Dockerfile.gpu

# GPU优化Dockerfile - 针对RTX 4090
FROM nvidia/cuda:12.2-devel-ubuntu22.04

# 设置非交互模式和环境变量
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV CUDA_VISIBLE_DEVICES=0
ENV PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512

# 更新系统并安装必要软件
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    python3-venv \
    git \
    curl \
    wget \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# 创建应用目录
WORKDIR /app

# 创建必要目录
RUN mkdir -p /app/data /app/models /app/logs

# 复制依赖文件
COPY requirements_gpu.txt .

# 升级pip并安装Python依赖
RUN pip3 install --no-cache-dir --upgrade pip && \
    pip3 install --no-cache-dir -r requirements_gpu.txt

# 复制应用文件
COPY *.py .
COPY *.md .
COPY .env.example .

# 设置Python路径
ENV PYTHONPATH=/app

# 创建启动脚本
RUN echo '#!/bin/bash\n\
export CUDA_VISIBLE_DEVICES=0\n\
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512\n\
export TOKENIZERS_PARALLELISM=false\n\
python3 -c "import torch; print(f'"'"'CUDA可用: {torch.cuda.is_available()}'"'"'); print(f'"'"'GPU数量: {torch.cuda.device_count()}'"'"')"\n\
python3 main.py' > /app/start.sh && chmod +x /app/start.sh

# 暴露端口
EXPOSE 8000 8001

# 健康检查
HEALTHCHECK --interval=30s --timeout=30s --start-period=60s --retries=3 \
    CMD curl -f http://localhost:8000/health || exit 1

# 启动命令
CMD ["/app/start.sh"]