Using kubernetes ray.io with truss.bridgeops.sh

Question

Pulumi · Accepted Answer

To deploy Ray on a Kubernetes cluster using Pulumi, we can take advantage of the available Kubernetes library within Pulumi to define the necessary resources. Ray doesn't have a dedicated Pulumi provider, but you can use the general Kubernetes provider to deploy applications such as Ray.

Ray is an open-source framework that provides a simple, universal API for building distributed applications. Truss is a tool for generating Kubernetes resource YAMLs, and you might use it to define your Kubernetes manifests, but here we will focus on directly using Pulumi to deploy Ray to a Kubernetes cluster.

Let's assume you have a Kubernetes cluster up and running. The following Pulumi program will create the necessary Kubernetes resources to get a basic Ray cluster running. This will include setting up the necessary service account, role, role binding, and stateful set for the Ray head node and worker nodes.

Here's a detailed Pulumi TypeScript program that sets up a Ray cluster:

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

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

const serviceAccount = new k8s.core.v1.ServiceAccount("ray-service-account", {
    metadata: {
        namespace: namespace.metadata.name,
    },
});

const role = new k8s.rbac.v1.Role("ray-role", {
    metadata: {
        namespace: namespace.metadata.name,
    },
    rules: [{
        apiGroups: [""],
        resources: ["pods", "services", "endpoints", "replicationcontrollers", "persistentvolumeclaims"],
        verbs: ["get", "list", "watch", "create", "update", "patch", "delete"],
    }, {
        apiGroups: ["extensions"],
        resources: ["replicasets", "deployments"],
        verbs: ["get", "list", "watch", "create", "update", "patch", "delete"],
    }],
});

const roleBinding = new k8s.rbac.v1.RoleBinding("ray-role-binding", {
    metadata: {
        namespace: namespace.metadata.name,
    },
    subjects: [{
        kind: "ServiceAccount",
        name: serviceAccount.metadata.name,
        namespace: namespace.metadata.name,
    }],
    roleRef: {
        kind: "Role",
        name: role.metadata.name,
        apiGroup: "rbac.authorization.k8s.io",
    },
});

// Define the head service for Ray
const headService = new k8s.core.v1.Service("ray-head-service", {
    metadata: {
        namespace: namespace.metadata.name,
        labels: {
            "ray.io/component": "head",
        },
    },
    spec: {
        selector: {
            "ray.io/component": "head",
        },
        ports: [{
            name: "client",
            protocol: "TCP",
            port: 10001,
            targetPort: 10001,
        }],
    },
});

// Define the head pod for Ray
const headPod = new k8s.core.v1.Pod("ray-head-pod", {
    metadata: {
        namespace: namespace.metadata.name,
        labels: {
            "ray.io/component": "head",
        },
    },
    spec: {
        serviceAccountName: serviceAccount.metadata.name,
        // ... additional pod configuration (image, ports, volumes, etc.)
        // Refer to the Ray documentation or your Ray configuration as needed.
    },
});

// Define any worker pods/deployments/statefulsets for Ray as necessary
// ...

// Example of stack export
export const headServiceName = headService.metadata.name;
```

In this program, you start by creating a namespace for the Ray-related resources to keep them separate from other applications in the cluster. Then, you create a `ServiceAccount`, `Role`, and `RoleBinding` to ensure the Ray services have the proper permissions to function within the Kubernetes cluster.

The `headService` variable defines the Kubernetes service required for Ray's head node. This is a simplified example, and in practice, you'll need to configure these resources with the correct properties such as the container image to use for the head node, the necessary environment variables, volumes, and any other specifics required by Ray.

After setting up the head node components, you would typically define additional workers that connect to the Ray head node. These could be represented as other pods, deployments, or stateful sets, depending on your scaling requirements.

Lastly, the `export` statement allows you to output values from your Pulumi program, such as the generated head service name, which can be very useful for further configurations or when integrating this setup into a larger system.

This program serves as a starting point for deploying Ray using Pulumi on Kubernetes. The exact details of the Ray components' configurations (e.g., the head node and workers setup) will depend on the specific usage requirements, image repositories, and Ray configuration you plan to use. For fine-tuning, you should consult Ray's documentation and interpret that in the context of Pulumi's Kubernetes API.