xgboost/tests/ci_build/Dockerfile.gpu
Jiaming Yuan 39c1488a42
[backport] Update CUDA docker image and NCCL. (#8139) (#8162)
* Update CUDA docker image and NCCL. (#8139)

* Rest of the CI.

* CPU test dependencies.
2022-08-12 18:57:42 +08:00

43 lines
1.6 KiB
Docker

ARG CUDA_VERSION_ARG
FROM nvidia/cuda:$CUDA_VERSION_ARG-runtime-ubuntu18.04
ARG CUDA_VERSION_ARG
# Environment
ENV DEBIAN_FRONTEND noninteractive
SHELL ["/bin/bash", "-c"] # Use Bash as shell
# Install all basic requirements
RUN \
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub && \
apt-get update && \
apt-get install -y wget unzip bzip2 libgomp1 build-essential openjdk-8-jdk-headless && \
# Python
wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
bash Miniconda3.sh -b -p /opt/python
ENV PATH=/opt/python/bin:$PATH
# Create new Conda environment with cuDF, Dask, and cuPy
RUN \
conda install -c conda-forge mamba && \
mamba create -n gpu_test -c rapidsai-nightly -c rapidsai -c nvidia -c conda-forge -c defaults \
python=3.8 cudf=22.04* rmm=22.04* cudatoolkit=$CUDA_VERSION_ARG dask dask-cuda=22.04* dask-cudf=22.04* cupy \
numpy pytest scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis \
pyspark cloudpickle cuda-python=11.7.0
ENV GOSU_VERSION 1.10
ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/
# Install lightweight sudo (not bound to TTY)
RUN set -ex; \
wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$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"]