Nvidia CUDA开发环境 Docker容器启用显卡
April 28th, 2020
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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 <limc@limc.com.cn> #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
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