坑边闲话:NVIDIA GPU 因为源码保护,无法直接集成到 GPLv2 开源协议的 Linux 内核里,因此需要采用一些稍微麻烦的方法进行部署。此外,CUDA 套件和 GPU driver 的关系,以及 NVIDIA-Container-runtime 的存在意义也是许多新手搞不明白的地方。

1. 前言·

全文以 amd64 体系结构的 Debian 12 系统为例。首先要牢记以下链接,因为它是上游仓库的地址。

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# NVIDIA 官方上游仓库
https://developer.download.nvidia.cn/compute/cuda/repos/debian12/x86_64/

1.1 深入理解 CUDA 和 Driver 的一体性·

与 NVIDIA 软件相关的名词列举如下:

  • GPU Driver
  • NVIDIA kernel module
  • CUDA toolkit
  • cuDNN
  • PyTorch

接下来分别介绍其含义及存在意义。

1.1.1 GPU 驱动·

驱动程序控制 CPU 如何调用位于 PCIe 总线上的显卡。不安装驱动将无法使用 GPU 的任何功能。NVIDIA 驱动程序允许你通过 PCIe 通信协议调用显卡,给显卡提交任务并接受返回。

NVIDIA 驱动一般以 kmod 即内核扩展模块的方式提供,因为无法嵌入到 kernel,所以这是保证性能的唯一办法。尽管有些移动系统能加载用户态的显卡驱动,但那毕竟是特例。

1.1.2 CUDA·

CUDA 是一个并行计算、编程平台。它可以利用 GPU 并行极大加速某些运算,比如向量计算。

CUDA 包含:

  • CUDA compiler 编译工具链
  • CUDA runtime (cudart)
  • CUDA libraries 和工具 (如 nvidia-smi)

CUDA 可以算是某些语言的扩展。比如标准 C/C++ 语言写的代码只能在 CPU 上跑,而 CUDA 类似该语言的扩展。有了 CUDA 扩展,C++ 可以轻松调用 GPU 进行计算,以前这种异构平台上的编程难度是很高的。有了 CUDA,程序员可以让应用程序同时使用 CPU 和 GPU 完成异构计算。其中,应用程序的串行部分在 CPU 上运行,而并行部分则卸载到 GPU 上。因此,CUDA 可以逐步应用于现有的应用程序。CPU 和 GPU 被视为独立的设备,拥有各自的内存空间。这种配置也允许在 CPU 和 GPU 上同时进行计算而不需要争夺内存资源。

图 1. 组成 CUDA 的各种模块,Toolkit 部分用来构建 CUDA 应用,Driver 部分用来执行 CUDA 程序。其中,Driver 又分为用户模式的 libcuda.so 和内核模式的 nvidia.ko 两部分。Driver 部分集成在显卡驱动里。

简单总结一下

  • CUDA Toolkit 提供了编程的接口,如头文件、部分源代码,使得程序员能开发支持 CUDA 的应用程序。
  • CUDA 的运行时库(内核库与用户态共享库)能够让 CUDA 程序运行起来。

具有 CUDA 功能的 GPU 有数百个内核,可以共同运行成千上万的计算线程。这些内核拥有共享资源,包括一个寄存器文件和一个共享存储器。片上共享内存允许在这些内核上运行的并行任务共享数据,而无需通过系统内存总线发送数据。

对程序员来说,CUDA 是一套 GPU 并行的编程接口。引用了 CUDA 头文件写出来的程序,能调用 NVIDIA 的 GPU 驱动做计算加速。

CUDA 版本和 NVIDIA 显卡驱动的版本之间确实存在绑定关系。每个 CUDA 版本都有一个最低的 NVIDIA 驱动版本要求。这意味着,为了使特定版本的 CUDA 正常工作,你的系统上必须至少安装该 CUDA 版本所要求的 NVIDIA 驱动版本。这种绑定关系的存在是因为 CUDA 的新版本可能会引入新的功能或优化,这些功能或优化可能需要更新的驱动支持。因此,旧版本的驱动可能不支持新版本的 CUDA.

