wehub-resource-sync
103 lines
3.6 KiB
Docker
103 lines
3.6 KiB
Docker
# NVIDIA Jetson embedded device support with GPU accelerated local model execution for https://github.com/microsoft/JARVIS
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# Base image for ffmpeg build env: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack/tags
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FROM nvcr.io/nvidia/l4t-jetpack:r35.2.1 AS build
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RUN apt update && apt install -y --no-install-recommends \
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build-essential git libass-dev libx264-dev cmake && \
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rm -rf /var/lib/apt/lists/*
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# Build ffmpeg dependency libraries
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RUN git clone https://github.com/jocover/jetson-ffmpeg.git && \
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cd jetson-ffmpeg && \
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sed -i 's=Libs: -L${libdir} -lnvmpi=Libs: -L${libdir} -lnvmpi -L/usr/lib/aarch64-linux-gnu/tegra -lnvbufsurface=g' nvmpi.pc.in && \
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mkdir build && \
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cd build && \
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cmake .. && \
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make -j$(nproc) && \
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sudo make install && \
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sudo ldconfig && \
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git clone git://source.ffmpeg.org/ffmpeg.git -b release/4.2 --depth=1 && \
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cd ffmpeg && \
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wget https://github.com/jocover/jetson-ffmpeg/raw/master/ffmpeg_nvmpi.patch && \
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git apply ffmpeg_nvmpi.patch && \
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./configure --enable-nvmpi --enable-libass --enable-libx264 --enable-gpl && \
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make -j$(nproc)
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# Base image: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch/tags
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# For running JARVIS application layer
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from nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
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ENV LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
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COPY --from=build /usr/local/lib/libnvmpi.a /usr/local/lib
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COPY --from=build /usr/local/lib/libnvmpi.so.1.0.0 /usr/local/lib
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COPY --from=build jetson-ffmpeg/build/ffmpeg/ffmpeg /usr/local/bin
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COPY --from=build jetson-ffmpeg/build/ffmpeg/ffprobe /usr/local/bin
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RUN ln /usr/local/lib/libnvmpi.so.1.0.0 /usr/local/lib/libnvmpi.so
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ENV MAKEFLAGS="-j$(nproc)"
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COPY ./server/requirements.txt .
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# Install model server dependencies
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RUN apt update && apt remove -y \
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opencv-dev opencv-libs opencv-licenses opencv-main opencv-python opencv-scripts python3-numpy && \
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rm -rf /var/lib/apt/lists/*
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RUN python3 -m pip install importlib-metadata==4.13.0 && \
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python3 -m pip install -r requirements.txt && \
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rm -rf requirements.txt
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# Update torch deps via reinstall
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RUN python3 -m pip install torch==2.0.0a0+ec3941ad.nv23.2 torchaudio==0.13.1+b90d798 torchvision==0.14.1a0+5e8e2f1
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# Downgrade opencv-python to v4.5
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RUN python3 -m pip install opencv-python==4.5.5.64
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# Install nvidia-opencv-dev
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RUN apt update && apt install -y --no-install-recommends \
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nvidia-opencv-dev && \
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rm -rf /var/lib/apt/lists/*
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# Fix loading of scikit dep at runtime
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ENV LD_PRELOAD='/usr/local/lib/python3.8/dist-packages/scikit_learn.libs/libgomp-d22c30c5.so.1.0.0'
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# Install nodejs npm from nodesource
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ENV NVM_DIR /root/.nvm
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ENV NODE_VERSION v18.16.0
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RUN wget -q -O - https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.3/install.sh | bash && \
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. "$NVM_DIR/nvm.sh" && \
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nvm install $NODE_VERSION && \
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nvm alias default $NODE_VERSION && \
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nvm use default
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ENV NODE_PATH $NVM_DIR/versions/node/$NODE_VERSION/lib/node_modules
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ENV PATH $NVM_DIR/versions/node/$NODE_VERSION/bin:$PATH
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WORKDIR /app
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# Copy source files
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COPY . .
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# Install web server dependencies
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RUN apt update && apt install -y --no-install-recommends \
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xdg-utils && \
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rm -rf /var/lib/apt/lists/* && \
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cd web && \
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npm install
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# Download local models
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# RUN apt update && apt install -y --no-install-recommends \
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# git-lfs && \
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# rm -rf /var/lib/apt/lists/* && \
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# cd server/models && \
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# bash download.sh
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# Expose the model server ports
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EXPOSE 8004
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EXPOSE 8005
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# Expose the web server port
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EXPOSE 9999
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WORKDIR /app/server
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# Start the model and web server
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CMD python3 models_server.py --config configs/config.default.yaml; |