dmlc--dgl
Quan (Andy) Gan
00f2099934
* upgrade CI pytorch version * update * revert tensorflow image * old image (220816) backup * Update Dockerfile.ci_gpu
45 行
1.8 KiB
Docker
45 行
1.8 KiB
Docker
# CI docker GPU env
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FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
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ENV TZ=US
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RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
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# Update outdated public key from NVIDIA
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RUN apt-key del 3bf863cc
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RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
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RUN apt-get update --fix-missing
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COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
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RUN bash /install/ubuntu_install_core.sh
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COPY install/ubuntu_install_build.sh /install/ubuntu_install_build.sh
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RUN bash /install/ubuntu_install_build.sh
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# python
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COPY install/ubuntu_install_conda.sh /install/ubuntu_install_conda.sh
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RUN bash /install/ubuntu_install_conda.sh
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ENV CONDA_ALWAYS_YES="true"
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COPY install/conda_env/torch_gpu.yml /install/conda_env/torch_gpu.yml
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COPY install/conda_env/torch_gpu_pip.txt /install/conda_env/torch_gpu_pip.txt
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RUN ["/bin/bash", "-i", "-c", "conda env create -f /install/conda_env/torch_gpu.yml"]
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COPY install/conda_env/tensorflow_gpu.yml /install/conda_env/tensorflow_gpu.yml
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RUN ["/bin/bash", "-i", "-c", "conda env create -f /install/conda_env/tensorflow_gpu.yml"]
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COPY install/conda_env/mxnet_gpu.yml /install/conda_env/mxnet_gpu.yml
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RUN ["/bin/bash", "-i", "-c", "conda env create -f /install/conda_env/mxnet_gpu.yml"]
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ENV CONDA_ALWAYS_YES=
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# Environment variables
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ENV PATH=/usr/local/nvidia/bin:${PATH}
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ENV PATH=/usr/local/cuda/bin:${PATH}
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ENV CPLUS_INCLUDE_PATH=/usr/local/cuda/include:${CPLUS_INCLUDE_PATH}
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ENV C_INCLUDE_PATH=/usr/local/cuda/include:${C_INCLUDE_PATH}
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ENV LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/nvidia/lib64:${LIBRARY_PATH}
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ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/nvidia/lib64:${LD_LIBRARY_PATH}
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ENV CUDA_VISIBLE_DEVICES=0
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ENV TF_FORCE_GPU_ALLOW_GROWTH=true
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