> ## Documentation Index
> Fetch the complete documentation index at: https://docs.siderolabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# KubeRay

> Install Ray on Kubernetes.

export const version = 'v1.13';

[Ray](https://www.ray.io/) is a project for running machine learning jobs in a cluster of machines. [KubeRay](https://github.com/ray-project/kuberay) is an operator for installing Ray on top of Kubernetes.
For up-to-date installation guide check with the [upstream Ray documentation](https://docs.ray.io/en/latest/cluster/kubernetes/getting-started/kuberay-operator-installation.html).

## Install Ray operator with Helm

Create a Kubernetes cluster with [`talosctl`](../../omni/getting-started/how-to-install-talosctl) or via [Omni](../../omni/overview/what-is-omni).

Deploy the operator via Helm.

```sh theme={null}
helm repo add kuberay https://ray-project.github.io/kuberay-helm/
helm repo update

helm install kuberay-operator kuberay/kuberay-operator --version 1.4.2
```

Once the operator pod is running you can create a Ray cluster.

```sh theme={null}
helm install raycluster kuberay/ray-cluster --version 1.4.2
```

Get the status of the cluster:

```sh theme={null}
kubectl get rayclusters
```

The output should look like this.

```
NAME                 DESIRED WORKERS   AVAILABLE WORKERS   CPUS   MEMORY   GPUS   STATUS   AGE
raycluster-kuberay   1                 1                   2      3G       0      ready    13m
```

From here you can follow the [Ray documentation to run a RayJob](https://docs.ray.io/en/latest/cluster/kubernetes/getting-started/rayjob-quick-start.html).