Run a PaddleJob
This page shows how to leverage Kueue’s scheduling and resource management capabilities when running Trainer PaddleJobs.
This guide is for batch users that have a basic understanding of Kueue. For more information, see Kueue’s overview.
Warning
Deprecation Notice: The integration with Kubeflow Trainer v1 (including PaddleJob) is deprecated in Kueue and will be removed in a future release, tentatively v0.20.
Kubeflow Trainer v1 is now legacy. We strongly recommend migrating to Kubeflow Trainer v2 (which is supported in Kueue via TrainJob), or using an alternative framework such as JobSet to run your jobs. See the Kubeflow Trainer v1 to v2 migration guide for details on how to migrate.
Before you begin
Check administer cluster quotas for details on the initial cluster setup.
Check the Trainer installation guide.
Note that the minimum requirement trainer 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 Trainer, prior to v0.8.1, you need to restart Kueue after the installation. You can do it by running:kubectl delete pods -l control-plane=controller-manager -n kueue-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/trainer/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"
Feedback
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.