Deploying a Static Ray Cluster on Kubernetes¶
This document gives an example of how to manually deploy a non-autoscaling Ray cluster on Kubernetes.
Learn about deploying an autoscaling Ray cluster using the Ray Helm chart.
Creating a Ray Namespace¶
First, create a Kubernetes Namespace for Ray resources on your cluster. The
following commands will create resources under this Namespace, so if you want
to use a different one than
ray, please be sure to also change the
namespace fields in the provided
yaml files and anytime you see a
flag passed to
$ kubectl create namespace ray
Starting a Ray Cluster¶
A Ray cluster consists of a single head node and a set of worker nodes (the provided ray-cluster.yaml file will start 3 worker nodes). In the example Kubernetes configuration, this is implemented as:
ray-headKubernetes Service that enables the worker nodes to discover the location of the head node on start up. This Service also enables access to the Ray Client and Ray Dashboard.
ray-headKubernetes Deployment that backs the
ray-headService with a single head node pod (replica).
ray-workerKubernetes Deployment with multiple worker node pods (replicas) that connect to the
ray-headpod using the
Note that because the head and worker nodes are Deployments, Kubernetes will automatically restart pods that crash to maintain the correct number of replicas.
If a worker node goes down, a replacement pod will be started and joined to the cluster.
If the head node goes down, it will be restarted. This will start a new Ray cluster. Worker nodes that were connected to the old head node will crash and be restarted, connecting to the new head node when they come back up.
Try deploying a cluster with the provided Kubernetes config by running the following command:
$ kubectl apply -f ray/doc/kubernetes/ray-cluster.yaml
Verify that the pods are running by running
kubectl get pods -n ray. You
may have to wait up to a few minutes for the pods to enter the ‘Running’
state on the first run.
$ kubectl -n ray get pods NAME READY STATUS RESTARTS AGE ray-head-5455bb66c9-6bxvz 1/1 Running 0 10s ray-worker-5c49b7cc57-c6xs8 1/1 Running 0 5s ray-worker-5c49b7cc57-d9m86 1/1 Running 0 5s ray-worker-5c49b7cc57-kzk4s 1/1 Running 0 5s
You might see a nonzero number of RESTARTS for the worker pods. That can happen when the worker pods start up before the head pod and the workers aren’t able to connect. This shouldn’t affect the behavior of the cluster.
To change the number of worker nodes in the cluster, change the
field in the worker deployment configuration in that file and then re-apply
the config as follows:
# Edit 'ray/doc/kubernetes/ray-cluster.yaml' and change the 'replicas' # field under the ray-worker deployment to, e.g., 4. # Re-apply the new configuration to the running deployment. $ kubectl apply -f ray/doc/kubernetes/ray-cluster.yaml service/ray-head unchanged deployment.apps/ray-head unchanged deployment.apps/ray-worker configured # Verify that there are now the correct number of worker pods running. $ kubectl -n ray get pods NAME READY STATUS RESTARTS AGE ray-head-5455bb66c9-6bxvz 1/1 Running 0 30s ray-worker-5c49b7cc57-c6xs8 1/1 Running 0 25s ray-worker-5c49b7cc57-d9m86 1/1 Running 0 25s ray-worker-5c49b7cc57-kzk4s 1/1 Running 0 25s ray-worker-5c49b7cc57-zzfg2 1/1 Running 0 0s
To validate that the restart behavior is working properly, try killing pods and checking that they are restarted by Kubernetes:
# Delete a worker pod. $ kubectl -n ray delete pod ray-worker-5c49b7cc57-c6xs8 pod "ray-worker-5c49b7cc57-c6xs8" deleted # Check that a new worker pod was started (this may take a few seconds). $ kubectl -n ray get pods NAME READY STATUS RESTARTS AGE ray-head-5455bb66c9-6bxvz 1/1 Running 0 45s ray-worker-5c49b7cc57-d9m86 1/1 Running 0 40s ray-worker-5c49b7cc57-kzk4s 1/1 Running 0 40s ray-worker-5c49b7cc57-ypq8x 1/1 Running 0 0s # Delete the head pod. $ kubectl -n ray delete pod ray-head-5455bb66c9-6bxvz pod "ray-head-5455bb66c9-6bxvz" deleted # Check that a new head pod was started and the worker pods were restarted. $ kubectl -n ray get pods NAME READY STATUS RESTARTS AGE ray-head-5455bb66c9-gqzql 1/1 Running 0 0s ray-worker-5c49b7cc57-d9m86 1/1 Running 1 50s ray-worker-5c49b7cc57-kzk4s 1/1 Running 1 50s ray-worker-5c49b7cc57-ypq8x 1/1 Running 1 10s # You can even try deleting all of the pods in the Ray namespace and checking # that Kubernetes brings the right number back up. $ kubectl -n ray delete pods --all $ kubectl -n ray get pods NAME READY STATUS RESTARTS AGE ray-head-5455bb66c9-7l6xj 1/1 Running 0 10s ray-worker-5c49b7cc57-57tpv 1/1 Running 0 10s ray-worker-5c49b7cc57-6m4kp 1/1 Running 0 10s ray-worker-5c49b7cc57-jx2w2 1/1 Running 0 10s
Now that we have a running cluster, we can execute Ray programs.
To delete a running Ray cluster, you can run the following command:
kubectl delete -f ray/doc/kubernetes/ray-cluster.yaml
Questions or Issues?¶
You can post questions or issues or feedback through the following channels: