[Doc][KubeRay] Improve KubeRay docs based on CUJ (#45212)

doc/source/cluster/kubernetes/getting-started/raycluster-quick-start.md

@@ -124,24 +124,23 @@ Now that we have the name of the service, we can use port-forwarding to access t
 ```sh
 # Execute this in a separate shell.
 kubectl port-forward service/raycluster-kuberay-head-svc 8265:8265
-
-# Visit ${YOUR_IP}:8265 in your browser for the Dashboard (e.g. 127.0.0.1:8265)
 ```
 
-Note: We use port-forwarding in this guide as a simple way to experiment with a RayCluster's services. For production use-cases, you would typically either 
-- Access the service from within the Kubernetes cluster or
-- Use an ingress controller to expose the service outside the cluster.
-
-See the {ref}`networking notes <kuberay-networking>` for details.
-
 Now that we have access to the Dashboard port, we can submit jobs to the RayCluster:
 
 ```sh
 # The following job's logs will show the Ray cluster's total resource capacity, including 2 CPUs.
 ray job submit --address http://localhost:8265 -- python -c "import ray; ray.init(); print(ray.cluster_resources())"
 ```
 
-## Step 5: Cleanup
+## Step 5: Access the Ray Dashboard
+
+Visit `${YOUR_IP}:8265` in your browser for the Dashboard. For example, `127.0.0.1:8265`.
+See the job you submitted in Step 4 in the **Recent jobs** pane as shown below.
+
+![Ray Dashboard](../images/ray-dashboard.png)
+
+## Step 6: Cleanup
 
 ```sh
 # [Step 5.1]: Delete the RayCluster CR

doc/source/cluster/kubernetes/user-guides/k8s-cluster-setup.md

@@ -9,9 +9,9 @@ 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.
+Most KubeRay documentation only requires a local Kubernetes cluster such as [Kind](https://kind.sigs.k8s.io/).
+Some KubeRay examples require GPU nodes, which can be provided by a managed Kubernetes service.
+We collect a few helpful links for users who are getting started with a managed Kubernetes service to launch a Kubernetes cluster equipped with GPUs.
 
 (gke-setup)=
 # Setting up a GKE cluster (Google Cloud)