ARG CUDA_VERSION_ARG
FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos6
ARG CUDA_VERSION_ARG

# Environment
ENV DEBIAN_FRONTEND noninteractive
ENV DEVTOOLSET_URL_ROOT http://vault.centos.org/6.9/sclo/x86_64/rh/devtoolset-4/

COPY CentOS-Base.repo /etc/yum.repos.d/

# Install all basic requirements
RUN \
    yum install -y epel-release && \
    yum -y update && \
    yum install -y tar unzip wget xz git patchelf && \
    yum install -y $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-5.3.1-6.1.el6.x86_64.rpm \
                   $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-c++-5.3.1-6.1.el6.x86_64.rpm \
                   $DEVTOOLSET_URL_ROOT/devtoolset-4-binutils-2.25.1-8.el6.x86_64.rpm \
                   $DEVTOOLSET_URL_ROOT/devtoolset-4-runtime-4.1-3.sc1.el6.x86_64.rpm \
                   $DEVTOOLSET_URL_ROOT/devtoolset-4-libstdc++-devel-5.3.1-6.1.el6.x86_64.rpm && \
    # Python
    wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
    bash Miniconda3.sh -b -p /opt/python && \
    /opt/python/bin/python -m pip install auditwheel && \
    # CMake
    wget -nv -nc https://cmake.org/files/v3.14/cmake-3.14.0-Linux-x86_64.sh --no-check-certificate && \
    bash cmake-3.14.0-Linux-x86_64.sh --skip-license --prefix=/usr && \
    # Ninja
    mkdir -p /usr/local && \
    cd /usr/local/ && \
    wget -nv -nc https://github.com/ninja-build/ninja/archive/v1.10.0.tar.gz --no-check-certificate && \
    tar xf v1.10.0.tar.gz && mv ninja-1.10.0 ninja && rm -v v1.10.0.tar.gz && \
    cd ninja && \
    /opt/python/bin/python ./configure.py --bootstrap

# NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
RUN \
    export CUDA_SHORT=`echo $CUDA_VERSION_ARG | grep -o -E '[0-9]+\.[0-9]'` && \
    export NCCL_VERSION=2.4.8-1 && \
    wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
    rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
    yum -y update && \
    yum install -y libnccl-${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/python/bin:/usr/local/ninja:$PATH
ENV CC=/opt/rh/devtoolset-4/root/usr/bin/gcc
ENV CXX=/opt/rh/devtoolset-4/root/usr/bin/c++
ENV CPP=/opt/rh/devtoolset-4/root/usr/bin/cpp

ENV GOSU_VERSION 1.10

# Install lightweight sudo (not bound to TTY)
RUN set -ex; \
    wget -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"]