Nvidia CUDA开发环境 Docker容器启用显卡

Nvidia CUDA开发环境 Docker容器启用显卡 1.准备docker>19.03 环境，配置好nvidia-container-toolkit 2.确定本机已安装的显卡驱动版本，匹配需要的容器版本 3.Pull基础docker镜像，可以从官方或者dockerhub下载 https://ngc.nvidia.com/catalog/containers/nvidia:cuda/tags https://gitlab.com/nvidia/container-images/cuda cuda10-py36-conda的Dockerfile



FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
MAINTAINER Limc 

#close frontend
ENV DEBIAN_FRONTEND noninteractive

# add cuda user
# --disabled-password = Don't assign a password
# using root group for OpenShift compatibility
ENV CUDA_USER_NAME=cuda10
ENV CUDA_USER_GROUP=root

# add user
RUN adduser --system --group --disabled-password --no-create-home --disabled-login $CUDA_USER_NAME
RUN adduser $CUDA_USER_NAME $CUDA_USER_GROUP

# Install basic dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
cmake \
git \
wget \
libopencv-dev \
libsnappy-dev \
python-dev \
python-pip \
#tzdata \
vim

# Install conda for python
RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh -O ~/miniconda.sh && \
/bin/bash ~/miniconda.sh -b -p /opt/conda && \
rm ~/miniconda.sh

# Set locale
ENV LANG C.UTF-8 LC_ALL=C.UTF-8

ENV PATH /opt/conda/bin:$PATH

RUN ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh  && \
echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
echo "conda activate base" >> ~/.bashrc && \
find /opt/conda/ -follow -type f -name '*.a' -delete && \
find /opt/conda/ -follow -type f -name '*.js.map' -delete && \
/opt/conda/bin/conda clean -afy


# copy entrypoint.sh
#COPY ./entrypoint.sh /entrypoint.sh
# install 
#ENTRYPOINT ["/entrypoint.sh"]

# Initialize workspace
COPY ./app /app
# make workdir
WORKDIR /app

# update pip if nesseary
#RUN pip install --upgrade --no-cache-dir pip
# install gunicorn
# RUN pip install --no-cache-dir -r ./requirements.txt

# install use conda
#RUN conda install --yes --file ./requirements.txt
RUN while read requirement; do conda install --yes $requirement; done < requirements.txt


# copy entrypoint.sh
COPY ./entrypoint.sh /entrypoint.sh
# install 
ENTRYPOINT ["/entrypoint.sh"]

# switch to non-root user
USER $CUDA_USER_NAME

运行容器Makefile




IMG:=`cat Name`
GPU_OPT:=all
MOUNT_ETC:=
MOUNT_LOG:=
MOUNT_APP:=-v `pwd`/work/app:/app
MOUNT:=$(MOUNT_ETC) $(MOUNT_LOG) $(MOUNT_APP)
EXT_VOL:=
PORT_MAP:=
LINK_MAP:=
RESTART:=no
CONTAINER_NAME:=docker-cuda10-py36-hello

echo:
echo $(IMG)

run:
docker rm $(CONTAINER_NAME) || echo
docker run -d --gpus $(GPU_OPT) --name $(CONTAINER_NAME) $(LINK_MAP) $(PORT_MAP) --restart=$(RESTART) \
$(EXT_VOL) $(MOUNT) $(IMG)

run_i:
docker rm $(CONTAINER_NAME) || echo
docker run -i -t --gpus $(GPU_OPT) --name $(CONTAINER_NAME) $(LINK_MAP) $(PORT_MAP) \
$(EXT_VOL) $(MOUNT) $(IMG) /bin/bash 

exec_i:
docker exec -i -t --name $(CONTAINER_NAME)  /bin/bash 

stop:
docker stop $(CONTAINER_NAME)

rm: stop
docker rm $(CONTAINER_NAME)

Entrypoint.sh



set -e

# Add python as command if needed
if [ "${1:0:1}" = '-' ]; then
set -- python "$@"
fi

# Drop root privileges if we are running gunicorn
# allow the container to be started with `--user`
if [ "$1" = 'python' -a "$(id -u)" = '0' ]; then
# Change the ownership of user-mutable directories to gunicorn
for path in \
/app \
/usr/local/cuda/ \
; do
chown -R cuda10:root "$path"
done

set -- su-exec python "$@"
#exec su-exec elasticsearch "$BASH_SOURCE" "$@"
fi

# As argument is not related to gunicorn,
# then assume that user wants to run his own process,
# for example a `bash` shell to explore this image
exec "$@"

几个注意点 1.显卡运行需要root用户权限，否则会出现以下， docker: Error response from daemon: OCI runtime create failed: container_linux.go:345 考虑安全性可以在容器内创建新用户并加入到root组 2.本机显卡驱动和CUDA必须匹配官方容器的版本，cudnn则不需要匹配，可以使用多个不同版本的cudnn，但是必须满足显卡要求的使用范围 3.docker运行容器非正常结束时会占用显卡，如果卡死，会造成容器外部无法使用，重启docker-daemon也无效，这时只能重启电脑完整的源代码 https://github.com/limccn/ultrasound-nerve-segmentation-in-tensorflow/commit/d7de1cbeb641d2fae4f5a78ff590a0254667b398 参考 https://gitlab.com/nvidia/container-images/cuda