63 lines
1.6 KiB
Docker
63 lines
1.6 KiB
Docker
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# DocProcesser Dockerfile
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# Optimized for RTX 5080 GPU deployment
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# Use NVIDIA CUDA base image with Python 3.11
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FROM nvidia/cuda:12.8.0-runtime-ubuntu24.04
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# Set environment variables
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PIP_NO_CACHE_DIR=1 \
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PIP_DISABLE_PIP_VERSION_CHECK=1
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# Set working directory
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3.11 \
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python3.11-venv \
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python3.11-dev \
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python3-pip \
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libgl1-mesa-glx \
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libglib2.0-0 \
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libsm6 \
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libxext6 \
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libxrender-dev \
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libgomp1 \
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curl \
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&& rm -rf /var/lib/apt/lists/* \
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&& ln -sf /usr/bin/python3.11 /usr/bin/python \
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&& ln -sf /usr/bin/python3.11 /usr/bin/python3
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# Install uv for fast package management
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RUN curl -LsSf https://astral.sh/uv/install.sh | sh
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ENV PATH="/root/.local/bin:$PATH"
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# Copy dependency files first for better caching
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COPY pyproject.toml ./
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# Create virtual environment and install dependencies
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RUN uv venv /app/.venv
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ENV PATH="/app/.venv/bin:$PATH"
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ENV VIRTUAL_ENV="/app/.venv"
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RUN uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -e .
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# Copy application code
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COPY app/ ./app/
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# Create model directories (models should be mounted at runtime)
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RUN mkdir -p /app/app/model/DocLayout /app/app/model/PP-DocLayout
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# Expose port
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EXPOSE 8053
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8053/health || exit 1
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# Run the application
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8053", "--workers", "1"]
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