NVIDIA 通常会在其官方文档中列出每个 CUDA 版本与其对应的驱动版本的关系。如果你打算安装特定版本的 CUDA,建议先查看 NVIDIA 的官方文档,以确保你也安装了合适的驱动版本。另外,虽然每个 CUDA 版本都有一个最低的驱动版本要求,但这并不意味着你不能在更高版本的驱动上使用该 CUDA 版本。通常,新版本的驱动是向下兼容的,这意味着它们应该能够支持旧版本的 CUDA. 但是,为了确保最佳的兼容性和性能,最好还是遵循 NVIDIA 的官方建议,安装推荐的驱动版本。

每个 CUDA tookkit 安装包都集成了 NVIDIA 的显卡驱动包,第一是为了简便,第二是防止用户在 CUDA 版本和驱动版本上出现麻烦。

CUDA Toolkit Minimum Required Driver Version for CUDA Minor Version Compatibility
Linux x86_64 Driver Version Windows x86_64 Driver Version
CUDA 12.2.x >=525.60.13 >=525.41
CUDA 12.1.x >=525.60.13 >=527.41
CUDA 12.0.x >=525.60.13 >=527.41
CUDA 11.8.x >=450.80.02 >=452.39
CUDA 11.7.x >=450.80.02 >=452.39
CUDA 11.6.x >=450.80.02 >=452.39
CUDA 11.5.x >=450.80.02 >=452.39
CUDA 11.4.x >=450.80.02 >=452.39
CUDA 11.3.x >=450.80.02 >=452.39
CUDA 11.2.x >=450.80.02 >=452.39
CUDA 11.1 (11.1.0) >=450.80.02 >=452.39
CUDA 11.0 (11.0.3) >=450.36.06 >=451.22

1.1.3 cuDNN·

NVIDIA CUDA Deep Neural Network Library (cuDNN) 是一个专用的领域内 GPU 加速库,专为深度神经网络设计。它包含了很多深度神经网络里需要的操作,而且这些操作经过了高度的优化,比如

  • Convolution forward and backward, including cross-correlation
  • Matrix multiplication,矩阵乘法
  • Pooling forward and backward
  • Softmax forward and backward
  • Neuron activations forward and backward: relu, tanh, sigmoid, elu, gelu, softplus, swish
  • Arithmetic, mathematical, relational and logical pointwise operations,算数与逻辑
  • Tensor transformation functions
  • LRN, LCN and batch normalization forward and backward

1.2 Debian 的 apt-secure 机制·

Debian wiki 参考

使用 apt update && apt install xxx 非常方便快捷,但是也面临一个问题,那就是如何保证上游的 package 仓库是可信的。如果你的请求被中间人拦截该如何处理?TLS 和 PKI 体系已经解决了这个问题,但是 apt-secure 并没有采用证书加 TLS 的方式,而是采用 PGP 签名。过程大概如下:

  • 对于每一个 xxx.deb 安装包,里面集成了一个数字签名。数字签名由官方仓库提供,没有官方私钥的中间人无法伪造数字签名。
  • apt 客户端安装了官方上游的公钥
  • apt 客户端得到了 package 之后,用公钥进行验签。

综上,在使用 NVIDIA、Microsoft 等大公司提供的 apt repository 时,第一步就是将它们的 PGP 公钥添加到自己的 keyring 文件夹里。

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# ls -1 /usr/share/keyrings
cuda-archive-keyring.gpg # NVIDIA 官方 gpg 公钥
nvidia-container-toolkit-keyring.gpg # NVIDIA 容器官方 gpg 公钥
debian-archive-bookworm-automatic.gpg
debian-archive-bookworm-security-automatic.gpg
debian-archive-bookworm-stable.gpg
debian-archive-bullseye-automatic.gpg
debian-archive-bullseye-security-automatic.gpg
debian-archive-bullseye-stable.gpg
debian-archive-buster-automatic.gpg
debian-archive-buster-security-automatic.gpg
debian-archive-buster-stable.gpg
debian-archive-keyring.gpg
debian-archive-removed-keys.gpg

下面是 /etc/apt/sources.list.d 目录里的文件内容

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# for i in $(ls /etc/apt/sources.list.d) ; do cat /etc/apt/sources.list.d/$i ; done
deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/ /
deb [arch=amd64 signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian bookworm stable
deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main
deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/deb/$(ARCH) /
deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/experimental/deb/$(ARCH) /

