doc_processer/Dockerfile

# DocProcesser Dockerfile
# Optimized for RTX 5080 GPU deployment

# Use NVIDIA CUDA base image with Python 3.10
FROM nvidia/cuda:12.8.0-runtime-ubuntu24.04

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    PIP_DISABLE_PIP_VERSION_CHECK=1 \
    # Model cache directories - mount these at runtime
    MODELSCOPE_CACHE=/root/.cache/modelscope \
    HF_HOME=/root/.cache/huggingface \
    # Application config (override defaults for container)
    # Use 127.0.0.1 for --network host mode, or override with -e for bridge mode
    PP_DOCLAYOUT_MODEL_DIR=/root/.cache/modelscope/hub/models/PaddlePaddle/PP-DocLayoutV2 \
    PADDLEOCR_VL_URL=http://127.0.0.1:8000/v1

# Set working directory
WORKDIR /app

# Install system dependencies and Python 3.10 from deadsnakes PPA
RUN apt-get update && apt-get install -y --no-install-recommends \
    software-properties-common \
    && add-apt-repository -y ppa:deadsnakes/ppa \
    && apt-get update && apt-get install -y --no-install-recommends \
    python3.10 \
    python3.10-venv \
    python3.10-dev \
    python3.10-distutils \
    libgl1 \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    curl \
    pandoc \
    && rm -rf /var/lib/apt/lists/* \
    && ln -sf /usr/bin/python3.10 /usr/bin/python \
    && ln -sf /usr/bin/python3.10 /usr/bin/python3 \
    && curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10

# Install uv via pip (more reliable than install script)
RUN python3.10 -m pip install uv -i https://pypi.tuna.tsinghua.edu.cn/simple
ENV PATH="/app/.venv/bin:$PATH"
ENV VIRTUAL_ENV="/app/.venv"

# Copy dependency files first for better caching
COPY pyproject.toml ./
COPY wheels/ ./wheels/

# Create virtual environment and install dependencies
RUN uv venv /app/.venv --python python3.10 \
    && uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -e . \
    && rm -rf ./wheels

# Copy application code
COPY app/ ./app/

# Create model cache directories (mount from host at runtime)
RUN mkdir -p /root/.cache/modelscope \
    /root/.cache/huggingface \
    /root/.paddlex \
    /app/app/model/DocLayout \
    /app/app/model/PP-DocLayout

# Declare volumes for model cache (mount at runtime to avoid re-downloading)
VOLUME ["/root/.cache/modelscope", "/root/.cache/huggingface", "/root/.paddlex"]

# Expose port
EXPOSE 8053

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:8053/health || exit 1

# Run the application
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8053", "--workers", "1"]

# =============================================================================
# Usage: Mount local model cache to avoid downloading
#
# Option 1: Use host network (simplest, can access localhost services)
# docker run --gpus all --network host \
#   -v /home/yoge/.paddlex:/root/.paddlex:ro \
#   -v /home/yoge/.cache/modelscope:/root/.cache/modelscope:ro \
#   -v /home/yoge/.cache/huggingface:/root/.cache/huggingface:ro \
#   doc_processer:latest
#
# Option 2: Use bridge network with host.docker.internal (Linux needs --add-host)
# docker run --gpus all -p 8053:8053 \
#   --add-host=host.docker.internal:host-gateway \
#   -v /home/yoge/.paddlex:/root/.paddlex:ro \
#   -v /home/yoge/.cache/modelscope:/root/.cache/modelscope:ro \
#   -v /home/yoge/.cache/huggingface:/root/.cache/huggingface:ro \
#   doc_processer:latest
# =============================================================================