polyaxon.polyflow.run.kubeflow.xgboost_job.V1XGBoostJob(kind='xgboost_job', clean_pod_policy=None, scheduling_policy=None, master=None, worker=None)
Kubeflow XGBoost-Job provides an interface to train distributed experiments with XGBoost.
run: kind: xgbjob cleanPodPolicy: schedulingPolicy: master: worker:
from polyaxon.polyflow import V1KFReplica, V1XGBoost from polyaxon.k8s import k8s_schemas xgb_job = V1XGBoost( clean_pod_policy='All', master=V1KFReplica(...), worker=V1KFReplica(...), )
The kind signals to the CLI, client, and other tools that this component’s runtime is a xgbjob.
If you are using the python client to create the runtime, this field is not required and is set by default.
run: kind: xgbjob
Controls the deletion of pods when a job terminates.
The policy can be one of the following values: [
run: kind: xgbjob cleanPodPolicy: 'All' ...
SchedulingPolicy encapsulates various scheduling policies of the distributed training
job, for example
minAvailable for gang-scheduling.
run: kind: xgbjob schedulingPolicy: ... ...
The master replica in the distributed XGBoostJob.
run: kind: xgbjob ps: replicas: 2 container: ... ...
The server replica in the distributed XGBoostJob.
run: kind: xgbjob worker: replicas: 2 container: ... ...