366f3cb9d8
* Ensure RMM is 0.18 or later * Add use_rmm flag to global configuration * Modify XGBCachingDeviceAllocatorImpl to skip CUB when use_rmm=True * Update the demo * [CI] Pin NumPy to 1.19.4, since NumPy 1.19.5 doesn't work with latest Shap
48 lines
1.7 KiB
Docker
48 lines
1.7 KiB
Docker
ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-ubuntu18.04
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ARG CUDA_VERSION_ARG
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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SHELL ["/bin/bash", "-c"] # Use Bash as shell
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# Install all basic requirements
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RUN \
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apt-get update && \
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apt-get install -y wget unzip bzip2 libgomp1 build-essential ninja-build git && \
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# Python
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wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
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bash Miniconda3.sh -b -p /opt/python && \
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# CMake
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wget -nv -nc https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.sh --no-check-certificate && \
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bash cmake-3.13.0-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 $CUDA_VERSION_ARG | grep -o -E '[0-9]+\.[0-9]'` && \
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export NCCL_VERSION=2.7.5-1 && \
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apt-get update && \
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apt-get install -y --allow-downgrades --allow-change-held-packages libnccl2=${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-dev=${NCCL_VERSION}+cuda${CUDA_SHORT}
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ENV PATH=/opt/python/bin:$PATH
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# Create new Conda environment with RMM
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RUN \
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conda create -n gpu_test -c nvidia -c rapidsai -c conda-forge -c defaults \
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python=3.7 rmm=0.18* cudatoolkit=$CUDA_VERSION_ARG
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ENV GOSU_VERSION 1.10
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# Install lightweight sudo (not bound to TTY)
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RUN set -ex; \
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wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$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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