2. 安装 NVIDIA 显卡驱动·

NVIDIA 官方参考

2.1 前期准备·

NVIDIA 维护了一个与 CUDA 相关的开发者上游仓库,里面包含了与驱动、CUDA、cuDNN 相关的一切。接下来无非是让系统将 NVIDIA 上游添加到系统可信列表,随后即可使用 apt-get 相关命令操作。

命令如下:

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# (1) 确保安装了 NVIDIA GPU 硬件
lspci | grep -i nvidia

# (2) 确认 CPU 体系结构,输出应该包含 x86_64
uname -m && cat /etc/*release

# (3) 确认是否已安装 gcc
gcc --version

完成上述三步,可继续安装。

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sudo add-apt-repository contrib

# 移除老的签名密钥
sudo apt-key del 7fa2af80

# 计算 distro 和 arch
distro=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g') ; echo "$distro"
arch=$(uname -m)

# 安装签名密钥
wget https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb

随后即可正式开始安装:

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sudo apt-get update
sudo apt-get install cuda-toolkit nvidia-driver

2.2 调整 NVIDIA apt 库优先级·

Debian 上游中含有 NVIDIA 的显卡驱动稳定版,然而其版本比较老旧,若用户要发挥最新显卡的全部计算潜力,需手动指定从 NVIDIA apt 仓库下载。为此,我们编辑 /etc/apt/preferences.d/stable 文件:

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sudo vim /etc/apt/preferences.d/stable

在其中调整或者输入,使得出现如下布局:

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Package: *
Pin: release a=stable
Pin-Priority: 900

Package: *
Pin: origin "developer.download.nvidia.com"
Pin-Priority: 950

[...]

其中,900 是 Debian 默认的 stable 上游的优先级,数字越大优先级越高。我们将 NVIDIA 上游 developer.download.nvidia.com 的优先级定义为 950,因此在下载同名的软件时,将优先从 NVIDIA 仓库下载,比如下载 nvidia-driver 驱动程序时将会出现此种判断。

注意

调整优先级步骤非常关键!如果不调整,NVIDIA 仓库默认的优先级为 500,导致 apt 一般不会从该仓库下载任何程序。

2.3 移除所有 NVIDIA 相关组件·

对于已经安装过其他版本驱动的 Debian 系统,首先要彻底清除所有 NVIDIA 相关组件。

在 Debian 12 上彻底删除 NVIDIA 驱动及其相关组件,需要进行一系列操作,包括卸载驱动程序、清除相关库文件以及删除配置文件。

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sudo apt remove --purge nvidia\*
sudo apt remove --purge libnvidia\*
sudo apt remove --purge firmware-nvidia-gsp
sudo apt remove --purge libcuda1

随后,需要手动重启。请开启 SSH 服务,防止重启后桌面环境异常。

2.4 安装驱动·

如果只需要核心驱动程序,可使用如下命令安装最新驱动:

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sudo apt install nvidia-driver

如果需要 CUDA 运行时库,需要安装 CUDA Driver,其中已经包含了 nvidia-driver.

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sudo apt install cuda-drivers

提醒

NVIDIA 的仓库一般有以下三个常需要安装的组件,其作用如下:

  • nvidia-driver 包含了 NVIDIA 的官方 Linux 显卡驱动,用于提供 GPU 的基本操作和直接控制。它包括了对图形渲染、显示管理和基本的计算任务的支持。
  • cuda-drivers 是一个元包(所谓元包,指的是其本身不包含任何程序,只包含对其他实体包的引用,通过安装元包可方便地安装一系列包),用于安装支持 CUDA 应用程序所需的所有驱动程序和库。这些包括但不限于显卡驱动程序、CUDA 运行时库、CUDA 核心库等。
  • cuda-toolkit 提供了开发和运行 CUDA 应用程序所需的工具和库。这包括编译器、库以及其他工具,允许开发者编写和测试使用 GPU 进行计算加速的程序。

一般来说,只需安装 cuda-drivers 即可,若需要使用 CUDA 进行开发,则需要安装 cuda-toolkit.

