Deploying on Kubernetes

Note

The easiest way to run a Ray cluster is by using the built-in autoscaler, which has support for running on top of Kubernetes. Please see the autoscaler documentation for details.

Warning

Running Ray on Kubernetes is still a work in progress. If you have a suggestion for how to improve this documentation or want to request a missing feature, please get in touch using one of the channels in the Questions or Issues? section below.

This document assumes that you have access to a Kubernetes cluster and have kubectl installed locally and configured to access the cluster. It will first walk you through how to deploy a Ray cluster on your existing Kubernetes cluster, then explore a few different ways to run programs on the Ray cluster.

The configuration yaml files used here are provided in the Ray repository as examples to get you started. When deploying real applications, you will probably want to build and use your own container images, add more worker nodes to the cluster (or use the Kubernetes Horizontal Pod Autoscaler), and change the resource requests for the head and worker nodes. Refer to the provided yaml files to be sure that you maintain important configuration options for Ray to function properly.

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 -n flag passed to kubectl.

$ kubectl create -f ray/doc/kubernetes/ray-namespace.yaml

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:

  • A ray-head Kubernetes Service that enables the worker nodes to discover the location of the head node on start up.
  • A ray-head Kubernetes Deployment that backs the ray-head Service with a single head node pod (replica).
  • A ray-worker Kubernetes Deployment with multiple worker node pods (replicas) that connect to the ray-head pod using the ray-head Service.

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

Note

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 replicas 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 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 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

Running Ray Programs

This section assumes that you have a running Ray cluster (if you don’t, please refer to the section above to get started) and will walk you through three different options to run a Ray program on it:

  1. Using kubectl exec to run a Python script.
  2. Using kubectl exec -it bash to work interactively in a remote shell.
  3. Submitting a Kubernetes Job.

Running a program using ‘kubectl exec’

To run an example program that tests object transfers between nodes in the cluster, try the following commands (don’t forget to replace the head pod name - you can find it by running kubectl -n ray get pods):

# Copy the test script onto the head node.
$ kubectl -n ray cp ray/doc/kubernetes/example.py ray-head-5455bb66c9-7l6xj:/example.py

# Run the example program on the head node.
$ kubectl -n ray exec ray-head-5455bb66c9-7l6xj -- python example.py
# You should see repeated output for 10 iterations and then 'Success!'

Running a program in a remote shell

You can also run tasks interactively on the cluster by connecting a remote shell to one of the pods.

# Copy the test script onto the head node.
$ kubectl -n ray cp ray/doc/kubernetes/example.py ray-head-5455bb66c9-7l6xj:/example.py

# Get a remote shell to the head node.
$ kubectl -n ray exec -it ray-head-5455bb66c9-7l6xj -- bash

# Run the example program on the head node.
root@ray-head-6f566446c-5rdmb:/# python example.py
# You should see repeated output for 10 iterations and then 'Success!'

You can also start an IPython interpreter to work interactively:

# From your local machine.
$ kubectl -n ray exec -it ray-head-5455bb66c9-7l6xj -- ipython

# From a remote shell on the head node.
$ kubectl -n ray exec -it ray-head-5455bb66c9-7l6xj -- bash
root@ray-head-6f566446c-5rdmb:/# ipython

Once you have the IPython interpreter running, try running the following example program:

from collections import Counter
import socket
import time
import ray

ray.init(address="$RAY_HEAD_SERVICE_HOST:$RAY_HEAD_SERVICE_PORT_REDIS_PRIMARY")

@ray.remote
def f(x):
    time.sleep(0.01)
    return x + (socket.gethostname(), )

# Check that objects can be transferred from each node to each other node.
%time Counter(ray.get([f.remote(f.remote(())) for _ in range(100)]))

Submitting a Job

You can also submit a Ray application to run on the cluster as a Kubernetes Job. The Job will run a single pod running the Ray driver program to completion, then terminate the pod but allow you to access the logs.

To submit a Job that downloads and executes an example program that tests object transfers between nodes in the cluster, run the following command:

$ kubectl create -f ray/doc/kubernetes/ray-job.yaml
job.batch/ray-test-job-kw5gn created

To view the output of the Job, first find the name of the pod that ran it, then fetch its logs:

$ kubectl -n ray get pods
NAME                          READY   STATUS      RESTARTS   AGE
ray-head-5455bb66c9-7l6xj     1/1     Running     0          15s
ray-test-job-kw5gn-5g7tv      0/1     Completed   0          10s
ray-worker-5c49b7cc57-57tpv   1/1     Running     0          15s
ray-worker-5c49b7cc57-6m4kp   1/1     Running     0          15s
ray-worker-5c49b7cc57-jx2w2   1/1     Running     0          15s

# Fetch the logs. You should see repeated output for 10 iterations and then
# 'Success!'
$ kubectl -n ray logs ray-test-job-kw5gn-5g7tv

To clean up the resources created by the Job after checking its output, run the following:

# List Jobs run in the Ray namespace.
$ kubectl -n ray get jobs
NAME                 COMPLETIONS   DURATION   AGE
ray-test-job-kw5gn   1/1           10s        30s

# Delete the finished Job.
$ kubectl -n ray delete job ray-test-job-kw5gn

# Verify that the Job's pod was cleaned up.
$ kubectl -n ray get pods
NAME                          READY   STATUS      RESTARTS   AGE
ray-head-5455bb66c9-7l6xj     1/1     Running     0          60s
ray-worker-5c49b7cc57-57tpv   1/1     Running     0          60s
ray-worker-5c49b7cc57-6m4kp   1/1     Running     0          60s
ray-worker-5c49b7cc57-jx2w2   1/1     Running     0          60s

Cleaning Up

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:

  1. ray-dev@googlegroups.com: For discussions about development or any general questions and feedback.
  2. StackOverflow: For questions about how to use Ray.
  3. GitHub Issues: For bug reports and feature requests.