V1TFJob

polyaxon.polyflow.run.kubeflow.tf_job.V1TFJob(kind='tfjob', clean_pod_policy=None, chief=None, ps=None, worker=None, evaluator=None)

Kubeflow TF-Job provides an interface to train distributed experiments with TensorFlow.

YAML usage

run:
  kind: tfjob
  cleanPodPolicy:
  chief:
  ps:
  worker:
  evaluator:

Python usage

from polyaxon.polyflow import V1KFReplica, V1TFJob
from polyaxon.k8s import k8s_schemas
tf_job = V1TFJob(
    clean_pod_policy='All',
    chief=V1KFReplica(...),
    ps=V1KFReplica(...),
    worker=V1KFReplica(...),
    evaluator=V1KFReplica(...),
)

Fields

kind

The kind signals to the CLI, client, and other tools that this component's runtime is a tfjob.

If you are using the python client to create the runtime, this field is not required and is set by default.

run:
  kind: tfjob

cleanPodPolicy

Controls the deletion of pods when a job terminates. The policy can be one of the following values: [All, Running, None]

run:
  kind: tfjob
  cleanPodPolicy: 'All'
 ...

chief

The chief is responsible for orchestrating training and performing tasks like checkpointing the model.

run:
  kind: tfjob
  chief:
    replicas: 1
    container:
      ...
 ...

ps

The ps are parameter servers; these servers provide a distributed data store for the model parameters.

run:
  kind: tfjob
  ps:
    replicas: 2
    container:
      ...
 ...

worker

The workers do the actual work of training the model. In some cases, worker 0 might also act as the chief.

run:
  kind: tfjob
  worker:
    replicas: 2
    container:
      ...
 ...

evaluator

The evaluators can be used to compute evaluation metrics as the model is trained.

run:
  kind: tfjob
  evaluator:
    replicas: 1
    container:
      ...
 ...