3. 安装 CUDA toolkit·

该步骤将在你的计算机上安装:

  • cuda 编译器

  • 调试器

  • cuda 数学库

  • 等等

  • 详情(1600 多行,小心展开

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Package: libnvidia-cfg1
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Package: libnvidia-eglcore
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Package: libnvidia-encode1
Package: libnvidia-fbc1
Package: libnvidia-fbc1
Package: libnvidia-glcore
Package: libnvidia-glcore
Package: libnvidia-glvkspirv
Package: libnvidia-glvkspirv
Package: libnvidia-ml1
Package: libnvidia-ml1
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Package: libnvidia-nvvm4
Package: libnvidia-opticalflow1
Package: libnvidia-opticalflow1
Package: libnvidia-ptxjitcompiler1
Package: libnvidia-ptxjitcompiler1
Package: libnvidia-rtcore
Package: libnvidia-wayland-client
Package: libnvjpeg-11-8
Package: libnvjpeg-dev-11-8
Package: libnvoptix1
Package: libxnvctrl0
Package: libxnvctrl-dev
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Package: nvidia-alternative
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Package: nvidia-egl-common
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Package: nvidia-kernel-open-dkms
Package: nvidia-kernel-source
Package: nvidia-kernel-support
Package: nvidia-legacy-check
Package: nvidia-libopencl1
Package: nvidia-libopencl1
Package: nvidia-modprobe
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-opencl-common
Package: nvidia-opencl-common
Package: nvidia-opencl-icd
Package: nvidia-opencl-icd
Package: nvidia-persistenced
Package: nvidia-settings
Package: nvidia-smi
Package: nvidia-vdpau-driver
Package: nvidia-vdpau-driver
Package: nvidia-vulkan-common
Package: nvidia-vulkan-common
Package: nvidia-vulkan-icd
Package: nvidia-vulkan-icd
Package: nvidia-xconfig
Package: xserver-xorg-video-nvidia
Package: libcudnn8
Package: libcudnn8-dev
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Package: cuda-drivers
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Package: cuda-drivers-fabricmanager
Package: libcuda1
Package: libcuda1
Package: libcuda1-i386
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Package: libegl-nvidia0
Package: libegl-nvidia0
Package: libgl1-nvidia-glvnd-glx
Package: libgl1-nvidia-glvnd-glx
Package: libgles-nvidia1
Package: libgles-nvidia1
Package: libgles-nvidia2
Package: libgles-nvidia2
Package: libglx-nvidia0
Package: libglx-nvidia0
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Package: libnvcuvid1
Package: libnvidia-allocator1
Package: libnvidia-allocator1
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Package: libnvidia-compiler
Package: libnvidia-compiler
Package: libnvidia-eglcore
Package: libnvidia-eglcore
Package: libnvidia-encode1
Package: libnvidia-encode1
Package: libnvidia-fbc1
Package: libnvidia-fbc1
Package: libnvidia-glcore
Package: libnvidia-glcore
Package: libnvidia-glvkspirv
Package: libnvidia-glvkspirv
Package: libnvidia-ml1
Package: libnvidia-ml1
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Package: libnvidia-nvvm4
Package: libnvidia-nvvm4
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Package: libnvidia-opticalflow1
Package: libnvidia-ptxjitcompiler1
Package: libnvidia-ptxjitcompiler1
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Package: libnvidia-wayland-client
Package: libnvoptix1
Package: libxnvctrl0
Package: libxnvctrl-dev
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Package: nvidia-alternative
Package: nvidia-cuda-mps
Package: nvidia-detect
Package: nvidia-driver
Package: nvidia-driver-bin
Package: nvidia-driver-libs
Package: nvidia-driver-libs
Package: nvidia-driver-libs-i386
Package: nvidia-egl-common
Package: nvidia-egl-common
Package: nvidia-egl-icd
Package: nvidia-egl-icd
Package: nvidia-fabricmanager-515
Package: nvidia-fabricmanager-dev-515
Package: nvidia-kernel-dkms
Package: nvidia-kernel-open
Package: nvidia-kernel-open-dkms
Package: nvidia-kernel-source
Package: nvidia-kernel-support
Package: nvidia-legacy-check
Package: nvidia-libopencl1
Package: nvidia-libopencl1
Package: nvidia-modprobe
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-opencl-common
Package: nvidia-opencl-common
Package: nvidia-opencl-icd
Package: nvidia-opencl-icd
Package: nvidia-persistenced
Package: nvidia-settings
Package: nvidia-smi
Package: nvidia-vdpau-driver
Package: nvidia-vdpau-driver
Package: nvidia-vulkan-common
Package: nvidia-vulkan-common
Package: nvidia-vulkan-icd
Package: nvidia-vulkan-icd
Package: nvidia-xconfig
Package: xserver-xorg-video-nvidia
Package: cuda-compat-11-7
Package: cuda-drivers-515
Package: cuda-drivers
Package: cuda-drivers-fabricmanager-515
Package: cuda-drivers-fabricmanager
Package: libcuda1
Package: libcuda1
Package: libcuda1-i386
Package: libegl1-nvidia
Package: libegl-nvidia0
Package: libegl-nvidia0
Package: libgl1-nvidia-glvnd-glx
Package: libgl1-nvidia-glvnd-glx
Package: libgles-nvidia1
Package: libgles-nvidia1
Package: libgles-nvidia2
Package: libgles-nvidia2
Package: libglx-nvidia0
Package: libglx-nvidia0
Package: libnvcuvid1
Package: libnvcuvid1
Package: libnvidia-allocator1
Package: libnvidia-allocator1
Package: libnvidia-cfg1
Package: libnvidia-compiler
Package: libnvidia-compiler
