[Doc] Update KubeRay version to 1.0.0 (#40937)

doc/source/cluster/kubernetes/examples/gpu-training-example.md

@@ -40,10 +40,10 @@ kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container
 # (Method 2) "gcloud container clusters get-credentials <your-cluster-name> --region <your-region> --project <your-project>"
 # (Method 3) "kubectl config use-context ..."
 
-# Install both CRDs and KubeRay operator v1.0.0-rc.0.
+# Install both CRDs and KubeRay operator v1.0.0.
 helm repo add kuberay https://ray-project.github.io/kuberay-helm/
 helm repo update
-helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0
 
 # Create a Ray cluster
 kubectl apply -f https://raw.githubusercontent.com/ray-project/ray/master/doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml
@@ -116,7 +116,7 @@ It is optional.
 # Create the KubeRay operator
 helm repo add kuberay https://ray-project.github.io/kuberay-helm/
 helm repo update
-helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0
 
 # Create a Ray cluster
 kubectl apply -f https://raw.githubusercontent.com/ray-project/ray/master/doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml

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

@@ -19,7 +19,7 @@ Note that the YAML file in this example uses `serveConfigV2`, which is supported
 
 ```sh
 # Download `ray-service.mobilenet.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-service.mobilenet.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-service.mobilenet.yaml
 
 # Create a RayService
 kubectl apply -f ray-service.mobilenet.yaml

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

@@ -39,12 +39,12 @@ It should be scheduled on the CPU pod.
 
 ## Step 2: Submit the RayJob
 
-Create the RayJob custom resource. The RayJob spec is defined in [ray-job.batch-inference.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-job.batch-inference.yaml).
+Create the RayJob custom resource. The RayJob spec is defined in [ray-job.batch-inference.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0/ray-operator/config/samples/ray-job.batch-inference.yaml).
 
 Download the file with `curl`:
 
 ```bash
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-job.batch-inference.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-job.batch-inference.yaml
 ```
 
 Note that the `RayJob` spec contains a spec for the `RayCluster` that is to be created for the job. For this tutorial, we use a single-node cluster with 4 GPUs. For production use cases, we recommend using a multi-node cluster where the head node does not have GPUs, so that Ray can automatically schedule GPU workloads on worker nodes and they won't interfere with critical Ray processes on the head node.

doc/source/cluster/kubernetes/examples/stable-diffusion-rayservice.md

@@ -18,7 +18,7 @@ Please note that the YAML file in this example uses `serveConfigV2`, which is su
 
 ```sh
 # Step 3.1: Download `ray-service.stable-diffusion.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-service.stable-diffusion.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-service.stable-diffusion.yaml
 
 # Step 3.2: Create a RayService
 kubectl apply -f ray-service.stable-diffusion.yaml

doc/source/cluster/kubernetes/examples/text-summarizer-rayservice.md

@@ -17,7 +17,7 @@ Please note that the YAML file in this example uses `serveConfigV2`, which is su
 
 ```sh
 # Step 3.1: Download `ray-service.text-summarizer.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-service.text-summarizer.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-service.text-summarizer.yaml
 
 # Step 3.2: Create a RayService
 kubectl apply -f ray-service.text-summarizer.yaml

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

@@ -26,8 +26,8 @@ Deploy the KubeRay operator with the [Helm chart repository](https://github.com/
 helm repo add kuberay https://ray-project.github.io/kuberay-helm/
 helm repo update
 
-# Install both CRDs and KubeRay operator v1.0.0-rc.0.
-helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0
+# Install both CRDs and KubeRay operator v1.0.0.
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0
 
 # Confirm that the operator is running in the namespace `default`.
 kubectl get pods
@@ -43,7 +43,7 @@ Once the KubeRay operator is running, we are ready to deploy a RayCluster. To do
 
 ```sh
 # Deploy a sample RayCluster CR from the KubeRay Helm chart repo:
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0
+helm install raycluster kuberay/ray-cluster --version 1.0.0
 
 # Once the RayCluster CR has been created, you can view it by running:
 kubectl get rayclusters

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

@@ -54,7 +54,7 @@ Please note that the YAML file in this example uses `serveConfigV2` to specify a
 
 ```sh
 # Step 3.1: Download `ray_v1alpha1_rayjob.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml
 
 # Step 3.2: Create a RayJob
 kubectl apply -f ray_v1alpha1_rayjob.yaml

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

@@ -40,7 +40,7 @@ Please note that the YAML file in this example uses `serveConfigV2` to specify a
 
 ```sh
 # Step 3.1: Download `ray_v1alpha1_rayservice.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray_v1alpha1_rayservice.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray_v1alpha1_rayservice.yaml
 
 # Step 3.2: Create a RayService
 kubectl apply -f ray_v1alpha1_rayservice.yaml

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

@@ -32,10 +32,10 @@ Three 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 1.0.0-rc.0
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0
 
 # Step 2: Install a RayCluster
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0
+helm install raycluster kuberay/ray-cluster --version 1.0.0
 
 # Step 3: Edit the `ray-operator/config/samples/ray-cluster-alb-ingress.yaml`
 #
@@ -122,10 +122,10 @@ 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 1.0.0-rc.0
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0
 
 # Step 2: Install a RayCluster
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0
+helm install raycluster kuberay/ray-cluster --version 1.0.0
 
 # 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`)
 
@@ -185,12 +185,12 @@ 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 1.0.0-rc.0
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0
 
 # 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/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.separate-ingress.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-cluster.separate-ingress.yaml
 kubectl apply -f ray-operator/config/samples/ray-cluster.separate-ingress.yaml
 
 # Step 5: Check the ingress created in Step 4.

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

@@ -42,7 +42,7 @@ kustomize version --short
 ```sh
 # Create a RayCluster CR, and the KubeRay operator will reconcile a Ray cluster
 # with 1 head Pod and 1 worker Pod.
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 --set image.tag=2.2.0-py38-cpu
+helm install raycluster kuberay/ray-cluster --version 1.0.0 --set image.tag=2.2.0-py38-cpu
 
 # Check RayCluster
 kubectl get pod -l ray.io/cluster=raycluster-kuberay

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

@@ -87,7 +87,7 @@ kubectl get service
  * `# 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/v1.0.0-rc.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.
+* Three required environment variables are defined in [ray-cluster.embed-grafana.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0/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/pyspy.md

@@ -33,7 +33,7 @@ Follow [this document](kuberay-operator-deploy) to install the latest stable Kub
 
 ```bash
 # Download `ray-cluster.py-spy.yaml`
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.py-spy.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-cluster.py-spy.yaml
 
 # Create a RayCluster
 kubectl apply -f ray-cluster.py-spy.yaml

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

@@ -35,7 +35,7 @@ batchScheduler:
 * Pass the `--set batchScheduler.enabled=true` flag when running on the command line:
 ```shell
 # Install the Helm chart with --enable-batch-scheduler flag set to true 
-helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0 --set batchScheduler.enabled=true
+helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0 --set batchScheduler.enabled=true
 ```
 
 ### Step 4: Install a RayCluster with the Volcano scheduler
@@ -45,7 +45,7 @@ The RayCluster custom resource must include the `ray.io/scheduler-name: volcano`
 ```shell
 # Path: kuberay/ray-operator/config/samples
 # Includes label `ray.io/scheduler-name: volcano` in the metadata.labels
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.volcano-scheduler.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-cluster.volcano-scheduler.yaml
 kubectl apply -f ray-cluster.volcano-scheduler.yaml
 
 # Check the RayCluster
@@ -113,7 +113,7 @@ Next, create a RayCluster with a head node (1 CPU + 2Gi of RAM) and two workers
 ```shell
 # Path: kuberay/ray-operator/config/samples
 # Includes the `ray.io/scheduler-name: volcano` and `volcano.sh/queue-name: kuberay-test-queue` labels in the metadata.labels
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.volcano-scheduler-queue.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-cluster.volcano-scheduler-queue.yaml
 kubectl apply -f ray-cluster.volcano-scheduler-queue.yaml
 ```
 

doc/source/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.md

@@ -124,7 +124,7 @@ Therefore, the YAML file includes `python-multipart` in the runtime environment.
 In the [MobileNet example](kuberay-mobilenet-rayservice-example), the [mobilenet.py](https://github.com/ray-project/serve_config_examples/blob/master/mobilenet/mobilenet.py) consists of two functions: `__init__()` and `__call__()`.
 The function `__call__()` is only called when the Serve application receives a request.
 
-* Example 1: Remove `python-multipart` from the runtime environment in [the MobileNet YAML](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-service.mobilenet.yaml).
+* Example 1: Remove `python-multipart` from the runtime environment in [the MobileNet YAML](https://github.com/ray-project/kuberay/blob/v1.0.0/ray-operator/config/samples/ray-service.mobilenet.yaml).
  * The `python-multipart` library is only required for the `__call__` method. Therefore, we can only observe the dependency issue when we send a request to the application.
  * Example error message:
  ```bash
@@ -139,7 +139,7 @@ The function `__call__()` is only called when the Serve application receives a r
  AssertionError: The `python-multipart` library must be installed to use form parsing..
  ```
 
-* Example 2: Update the image from `rayproject/ray-ml:2.5.0` to `rayproject/ray:2.5.0` in [the MobileNet YAML](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-service.mobilenet.yaml). The latter image does not include `tensorflow`.
+* Example 2: Update the image from `rayproject/ray-ml:2.5.0` to `rayproject/ray:2.5.0` in [the MobileNet YAML](https://github.com/ray-project/kuberay/blob/v1.0.0/ray-operator/config/samples/ray-service.mobilenet.yaml). The latter image does not include `tensorflow`.
  * The `tensorflow` library is imported in the [mobilenet.py](https://github.com/ray-project/serve_config_examples/blob/master/mobilenet/mobilenet.py).
  * Example error message:
  ```bash
@@ -162,7 +162,7 @@ The function `__call__()` is only called when the Serve application receives a r
 ### Issue 4: Incorrect `import_path`.
 
