快速搭建所需开发环境
Docker文档：https://docs.docker.com/，Docker安装指南： Install Docker Engine on Ubuntu
Dokcer安装
Uninstall old versions ~$ sudo apt-get remove docker docker-engine docker.io containerd runc ~$ sudo apt-get install curl Install using the repository ~$ curl https://get.docker.com | sh \ && sudo systemctl --now enable docker Verify that Docker Engine is installed correctly by running the hello-world image. ~$ sudo docker run hello-worldDocker测试
# 启动docker服务 $ sudo service docker start # Docker: hello-world $ sudo docker run hello-world其他Docker命令：
Usage: service docker {start|stop|restart|status} 查看镜像 $ sudo docker images 查看容器 $ sudo docker container ls -aTips：Docker中一般Crtl+C退出，传送门：停止、删除所有的 docker 容器和镜像。
Nvidia-docker安装
查看nvidia版本
$ nvidia-smi Thu Nov 26 10:34:37 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 GeForce RTX 2060 Off | 00000000:01:00.0 On | N/A | | 0% 37C P8 9W / 190W | 301MiB / 5931MiB | 2% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | 0 N/A N/A 942 G /usr/lib/xorg/Xorg 35MiB | | 0 N/A N/A 2278 G /usr/lib/xorg/Xorg 96MiB | | 0 N/A N/A 2404 G /usr/bin/gnome-shell 150MiB | | 0 N/A N/A 4051 G /usr/lib/firefox/firefox 3MiB | +-----------------------------------------------------------------------------+参考链接：官网 installation guide
Github：NVIDIA/nvidia-docker# $ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \ && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \ sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list $ sudo apt-get update $ sudo apt-get install -y nvidia-container-toolkit $ sudo nvidia-ctk runtime configure --runtime=docker $ sudo systemctl restart docker $ docker pull nvidia/cudagl:11.0-base # 测试 $ docker run --rm --gpus all nvidia/cudagl:11.0-base nvidia-smi Thu Nov 26 02:30:34 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 GeForce RTX 2060 Off | 00000000:01:00.0 On | N/A | | 0% 37C P8 10W / 190W | 307MiB / 5931MiB | 13% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| +-----------------------------------------------------------------------------+测试
$ sudo docker run --runtime=nvidia --rm nvidia/cudagl:11.0-base nvidia-smi
Docker 容器 GUI
$ sudo apt-get install x11-xserver-utils # 关闭权限控制，允许其他X客户端绘制 $ xhost + access control disabled, clients can connect from any host $ docker run -e DISPLAY=$DISPLAY -e GDK_SCALE -e GDK_DPI_SCAL -v /tmp/.X11-unix:/tmp/.X11-unix --rm -it container-name-or-id若遇到X Error时，添加参数：--ipc=host 或 --env="QT_X11_NO_MITSHM=1"，参考链接：
How to fix X Error: BadAccess, BadDrawable, BadShmSeg while running graphical application using Docker?
Docker: gazebo: cannot connect to X server
若遇到 libGL error: No matching fbConfigs or visuals found libGL error... ，参考链接：
使用docker时出现libGL error: No matching fbConfigs or visuals found libGL error: failed to load driver...
已成功测试上述链接中的 pull image 方式
使用nvidia-smi查看nvidia driver和cuda版本，根据 nvidia/cudagl ，选择合适的TAG$ sudo apt-get install x11-xserver-utils $ nvidia-smi $ docker pull nvidia/cudagl:11.0-base # 关闭权限控制，允许其他X客户端绘制 $ xhost + access control disabled, clients can connect from any host $ sudo docker run --rm --runtime=nvidia -it -e DISPLAY=$DISPLAY -e GDK_SCALE -e GDK_DPI_SCAL -v /tmp/.X11-unix:/tmp/.X11-unix nvidia/cudagl:11.0-base $ apt-get update $ apt-get install mesa-utils $ glxgears
创建新的长期镜像：
$ sudo apt-get install x11-xserver-utils $ nvidia-smi $ docker pull nvidia/cudagl:10.2-base # 关闭权限控制，允许其他X客户端绘制 $ xhost + access control disabled, clients can connect from any host $ sudo docker run -it --name isaac --runtime=nvidia -it -e DISPLAY=$DISPLAY -e GDK_SCALE -e GDK_DPI_SCAL -v /tmp/.X11-unix:/tmp/.X11-unix -v /data/isaac:/data/isaac nvidia/cudagl:10.2-base $ apt-get update $ apt-get install mesa-utils $ glxgears $ exit $ sudo docker ps -a $ sudo docker start isaac $ sudo docker attach isaac
如果遇到如下报错：
Nvidia-container-cli: initialization error: load library failed: libnvidia-ml.so.1: cannot open shared object file: no such file or directory
上述报错目前没有找到很好的解决方法，应该是某个安装包意外修改了系统配置，导致出现问题，重装系统可以顺利解决此问题。