tensorflow serving 编写配置文件platform_config_file的方法

1、安装grpc

gRPC 的安装：

        $ pip install grpcio

安装 ProtoBuf 相关的 python 依赖库：

          $ pip install protobuf

安装 python grpc 的 protobuf 编译工具：

         $ pip install grpcio-tools

2、在serving目录运行脚本，生成*_pb2.py文件

# run at root of tensorflow_serving repo

TARGET_DIR="$1"

python -m grpc.tools.protoc 
    -I . -I ./tensorflow 
    --python_out "$TARGET_DIR" 
    tensorflow_serving/servables/tensorflow/saved_model_bundle_source_adapter.proto 
    tensorflow_serving/servables/tensorflow/session_bundle_config.proto 
    tensorflow_serving/config/platform_config.proto

pushd $TARGET_DIR

touch tensorflow_serving/__init__.py
touch tensorflow_serving/config/__init__.py
touch tensorflow_serving/servables/__init__.py
touch tensorflow_serving/servables/tensorflow/__init__.py

popd

sh gen-tf-serving-proto-py.sh /tmp

3、将生成的*_pb2.py文件cp出来

cp -r /tmp/tensorflow_serving .

4、在当前目录运行gen-platform-config.py

# -*- coding: utf-8 -*-


import tensorflow as tf

from tensorflow_serving.config import platform_config_pb2
from tensorflow_serving.servables.tensorflow import session_bundle_config_pb2
from tensorflow_serving.servables.tensorflow import saved_model_bundle_source_adapter_pb2


session_config = tf.ConfigProto()
# config whatever you want
session_config.gpu_options.allow_growth = True
session_config.gpu_options.per_process_gpu_memory_fraction = 0.4

legacy_config=session_bundle_config_pb2.SessionBundleConfig(session_config=session_config)
adapter = saved_model_bundle_source_adapter_pb2.SavedModelBundleSourceAdapterConfig(legacy_config=legacy_config)

config_map = platform_config_pb2.PlatformConfigMap()
config_map.platform_configs['tensorflow'].source_adapter_config.Pack(adapter)

print(config_map)

5、生成platform_config_file.cfg文件

platform_configs {
  key: "tensorflow"
  value {
    source_adapter_config {
      [type.googleapis.com/tensorflow.serving.SavedModelBundleSourceAdapterConfig] {
        legacy_config {
          session_config {
            gpu_options {
              per_process_gpu_memory_fraction: 0.4
              allow_growth: true
            }
          }
        }
      }
    }
  }
}

6、运行tf_serving时添加参数--platform_config_file=./conf/platform_config_file.cfg

7、若同时需要配置batching_parameters_file，则需要将batching参数写入到platform_config_file.cfg内

platform_configs {
  key: "tensorflow"
  value {
    source_adapter_config {
      [type.googleapis.com/tensorflow.serving.SavedModelBundleSourceAdapterConfig] {
        legacy_config {
      batching_parameters {
        max_batch_size { value: 1000000 }
        batch_timeout_micros { value: 200000000 }
        max_enqueued_batches { value: 1000000 }
        num_batch_threads { value: 36 }  
          }
          session_config {
            allow_soft_placement: true
            gpu_options {
              per_process_gpu_memory_fraction: 0.4
              allow_growth: true
            }
          }
        }
      }
    }
  }
}

详细信息参照：https://github.com/tensorflow/serving/issues/342

我运行后生成的cfg文件为

platform_configs {
  key: "tensorflow"
  value {
    source_adapter_config {
      type_url: "type.googleapis.com/tensorflow.serving.SavedModelBundleSourceAdapterConfig"
      value: "302>1722
213	232231231231231231331? 01"
    }
  }
}

并不能生成清晰的text格式的配置文件，目前还未找到原因