Fault-Tolerant AI Workloads with Ray on Kubernetes

Question

Pulumi · Accepted Answer

Creating a fault-tolerant AI workload with Ray on Kubernetes involves several components that need to be carefully orchestrated to ensure resiliency and fault tolerance. Here's how you can achieve this with Pulumi using Python as a programming language.

### Overview

**Ray** is an open-source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

**Kubernetes** is an open-source platform designed to automate deploying, scaling, and operating application containers.

**Pulumi** is an infrastructure as code tool that allows you to define and manage your infrastructure using code in numerous languages, here we're using Python.

To deploy Ray on Kubernetes using Pulumi, you will need to:
- Create a Kubernetes cluster, if you don't already have one.
- Use Ray's Kubernetes Operator to manage the Ray cluster lifecycle.
- Ensure the Ray Operator and associated services are deployed into the cluster.

### Pre-requisites

Before we dive into the code, you need to have the following pre-requisites met:
- Pulumi CLI installed and configured to use a cloud provider for creating a Kubernetes cluster.
- `kubectl` installed and configured to interact with the cluster.
- Access to a Kubernetes cluster - this could be on any cloud provider or on-premises.

In this guide, I will assume you are working with a managed Kubernetes service such as Amazon EKS, Microsoft AKS, or Google GKE.

### Pulumi Program for Ray on Kubernetes

Now, let's write a Pulumi program to deploy Ray on Kubernetes. We will use the `pulumi_kubernetes` library which interfaces with Kubernetes to manage the resources.

```python
import pulumi
import pulumi_kubernetes as kubernetes
from pulumi_kubernetes.apps.v1 import Deployment

# We'll use a pre-existing Kubernetes cluster context,
# which should be configured in your local kubeconfig or Pulumi.
kubeconfig = pulumi.Config('kubernetes').get('kubeconfig')

# If you don't have a cluster, you'll need to create one using Pulumi providers for
# AWS (EKS), Azure (AKS), or Google Cloud (GKE) before this step.

# Create a Namespace for Ray services.
ray_namespace = kubernetes.core.v1.Namespace(
    "ray",
    metadata={"name": "ray"}
)

# Deploy the Ray Operator which will manage Ray clusters.
ray_operator = Deployment(
    "ray-operator",
    metadata={
        "namespace": ray_namespace.metadata["name"],
        "labels": {"component": "ray-operator"}
    },
    spec={
        "selector": {"matchLabels": {"component": "ray-operator"}},
        "replicas": 1,
        "template": {
            "metadata": {"labels": {"component": "ray-operator"}},
            "spec": {
                "containers": [{
                    "name": "ray-operator",
                    "image": "rayproject/ray-operator:latest",
                    # Making sure to add necessary permissions for the Ray operator
                    # to manage the Ray clusters.
                }]
            }
        }
    }
)

# Now you would create the actual Ray cluster using the Ray CRD (Custom Resource Definition)
# provided by the Ray operator, please refer to the official Ray Kubernetes documentation
# to configure the Ray cluster as per your use case:
# https://docs.ray.io/en/latest/cluster/kubernetes.html

pulumi.export('ray_namespace', ray_namespace.metadata["name"])
```

This Pulumi program sets up the necessary Kubernetes resources to run Ray. Let me explain step by step:

- We start by importing the necessary modules from Pulumi's Kubernetes SDK.
- We create a Kubernetes Namespace dedicated to Ray. This allows us to organize and manage all Ray-related resources within this namespace.
- We deploy the Ray Operator into our Kubernetes cluster. The Ray Operator is responsible for managing Ray clusters on Kubernetes. It watches for instances of Ray custom resources and manages the Ray head and worker nodes for each instance of the custom resource.
- At the end of the program, we export the namespace name to use it when interacting with Ray clusters via `kubectl` or Pulumi.

Please note that creating the Ray cluster itself would involve defining a custom resource for Ray, which goes in detail and depends on your specific workload. For further customization and details, you should consult the [Ray on Kubernetes official documentation](https://docs.ray.io/en/latest/cluster/kubernetes.html).

Remember, this Pulumi program assumes you have an existing Kubernetes cluster and adds the Ray components to it. If you need a complete example that includes creating a Kubernetes cluster, you would add the cluster creation scripts before deploying the Ray operator.