Dockerfile

FROM tensorflow/tensorflow:latest-py3-jupyter

# 设置工作空间为/app
WORKDIR /app

# 把当前目录下的文件拷贝到 容器里的/app里
ADD . /app

# 安装requirements.txt中指定的依赖
RUN pip install --no-cache=False --trusted-host pypi.python.org -r requirements.txt

# 开放5000端口
EXPOSE 5000 8888

# 设置 NAME 这个环境变量
ENV NAME World
#ENV http_proxy http://127.0.0.1:12333
#ENV https_proxy http://127.0.0.1:12333

# 当容器启动时，运行app.py
CMD ["python", "run_keras_server.py"]

requirements.txt

flask
numpy
keras
Pillow
gunicorn
gevent

/etc/default/docker

＃修改文件/etc/default/docker，新增
export ALL_PROXY=socks5://127.0.0.1:1080

重启docker

sudo service docker stop
sudo service docker start

＃docker 命令

docker build --network=host -t simple-keras .
docker run -d --name simple-keras --runtime=nvidia -v /home/reno/.keras:/root/.keras -v /home/reno/Github/jupyter:/tf/notebooks -p 8888:8888 -p 5000:5000 simple-keras:latest

flask

docker无法映射flask服务端口，从而访问127.0.0.1:5000的问题，需要在代码中注明地址0.0.0.0

# if this is the main thread of execution first load the model and
# then start the server
if __name__ == "__main__":
    print(("* Loading Keras model and Flask starting server..."
           "please wait until server has fully started"))
    load_model()
    app.run(host="0.0.0.0", debug=False, threaded=False)