[Docs][Kuberay] Update version to 1.5.0 (#58452)

Signed-off-by: Future-Outlier <[email protected]>

Author: Future-Outlier

doc/source/cluster/kubernetes/examples/mobilenet-rayservice.md

@@ -19,7 +19,7 @@ Note that the YAML file in this example uses `serveConfigV2`. You need KubeRay v
 
 ```sh
 # Create a RayService
-kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-service.mobilenet.yaml
+kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-service.mobilenet.yaml
 ```
 
 * The [mobilenet.py](https://github.com/ray-project/serve_config_examples/blob/master/mobilenet/mobilenet.py) file needs `tensorflow` as a dependency. Hence, the YAML file uses `rayproject/ray-ml` image instead of `rayproject/ray` image.

doc/source/cluster/kubernetes/examples/rayjob-batch-inference-example.md

@@ -37,12 +37,12 @@ The KubeRay operator Pod must be on the CPU node if you have set up the taint fo
 
 ## Step 2: Submit the RayJob
 
-Create the RayJob custom resource with [ray-job.batch-inference.yaml](https://github.com/ray-project/kuberay/blob/v1.4.2/ray-operator/config/samples/ray-job.batch-inference.yaml).
+Create the RayJob custom resource with [ray-job.batch-inference.yaml](https://github.com/ray-project/kuberay/blob/v1.5.0/ray-operator/config/samples/ray-job.batch-inference.yaml).
 
 Download the file with `curl`:
 
 ```bash
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-job.batch-inference.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-job.batch-inference.yaml
 ```
 
 Note that the `RayJob` spec contains a spec for the `RayCluster`. This tutorial uses a single-node cluster with 4 GPUs. For production use cases, use a multi-node cluster where the head node doesn't have GPUs, so that Ray can automatically schedule GPU workloads on worker nodes which won't interfere with critical Ray processes on the head node.

doc/source/cluster/kubernetes/getting-started/kuberay-operator-installation.md

@@ -17,15 +17,15 @@ kind create cluster --image=kindest/node:v1.26.0
 ```sh
 helm repo add kuberay https://ray-project.github.io/kuberay-helm/
 helm repo update
-# Install both CRDs and KubeRay operator v1.4.2.
-helm install kuberay-operator kuberay/kuberay-operator --version 1.4.2
+# Install both CRDs and KubeRay operator v1.5.0.
+helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0
 ```
 
 ### Method 2: Kustomize
 
 ```sh
 # Install CRD and KubeRay operator.
-kubectl create -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=v1.4.2"
+kubectl create -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=v1.5.0"
 ```
 
 ## Step 3: Validate Installation

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

@@ -28,7 +28,7 @@ Once the KubeRay operator is running, you're ready to deploy a RayCluster. Creat
 
 ```sh
 # Deploy a sample RayCluster CR from the KubeRay Helm chart repo:
-helm install raycluster kuberay/ray-cluster --version 1.4.2
+helm install raycluster kuberay/ray-cluster --version 1.5.0
 ```
 
 

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

@@ -96,7 +96,7 @@ Follow the [KubeRay Operator Installation](kuberay-operator-deploy) to install t
 ## Step 3: Install a RayJob
 
 ```sh
-kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-job.sample.yaml
+kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-job.sample.yaml
 ```
 
 ## Step 4: Verify the Kubernetes cluster status
@@ -163,13 +163,13 @@ The Python script `sample_code.py` used by `entrypoint` is a simple Ray script t
 ## Step 6: Delete the RayJob
 
 ```sh
-kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-job.sample.yaml
+kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-job.sample.yaml
 ```
 
 ## Step 7: Create a RayJob with `shutdownAfterJobFinishes` set to true
 
 ```sh
-kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-job.shutdown.yaml
+kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-job.shutdown.yaml
 ```
 
 The `ray-job.shutdown.yaml` defines a RayJob custom resource with `shutdownAfterJobFinishes: true` and `ttlSecondsAfterFinished: 10`.
@@ -197,7 +197,7 @@ kubectl get raycluster
 
 ```sh
 # Step 10.1: Delete the RayJob
-kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-job.shutdown.yaml
+kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-job.shutdown.yaml
 
 # Step 10.2: Delete the KubeRay operator
 helm uninstall kuberay-operator

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

@@ -3,7 +3,7 @@
 
 ## Prerequisites
 
-This guide mainly focuses on the behavior of KubeRay v1.4.2 and Ray 2.46.0.
+This guide mainly focuses on the behavior of KubeRay v1.5.0 and Ray 2.46.0.
 
 ## What's a RayService?
 
@@ -35,7 +35,7 @@ Note that the YAML file in this example uses `serveConfigV2` to specify a multi-
 ## Step 3: Install a RayService
 
 ```sh
-kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-service.sample.yaml
+kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-service.sample.yaml
 ```
 
 ## Step 4: Verify the Kubernetes cluster status
@@ -129,7 +129,7 @@ curl -X POST -H 'Content-Type: application/json' rayservice-sample-serve-svc:800
 
 ```sh
 # Delete the RayService.
-kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-service.sample.yaml
+kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-service.sample.yaml
 
