ray-project · richardliaw · Oct 13, 2023 · Oct 12, 2023
@@ -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/master/ray-operator/config/samples/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
 
 # Create a RayService
 kubectl apply -f ray-service.mobilenet.yaml

@@ -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/master/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-rc.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/master/ray-operator/config/samples/ray-job.batch-inference.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.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.

@@ -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/master/ray-operator/config/samples/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
 
 # Step 3.2: Create a RayService
 kubectl apply -f ray-service.stable-diffusion.yaml

@@ -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/master/ray-operator/config/samples/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
 
 # Step 3.2: Create a RayService
 kubectl apply -f ray-service.text-summarizer.yaml

@@ -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/master/ray-operator/config/samples/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
 
 # Step 3.2: Create a RayJob
 kubectl apply -f ray_v1alpha1_rayjob.yaml

@@ -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/master/ray-operator/config/samples/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
 
 # Step 3.2: Create a RayService
 kubectl apply -f ray_v1alpha1_rayservice.yaml

@@ -190,7 +190,7 @@ helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.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/master/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
 kubectl apply -f ray-operator/config/samples/ray-cluster.separate-ingress.yaml
 
 # Step 5: Check the ingress created in Step 4.

@@ -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/master/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-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.
   ```yaml
   env:
     - name: RAY_GRAFANA_IFRAME_HOST

@@ -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/master/ray-operator/config/samples/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
 
 # Create a RayCluster
 kubectl apply -f ray-cluster.py-spy.yaml

@@ -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/master/ray-operator/config/samples/ray-cluster.volcano-scheduler.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.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/master/ray-operator/config/samples/ray-cluster.volcano-scheduler-queue.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.volcano-scheduler-queue.yaml
 kubectl apply -f ray-cluster.volcano-scheduler-queue.yaml
 ```
 

@@ -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/master/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-rc.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/master/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-rc.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/master/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-rc.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.

@@ -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/master/ray-operator/config/samples/ray-cluster.autoscaler.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.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/master/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-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.
 
 * `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/master/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-rc.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

@@ -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/master/ray-operator/config/samples/ray-cluster.gke-bucket.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.gke-bucket.yaml
 ```
 
 The key parts are the following lines:

@@ -37,7 +37,7 @@ Follow [this document](kuberay-operator-deploy) to install the latest stable Kub
 ### Step 3: Install a RayCluster with GCS FT enabled
 
 ```sh
-curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.external-redis.yaml
+curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml
 kubectl apply -f ray-cluster.external-redis.yaml
 ```
 
@@ -58,7 +58,7 @@ kubectl get configmaps
 # ...
 ```
 
-The [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.external-redis.yaml) file defines Kubernetes resources for RayCluster, Redis, and ConfigMaps.
+The [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml) file defines Kubernetes resources for RayCluster, Redis, and ConfigMaps.
 There are two ConfigMaps in this example: `ray-example` and `redis-config`.
 The `ray-example` ConfigMap houses two Python scripts: `detached_actor.py` and `increment_counter.py`.
 
@@ -137,7 +137,7 @@ KEYS *
 HGETALL 864b004c-6305-42e3-ac46-adfa8eb6f752
 ```
 
-In [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.external-redis.yaml), the `ray.io/external-storage-namespace` annotation isn't set for the RayCluster.
+In [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml), the `ray.io/external-storage-namespace` annotation isn't set for the RayCluster.
 Therefore, KubeRay automatically injects the environment variable `RAY_external_storage_namespace` to all Ray Pods managed by the RayCluster with the RayCluster's UID as the external storage namespace by default.
 See [this section](kuberay-external-storage-namespace) to learn more about the annotation.
 
@@ -172,7 +172,7 @@ kubectl get pods -l=ray.io/is-ray-node=yes
 # raycluster-external-redis-worker-small-group-yyyyy   1/1     Running   0             xxm
 ```
 
-In [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.external-redis.yaml), the `RAY_gcs_rpc_server_reconnect_timeout_s` environment variable isn't set in the specifications for either the head Pod or the worker Pod within the RayCluster.
+In [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml), the `RAY_gcs_rpc_server_reconnect_timeout_s` environment variable isn't set in the specifications for either the head Pod or the worker Pod within the RayCluster.
 Therefore, KubeRay automatically injects the `RAY_gcs_rpc_server_reconnect_timeout_s` environment variable with the value **600** to the worker Pod and uses the default value **60** for the head Pod.
 The timeout value for worker Pods must be longer than the timeout value for the head Pod so that the worker Pods don't terminate before the head Pod restarts from a failure.
 
@@ -204,7 +204,7 @@ kind delete cluster
 
 ## KubeRay GCS fault tolerance configurations
 
-The [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.external-redis.yaml) used in the quickstart example contains detailed comments about the configuration options.
+The [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml) used in the quickstart example contains detailed comments about the configuration options.
 ***Read this section in conjunction with the YAML file.***
 
 ### 1. Enable GCS fault tolerance
@@ -221,7 +221,7 @@ The [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blo
 
 * **`redis-password`** in head's `rayStartParams`:
 Use this option to specify the password for the Redis service, thus allowing the Ray head to connect to it.
-In the [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.external-redis.yaml), the RayCluster custom resource uses an environment variable `REDIS_PASSWORD` to store the password from a Kubernetes secret.
+In the [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml), the RayCluster custom resource uses an environment variable `REDIS_PASSWORD` to store the password from a Kubernetes secret.
     ```yaml
     rayStartParams:
       redis-password: $REDIS_PASSWORD
@@ -240,7 +240,7 @@ In the [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/
 
 * **`RAY_REDIS_ADDRESS`** environment variable in head's Pod:
 Ray reads the `RAY_REDIS_ADDRESS` environment variable to establish a connection with the Redis server.
-In the [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.external-redis.yaml), the RayCluster custom resource uses the `redis` Kubernetes ClusterIP service name as the connection point to the Redis server. The ClusterIP service is also created by the YAML file.
+In the [ray-cluster.external-redis.yaml](https://github.com/ray-project/kuberay/blob/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.external-redis.yaml), the RayCluster custom resource uses the `redis` Kubernetes ClusterIP service name as the connection point to the Redis server. The ClusterIP service is also created by the YAML file.
     ```yaml
     template:
       spec:

@@ -45,7 +45,7 @@ Currently, for timing (1), we can set the container's `Command` and `Args` in Ra
     # Prerequisite: There is a KubeRay operator in the Kubernetes cluster.
 
     # Download `ray-cluster.head-command.yaml`
-    curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.head-command.yaml
+    curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-cluster.head-command.yaml
 
     # Create a RayCluster
     kubectl apply -f ray-cluster.head-command.yaml