XGBoost Training (XGBoostJob)
This page describes the XGBoostJob for training a machine learning model with XGBoost.
XGBoostJob is a Kubernetes
custom resource
to run XGBoost training jobs on Kubernetes. The Kubeflow implementation of
XGBoostJob is in the training-operator.
Note: XGBoostJob doesn’t work in a user namespace by default because of
Istio automatic sidecar injection.
In order to get it running, it needs the annotation sidecar.istio.io/inject: "false"
to disable it for either the PyTorchJob pods or namespace.
To view an example of how to add this annotation to your yaml file,
see the XGBoostJob documentation.
Creating a XGBoost training job
You can create a training job by defining an XGboostJob config file. See the
manifests for the IRIS example.
You may change the config file based on your requirements. E.g.: add CleanPodPolicy
in Spec to None to retain pods after job termination.
cat xgboostjob.yaml
Deploy the XGBoostJob resource to start training:
kubectl create -f xgboostjob.yaml
You should now be able to see the created pods matching the specified number of replicas.
kubectl get pods -l job-name=xgboost-dist-iris-test-train
Training takes 5-10 minutes on a cpu cluster. Logs can be inspected to see its training progress.
PODNAME=$(kubectl get pods -l job-name=xgboost-dist-iris-test-train,replica-type=master,replica-index=0 -o name)
kubectl logs -f ${PODNAME}
Monitoring a XGBoostJob
kubectl get -o yaml xgboostjobs xgboost-dist-iris-test-train
See the status section to monitor the job status. Here is sample output when the job is successfully completed.
apiVersion: kubeflow.org/v1
kind: XGBoostJob
metadata:
creationTimestamp: "2021-09-06T18:34:06Z"
generation: 1
name: xgboost-dist-iris-test-train
namespace: default
resourceVersion: "5844304"
selfLink: /apis/kubeflow.org/v1/namespaces/default/xgboostjobs/xgboost-dist-iris-test-train
uid: a1ea6675-3cb5-482b-95dd-68b2c99b8adc
spec:
runPolicy:
cleanPodPolicy: None
xgbReplicaSpecs:
Master:
replicas: 1
restartPolicy: Never
template:
spec:
containers:
- 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
image: docker.io/merlintang/xgboost-dist-iris:1.1
imagePullPolicy: Always
name: xgboost
ports:
- containerPort: 9991
name: xgboostjob-port
protocol: TCP
Worker:
replicas: 2
restartPolicy: ExitCode
template:
spec:
containers:
- args:
- --job_type=Train
- --xgboost_parameter="objective:multi:softprob,num_class:3"
- --n_estimators=10
- --learning_rate=0.1
image: docker.io/merlintang/xgboost-dist-iris:1.1
imagePullPolicy: Always
name: xgboost
ports:
- containerPort: 9991
name: xgboostjob-port
protocol: TCP
status:
completionTime: "2021-09-06T18:34:23Z"
conditions:
- lastTransitionTime: "2021-09-06T18:34:06Z"
lastUpdateTime: "2021-09-06T18:34:06Z"
message: xgboostJob xgboost-dist-iris-test-train is created.
reason: XGBoostJobCreated
status: "True"
type: Created
- lastTransitionTime: "2021-09-06T18:34:06Z"
lastUpdateTime: "2021-09-06T18:34:06Z"
message: XGBoostJob xgboost-dist-iris-test-train is running.
reason: XGBoostJobRunning
status: "False"
type: Running
- lastTransitionTime: "2021-09-06T18:34:23Z"
lastUpdateTime: "2021-09-06T18:34:23Z"
message: XGBoostJob xgboost-dist-iris-test-train is successfully completed.
reason: XGBoostJobSucceeded
status: "True"
type: Succeeded
replicaStatuses:
Master:
succeeded: 1
Worker:
succeeded: 2
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