Using GPU from a docker container?

Question

I\'m searching for a way to use the GPU from inside a docker container.

The container will execute arbitrary code so i don\'t want to use the privileged mode.

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Answer 1

Goal:

My goal was to make a CUDA enabled docker image without using nvidia/cuda as base image. Because I have some custom jupyter image, and I want to base from that.

Prerequisite:

The host machine had nvidia driver, CUDA toolkit, and nvidia-container-toolkit already installed. Please refer to the official docs, and to Rohit's answer.

Test that nvidia driver and CUDA toolkit is installed correctly with: nvidia-smi on the host machine, which should display correct "Driver Version" and "CUDA Version" and shows GPUs info.

Test that nvidia-container-toolkit is installed correctly with: docker run --rm --gpus all nvidia/cuda:latest nvidia-smi

Dockerfile

I found what I assume to be the official Dockerfile for nvidia/cuda here I "flattened" it, appended the contents to my Dockerfile and tested it to be working nicely:

FROM sidazhou/scipy-notebook:latest
# FROM ubuntu:18.04 

###########################################################################
# See https://gitlab.com/nvidia/container-images/cuda/-/blob/master/dist/10.1/ubuntu18.04-x86_64/base/Dockerfile
# See https://sarus.readthedocs.io/en/stable/user/custom-cuda-images.html
###########################################################################
USER root

###########################################################################
# base
RUN apt-get update && apt-get install -y --no-install-recommends \
    gnupg2 curl ca-certificates && \
    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub | apt-key add - && \
    echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
    echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list && \
    apt-get purge --autoremove -y curl \
    && rm -rf /var/lib/apt/lists/*

ENV CUDA_VERSION 10.1.243
ENV CUDA_PKG_VERSION 10-1=$CUDA_VERSION-1

# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
RUN apt-get update && apt-get install -y --no-install-recommends \
    cuda-cudart-$CUDA_PKG_VERSION \
    cuda-compat-10-1 \
    && ln -s cuda-10.1 /usr/local/cuda && \
    rm -rf /var/lib/apt/lists/*

# Required for nvidia-docker v1
RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
    echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf

ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64


###########################################################################
#runtime next
ENV NCCL_VERSION 2.7.8

RUN apt-get update && apt-get install -y --no-install-recommends \
    cuda-libraries-$CUDA_PKG_VERSION \
    cuda-npp-$CUDA_PKG_VERSION \
    cuda-nvtx-$CUDA_PKG_VERSION \
    libcublas10=10.2.1.243-1 \
    libnccl2=$NCCL_VERSION-1+cuda10.1 \
    && apt-mark hold libnccl2 \
    && rm -rf /var/lib/apt/lists/*

# apt from auto upgrading the cublas package. See https://gitlab.com/nvidia/container-images/cuda/-/issues/88
RUN apt-mark hold libcublas10


###########################################################################
#cudnn7 (not cudnn8) next

ENV CUDNN_VERSION 7.6.5.32

RUN apt-get update && apt-get install -y --no-install-recommends \
    libcudnn7=$CUDNN_VERSION-1+cuda10.1 \
    && apt-mark hold libcudnn7 && \
    rm -rf /var/lib/apt/lists/*


ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES all
ENV NVIDIA_REQUIRE_CUDA "cuda>=10.1"


###########################################################################
#docker build -t sidazhou/scipy-notebook-gpu:latest .

#docker run -itd -gpus all\
#  -p 8888:8888 \
#  -p 6006:6006 \
#  --user root \
#  -e NB_UID=$(id -u) \
#  -e NB_GID=$(id -g) \
#  -e GRANT_SUDO=yes \
#  -v ~/workspace:/home/jovyan/work \
#  --name sidazhou-jupyter-gpu \
#  sidazhou/scipy-notebook-gpu:latest

#docker exec sidazhou-jupyter-gpu python -c "import tensorflow as tf; print(tf.config.experimental.list_physical_devices('GPU'))"