MogFace WebUI GPU 部署教程：NVIDIA 驱动与 CUDA 环境配置

3. 第二步：安装 CUDA Toolkit 和 cuDNN

3.1 安装 CUDA Toolkit

推荐使用 CUDA 11.8：

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-ubuntu2204-11-8-local_11.8.0-520.61.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-11-8-local_11.8.0-520.61.05-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-11-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-11-8

添加环境变量：

echo 'export PATH=/usr/local/cuda-11.8/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

验证：nvcc --version

3.2 安装 cuDNN

# 假设已下载 deb 包
sudo dpkg -i cudnn-local-repo-ubuntu2204-*.deb
sudo cp /var/cudnn-local-repo-*/cudnn-local-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get install -y libcudnn8 libcudnn8-dev

3.3 安装 PyTorch with CUDA

python3 -m venv mogface_env
source mogface_env/bin/activate
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

验证：

import torch
print(f'CUDA 可用：{torch.cuda.is_available()}')

4. 第三步：拉取并启动 MogFace 镜像

4.1 安装 Docker 和 NVIDIA Container Toolkit

# 安装 Docker
sudo apt remove docker docker-engine docker.io containerd runc
sudo apt update
sudo apt install -y apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io
sudo usermod -aG docker $USER
newgrp docker

# 安装 NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo systemctl restart docker

4.2 拉取并运行镜像

docker pull your-registry/mogface-webui:gpu
docker run -d \
  --name mogface-webui \
  --gpus all \
  -p 7860:7860 \
  -p 8080:8080 \
  -v /path/to/your/data:/app/data \
  your-registry/mogface-webui:gpu

5. 验证部署与基本使用

5.1 访问 Web 界面

浏览器访问 http://你的服务器 IP:7860 或 http://localhost:7860。

5.2 测试 API 接口

curl http://localhost:8080/health
curl -X POST -F "[email protected]" http://localhost:8080/detect

5.3 测试脚本

import requests
import cv2
import matplotlib.pyplot as plt

def test_face_detection(image_path):
    url = "http://localhost:8080/detect"
    with open(image_path, 'rb') as f:
        response = requests.post(url, files={'image': f})
    result = response.json()
    if result['success']:
        print(f"检测到 {len(result['data']['faces'])} 个人脸")

6. 性能优化与生产部署建议

6.1 GPU 内存优化

import torch
torch.cuda.set_per_process_memory_fraction(0.8)
torch.cuda.empty_cache()

6.2 批量处理优化

使用 concurrent.futures.ThreadPoolExecutor 并行处理图片请求。

6.3 使用 Docker Compose

version: '3.8'
services:
  mogface-webui:
    image: your-registry/mogface-webui:gpu
    container_name: mogface-webui
    restart: unless-stopped
    ports:
      - "7860:7860"
      - "8080:8080"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

启动：docker-compose up -d

7. 总结

成功部署需完成三个步骤：

1. NVIDIA 驱动安装：确保 GPU 被系统识别。
2. CUDA 环境配置：版本匹配至关重要。
3. 镜像启动：正确映射端口和挂载数据卷。

常见问题排查：

• Web 界面打不开：检查防火墙及 7860 端口。
• 检测不到人脸：尝试降低置信度阈值。
• GPU 内存不足：减小输入尺寸或分批处理。