Job Scheduling

This guide describes how to use kube-batch to support gang-scheduling in Kubeflow, to allow jobs to run multiple pods at the same time.

Running jobs with gang-scheduling

To use gang-scheduling, you have to install kube-batch in your cluster first as a secondary scheduler of Kubernetes and configure operator to enable gang-scheduling.

  • Kube-batch’s introduction is here, and also check how to install it here.
  • Take tf-operator for example, enable gang-scheduling in tf-operator by setting true to --enable-gang-scheduling flag.

Note: Kube-batch and operator in Kubeflow achieve gang-scheduling by using pdb. operator will create the pdb of the job automatically. You can know more about pdb here.

To use kube-batch to schedule your job as a gang, you have to specify the schedulerName in each replica; for example.

apiVersion: "kubeflow.org/v1alpha2"
kind: "TFJob"
metadata:
  name: "tfjob-gang-scheduling"
spec:
  tfReplicaSpecs:
    Worker:
      replicas: 1
      template:
        spec:
          schedulerName: kube-batch
          containers:
          - args:
            - python
            - tf_cnn_benchmarks.py
            - --batch_size=32
            - --model=resnet50
            - --variable_update=parameter_server
            - --flush_stdout=true
            - --num_gpus=1
            - --local_parameter_device=cpu
            - --device=gpu
            - --data_format=NHWC
            image: gcr.io/kubeflow/tf-benchmarks-gpu:v20171202-bdab599-dirty-284af3
            name: tensorflow
            resources:
              limits:
                nvidia.com/gpu: 1
            workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure
    PS:
      replicas: 1
      template:
        spec:
          schedulerName: kube-batch
          containers:
          - args:
            - python
            - tf_cnn_benchmarks.py
            - --batch_size=32
            - --model=resnet50
            - --variable_update=parameter_server
            - --flush_stdout=true
            - --num_gpus=1
            - --local_parameter_device=cpu
            - --device=cpu
            - --data_format=NHWC
            image: gcr.io/kubeflow/tf-benchmarks-cpu:v20171202-bdab599-dirty-284af3
            name: tensorflow
            resources:
              limits:
                cpu: '1'
            workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure

About kube-batch and gang-scheduling

With using kube-batch to apply gang-scheduling, a job can run only if there are enough resources for all the pods of the job. Otherwise, all the pods will be in pending state waiting for enough resources. For example, if a job requiring N pods is created and there are only enough resources to schedule N-2 pods, then N pods of the job will stay pending.

Note: when in a high workload, if a pod of the job dies when the job is still running, it might give other pods chance to occupied the resources and cause deadlock.

Troubleshooting

If you keep getting problems related to RBAC in your kube-batch.

You can try to add the following rules into your clusterrole of scheduler used by kube-batch.

- apiGroups:
  - '*'
  resources:
  - '*'
  verbs:
  - '*'