Docker Compose构建TensorFlow Serving微服务环境配置

Docker Compose构建TensorFlow Serving微服务环境配置

在现代机器学习应用中，将TensorFlow模型部署为微服务是标准实践。本文将详细介绍如何使用Docker Compose快速搭建TensorFlow Serving微服务环境。

环境准备

首先创建项目目录结构：

project/
├── docker-compose.yml
├── models/
│   └── model.pb
└── config/
    └── serving_config.json

Docker Compose配置

version: '3.8'
services:
  tensorflow-serving:
    image: tensorflow/serving:latest-gpu
    container_name: tf-serving
    ports:
      - "8500:8500"   # gRPC端口
      - "8501:8501"   # HTTP端口
    volumes:
      - ./models:/models
      - ./config/serving_config.json:/serving_config.json
    command: |
      tensorflow_model_server \
        --model_base_path=/models \
        --model_name=model_name \
        --rest_api_port=8501 \
        --grpc_port=8500 \
        --config=/serving_config.json
    deploy:
      resources:
        limits:
          memory: 4G
        reservations:
          memory: 2G

负载均衡配置

使用Nginx进行负载均衡：

upstream tensorflow_servers {
    server tf-serving-1:8501;
    server tf-serving-2:8501;
}

server {
    listen 80;
    location / {
        proxy_pass http://tensorflow_servers;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

通过上述配置，可实现模型服务的容器化部署和负载均衡，提升系统可用性。