79 lines
2.4 KiB
Docker
79 lines
2.4 KiB
Docker
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive
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ENV TZ=Europe/Paris
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# Install system packages + Miniforge for PDAL >= 2.5 (Ubuntu 22.04 ships PDAL 2.3 which can't read COPC v1.1)
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RUN apt-get update && apt-get install -y --no-install-recommends \
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gdal-bin \
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python3-gdal \
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python3-pip \
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python3-dev \
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build-essential \
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wget \
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&& rm -rf /var/lib/apt/lists/* \
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&& wget -q https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O /tmp/miniforge.sh \
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&& bash /tmp/miniforge.sh -b -p /opt/conda \
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&& rm /tmp/miniforge.sh \
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&& /opt/conda/bin/conda install -y -c conda-forge pdal \
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&& ln -sf /opt/conda/bin/pdal /usr/local/bin/pdal \
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&& /opt/conda/bin/conda clean -afy
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WORKDIR /app
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# Install Python packages via pip
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COPY requirements.txt .
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RUN pip3 install --no-cache-dir \
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numpy \
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matplotlib \
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whitebox \
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rasterio \
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'laspy[lazrs]' \
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lazrs \
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scikit-image \
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scikit-learn \
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scipy \
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tqdm \
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Pillow \
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pytest \
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numba \
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rio-cogeo \
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titiler.core \
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fastapi \
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uvicorn \
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piexif \
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pillow-avif-plugin \
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cmcrameri
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# Install CuPy for GPU acceleration (optional - will fallback to numpy if not available)
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# We use CuPy 13.4 (CUDA 11.x wheel) because CuPy 14.x dropped JIT compilation
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# support. With CUPY_CUDA_COMPILE_WITH_CACHE=1, CuPy 13.4 compiles kernels at
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# runtime for GPU architectures not in the pre-built wheel (e.g. sm_89 / RTX 4060 Ti).
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# The devel image includes nvcc, required for JIT compilation.
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# nvcc stays in PATH for the 'lidar' user.
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ENV CUPY_CUDA_COMPILE_WITH_CACHE=1
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ENV PATH=/usr/local/cuda/bin:${PATH}
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RUN pip3 install --no-cache-dir cupy-cuda11x==13.4.0 || echo "CuPy not available - GPU acceleration disabled"
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# Copy and install the pipeline package
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COPY setup.py .
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COPY lidar_pipeline/ ./lidar_pipeline/
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RUN pip3 install --no-cache-dir .
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# Copy backward-compatible entry point
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COPY process_lidar.py /usr/local/bin/
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RUN chmod +x /usr/local/bin/process_lidar.py
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# Create user with uid/gid 1000:1000 and run as that user
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RUN groupadd -g 1000 lidar && \
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useradd -u 1000 -g lidar -m lidar && \
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mkdir -p /data/output /data/input && \
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chown -R lidar:lidar /data /data/output /data/input
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WORKDIR /data
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USER lidar
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VOLUME ["/data"]
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CMD ["python3", "-m", "lidar_pipeline", "/data/input", "-o", "/data/output"] |