133 lines
4.2 KiB
Docker
133 lines
4.2 KiB
Docker
ARG UBUNTU_VERSION=24.04
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ARG NVIDIA_CUDA_VERSION=12.9.1
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#
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# Docker builder stage.
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#
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FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} AS builder
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ARG COLMAP_GIT_COMMIT=main
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ARG CUDA_ARCHITECTURES=86
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ENV QT_XCB_GL_INTEGRATION=xcb_egl
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# Prevent stop building ubuntu at time zone selection.
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ENV DEBIAN_FRONTEND=noninteractive
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# Prepare and empty machine for building.
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RUN apt-get update && \
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apt-get install -y \
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git \
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cmake \
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ninja-build \
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build-essential \
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libboost-program-options-dev \
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libboost-graph-dev \
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libboost-system-dev \
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libeigen3-dev \
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libopenimageio-dev \
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openimageio-tools \
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libmetis-dev \
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libgoogle-glog-dev \
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libgtest-dev \
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libgmock-dev \
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libsqlite3-dev \
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libglew-dev \
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qt6-base-dev \
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libqt6opengl6-dev \
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libqt6openglwidgets6 \
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libcgal-dev \
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libcurl4-openssl-dev \
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libssl-dev \
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libmkl-full-dev \
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libsuitesparse-dev \
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libcudss0-dev-cuda-12 \
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libcudss0-static-cuda-12
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# Install Ceres Solver with CUDA support. Disable testing and examples since
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# the GPU isn't available to us at build time
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RUN git clone --recursive https://github.com/ceres-solver/ceres-solver
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RUN mkdir ceres-bin && \
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cd ceres-bin && \
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cmake ../ceres-solver \
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-DUSE_CUDA=ON \
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-DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES} \
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-DBUILD_TESTING=OFF \
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-DBUILD_EXAMPLES=OFF \
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-DCMAKE_BUILD_TYPE=Release \
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-Dcudss_DIR=/usr/lib/x86_64-linux-gnu/libcudss/12/cmake/cudss \
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-Dcudss_INCLUDE_DIR=/usr/include/libcudss/12 && \
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make -j8 && \
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make test && \
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make install
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# Fix issue in Ubuntu's openimageio CMake config.
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# We don't depend on any of openimageio's OpenCV functionality,
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# but it still requires the OpenCV include directory to exist.
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RUN mkdir -p /usr/include/opencv4
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# Build and install COLMAP.
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RUN git clone --recursive https://github.com/colmap/colmap.git
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RUN cd colmap && \
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git fetch https://github.com/colmap/colmap.git ${COLMAP_GIT_COMMIT} && \
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git checkout FETCH_HEAD && \
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mkdir build && \
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cd build && \
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cmake .. \
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-GNinja \
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-DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES} \
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-DCMAKE_INSTALL_PREFIX=/colmap-install \
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-DBLA_VENDOR=Intel10_64lp \
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-Dcudss_DIR=/usr/lib/x86_64-linux-gnu/libcudss/12/cmake/cudss \
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-Dcudss_INCLUDE_DIR=/usr/include/libcudss/12 && \
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ninja install
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#
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# Docker runtime stage.
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#
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FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} AS runtime
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# Minimal dependencies to run COLMAP binary compiled in the builder stage.
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# Note: this reduces the size of the final image considerably, since all the
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# build dependencies are not needed.
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RUN apt-get update && \
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apt-get install -y --no-install-recommends --no-install-suggests \
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libboost-program-options1.83.0 \
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libspqr4 \
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libcholmod5 \
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libc6 \
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libomp5 \
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libopengl0 \
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libmetis5 \
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libopenimageio2.4t64 \
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libgcc-s1 \
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libgl1 \
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libglew2.2 \
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libgoogle-glog0v6t64 \
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libqt6core6 \
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libqt6gui6 \
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libqt6widgets6 \
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libqt6openglwidgets6 \
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libcurl4 \
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libssl3t64 \
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libmkl-locale \
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libmkl-intel-lp64 \
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libmkl-intel-thread \
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libmkl-core \
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libcudss0-cuda-12 && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists/*
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# Copy all files from /colmap-install/ in the builder stage to /usr/local/ in
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# the runtime stage. This simulates installing COLMAP in the default location
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# (/usr/local/), which simplifies environment variables. It also allows the user
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# of this Docker image to use it as a base image for compiling against COLMAP as
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# a library. For instance, CMake will be able to find COLMAP easily with the
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# command: find_package(COLMAP REQUIRED).
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COPY --from=builder /colmap-install/ /usr/local/
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COPY --from=builder /usr/local/include/ /usr/local/include/
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COPY --from=builder /usr/local/lib/ /usr/local/lib/
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COPY --from=builder /usr/lib/x86_64-linux-gnu/libcudss /usr/lib/x86_64-linux-gnu/libcudss
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ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/libcudss/12:$LD_LIBRARY_PATH
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WORKDIR /workspace
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