[Doc] Run pre-commit on cluster docs

doc/source/cluster/cli.rst

@@ -56,4 +56,4 @@ This section contains commands for managing Ray clusters.
 
 .. click:: ray.scripts.scripts:monitor
    :prog: ray monitor
-   :show-nested:
+   :show-nested:

doc/source/cluster/configure-manage-dashboard.md

@@ -24,10 +24,10 @@ Pass the keyword argument ``dashboard_port`` in your call to ``ray.init()``.
 :::{tab-item} VM Cluster Launcher
 Include the ``--dashboard-port`` argument in the `head_start_ray_commands` section of the [Cluster Launcher's YAML file](https://github.com/ray-project/ray/blob/0574620d454952556fa1befc7694353d68c72049/python/ray/autoscaler/aws/example-full.yaml#L172).
 ```yaml
-head_start_ray_commands: 
-  - ray stop 
+head_start_ray_commands:
+  - ray stop
   # Replace ${YOUR_PORT} with the port number you need.
-  - ulimit -n 65536; ray start --head --dashboard-port=${YOUR_PORT} --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml 
+  - ulimit -n 65536; ray start --head --dashboard-port=${YOUR_PORT} --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
 
 ```
 :::
@@ -66,7 +66,7 @@ The dashboard is now visible at ``http://localhost:8265``.
 :::{tab-item} KubeRay
 
 The KubeRay operator makes Dashboard available via a Service targeting the Ray head pod, named ``<RayCluster name>-head-svc``. Access
-Dashboard from within the Kubernetes cluster at ``http://<RayCluster name>-head-svc:8265``. 
+Dashboard from within the Kubernetes cluster at ``http://<RayCluster name>-head-svc:8265``.
 
 There are two ways to expose Dashboard outside the Cluster:
 
@@ -77,7 +77,7 @@ Follow the [instructions](kuberay-ingress) to set up ingress to access Ray Dashb
 You can also view the dashboard from outside the Kubernetes cluster by using port-forwarding:
 
 ```shell
-$ kubectl port-forward service/${RAYCLUSTER_NAME}-head-svc 8265:8265 
+$ kubectl port-forward service/${RAYCLUSTER_NAME}-head-svc 8265:8265
 # Visit ${YOUR_IP}:8265 for the Dashboard (e.g. 127.0.0.1:8265 or ${YOUR_VM_IP}:8265)
 ```
 
@@ -199,7 +199,7 @@ Grafana is a tool that supports advanced visualizations of Prometheus metrics an
 To view embedded time-series visualizations in Ray Dashboard, the following must be set up:
 
 1. The head node of the cluster is able to access Prometheus and Grafana.
-2. The browser of the dashboard user is able to access Grafana. 
+2. The browser of the dashboard user is able to access Grafana.
 
 Configure these settings using the `RAY_GRAFANA_HOST`, `RAY_PROMETHEUS_HOST`, `RAY_PROMETHEUS_NAME`, and `RAY_GRAFANA_IFRAME_HOST` environment variables when you start the Ray Clusters.
 

doc/source/cluster/faq.rst

@@ -76,7 +76,7 @@ connections. The solution for this problem is to start the worker nodes more slo
 Problems getting a SLURM cluster to work
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-A class of issues exist with starting Ray on SLURM clusters. While the exact causes aren't understood, (as of June 2023), some Ray 
+A class of issues exist with starting Ray on SLURM clusters. While the exact causes aren't understood, (as of June 2023), some Ray
 improvements mitigate some of the resource contention. Some of the issues
 reported are as follows:
 
@@ -100,4 +100,4 @@ any of the options `--entrypoint-num-cpus`, `--entrypoint-num-gpus`,
 `--entrypoint-resources` or `--entrypoint-memory` to `ray job submit`, or the
 corresponding arguments if using the Python SDK. If these are specified, the
 job entrypoint will be scheduled on a node that has the requested resources
-available.
+available.

doc/source/cluster/kubernetes/benchmarks/memory-scalability-benchmark.md

@@ -86,4 +86,4 @@ In addition, the number of custom resources in the Kubernetes cluster does not h
 * Note that the x-axis "Number of Pods" is the number of Pods that are created rather than running.
 If the Kubernetes cluster does not have enough computing resources, the GKE Autopilot adds a new Kubernetes node into the cluster.
 This process may take a few minutes, so some Pods may be pending in the process.
-This lag may can explain why the memory usage is somewhat throttled.
+This lag may can explain why the memory usage is somewhat throttled.

doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml

@@ -1,4 +1,4 @@
-# This is a RayCluster configuration for PyTorch image training benchmark with a 1Gi training set. 
+# This is a RayCluster configuration for PyTorch image training benchmark with a 1Gi training set.
 apiVersion: ray.io/v1alpha1
 kind: RayCluster
 metadata:

doc/source/cluster/kubernetes/configs/static-ray-cluster-networkpolicy.yaml

@@ -1,4 +1,4 @@
-# If your Kubernetes has a default deny network policy for pods, you need to manually apply this network policy 
+# If your Kubernetes has a default deny network policy for pods, you need to manually apply this network policy
 # to allow the bidirectional communication among the head and worker nodes in the Ray cluster.
 
 # Ray Head Ingress
@@ -92,4 +92,4 @@ spec:
   - to:
     - podSelector:
         matchLabels:
-          app: ray-cluster-head
+          app: ray-cluster-head

doc/source/cluster/kubernetes/configs/static-ray-cluster.tls.yaml

@@ -14,7 +14,7 @@ apiVersion: v1
 kind: ConfigMap
 metadata:
   name: tls
-data: 
+data:
   gencert_head.sh: |
     #!/bin/sh
     ## Create tls.key
@@ -380,4 +380,4 @@ spec:
             # Kubernetes testing environments such as Kind and minikube.
           requests:
             cpu: "500m"
-            memory: "1G"
+            memory: "1G"

doc/source/cluster/kubernetes/configs/static-ray-cluster.with-fault-tolerance.yaml

@@ -1,5 +1,5 @@
-# This section is only required for deploying Redis on Kubernetes for the purpose of enabling Ray 
-# to write GCS metadata to an external Redis for fault tolerance. If you have already deployed Redis 
+# This section is only required for deploying Redis on Kubernetes for the purpose of enabling Ray
+# to write GCS metadata to an external Redis for fault tolerance. If you have already deployed Redis
 # on Kubernetes, this section can be removed.
 kind: ConfigMap
 apiVersion: v1