ubuntu-intall-cuda

发表于 更新于 分类于 Waline：

Ubuntu20.04如何安装CUDA

  1. 安装显卡驱动
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#更新系统，安装必须用到的工具
exxk@exxk:~$ sudo apt update && sudo apt upgrade -y
exxk@exxk:~$ sudo apt install -y curl wget gnupg lsb-release
#添加 NVIDIA 驱动的 PPA
exxk@exxk:~$ sudo add-apt-repository ppa:graphics-drivers/ppa
exxk@exxk:~$ sudo apt update
#检测可用驱动： 它会列出所有设备（如显卡）和相应的推荐驱动。如果知道安装什么去驱动了，可以不用安装
exxk@exxk:~$ sudo apt install -y ubuntu-drivers-common
exxk@exxk:~$ ubuntu-drivers devices
ERROR:root:could not open aplay -l
Traceback (most recent call last):
  File "/usr/share/ubuntu-drivers-common/detect/sl-modem.py", line 35, in detect
    aplay = subprocess.Popen(
  File "/usr/lib/python3.8/subprocess.py", line 858, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
  File "/usr/lib/python3.8/subprocess.py", line 1704, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'aplay' #系统试图使用aplay来检测音频设备，但该工具未安装，可以忽略该错误
== /sys/devices/pci0000:00/0000:00:10.0 ==
modalias : pci:v000010DEd000025E2sv000017AAsd0000382Dbc03sc00i00
vendor   : NVIDIA Corporation
driver   : nvidia-driver-470 - distro non-free
driver   : nvidia-driver-535-server - distro non-free
driver   : nvidia-driver-535 - distro non-free
driver   : nvidia-driver-550-open - third-party non-free
driver   : nvidia-driver-535-open - distro non-free
driver   : nvidia-driver-550 - third-party non-free
driver   : nvidia-driver-560 - third-party non-free recommended #推荐的驱动
driver   : nvidia-driver-535-server-open - distro non-free
driver   : nvidia-driver-545-open - third-party non-free
driver   : nvidia-driver-470-server - distro non-free
driver   : nvidia-driver-555-open - third-party non-free
driver   : nvidia-driver-545 - third-party non-free
driver   : nvidia-driver-555 - third-party non-free
driver   : nvidia-driver-560-open - third-party non-free
driver   : xserver-xorg-video-nouveau - distro free builtin
# exxk@exxk:~$ sudo apt install -y nvidia-driver-560 该安装方式会安装桌面，其实用不到，因此采用下面的安装方式
exxk@exxk:~$ sudo apt install -y nvidia-driver-560 --no-install-recommends
exxk@exxk:~$ sudo reboot
exxk@exxk:~$ nvidia-smi
Tue Nov 19 07:27:03 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03              Driver Version: 560.35.03      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| 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  NVIDIA GeForce RTX 3050 ...    Off |   00000000:00:10.0 Off |                  N/A |
| N/A   45C    P8              3W /   60W |      15MiB /   4096MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A       933      G   /usr/lib/xorg/Xorg                              4MiB |
+-----------------------------------------------------------------------------------------+
  1. 安装docker(可以在安装系统时勾选上docker，这一步就可以省略，建议不要省略，安装系统勾选安装的docker 版本较低)
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#安装 Docker 仓库密钥和工具
exxk@exxk:~$ sudo apt install -y apt-transport-https ca-certificates curl software-properties-common
exxk@exxk:~$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
#添加 Docker 仓库
exxk@exxk:~$ echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
exxk@exxk:~$ sudo apt update
#安装 Docker
exxk@exxk:~$ sudo apt install -y docker-ce docker-ce-cli containerd.io
#将当前用户添加到 Docker 组
exxk@exxk:~$ sudo usermod -aG docker $USER
exxk@exxk:~$ sudo reboot
#查看docker版本，如果没有添加当前用户到docker组，需要加sudo
exxk@exxk:~$ docker version
Client: Docker Engine - Community
 Version:           27.3.1
 API version:       1.47
 Go version:        go1.22.7
 Git commit:        ce12230
 Built:             Fri Sep 20 11:41:03 2024
 OS/Arch:           linux/amd64
 Context:           default

