Run A RayJob
This page shows how to leverage Kueue’s scheduling and resource management capabilities when running KubeRay’s RayJob.
This guide is for batch users that have a basic understanding of Kueue. For more information, see Kueue’s overview.
Before you begin
Make sure you are using Kueue v0.6.0 version or newer and KubeRay v1.1.0 or newer.
Check Administer cluster quotas for details on the initial Kueue setup.
See KubeRay Installation for installation and configuration details of KubeRay.
Note
In order to use RayJob, prior to v0.8.1, you need to restart Kueue after the installation. You can do it by running:kubectl delete pods -lcontrol-plane=controller-manager -nkueue-system
.RayJob definition
When running RayJobs on Kueue, take into consideration the following aspects:
a. Queue selection
The target local queue should be specified in the metadata.labels
section of the RayJob configuration.
metadata:
labels:
kueue.x-k8s.io/queue-name: user-queue
b. Configure the resource needs
The resource needs of the workload can be configured in the spec.rayClusterSpec
.
spec:
rayClusterSpec:
headGroupSpec:
template:
spec:
containers:
- resources:
requests:
cpu: "1"
workerGroupSpecs:
- template:
spec:
containers:
- resources:
requests:
cpu: "1"
c. Limitations
- A Kueue managed RayJob cannot use an existing RayCluster.
- The RayCluster should be deleted at the end of the job execution,
spec.ShutdownAfterJobFinishes
should betrue
. - Because Kueue will reserve resources for the RayCluster,
spec.rayClusterSpec.enableInTreeAutoscaling
should befalse
. - Because a Kueue workload can have a maximum of 8 PodSets, the maximum number of
spec.rayClusterSpec.workerGroupSpecs
is 7.
Example RayJob
In this example, the code is provided to the Ray framework via a ConfigMap.
apiVersion: v1
kind: ConfigMap
metadata:
name: ray-job-code-sample
data:
sample_code.py: |
import ray
import os
import requests
ray.init()
@ray.remote
class Counter:
def __init__(self):
# Used to verify runtimeEnv
self.name = os.getenv("counter_name")
assert self.name == "test_counter"
self.counter = 0
def inc(self):
self.counter += 1
def get_counter(self):
return "{} got {}".format(self.name, self.counter)
counter = Counter.remote()
for _ in range(5):
ray.get(counter.inc.remote())
print(ray.get(counter.get_counter.remote()))
# Verify that the correct runtime env was used for the job.
assert requests.__version__ == "2.26.0"
The RayJob looks like the following:
apiVersion: ray.io/v1
kind: RayJob
metadata:
name: rayjob-sample
labels:
kueue.x-k8s.io/queue-name: user-queue
spec:
suspend: true
shutdownAfterJobFinishes: true
# submissionMode specifies how RayJob submits the Ray job to the RayCluster.
# The default value is "K8sJobMode", meaning RayJob will submit the Ray job via a submitter Kubernetes Job.
# The alternative value is "HTTPMode", indicating that KubeRay will submit the Ray job by sending an HTTP request to the RayCluster.
# submissionMode: "K8sJobMode"
entrypoint: python /home/ray/samples/sample_code.py
# shutdownAfterJobFinishes specifies whether the RayCluster should be deleted after the RayJob finishes. Default is false.
# shutdownAfterJobFinishes: false
# ttlSecondsAfterFinished specifies the number of seconds after which the RayCluster will be deleted after the RayJob finishes.
# ttlSecondsAfterFinished: 10
# activeDeadlineSeconds is the duration in seconds that the RayJob may be active before
# KubeRay actively tries to terminate the RayJob; value must be positive integer.
# activeDeadlineSeconds: 120
# RuntimeEnvYAML represents the runtime environment configuration provided as a multi-line YAML string.
# See https://docs.ray.io/en/latest/ray-core/handling-dependencies.html for details.
# (New in KubeRay version 1.0.)
runtimeEnvYAML: |
pip:
- requests==2.26.0
- pendulum==2.1.2
env_vars:
counter_name: "test_counter"
# Suspend specifies whether the RayJob controller should create a RayCluster instance.
# If a job is applied with the suspend field set to true, the RayCluster will not be created and we will wait for the transition to false.
# If the RayCluster is already created, it will be deleted. In the case of transition to false, a new RayCluste rwill be created.
# suspend: false
# rayClusterSpec specifies the RayCluster instance to be created by the RayJob controller.
rayClusterSpec:
rayVersion: '2.9.0' # should match the Ray version in the image of the containers
# Ray head pod template
headGroupSpec:
# The `rayStartParams` are used to configure the `ray start` command.
# See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
# See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
rayStartParams:
dashboard-host: '0.0.0.0'
#pod template
template:
spec:
containers:
- name: ray-head
image: rayproject/ray:2.9.0
ports:
- containerPort: 6379
name: gcs-server
- containerPort: 8265 # Ray dashboard
name: dashboard
- containerPort: 10001
name: client
resources:
limits:
cpu: "1"
requests:
cpu: "1"
volumeMounts:
- mountPath: /home/ray/samples
name: code-sample
volumes:
# You set volumes at the Pod level, then mount them into containers inside that Pod
- name: code-sample
configMap:
# Provide the name of the ConfigMap you want to mount.
name: ray-job-code-sample
# An array of keys from the ConfigMap to create as files
items:
- key: sample_code.py
path: sample_code.py
workerGroupSpecs:
# the pod replicas in this group typed worker
- replicas: 1
minReplicas: 1
maxReplicas: 5
# logical group name, for this called small-group, also can be functional
groupName: small-group
# The `rayStartParams` are used to configure the `ray start` command.
# See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
# See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
rayStartParams: {}
#pod template
template:
spec:
containers:
- name: ray-worker # must consist of lower case alphanumeric characters or '-', and must start and end with an alphanumeric character (e.g. 'my-name', or '123-abc'
image: rayproject/ray:2.9.0
lifecycle:
preStop:
exec:
command: [ "/bin/sh","-c","ray stop" ]
resources:
limits:
cpu: "1"
requests:
cpu: "1"
# SubmitterPodTemplate is the template for the pod that will run the `ray job submit` command against the RayCluster.
# If SubmitterPodTemplate is specified, the first container is assumed to be the submitter container.
# submitterPodTemplate:
# spec:
# restartPolicy: Never
# containers:
# - name: my-custom-rayjob-submitter-pod
# image: rayproject/ray:2.9.0
# # If Command is not specified, the correct command will be supplied at runtime using the RayJob spec `entrypoint` field.
# # Specifying Command is not recommended.
# # command: ["sh", "-c", "ray job submit --address=http://$RAY_DASHBOARD_ADDRESS --submission-id=$RAY_JOB_SUBMISSION_ID -- echo hello world"]
You can run this RayJob with the following commands:
# Create the code ConfigMap (once)
kubectl apply -f ray-job-code-sample.yaml
# Create a RayJob. You can run this command multiple times
# to observe the queueing and admission of the jobs.
kubectl create -f ray-job-sample.yaml
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