操作系统： Ubuntu 16.04/18.04
安装 Nvidia Driver
推荐使用 graphics drivers PPA 安装 Nvidia 驱动。
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
检测推荐的 Nvidia 显卡驱动：
ubuntu-drivers devices
安装 Nvidia 驱动（以下是 RTX2060 上的情况）：
# Ubuntu 16.04 only search 430 for CUDA < 10.2
apt-cache search nvidia
sudo apt install nvidia-430
# Ubuntu 18.04 could search 440 for CUDA <= 10.2
apt-cache search nvidia | grep ^nvidia-driver
sudo apt install nvidia-driver-440
驱动对应的 CUDA 版本，请见 CUDA Compatibility 。
最后， sudo reboot 重启。之后，运行 nvidia-smi 输出 Nvidia 驱动信息:
$ nvidia-smi
Fri Apr 17 07:31:55 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2060 Off | 00000000:01:00.0 Off | N/A |
| N/A 40C P8 5W / N/A | 263MiB / 5934MiB | 3% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1560 G /usr/lib/xorg/Xorg 144MiB |
| 0 1726 G /usr/bin/gnome-shell 76MiB |
| 0 2063 G ...uest-channel-token=10544833948196615517 39MiB |
+-----------------------------------------------------------------------------+
如果安装 CUDA Toolkit ，请先了解 CUDA Compatibility 。安装 CUDA Toolkit 时，注意其携带的驱动版本，最好将其与驱动分别进行安装。而驱动从官方上直接找合适的版本。
安装 Docker
# update the apt package index
sudo apt-get update
# install packages to allow apt to use a repository over HTTPS
sudo apt-get install apt-transport-https ca-certificates curl gnupg2 software-properties-common
# add Docker’s official GPG key
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
# set up the stable repository
sudo add-apt-repository \
"deb [arch=amd64] https://mirrors.tuna.tsinghua.edu.cn/docker-ce/linux/ubuntu \
$(lsb_release -cs) \
stable"
# update the apt package index
sudo apt-get update
# install the latest version of Docker Engine and containerd
sudo apt-get install docker-ce docker-ce-cli containerd.io
之后，将 Docker 设为 non-root 用户可用：
sudo groupadd docker
sudo usermod -aG docker $USER
参考
安装 Nvidia Docker
# add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
使用
#### Test nvidia-smi with the latest official CUDA image
docker run --gpus all nvidia/cuda:10.0-base nvidia-smi
# Start a GPU enabled container on two GPUs
docker run --gpus 2 nvidia/cuda:10.0-base nvidia-smi
# Starting a GPU enabled container on specific GPUs
docker run --gpus '"device=1,2"' nvidia/cuda:10.0-base nvidia-smi
docker run --gpus '"device=UUID-ABCDEF,1"' nvidia/cuda:10.0-base nvidia-smi
# Specifying a capability (graphics, compute, ...) for my container
# Note this is rarely if ever used this way
docker run --gpus all,capabilities=utility nvidia/cuda:10.0-base nvidia-smi
$ docker run --gpus all nvidia/cuda:10.2-base-ubuntu16.04 nvidia-smi
Unable to find image 'nvidia/cuda:10.2-base-ubuntu16.04' locally
10.2-base-ubuntu16.04: Pulling from nvidia/cuda
976a760c94fc: Pull complete
c58992f3c37b: Pull complete
0ca0e5e7f12e: Pull complete
f2a274cc00ca: Pull complete
708a53113e13: Pull complete
7dde2dc03189: Pull complete
2d21d4aba891: Pull complete
Digest: sha256:1423b386bb4f950d12b3b0f3ad51eba42d754ee73f8fc4a60657a1904993b68c
Status: Downloaded newer image for nvidia/cuda:10.2-base-ubuntu16.04
Fri Apr 24 08:17:26 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2060 Off | 00000000:01:00.0 Off | N/A |
| N/A 38C P8 10W / N/A | 523MiB / 5934MiB | 21% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
参考
结语
Go coding!
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