Server: Docker Engine - Community
 Engine:
  Version:          27.3.1
  API version:      1.47 (minimum version 1.24)
  Go version:       go1.22.7
  Git commit:       41ca978
  Built:            Fri Sep 20 11:41:03 2024
  OS/Arch:          linux/amd64
  Experimental:     false
 containerd:
  Version:          1.7.23
  GitCommit:        57f17b0a6295a39009d861b89e3b3b87b005ca27
 runc:
  Version:          1.1.14
  GitCommit:        v1.1.14-0-g2c9f560
 docker-init:
  Version:          0.19.0
  GitCommit:        de40ad0
  1. 安装 NVIDIA Container Toolkit
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# 配置生产存储库：
exxk@exxk:~$ 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/stable/deb/nvidia-container-toolkit.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
#（可选）配置存储库以使用实验性软件包：
exxk@exxk:~$ sudo sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list   
#从存储库更新包列表：
exxk@exxk:~$ sudo apt-get update
#安装 NVIDIA Container Toolkit 软件包：
exxk@exxk:~$ sudo apt-get install -y nvidia-container-toolkit
#配置并重启 Docker
exxk@exxk:~$ sudo nvidia-ctk runtime configure --runtime=docker
exxk@exxk:~$ sudo systemctl restart docker
#运行一个简单的容器测试 GPU 是否可用
exxk@exxk:~$ docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu20.04 nvidia-smi
Tue Nov 19 07:58:08 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03              Driver Version: 560.35.03      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| 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  NVIDIA GeForce RTX 3050 ...    Off |   00000000:00:10.0 Off |                  N/A |
| N/A   39C    P8              3W /   60W |      15MiB /   4096MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
+-----------------------------------------------------------------------------------------+

CUDA使用

镜像如何选择

NVIDIA 提供了官方的 nvidia/cuda Docker 镜像，用于构建和运行 GPU 加速的容器化应用。

镜像说明： nvidia/cuda:<CUDA版本>--<镜像类型>-<基础系统>

  • 镜像类型：

    名称 标识 介绍 大小
    基础镜像 nvidia/cuda:-base 不包含运行时库，只包含基本的 CUDA 工具链(不包含运行时库，只包含基本的 CUDA 工具链) 100MB
    运行时镜像 nvidia/cuda:-runtime 部署已经编译好的应用程序(不包含开发工具) >2G
    开发镜像 nvidia/cuda:-devel 开发和调试 CUDA 应用程序 >3G
    完整镜像 nvidia/cuda:-full 包含开发镜像的所有内容，并添加了额外的示例代码和文档 没有

  • CUDA版本：运行和驱动对应版本，一般可以向下兼容，根据我的驱动推荐使用 CUDA 12.6（与您的驱动版本一致），也可以使用 CUDA 12.x 或 11.x

  • cuDNN：包含 NVIDIA 的 cuDNN 库（深度学习加速库），这是深度学习框架（如 TensorFlow、PyTorch）的核心组件

  • 基础系统：推荐使用宿主机的系统，例如：宿主机是Ubuntu就Ubuntu，宿主机 CentOS就 CentOS

根据我的环境，推荐镜像：docker pull nvidia/cuda:12.6.2-cudnn-devel-ubuntu20.04

使用

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#-w /workspace 指定工作目录，-p 8888:8888提前预设Jupyter的端口
docker run -it --gpus all --name cuda-dev \
  -v /home/exxk/workspace:/workspace \
  -w /workspace \
  -p 8888:8888 \
  nvidia/cuda:12.6.2-cudnn-devel-ubuntu20.04 bash 
root@287c0288ad93:/workspace# apt update
root@287c0288ad93:/workspace# apt install -y python3 python3-pip
#上传代码到/home/exxk/workspace