TensorFlow Serving: Update model_config (add additional models) at runtime

I'm busy configuring a TensorFlow Serving client that asks a TensorFlow Serving server to produce predictions on a given input image, for a given model.

If the model being requested has not yet been served, it is downloaded from a remote URL to a folder where the server's models are located. (The client does this). At this point I need to update the model_config and trigger the server to reload it.

This functionality appears to exist (based on https://github.com/tensorflow/serving/pull/885 and https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/model_service.proto#L22), but I can't find any documentation on how to actually use it.

I am essentially looking for a python script with which I can trigger the reload from client side (or otherwise to configure the server to listen for changes and trigger the reload itself).

So it took me ages of trawling through pull requests to finally find a code example for this. For the next person who has the same question as me, here is an example of how to do this. (You'll need the tensorflow_serving package for this; pip install tensorflow-serving-api).

Based on this pull request (which at the time of writing hadn't been accepted and was closed since it needed review): https://github.com/tensorflow/serving/pull/1065

from tensorflow_serving.apis import model_service_pb2_grpc
from tensorflow_serving.apis import model_management_pb2
from tensorflow_serving.config import model_server_config_pb2

import grpc

def add_model_config(host, name, base_path, model_platform):
  channel = grpc.insecure_channel(host) 
  stub = model_service_pb2_grpc.ModelServiceStub(channel)
  request = model_management_pb2.ReloadConfigRequest() 
  model_server_config = model_server_config_pb2.ModelServerConfig()

  #Create a config to add to the list of served models
  config_list = model_server_config_pb2.ModelConfigList()       
  one_config = config_list.config.add()
  one_config.name= name
  one_config.base_path=base_path
  one_config.model_platform=model_platform

  model_server_config.model_config_list.CopyFrom(config_list)

  request.config.CopyFrom(model_server_config)

  print(request.IsInitialized())
  print(request.ListFields())

  response = stub.HandleReloadConfigRequest(request,10)
  if response.status.error_code == 0:
      print("Reload sucessfully")
  else:
      print("Reload failed!")
      print(response.status.error_code)
      print(response.status.error_message)


add_model_config(host="localhost:8500", 
                    name="my_model", 
                    base_path="/models/my_model", 
                    model_platform="tensorflow")