            "metadata": {
                "labels": {
                    "component": "ray-head"
                }
            },
            "spec": {
                "containers": [{
                    "name": "ray-head",
                    "image": "rayproject/ray:latest",  # Use the latest Ray image
                    "command": [
                        "ray", "start",
                        "--head",
                        "--port=6379",
                        "--node-manager-port=6380",
                        "--object-manager-port=6381",
                        "--autoscaling-config=~/ray_bootstrap_config.yaml"
                    ],
                    "ports": [
                        {"containerPort": 6379},
                        {"containerPort": 6380},
                        {"containerPort": 6381}
                    ],
                    "env": [
                        # Define environment variables for Ray configuration (if needed)
                    ]
                }],
                # Optional: Define persistent volume claims, node selectors, etc.
            }
        }
    })

# Define the Ray worker node deployment (optional, if you need scalable workers)
ray_worker_node = Deployment(
    "ray-worker-node",
    spec={
        "selector": {
            "matchLabels": {
                "component": "ray-worker"
            }
        },
        "replicas": 3,  # Define the number of worker replicas
        "template": {
            "metadata": {
                "labels": {
                    "component": "ray-worker"
                }
            },
            "spec": {
                "containers": [{
                    "name": "ray-worker",
                    "image": "rayproject/ray:latest",  # Use the latest Ray image
                    "command": [
                        "ray", "start",
                        "--address=<head-service-ip>:6379",  # Connect to the Ray head node
                        # Note that `<head-service-ip>` needs to be replaced with the actual head node service IP
                    ],
                    # Optional: Define resource requests and limits for each worker
                    "env": [
                        # Define environment variables for Ray configuration (if needed)
                    ]
                }],
                # Optional: Define persistent volume claims, node selectors, etc.
            }
        }
    })

# Export the Ray head node service endpoint
pulumi.export('ray_head_node_endpoint', ray_head_node.status.apply(lambda status: status.load_balancer.ingress[0].ip if status.load_balancer.ingress else "pending"))

# Note: The above program assumes that you already have a Kubernetes cluster configured and Pulumi set up accordingly.

Let's break down what this program does:

It creates two deployments, one for the Ray head node and, optionally, one for Ray worker nodes.
The head node is the primary node that orchestrates the execution of tasks across the worker nodes.
The image rayproject/ray:latest is used for both head and worker nodes, but you could pin the image to a specific Ray version if needed.
The command line arguments starting with -- are Ray-specific options used for configuring the head and worker nodes.
The ray_head_node_endpoint is exported, which gives you the IP address once the Ray head node is successfully deployed and available. This allows you to connect to your Ray cluster.

With this setup, you would then be able to run ML pipelines defined in Python that use Ray's API for distributed computation.

Remember to replace the <head-service-ip> placeholder with the actual IP address or DNS name for the head node service. You might want to define a Kubernetes service resource for the head node to make it accessible to the worker nodes and your pipeline code.

This program is a stepping stone to get Ray running on Kubernetes. The actual ML pipeline would need to be defined in Python code and use Ray's APIs for execution. Ray's documentation provides a comprehensive guide on how to define and run these types of distributed tasks.