基于Docker的大模型服务部署最佳实践

基于Docker的大模型服务部署最佳实践

在大模型微服务化改造过程中，容器化部署已成为主流实践。本文将分享基于Docker的高效部署方案。

部署架构

+----------------+     +----------------+     +----------------+
|   Nginx        | --> |  Model Service | --> |   Redis        |
| Load Balancer  |     |   API Server   |     | Cache Layer    |
+----------------+     +----------------+     +----------------+

Dockerfile构建

FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY model/ ./model/
COPY app.py ./

EXPOSE 8000
CMD ["python", "app.py"]

部署脚本

#!/bin/bash
# docker-compose.yml
version: '3.8'
services:
  model-service:
    build: .
    ports:
      - "8000:8000"
    environment:
      - MODEL_PATH=/app/model
      - LOG_LEVEL=INFO
    volumes:
      - ./logs:/app/logs
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

监控配置

建议集成Prometheus + Grafana监控体系，重点关注GPU利用率、内存使用率等关键指标。

部署验证：

docker-compose up -d
# 查看日志
sudo docker logs -f model-service
# 验证服务
curl http://localhost:8000/health