Run a XGBoostJob
This page shows how to leverage Kueue’s scheduling and resource management capabilities when running Training Operator XGBoostJobs.
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 XGBoostJobs as an allowed workload.
XGBoostJob definition
a. Queue selection
The target local queue should be specified in the metadata.labels section of the XGBoostJob configuration.
metadata:
labels:
kueue.x-k8s.io/queue-name: user-queue
b. Optionally set Suspend field in XGBoostJobs
spec:
runPolicy:
suspend: true
By default, Kueue will set suspend to true via webhook and unsuspend it when the XGBoostJob is admitted.
Sample XGBoostJob
This example is based on https://github.com/kubeflow/training-operator/blob/afba76bc5a168cbcbc8685c7661f36e9b787afd1/examples/xgboost/xgboostjob.yaml.
apiVersion: kubeflow.org/v1
kind: XGBoostJob
metadata:
name: xgboost-dist-iris-test-train
namespace: default
labels:
kueue.x-k8s.io/queue-name: user-queue
spec:
xgbReplicaSpecs:
Master:
replicas: 1
restartPolicy: Never
template:
spec:
containers:
- name: xgboost
image: docker.io/kubeflow/xgboost-dist-iris:latest
resources:
requests:
cpu: 0.5
memory: 256Mi
ports:
- containerPort: 9991
name: xgboostjob-port
imagePullPolicy: Always
args:
- --job_type=Train
- --xgboost_parameter=objective:multi:softprob,num_class:3
- --n_estimators=10
- --learning_rate=0.1
- --model_path=/tmp/xgboost-model
- --model_storage_type=local
Worker:
replicas: 2
restartPolicy: ExitCode
template:
spec:
containers:
- name: xgboost
image: docker.io/kubeflow/xgboost-dist-iris:latest
resources:
requests:
cpu: 0.5
memory: 256Mi
ports:
- containerPort: 9991
name: xgboostjob-port
imagePullPolicy: Always
args:
- --job_type=Train
- --xgboost_parameter="objective:multi:softprob,num_class:3"
- --n_estimators=10
- --learning_rate=0.1
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