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Dockerfile.rmm
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Dockerfile.rmm
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ARG CUDA_VERSION_ARG
FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-ubuntu18.04
ARG CUDA_VERSION_ARG
# Environment
ENV DEBIAN_FRONTEND noninteractive
SHELL ["/bin/bash", "-c"] # Use Bash as shell
# Install all basic requirements
RUN \
apt-get update && \
apt-get install -y wget unzip bzip2 libgomp1 build-essential ninja-build git && \
# Python
wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
bash Miniconda3.sh -b -p /opt/python && \
# CMake
wget -nv -nc https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.sh --no-check-certificate && \
bash cmake-3.13.0-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 $CUDA_VERSION_ARG | grep -o -E '[0-9]+\.[0-9]'` && \
export NCCL_VERSION=2.7.5-1 && \
apt-get update && \
apt-get install -y --allow-downgrades --allow-change-held-packages libnccl2=${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-dev=${NCCL_VERSION}+cuda${CUDA_SHORT}
ENV PATH=/opt/python/bin:$PATH
# Create new Conda environment with RMM
RUN \
conda create -n gpu_test -c rapidsai-nightly -c rapidsai -c nvidia -c conda-forge -c defaults \
python=3.7 rmm=21.08* cudatoolkit=$CUDA_VERSION_ARG
ENV GOSU_VERSION 1.10
# Install lightweight sudo (not bound to TTY)
RUN set -ex; \
wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$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"]