Package: libnvidia-eglcore
Package: libnvidia-eglcore
Package: libnvidia-encode1
Package: libnvidia-encode1
Package: libnvidia-fbc1
Package: libnvidia-fbc1
Package: libnvidia-glcore
Package: libnvidia-glcore
Package: libnvidia-glvkspirv
Package: libnvidia-glvkspirv
Package: libnvidia-ml1
Package: libnvidia-ml1
Package: libnvidia-nscq-515
Package: libnvidia-nvvm4
Package: libnvidia-nvvm4
Package: libnvidia-opticalflow1
Package: libnvidia-opticalflow1
Package: libnvidia-ptxjitcompiler1
Package: libnvidia-ptxjitcompiler1
Package: libnvidia-rtcore
Package: libnvidia-wayland-client
Package: libnvoptix1
Package: libxnvctrl0
Package: libxnvctrl-dev
Package: nvidia-alternative
Package: nvidia-alternative
Package: nvidia-cuda-mps
Package: nvidia-detect
Package: nvidia-driver
Package: nvidia-driver-bin
Package: nvidia-driver-libs
Package: nvidia-driver-libs
Package: nvidia-driver-libs-i386
Package: nvidia-egl-common
Package: nvidia-egl-common
Package: nvidia-egl-icd
Package: nvidia-egl-icd
Package: nvidia-fabricmanager-515
Package: nvidia-fabricmanager-dev-515
Package: nvidia-kernel-dkms
Package: nvidia-kernel-open
Package: nvidia-kernel-open-dkms
Package: nvidia-kernel-source
Package: nvidia-kernel-support
Package: nvidia-legacy-check
Package: nvidia-libopencl1
Package: nvidia-libopencl1
Package: nvidia-modprobe
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-opencl-common
Package: nvidia-opencl-common
Package: nvidia-opencl-icd
Package: nvidia-opencl-icd
Package: nvidia-persistenced
Package: nvidia-settings
Package: nvidia-smi
Package: nvidia-vdpau-driver
Package: nvidia-vdpau-driver
Package: nvidia-vulkan-common
Package: nvidia-vulkan-common
Package: nvidia-vulkan-icd
Package: nvidia-vulkan-icd
Package: nvidia-xconfig
Package: xserver-xorg-video-nvidia
Package: libcudnn8
Package: libcudnn8-dev
Package: cuda
Package: cuda-11-8
Package: cuda-cccl-11-8
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Package: cuda-compat-11-8
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Package: cuda-cudart-dev-11-8
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Package: cuda-cupti-dev-11-8
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Package: cuda-nsight-systems-11-8
Package: cuda-nvcc-11-8
Package: cuda-nvdisasm-11-8
Package: cuda-nvml-dev-11-8
Package: cuda-nvprof-11-8
Package: cuda-nvprune-11-8
Package: cuda-nvrtc-11-8
Package: cuda-nvrtc-dev-11-8
Package: cuda-nvtx-11-8
Package: cuda-nvvp-11-8
Package: cuda-profiler-api-11-8
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Package: libcufile-11-8
Package: libcufile-dev-11-8
Package: libcurand-11-8
Package: libcurand-dev-11-8
Package: libcusolver-11-8
Package: libcusolver-dev-11-8
Package: libcusparse-11-8
Package: libcusparse-dev-11-8
Package: libegl1-nvidia
Package: libegl-nvidia0
Package: libegl-nvidia0
Package: libgl1-nvidia-glvnd-glx
Package: libgl1-nvidia-glvnd-glx
Package: libgles-nvidia1
Package: libgles-nvidia1
Package: libgles-nvidia2
Package: libgles-nvidia2
Package: libglx-nvidia0
Package: libglx-nvidia0
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Package: libnvcuvid1
Package: libnvcuvid1
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Package: libnvidia-encode1
Package: libnvidia-fbc1
Package: libnvidia-fbc1
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Package: libnvidia-glcore
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Package: libnvidia-glvkspirv
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Package: libnvidia-nvvm4
Package: libnvidia-nvvm4
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Package: libnvidia-opticalflow1
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Package: libnvidia-rtcore
Package: libnvidia-wayland-client
Package: libnvjpeg-11-8
Package: libnvjpeg-dev-11-8
Package: libnvoptix1
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Package: libxnvctrl-dev
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Package: nvidia-driver
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Package: nvidia-driver-libs
Package: nvidia-driver-libs
Package: nvidia-driver-libs-i386
Package: nvidia-egl-common
Package: nvidia-egl-common
Package: nvidia-egl-icd
Package: nvidia-egl-icd
Package: nvidia-fabricmanager-520
Package: nvidia-fabricmanager-dev-520
Package: nvidia-fs
Package: nvidia-fs-dkms
Package: nvidia-gds
Package: nvidia-gds-11-8
Package: nvidia-installer-cleanup
Package: nvidia-kernel-common
Package: nvidia-kernel-dkms
Package: nvidia-kernel-open
Package: nvidia-kernel-open-dkms
Package: nvidia-kernel-source
Package: nvidia-kernel-support
Package: nvidia-legacy-check
Package: nvidia-libopencl1
Package: nvidia-libopencl1
Package: nvidia-modprobe
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-nonglvnd-vulkan-common
Package: nvidia-opencl-common
Package: nvidia-opencl-common
Package: nvidia-opencl-icd
Package: nvidia-opencl-icd
Package: nvidia-persistenced
Package: nvidia-settings
Package: nvidia-smi
Package: nvidia-support
Package: nvidia-vdpau-driver
Package: nvidia-vdpau-driver
Package: nvidia-vulkan-common
Package: nvidia-vulkan-common
Package: nvidia-vulkan-icd
Package: nvidia-vulkan-icd
Package: nvidia-xconfig
Package: xserver-xorg-video-nvidia

