XGBoost Training

Instructions for using XGBoost

This page describes XGBoostJob for training a machine learning model with XGBoost.

Installing Training Operator

If you haven’t already done so please follow the Getting Started Guide to deploy Kubeflow.

Verify that XGBoost support is included in your Kubeflow deployment

Check that the XGboost custom resource is installed

kubectl get crd

The output should include xgboostjobs.kubeflow.org

NAME                                           AGE
...
xgboostjobs.kubeflow.org                       4d
...

Creating a XGBoost Job

You can create XGBoost Job by defining a XGboostJob config file. See the manifests for the IRIS example. You may change the config file based on your requirements. eg: 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 XGBoost Job

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

Last modified 07.09.2021: Add XGboost doc for training (#2919) (80760122)