example/framework/scenario/tensorflow/ps/gpu/tensorflowdistributedtrainingwithdefaultscheduledgpu.yaml [22:56]:
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    task:
      retryPolicy:
        fancyRetryPolicy: false
        maxRetryCount: 0
      # Large timeout to force delete Pod as it may break the stateful batch.
      podGracefulDeletionTimeoutSec: 1800
      pod:
        spec:
          restartPolicy: Never
          # [PREREQUISITE]
          # User needs to setup the k8s cluster networking model and aware the
          # potential network overhead, if he want to disable the hostNetwork to
          # avoid the coordination of the containerPort usage.
          # And for this example, if the hostNetwork is disabled, it only needs
          # at least 1 node, otherwise, it needs at least 3 nodes since all the
          # 3 workers are specified with the same containerPort.
          # See https://kubernetes.io/docs/concepts/cluster-administration/networking
          hostNetwork: false
          containers:
          - name: tensorflow
            # Using official image to demonstrate this example.
            # The image contains and only contains tensorflow official code.
            image: frameworkcontroller/tensorflow-examples:gpu
            # For the tf_cnn_benchmarks usage, see
            # https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks
            workingDir: /tensorflow/benchmarks/scripts/tf_cnn_benchmarks
            # Using /mnt/frameworkbarrier/injector.sh to inject environment variables
            # without the need for image invasion and k8s DNS:
            # FB_{UpperCase({TaskRoleName})}_ADDRESSES=
            #   {Task[0].PodIP}:${FB_{UpperCase({TaskRoleName})}_PORT},...,
            #   {Task[TaskRole.TaskNumber-1].PodIP}:${FB_{UpperCase({TaskRoleName})}_PORT}
            # See more in ./example/framework/extension/frameworkbarrier.yaml
            command: [
            "sh", "-c",
            "FB_PS_PORT=4001 FB_WORKER_PORT=5001 . /mnt/frameworkbarrier/injector.sh &&
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example/framework/scenario/tensorflow/ps/gpu/tensorflowdistributedtrainingwithdefaultscheduledgpu.yaml [124:142]:
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    task:
      retryPolicy:
        fancyRetryPolicy: false
        maxRetryCount: 0
      # Large timeout to force delete Pod as it may break the stateful batch.
      podGracefulDeletionTimeoutSec: 1800
      pod:
        spec:
          restartPolicy: Never
          # [PREREQUISITE]
          # Same as ps TaskRole.
          hostNetwork: false
          containers:
          - name: tensorflow
            image: frameworkcontroller/tensorflow-examples:gpu
            workingDir: /tensorflow/benchmarks/scripts/tf_cnn_benchmarks
            command: [
            "sh", "-c",
            "FB_PS_PORT=4001 FB_WORKER_PORT=5001 . /mnt/frameworkbarrier/injector.sh &&
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