Run a PaddleJob
This page shows how to leverage Kueue’s scheduling and resource management capabilities when running Training Operator PaddleJobs.
This guide is for batch users that have a basic understanding of Kueue. For more information, see Kueue’s overview.
Before you begin
Check administer cluster quotas for details on the initial cluster setup.
Check the Training Operator installation guide.
Note that the minimum requirement training-operator version is v1.7.0.
You can modify kueue configurations from installed releases to include PaddleJobs as an allowed workload.
Note
In order to use Training Operator, 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.
PaddleJob definition
a. Queue selection
The target local queue should be specified in the metadata.labels section of the PaddleJob configuration.
metadata:
labels:
kueue.x-k8s.io/queue-name: user-queue
b. Optionally set Suspend field in PaddleJobs
spec:
runPolicy:
suspend: true
By default, Kueue will set suspend to true via webhook and unsuspend it when the PaddleJob is admitted.
Sample PaddleJob
This example is based on https://github.com/kubeflow/training-operator/blob/288d680a699237fb61a74ada005e202721815ff2/examples/paddlepaddle/simple-cpu.yaml.
apiVersion: kubeflow.org/v1
kind: PaddleJob
metadata:
name: paddle-simple-cpu
namespace: default
labels:
kueue.x-k8s.io/queue-name: user-queue
spec:
paddleReplicaSpecs:
Worker:
replicas: 2
restartPolicy: OnFailure
template:
spec:
containers:
- name: paddle
image: registry.baidubce.com/paddlepaddle/paddle:2.5.1
command:
- python
args:
- "-m"
- paddle.distributed.launch
- "run_check"
ports:
- containerPort: 37777
name: master
imagePullPolicy: Always
resources:
requests:
cpu: 1
memory: "256Mi"
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