通过Dockerfile创建Tensorflow grpc server镜像，创建项目目录

mkdir /opt/work/tensorflow

编写Dockerfile

FROM registry.cn-hangzhou.aliyuncs.com/denverdino/tensorflow:0.12.0
RUN mkdir -p /var/worker
ADD . /var/worker
RUN chmod 777 /var/worker/*

ENTRYPOINT ["/var/worker/grpc_tensorflow_server.py"]

镜像也可以直接使用registry.cn-beijing.aliyuncs.com/tensorflow-samples/tf_grpc_server:0.12.0

创建docker-compose.yaml文件

version: '2'
services:
  worker-0:
    image: registry-internal.cn-beijing.aliyuncs.com/tensorflow-samples/tf_grpc_server:0.12.0
    restart:  always
    container_name: tf-worker0
    command:
      - --cluster_spec=worker|tf-worker0:2222;tf-worker1:2222,ps|tf-ps0:2222
      - --job_name=worker
      - --task_id=0
  worker-1:
    image: registry-internal.cn-beijing.aliyuncs.com/tensorflow-samples/tf_grpc_server:0.12.0
    container_name: tf-worker1
    restart:  always
    command:
      - --cluster_spec=worker|tf-worker0:2222;tf-worker1:2222,ps|tf-ps0:2222
      - --job_name=worker
      - --task_id=1
  ps-0:
    image: registry-internal.cn-beijing.aliyuncs.com/tensorflow-samples/tf_grpc_server:0.12.0
    container_name: tf-ps0
    restart:  always
    command:
      - --cluster_spec=worker|tf-worker0:2222;tf-worker1:2222,ps|tf-ps0:2222
      - --job_name=ps
      - --task_id=0

利用docker-compose 命令启动并查看容器状态。