 # Uninstall the KubeRay operator.
 helm uninstall kuberay-operator

doc/source/cluster/kubernetes/k8s-ecosystem/ingress.md

@@ -33,10 +33,10 @@ Four examples show how to use ingress to access your Ray cluster:
 # Step 1: Install KubeRay operator and CRD
 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
+helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0
 
 # Step 2: Install a RayCluster
-helm install raycluster kuberay/ray-cluster --version 1.4.2
+helm install raycluster kuberay/ray-cluster --version 1.5.0
 
 # Step 3: Edit the `ray-operator/config/samples/ray-cluster-alb-ingress.yaml`
 #
@@ -123,10 +123,10 @@ Now run the following commands:
 # Step 1: Install KubeRay operator and CRD
 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
+helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0
 
 # Step 2: Install a RayCluster
-helm install raycluster kuberay/ray-cluster --version 1.4.2
+helm install raycluster kuberay/ray-cluster --version 1.5.0
 
 # Step 3: Edit ray-cluster-gclb-ingress.yaml to replace the service name with the name of the head service from the RayCluster. (Output of `kubectl get svc`)
 
@@ -186,12 +186,12 @@ kubectl wait --namespace ingress-nginx \
 # Step 3: Install KubeRay operator and CRD
 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
+helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0
 
 # Step 4: Install RayCluster and create an ingress separately.
 # More information about change of setting was documented in https://github.com/ray-project/kuberay/pull/699
 # and `ray-operator/config/samples/ray-cluster.separate-ingress.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.4.2/ray-operator/config/samples/ray-cluster.separate-ingress.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.5.0/ray-operator/config/samples/ray-cluster.separate-ingress.yaml
 kubectl apply -f ray-cluster.separate-ingress.yaml
 
 # Step 5: Check the ingress created in Step 4.
@@ -230,10 +230,10 @@ kubectl describe ingress raycluster-ingress-head-ingress
 # Step 1: Install KubeRay operator and CRD
 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
+helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0
 
 # Step 2: Install a RayCluster
-helm install raycluster kuberay/ray-cluster --version 1.4.2
+helm install raycluster kuberay/ray-cluster --version 1.5.0
 
 # Step 3: Edit the `ray-operator/config/samples/ray-cluster-agc-gatewayapi.yaml`
 #

doc/source/cluster/kubernetes/k8s-ecosystem/istio.md

@@ -66,7 +66,7 @@ In this mode, you _must_ disable the KubeRay init container injection by setting
 
 ```bash
 # Set ENABLE_INIT_CONTAINER_INJECTION=false on the KubeRay operator.
-helm upgrade kuberay-operator kuberay/kuberay-operator --version 1.4.2 \
+helm upgrade kuberay-operator kuberay/kuberay-operator --version 1.5.0 \
   --set env\[0\].name=ENABLE_INIT_CONTAINER_INJECTION \
   --set-string env\[0\].value=false
 

doc/source/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.md

@@ -56,7 +56,7 @@ kubectl get all -n prometheus-system
 * Set `metrics.serviceMonitor.enabled=true` when installing the KubeRay operator with Helm to create a ServiceMonitor that scrapes metrics exposed by the KubeRay operator's service.
   ```sh
   # Enable the ServiceMonitor and set the label `release: prometheus` to the ServiceMonitor so that Prometheus can discover it
-  helm install kuberay-operator kuberay/kuberay-operator --version 1.4.2 \
+  helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0 \
     --set metrics.serviceMonitor.enabled=true \ 
     --set metrics.serviceMonitor.selector.release=prometheus 
   ```
@@ -104,7 +104,7 @@ curl localhost:8080
   * `# HELP`: Describe the meaning of this metric.
   * `# TYPE`: See [this document](https://prometheus.io/docs/concepts/metric_types/) for more details.
 
-* Three required environment variables are defined in [ray-cluster.embed-grafana.yaml](https://github.com/ray-project/kuberay/blob/v1.4.2/ray-operator/config/samples/ray-cluster.embed-grafana.yaml). See [Configuring and Managing Ray Dashboard](https://docs.ray.io/en/latest/cluster/configure-manage-dashboard.html) for more details about these environment variables.
+* Three required environment variables are defined in [ray-cluster.embed-grafana.yaml](https://github.com/ray-project/kuberay/blob/v1.5.0/ray-operator/config/samples/ray-cluster.embed-grafana.yaml). See [Configuring and Managing Ray Dashboard](https://docs.ray.io/en/latest/cluster/configure-manage-dashboard.html) for more details about these environment variables.
   ```yaml
   env:
     - name: RAY_GRAFANA_IFRAME_HOST

doc/source/cluster/kubernetes/k8s-ecosystem/scheduler-plugins.md

@@ -29,7 +29,7 @@ You need to have the access to configure Kubernetes control plane to replace the
 KubeRay v1.4.0 and later versions support scheduler plugins.
 
 ```sh
-helm install kuberay-operator kuberay/kuberay-operator --version 1.4.2 --set batchScheduler.name=scheduler-plugins
+helm install kuberay-operator kuberay/kuberay-operator --version 1.5.0 --set batchScheduler.name=scheduler-plugins
 ```
 
 ## Step 4: Deploy a RayCluster with gang scheduling