参考

3.1 安装合适的 kernel-header (一般都有,可忽略)·

kernel-header 是内核对外的接口,当需要向内核提供兼容的功能模块时,需要用到内核的信息。比如安装驱动

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sudo apt update ; sudo apt install linux-headers-$(uname -r)

3.2 安装 CUDA toolkit 库·

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sudo apt update
sudo apt install cuda-toolkit

注意

cuda-toolkit 只含有基本的 cuda 相关组件,如果要安装完整版,需要执行 sudo apt install cuda,然而这并不兼容 535 版本的驱动。

4. 安装 cuDNN·

4.1 安装 zlib·

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sudo apt-get install zlib1g

4.2 在线式安装 cuDNN·

NVIDIA 的官网总是把手册写得无比详细,因此有的时候不好复现。根据我的观察,官方文档的操作是有点问题的,因为官方库里并没有文档所说的 cuda-debian11.pin 文件。只要添加了 cuda 的 NVIDIA 官方 repo,就能自由安装 NVIDIA DrivercuDNN 开发库。

按照上述教程安装 CUDA Toolkit,就可以直接安装 cuDNN,命令如下:

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sudo apt update
sudo apt install libcudnn8 libcudnn8-dev

4.3 测试 cuDNN·

直接上测试代码:

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cd ~
sudo apt update && sudo apt install libfreeimage3 libfreeimage-dev
git clone [email protected]:LittleNewton/cudnn-samples-v8.git ; cd cudnn-samples-v8/mnistCUDNN
make clean ; make ; ./mnistCUDNN

# 看到 Test passed! 就说明 OK 了。

5. 安装 PyTorch·

5.1 查看 CUDA 版本·

去如下官网下载合适的 PyTorch

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https://pytorch.org/get-started/locally/

