def _GetServingStatus()

in tfx/components/infra_validator/model_server_clients/tensorflow_serving_client.py [0:0]

• 15 lines of code
• 11 McCabe index (conditional complexity)

  def _GetServingStatus(self) -> types.ModelServingStatus:
    """Check whether the model is available for query or not.

    In TensorFlow Serving, model is READY if and only if the state from
    _GetModelStatus() is AVAILABLE. If returned state is END, it will never
    become READY therefore returns UNAVAILABLE. Otherwise it will return
    NOT_READY.

    Returns:
      A ModelState.
    """
    try:
      resp = self._GetModelStatus()
    except grpc.RpcError as e:
      logging.info('Model status is not available yet:\n%s', e)
      return types.ModelServingStatus.NOT_READY

    # When no versions available. (empty list)
    if not resp.model_version_status:
      return types.ModelServingStatus.NOT_READY

    # Wait until all serving model versions are in AVAILABLE state.
    # In TensorFlow Serving, model state lifecycle is
    #     START -> LOADING -> AVAILABLE -> UNLOADING -> END
    # if loaded successfully or
    #     START -> LOADING -> END
    # if loaded unsuccessfully. The model is available iff state is AVAILABLE.
    # The model is unavailable for goods iff state is END.
    # https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/get_model_status.proto
    if all(mvs.state == State.AVAILABLE
           for mvs in resp.model_version_status):
      return types.ModelServingStatus.READY
    if any(mvs.state == State.END
           for mvs in resp.model_version_status):
      return types.ModelServingStatus.UNAVAILABLE
    return types.ModelServingStatus.NOT_READY