/tensorflow_serving

show how to use tensorflow estimator train and export model, then serving model and call for prediction

Primary LanguageJava

tensorflow_serving

安装Tensorflow Serving

参见: 安装

Estimator训练和保存模型

实现代码

启动Serving

# 启动 build_raw_serving_input_receiver_fn 定义输入的模型，直接喂入 TensorProto
docker run -p 8500:8500 -p 8501:8501 \
   --mount type=bind,source=/home/gaoqing/tensorflow_serving/tf_model_raw,target=/models/test_dnn_raw \
   -t --entrypoint=tensorflow_model_server root/tensorflow-serving:latest \
   --port=8500 --rest_api_port=8501 \
   --enable_batching=true --model_name=test_dnn_raw --model_base_path=/models/test_dnn_raw &
   
# 启动 build_parsing_serving_input_receiver_fn 定义输入的模型，需要喂入 ExampleProto
docker run -p 9000:9000 -p 9001:9001 \
   --mount type=bind,source=/home/gaoqing/tensorflow_serving/tf_model,target=/models/test_dnn \
   -t --entrypoint=tensorflow_model_server root/tensorflow-serving:latest \
   --port=9000 --rest_api_port=9001 \
   --enable_batching=true --model_name=test_dnn --model_base_path=/models/test_dnn &

利用配置文件启动serving

脚本见 run.sh
配置文件见 models.config

gRPC调用