通过nvidia-smi 查看到的 CUDA 版本不对,它会取整数。

合理的做法,查看前面几行即可知道版本。

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cat /usr/local/cuda/version.json

5.2 安装合适的 PyTorch·

PyTorch 是一个开源的机器学习库,用于各种视觉和语言处理任务。它是由 Facebook 的人工智能研究团队(FAIR)开发的,并广泛用于学术研究和商业应用。PyTorch 提供了一组灵活的工具和库,用于构建和训练复杂的神经网络模型。

主要特点:

  • 动态计算图(Dynamic Computational Graphs):与其他一些深度学习框架(如 TensorFlow 的静态计算图)不同,PyTorch 使用动态计算图。这使得模型更容易调试和修改。
  • Pythonic 设计:PyTorch 的 API 和设计哲学都是非常 Pythonic 的,这使得它容易上手和使用。
  • GPU 加速:PyTorch 可以轻易地在 GPU 上运行,以加速计算。这是通过与 NVIDIA 的 CUDA 深度集成来实现的。
  • 丰富的 API 和库:PyTorch 提供了大量预构建的函数和库,包括自动微分(Autograd)、优化器(Optim)、数据加载(DataLoader)等,以简化机器学习的各个方面。
  • 社群支持:由于其开源性质和广泛的应用,PyTorch 有一个非常活跃的社群,这意味着你可以轻易地找到文档、教程和开源项目。
  • 多平台和多语言支持:除了 Python,PyTorch 也支持其他语言,如 C++. 它也可以在多个平台上运行,包括 Windows、Linux 和 macOS.
  • 与其他工具和库的集成:PyTorch 可以与其他机器学习和数据科学工具(如 NumPy、SciPy 等)轻易地集成。
  • 研究和生产的灵活性:PyTorch 不仅适用于研究,还可以用于生产环境。它提供了一系列工具,如 TorchScript,用于将模型转换为可以在其他环境中运行的格式。

因此,无论你是一名研究人员、数据科学家还是开发人员,PyTorch 都提供了一组强大和灵活的工具,用于构建和实现复杂的机器学习模型。

可以通过以下命令直接使用 conda 的上游 Python 库安装 PyTorch 库。

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conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

5.3 测试·

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# 期待输出 True
import torch
torch.cuda.is_available()

6. 安装 NVIDIA-Container 套件·

通过映射硬件描述符的方式暴露 GPU 接口给容器,即可让容器调用 GPU,不过这需要在容器里安装所有的驱动、CUDA 套件。如果需要多个容器,则需要反复安装这些代码,这无疑会造成极大的空间浪费。为此,NVIDIA 开发了适合容器环境的 GPU 运行时工具,即 nvidia-container-runtime,该套件允许用户创建并执行支持 GPU 加速的容器。

6.1 安装 Docker 引擎·

一般已经安装好了,此处不再赘述。可参考官方教程

6.2 安装 NVIDIA-Docker-Toolkit 运行时工具·

同之前一样,安装 nvidia-container-toolkit 也需要设置 GPG 密钥和上游 apt 库,操作如下:

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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

可开启部分实验性功能:

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# 可选项
sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list

随后,即可安装:

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# 安装
sudo apt update
sudo apt-get install -y nvidia-container-toolkit

仅仅安装 nvidia-container-toolkit 并不能直接生效,还需要修改 docker 运行时。这里我们以 docker-ce 引擎为例。其他容器技术,如 containerd、LXC、cri-o 等,请自行研究。

nvidia-ctk 的出现简化了后续的步骤,只需一行命令即可更改 Docker 的运行时。

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sudo nvidia-ctk runtime configure --runtime=docker

可以发现,执行完上述命令,/etc/docker/daemon.json 文件被修改了。

最后,重载服务并重启 docker 服务:

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sudo systemctl daemon-reload
sudo systemctl restart docker

现在,我们就可以在容器里使用 GPU 了,不妨做个简单测试:

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sudo systemctl restart docker

# 测试:拉取 cuda 容器,执行 `nvidia-smi` 命令查看能否使用 GPU
sudo docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu20.04 nvidia-smi

图 2. 注意看,在容器里,我们成功执行了  命令,输出显示 cuda 版本是 12.2,驱动版本是 535.86.10,这与主机相同,非常好。