xgboost/tests/ci_build/Dockerfile.gpu_build_centos7
Philip Hyunsu Cho 6d8afb2218
[CI] Require C++17 + CMake 3.18; Use CUDA 11.8 in CI (#8853)
* Update to C++17

* Turn off unity build

* Update CMake to 3.18

* Use MSVC 2022 + CUDA 11.8

* Re-create stack for worker images

* Allocate more disk space for Windows

* Tempiorarily disable clang-tidy

* RAPIDS now requires Python 3.10+

* Unpin cuda-python

* Use latest NCCL

* Use Ubuntu 20.04 in RMM image

* Mark failing mgpu test as xfail
2023-03-01 09:22:24 -08:00

60 lines
2.6 KiB
Docker

ARG CUDA_VERSION_ARG
FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos7
ARG CUDA_VERSION_ARG
ARG NCCL_VERSION_ARG
# Install all basic requirements
RUN \
curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/D42D0685.pub | sed '/^Version/d' \
> /etc/pki/rpm-gpg/RPM-GPG-KEY-NVIDIA && \
yum install -y epel-release centos-release-scl && \
yum-config-manager --enable centos-sclo-rh-testing && \
yum -y update && \
yum install -y tar unzip wget xz git which ninja-build devtoolset-8-gcc devtoolset-8-binutils devtoolset-8-gcc-c++ && \
# Python
wget -nv -O conda.sh https://github.com/conda-forge/miniforge/releases/download/22.11.1-2/Mambaforge-22.11.1-2-Linux-x86_64.sh && \
bash conda.sh -b -p /opt/mambaforge && \
/opt/mambaforge/bin/python -m pip install awscli && \
# CMake
wget -nv -nc https://cmake.org/files/v3.18/cmake-3.18.0-Linux-x86_64.sh --no-check-certificate && \
bash cmake-3.18.0-Linux-x86_64.sh --skip-license --prefix=/usr
# NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
RUN \
export CUDA_SHORT=`echo $CUDA_VERSION_ARG | grep -o -E '[0-9]+\.[0-9]'` && \
export NCCL_VERSION=$NCCL_VERSION_ARG && \
wget -nv -nc https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
yum -y update && \
yum install -y libnccl-${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-devel-${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-static-${NCCL_VERSION}+cuda${CUDA_SHORT} && \
rm -f nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm;
ENV PATH=/opt/mambaforge/bin:/usr/local/ninja:$PATH
ENV CC=/opt/rh/devtoolset-8/root/usr/bin/gcc
ENV CXX=/opt/rh/devtoolset-8/root/usr/bin/c++
ENV CPP=/opt/rh/devtoolset-8/root/usr/bin/cpp
ENV GOSU_VERSION 1.10
# Install gRPC
RUN git clone -b v1.49.1 https://github.com/grpc/grpc.git \
--recurse-submodules --depth 1 && \
pushd grpc && \
cmake -S . -B build -GNinja -DCMAKE_INSTALL_PREFIX=/opt/grpc -DCMAKE_CXX_VISIBILITY_PRESET=hidden && \
cmake --build build --target install && \
popd && \
rm -rf grpc
# Install lightweight sudo (not bound to TTY)
RUN set -ex; \
wget -nv -nc -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
chmod +x /usr/local/bin/gosu && \
gosu nobody true
# Default entry-point to use if running locally
# It will preserve attributes of created files
COPY entrypoint.sh /scripts/
WORKDIR /workspace
ENTRYPOINT ["/scripts/entrypoint.sh"]