ea850ecd20
* All Linux tests are now in Jenkins CI * Tests are now de-coupled from builds. We can now build XGBoost with one version of CUDA/JDK and test it with another version of CUDA/JDK * Builds (compilation) are significantly faster because 1) They use C5 instances with faster CPU cores; and 2) build environment setup is cached using Docker containers
60 lines
2.5 KiB
Docker
60 lines
2.5 KiB
Docker
ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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# Install all basic requirements
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RUN \
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yum -y update && \
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yum install -y tar unzip wget xz git centos-release-scl yum-utils && \
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yum-config-manager --enable centos-sclo-rh-testing && \
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yum -y update && \
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yum install -y devtoolset-4-gcc devtoolset-4-binutils devtoolset-4-gcc-c++ && \
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# Python
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wget https://repo.continuum.io/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh && \
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bash Miniconda3-4.5.12-Linux-x86_64.sh -b -p /opt/python && \
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# CMake
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wget -nv -nc https://cmake.org/files/v3.12/cmake-3.12.0-Linux-x86_64.sh --no-check-certificate && \
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bash cmake-3.12.0-Linux-x86_64.sh --skip-license --prefix=/usr
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# NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
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RUN \
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export CUDA_SHORT=`echo $CUDA_VERSION | egrep -o '[0-9]+\.[0-9]'` && \
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if [ "${CUDA_SHORT}" != "10.0" ]; then \
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wget https://developer.download.nvidia.com/compute/redist/nccl/v2.2/nccl_2.2.13-1%2Bcuda${CUDA_SHORT}_x86_64.txz && \
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tar xf "nccl_2.2.13-1+cuda${CUDA_SHORT}_x86_64.txz" && \
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cp nccl_2.2.13-1+cuda${CUDA_SHORT}_x86_64/include/nccl.h /usr/include && \
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cp nccl_2.2.13-1+cuda${CUDA_SHORT}_x86_64/lib/* /usr/lib && \
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rm -f nccl_2.2.13-1+cuda${CUDA_SHORT}_x86_64.txz && \
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rm -r nccl_2.2.13-1+cuda${CUDA_SHORT}_x86_64; else \
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wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
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rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
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yum -y update && \
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yum install -y libnccl-2.4.2-1+cuda10.0 libnccl-devel-2.4.2-1+cuda10.0 libnccl-static-2.4.2-1+cuda10.0 && \
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rm -f nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm; fi
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ENV PATH=/opt/python/bin:$PATH
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ENV CC=/opt/rh/devtoolset-4/root/usr/bin/gcc
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ENV CXX=/opt/rh/devtoolset-4/root/usr/bin/c++
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ENV CPP=/opt/rh/devtoolset-4/root/usr/bin/cpp
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# Install Python packages
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RUN \
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pip install numpy pytest scipy scikit-learn wheel kubernetes urllib3==1.22
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ENV GOSU_VERSION 1.10
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# Install lightweight sudo (not bound to TTY)
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RUN set -ex; \
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wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
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chmod +x /usr/local/bin/gosu && \
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gosu nobody true
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# Default entry-point to use if running locally
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# It will preserve attributes of created files
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COPY entrypoint.sh /scripts/
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WORKDIR /workspace
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ENTRYPOINT ["/scripts/entrypoint.sh"]
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