(kuberay-k8s-setup)= # Managed Kubernetes services ```{toctree} :hidden: aws-eks-gpu-cluster gcp-gke-gpu-cluster ``` The KubeRay operator and Ray can run on any cloud or on-prem Kubernetes cluster. The simplest way to provision a remote Kubernetes cluster is to use a cloud-based managed service. We collect a few helpful links for users who are getting started with a managed Kubernetes service. (gke-setup)= # Setting up a GKE cluster (Google Cloud) - {ref}`kuberay-gke-gpu-cluster-setup` (eks-setup)= # Setting up an EKS cluster (AWS) - {ref}`kuberay-eks-gpu-cluster-setup` (aks-setup)= # Setting up an AKS (Microsoft Azure) You can find the landing page for AKS [here](https://azure.microsoft.com/en-us/services/kubernetes-service/). If you have an account set up, you can immediately start experimenting with Kubernetes clusters in the provider's console. Alternatively, check out the [documentation](https://docs.microsoft.com/en-us/azure/aks/) and [quickstart guides](https://docs.microsoft.com/en-us/azure/aks/learn/quick-kubernetes-deploy-portal?tabs=azure-cli). To successfully deploy Ray on Kubernetes, you will need to configure pools of Kubernetes nodes; find guidance [here](https://docs.microsoft.com/en-us/azure/aks/use-multiple-node-pools).