Add requirements for GKE customer on ray-on-gke README

[brandonroyal]

TL;DR - the ray-on-gke readme should be updated with the requirements needed for setting up the prerequisite GKE clusters.

• GKE Cluster must have Workload Identity Enabled
• GKE Cluster must have KubeRay Operator deployed

Here are the details on the errors when these prerequisites are NOT met. These are based on a GKE Standard cluster (1.27.3-gke.100)

module.service_accounts.google_project_iam_binding.monitoring-viewer: Creation complete after 7s [id=ie-raycluster-0f2aa542/roles/monitoring.viewer]
╷
│ Error: unable to build kubernetes objects from release manifest: resource mapping not found for name: "example-cluster-kuberay" namespace: "" from "": no matches for kind "RayCluster" in version "ray.io/v1alpha1"
│ ensure CRDs are installed first
│ 
│   with module.kuberay.helm_release.ray-cluster,
│   on modules/kuberay/kuberay.tf line 15, in resource "helm_release" "ray-cluster":
│   15: resource "helm_release" "ray-cluster" {
│ 
╵

This is a result of the KubeRay operator not being installed on the cluster

The iam-role-binding will also fail. This is the result of Workload Identity not being enabled.

[github-actions]

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[rahuld-turing]

The documentation mentions kuberay-operator , however I can't find it in the modules folder? Was this removed?

.
├── LICENSE
├── README.md
├── infrastructure
│ ├── README.md
│ ├── backend.tf
│ ├── main.tf
│ ├── outputs.tf
│ ├── platform.tfvars
│ ├── variables.tf
│ └── versions.tf
├── modules
│ ├── gke-autopilot-private-cluster
│ ├── gke-autopilot-public-cluster
│ ├── gke-standard-private-cluster
│ ├── gke-standard-public-cluster
│ ├── jupyter
│ ├── jupyter_iap
│ ├── jupyter_service_accounts
│ ├── kuberay-cluster
│ ├── kuberay-logging
│ ├── kuberay-monitoring
│ ├── kuberay-operator
│ └── kuberay-serviceaccounts
└── tutorial.md

[fcabrera23]

This code was migrated to a new repo: https://github.com/ai-on-gke/quick-start-guides - If the problem persists, please open a issue in the new repo.