 You can refer to [the documentation](https://docs.ray.io/en/latest/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray.serve.schema.ServeApplicationSchema.import_path) for more details about the format of `import_path`.
-Taking [the MobileNet YAML file](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-service.mobilenet.yaml) as an example,
+Taking [the MobileNet YAML file](https://github.com/ray-project/kuberay/blob/v1.0.0/ray-operator/config/samples/ray-service.mobilenet.yaml) as an example,
 the `import_path` is `mobilenet.mobilenet:app`. The first `mobilenet` is the name of the directory in the `working_dir`,
 the second `mobilenet` is the name of the Python file in the directory `mobilenet/`,
 and `app` is the name of the variable representing Ray Serve application within the Python file.

doc/source/cluster/kubernetes/user-guides/configuring-autoscaling.md

@@ -61,7 +61,7 @@ Follow [this document](kuberay-operator-deploy) to install the latest stable Kub
 ### Step 3: Create a RayCluster custom resource with autoscaling enabled
 
 ```bash
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.autoscaler.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-cluster.autoscaler.yaml
 kubectl apply -f ray-cluster.autoscaler.yaml
 ```
 
@@ -85,7 +85,7 @@ kubectl get configmaps
 ```
 
 The RayCluster has one head Pod and zero worker Pods. The head Pod has two containers: a Ray head container and a Ray Autoscaler sidecar container.
-Additionally, the [ray-cluster.autoscaler.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.autoscaler.yaml) includes a ConfigMap named `ray-example` that houses two Python scripts: `detached_actor.py` and `terminate_detached_actor`.py.
+Additionally, the [ray-cluster.autoscaler.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0/ray-operator/config/samples/ray-cluster.autoscaler.yaml) includes a ConfigMap named `ray-example` that houses two Python scripts: `detached_actor.py` and `terminate_detached_actor`.py.
 
 * `detached_actor.py` is a Python script that creates a detached actor which requires 1 CPU.
  ```py
@@ -254,7 +254,7 @@ helm uninstall kuberay-operator
 (kuberay-autoscaling-config)=
 ## KubeRay Autoscaling Configurations
 
-The [ray-cluster.autoscaler.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.autoscaler.yaml) used in the quickstart example contains detailed comments about the configuration options.
+The [ray-cluster.autoscaler.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0/ray-operator/config/samples/ray-cluster.autoscaler.yaml) used in the quickstart example contains detailed comments about the configuration options.
 ***It's recommended to read this section in conjunction with the YAML file.***
 
 ### 1. Enabling autoscaling

doc/source/cluster/kubernetes/user-guides/gke-gcs-bucket.md

@@ -72,7 +72,7 @@ gsutil iam ch serviceAccount:[email protected]:rol
 You can download the RayCluster YAML manifest for this tutorial with `curl` as follows:
 
 ```bash
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.gke-bucket.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0/ray-operator/config/samples/ray-cluster.gke-bucket.yaml
 ```
 
 The key parts are the following lines:

doc/source/cluster/kubernetes/user-guides/helm-chart-rbac.md

@@ -69,9 +69,9 @@ kubectl get role
 #kuberay-operator-leader-election 2023-10-15T04:54:28Z
 
 # Install RayCluster in the `default`, `n1`, and `n2` namespaces.
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 -n n1
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 -n n2
+helm install raycluster kuberay/ray-cluster --version 1.0.0
+helm install raycluster kuberay/ray-cluster --version 1.0.0 -n n1
+helm install raycluster kuberay/ray-cluster --version 1.0.0 -n n2
 
 # You should create a RayCluster in these 3 namespaces.
 kubectl get raycluster -A
@@ -117,9 +117,9 @@ kubectl get role --all-namespaces | grep kuberay
 #default kuberay-operator-leader-election 2023-10-15T05:18:03Z
 
 # Install RayCluster in the `default`, `n1`, and `n2` namespaces.
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 -n n1
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 -n n2
+helm install raycluster kuberay/ray-cluster --version 1.0.0
+helm install raycluster kuberay/ray-cluster --version 1.0.0 -n n1
+helm install raycluster kuberay/ray-cluster --version 1.0.0 -n n2
 
 # KubeRay only creates a RayCluster in `default`.
 kubectl get raycluster -A
@@ -173,9 +173,9 @@ kubectl get role --all-namespaces | grep kuberay
 #n2 kuberay-operator 2023-10-15T05:34:27Z
 
 # Install RayCluster in the `default`, `n1`, and `n2` namespaces.
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 -n n1
-helm install raycluster kuberay/ray-cluster --version 1.0.0-rc.0 -n n2
+helm install raycluster kuberay/ray-cluster --version 1.0.0
+helm install raycluster kuberay/ray-cluster --version 1.0.0 -n n1
+helm install raycluster kuberay/ray-cluster --version 1.0.0 -n n2
 
 # KubeRay creates a RayCluster only in n1 and n2.
 kubectl get raycluster -A