105 lines
4.2 KiB
Docker
105 lines
4.2 KiB
Docker
FROM nvcr.io/nvidia/tensorrt:21.04-py3
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ARG CUDA=10.2
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ARG PYTHON_VERSION=3.8
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ARG TORCH_VERSION=1.8.0
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ARG TORCHVISION_VERSION=0.9.0
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ARG ONNXRUNTIME_VERSION=1.8.1
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ARG MMCV_VERSION=1.4.0
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ARG CMAKE_VERSION=3.20.0
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ARG PPLCV_VERSION=0.6.1
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ENV FORCE_CUDA="1"
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ENV DEBIAN_FRONTEND=noninteractive
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### change the system source for installing libs
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ARG USE_SRC_INSIDE=false
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RUN if [ ${USE_SRC_INSIDE} == true ] ; \
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then \
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sed -i s/archive.ubuntu.com/mirrors.aliyun.com/g /etc/apt/sources.list ; \
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sed -i s/security.ubuntu.com/mirrors.aliyun.com/g /etc/apt/sources.list ; \
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echo "Use aliyun source for installing libs" ; \
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else \
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echo "Keep the download source unchanged" ; \
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fi
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### update apt and install libs
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RUN apt-get update &&\
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apt-get install -y vim libsm6 libxext6 libxrender-dev libgl1-mesa-glx git wget libssl-dev libopencv-dev libspdlog-dev --no-install-recommends &&\
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rm -rf /var/lib/apt/lists/*
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RUN curl -fsSL -v -o ~/miniconda.sh -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
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chmod +x ~/miniconda.sh && \
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~/miniconda.sh -b -p /opt/conda && \
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rm ~/miniconda.sh && \
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/opt/conda/bin/conda install -y python=${PYTHON_VERSION} conda-build pyyaml numpy ipython cython typing typing_extensions mkl mkl-include ninja && \
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/opt/conda/bin/conda clean -ya
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### pytorch
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RUN /opt/conda/bin/conda install pytorch==${TORCH_VERSION} torchvision==${TORCHVISION_VERSION} cudatoolkit=${CUDA} -c pytorch
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ENV PATH /opt/conda/bin:$PATH
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### install mmcv-full
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RUN /opt/conda/bin/pip install mmcv-full==${MMCV_VERSION} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${TORCH_VERSION}/index.html
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WORKDIR /root/workspace
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### get onnxruntime
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RUN wget https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz \
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&& tar -zxvf onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz &&\
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pip install onnxruntime-gpu==${ONNXRUNTIME_VERSION}
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### cp trt from pip to conda
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RUN cp -r /usr/local/lib/python${PYTHON_VERSION}/dist-packages/tensorrt* /opt/conda/lib/python${PYTHON_VERSION}/site-packages/
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### update cmake
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RUN wget https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz &&\
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tar -zxvf cmake-${CMAKE_VERSION}.tar.gz &&\
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cd cmake-${CMAKE_VERSION} &&\
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./bootstrap &&\
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make &&\
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make install
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### install mmdeploy
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ENV ONNXRUNTIME_DIR=/root/workspace/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}
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ENV TENSORRT_DIR=/workspace/tensorrt
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ARG VERSION
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RUN git clone https://github.com/open-mmlab/mmdeploy &&\
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cd mmdeploy &&\
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if [ -z ${VERSION} ] ; then echo "No MMDeploy version passed in, building on master" ; else git checkout tags/v${VERSION} -b tag_v${VERSION} ; fi &&\
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git submodule update --init --recursive &&\
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rm -rf build &&\
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mkdir build &&\
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cd build &&\
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cmake -DMMDEPLOY_TARGET_BACKENDS=ort .. &&\
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make -j$(nproc) &&\
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cmake -DMMDEPLOY_TARGET_BACKENDS=trt .. &&\
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make -j$(nproc) &&\
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cd .. &&\
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pip install -e .
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### build sdk
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RUN wget https://github.com/openppl-public/ppl.cv/archive/refs/tags/v${PPLCV_VERSION}.zip &&\
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unzip v${PPLCV_VERSION}.zip && mv ppl.cv-${PPLCV_VERSION} ppl.cv &&\
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cd ppl.cv &&\
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./build.sh cuda
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RUN cd /root/workspace/mmdeploy &&\
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rm -rf build/CM* &&\
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mkdir -p build && cd build &&\
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cmake .. \
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-DMMDEPLOY_BUILD_SDK=ON \
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-DCMAKE_CXX_COMPILER=g++ \
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-Dpplcv_DIR=/root/workspace/ppl.cv/cuda-build/install/lib/cmake/ppl \
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-DTENSORRT_DIR=${TENSORRT_DIR} \
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-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
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-DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
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-DMMDEPLOY_TARGET_BACKENDS="trt" \
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-DMMDEPLOY_CODEBASES=all &&\
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cmake --build . -- -j$(nproc) && cmake --install . &&\
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cd install/example && mkdir -p build && cd build &&\
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cmake -DMMDeploy_DIR=/root/workspace/mmdeploy/build/install/lib/cmake/MMDeploy .. &&\
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cmake --build . && export SPDLOG_LEVEL=warn &&\
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if [ -z ${VERSION} ] ; then echo "Built MMDeploy master for GPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for GPU devices successfully!" ; fi
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ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${LD_LIBRARY_PATH}"
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