TensorFlow服务:在运行时更新模型配置(添加其他模型)

因此,我花了好几年的时间在pull请求中摸索,终于找到了一个代码示例。对于下一个和我有同样问题的人,这里有一个如何做到这一点的例子。(你需要 tensorflow_serving package 为此,; pip install tensorflow-serving-api

基于此拉取请求(在撰写本文时,该请求尚未被接受,并且由于需要审查而被关闭): 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")

添加模型 conf_filepath :使用参数 name base_path , model_platform 对于新模型。保持原始模型完好无损。

注意到@Karl的答案有一点不同-使用 MergeFrom CopyFrom

pip安装tensorflow服务api

import grpc
from google.protobuf import text_format
from tensorflow_serving.apis import model_service_pb2_grpc, model_management_pb2
from tensorflow_serving.config import model_server_config_pb2


def add_model_config(conf_filepath, host, name, base_path, model_platform):
    with open(conf_filepath, 'r+') as f:
        config_ini = f.read()
    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()
    config_list = model_server_config_pb2.ModelConfigList()
    model_server_config = text_format.Parse(text=config_ini, message=model_server_config)

    # Create a config to add to the list of served models
    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.MergeFrom(config_list)
    request.config.CopyFrom(model_server_config)

    response = stub.HandleReloadConfigRequest(request, 10)
    if response.status.error_code == 0:
        with open(conf_filepath, 'w+') as f:
            f.write(request.config.__str__())
        print("Updated TF Serving conf file")
    else:
        print("Failed to update model_config_list!")
        print(response.status.error_code)
        print(response.status.error_message)

我并没有在运行时使用配置文件来提供更多的模型,而是使用了servings的RESTAPI。我是这样做的。

tensorflow_model_server --rest_api_port=dynamic_port --model_base_path=<path to model> --model_name=<model_name>

我知道这个问题是关于更新配置文件的,但我在做上述解决方案之前来这里寻找答案。因此,如果其他人来这里希望提供多个模型,并在运行时添加更多,请使用上述模式。

如果您正在使用中描述的方法 this answer ,请注意,实际上您正在启动多个tensorflow模型服务器实例,而不是单个模型服务器,从而有效地使服务器竞争资源,而不是协同工作以优化尾部延迟。