tensorflow/serving with top n logits to return

I'm currently dealing with the challenge to serve my tensorflow models in a scalable way. As far as I know the recommended solution is to use the standard TensorFlow ModelServer. Common requirements are pretty well handled by this - but I want more. I want to decrease the transfered amount of data by parsing a parameter like "limit" to define the top n logits + probabilites to return.

During my research I identified the following solutions:

1) Create a more advanced SignatureDef during model building.

2) Customize the basic tensorflow/serving project with the mentioned functionality.

3) Serve the model with the standard Tensorflow Modelserver and build a postprocessing service to restructure resp. filter the result in the predefined way.

Can someone more experienced than me go into some details regarding my question? - codesnippets or links would be awesome.

Thanks in advance.

Your solution number 3,

"Serve the model with the standard Tensorflow Modelserver and build a postprocessing service to restructure resp. filter the result in the predefined way."

should be the best one.

Links and Code Snippets: If we consider the example of MNIST using TF Serving, the link for Saved Model is, https://github.com/tensorflow/serving/blob/87e32bb386f156fe208df633c1a7f489b57464e1/tensorflow_serving/example/mnist_saved_model.py,

and the link for Client code is https://github.com/tensorflow/serving/blob/87e32bb386f156fe208df633c1a7f489b57464e1/tensorflow_serving/example/mnist_client.py.

If we want values of top-n predictions, we can tweak the code of the function, _create_rpc_callback in the Client file as shown below.

def _create_rpc_callback(label, result_counter):
  """Creates RPC callback function.

  Args:
    label: The correct label for the predicted example.
    result_counter: Counter for the prediction result.
  Returns:
    The callback function.
  """
  def _callback(result_future):
    """Callback function.

    Calculates the statistics for the prediction result.

    Args:
      result_future: Result future of the RPC.
    """
    exception = result_future.exception()
    if exception:
      result_counter.inc_error()
      print(exception)
    else:
      sys.stdout.write('.')
      sys.stdout.flush()
      response = numpy.array(result_future.result().outputs['scores'].float_val)
      print('Top 4 responses = ', response[0:4]) 

The print statement in the last line will print Top-4 Predictions.