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。
1 2 # NVIDIA 官方上游仓库 https://developer.download.nvidia.cn/compute/cuda/repos/debian11/x86_64/
注意，目前（2023 年 9 月 30 日） debian 12 的 NVIDIA dpkg 库还没有完成，用 Debian 11 的库也可以兼容。
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。
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。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 cuda-archive-keyring.gpg nodesource.gpg nvidia-container-toolkit-keyring.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 debian-archive-stretch-automatic.gpg debian-archive-stretch-security-automatic.gpg debian-archive-stretch-stable.gpg
下面是 /etc/apt/sources.list.d 目录里的文件内容
1 2 3 4 5 6 deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/ / deb [signed-by=/usr/share/keyrings/nodesource.gpg] https://deb.nodesource.com/node_18.x bullseye main deb-src [signed-by=/usr/share/keyrings/nodesource.gpg] https://deb.nodesource.com/node_18.x bullseye main deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/debian10/$(ARCH) /
2. 安装 NVIDIA 显卡驱动·
NVIDIA 官方参考
2.1 通过 Debian 包管理器安装（推荐）·
NVIDIA 维护了一个与 CUDA 相关的开发者上游仓库，里面包含了与驱动、CUDA、cuDNN 相关的一切。接下来无非是让系统将 NVIDIA 上游添加到系统可信列表，随后即可使用 apt-get 相关命令操作。
命令如下：
1 2 3 4 5 6 7 8 9 10 11 12 13 sudo apt-get install linux-headers-$(uname -r) distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g' ) ; echo "$distribution " wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution /x86_64/cuda-keyring_1.1-1_all.deb sudo dpkg -i cuda-keyring_1.1-1_all.deb sudo apt-get update sudo apt-get -y install cuda-drivers
如果网速太慢，也可以使用阿里云镜像：
1 2 3 4 5 6 wget https://mirrors.aliyun.com/nvidia-cuda/debian11/x86_64/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.1-1_all.deb sudo apt-get update sudo apt-get -y install cuda-drivers
2.2 通过 Runfile 安装 （不推荐！ ）·
2.2.1 禁用 Nouveau·
为了安装合适的 Nvidia 显卡驱动，首先需要禁用 Nouveau. Nouveau 是社区通过逆向得到的 NVIDIA GPU 驱动程序，不是很完美。既然选择了闭源驱动，Nouveau 就可以卸载或者关闭了。
创建并编辑文件：
1 sudo vim /etc/modprobe.d/blacklist-nouveau.conf
填写入以下内容：
1 2 blacklist nouveau options nouveau modeset=0
重新生成内核 initramfs.
1 sudo update-initramfs -u
随后即可进入 官网 下载 CUDA driver 套装安装包，但是不推荐这种使用 .run 文件的方式安装。
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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cuda-11-5 Package: cuda-11-5 Package: cuda-11-5 Package: cuda Package: cuda Package: cuda Package: cuda-11-6 Package: cuda-11-6 Package: cuda-11-6 Package: cuda Package: cuda Package: cuda-cccl-11-5 Package: cuda-cccl-11-6 Package: cuda-command-line-tools-11-5 Package: cuda-command-line-tools-11-5 Package: cuda-command-line-tools-11-5 Package: cuda-command-line-tools-11-6 Package: cuda-command-line-tools-11-6 Package: cuda-command-line-tools-11-6 Package: cuda-compat-11-5 Package: cuda-compat-11-6 Package: cuda-compat-11-6 Package: cuda-compiler-11-5 Package: cuda-compiler-11-5 Package: cuda-compiler-11-5 Package: cuda-compiler-11-6 Package: cuda-compiler-11-6 Package: cuda-compiler-11-6 Package: cuda-cudart-11-5 Package: cuda-cudart-11-5 Package: cuda-cudart-11-6 Package: cuda-cudart-dev-11-5 Package: cuda-cudart-dev-11-5 Package: cuda-cudart-dev-11-6 Package: cuda-cuobjdump-11-5 Package: cuda-cuobjdump-11-5 Package: cuda-cuobjdump-11-6 Package: cuda-cuobjdump-11-6 Package: cuda-cuobjdump-11-6 Package: cuda-cupti-11-5 Package: cuda-cupti-11-5 Package: cuda-cupti-11-6 Package: cuda-cupti-11-6 Package: cuda-cupti-11-6 Package: cuda-cupti-dev-11-5 Package: cuda-cupti-dev-11-5 Package: cuda-cupti-dev-11-6 Package: cuda-cupti-dev-11-6 Package: cuda-cupti-dev-11-6 Package: cuda-cuxxfilt-11-5 Package: cuda-cuxxfilt-11-5 Package: cuda-cuxxfilt-11-6 Package: cuda-cuxxfilt-11-6 Package: cuda-cuxxfilt-11-6 Package: cuda-demo-suite-11-5 Package: cuda-demo-suite-11-6 Package: cuda-documentation-11-5 Package: cuda-documentation-11-5 Package: cuda-documentation-11-6 Package: cuda-documentation-11-6 Package: cuda-documentation-11-6 Package: cuda-driver-dev-11-5 Package: cuda-driver-dev-11-5 Package: cuda-driver-dev-11-6 Package: cuda-drivers Package: cuda-drivers-495 Package: cuda-drivers Package: cuda-drivers Package: cuda-drivers-510 Package: cuda-drivers-510 Package: cuda-drivers-fabricmanager Package: cuda-drivers-fabricmanager-495 Package: cuda-drivers-fabricmanager 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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-520 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: libnvjpeg-11-8 Package: libnvjpeg-dev-11-8 Package: libnvoptix1 Package: libxnvctrl0 Package: libxnvctrl-dev Package: nsight-compute-2022.3.0 Package: nsight-systems-2022.4.2 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: 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nvidia-vulkan-icd Package: nvidia-xconfig Package: xserver-xorg-video-nvidia Package: libcudnn8 Package: libcudnn8-dev 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: 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 Package: cuda-command-line-tools-11-8 Package: cuda-compat-11-8 Package: cuda-compiler-11-8 Package: cuda-cudart-11-8 Package: cuda-cudart-dev-11-8 Package: cuda-cuobjdump-11-8 Package: cuda-cupti-11-8 Package: cuda-cupti-dev-11-8 Package: cuda-cuxxfilt-11-8 Package: cuda-demo-suite-11-8 Package: cuda-documentation-11-8 Package: cuda-driver-dev-11-8 Package: cuda-drivers-520 Package: cuda-drivers Package: cuda-drivers-fabricmanager-520 Package: cuda-drivers-fabricmanager Package: cuda-gdb-11-8 Package: cuda-gdb-src-11-8 Package: cuda-libraries-11-8 Package: cuda-libraries-dev-11-8 Package: cuda-memcheck-11-8 Package: cuda-minimal-build-11-8 Package: cuda-nsight-11-8 Package: cuda-nsight-compute-11-8 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 Package: cuda-runtime-11-8 Package: cuda-sanitizer-11-8 Package: cuda-toolkit-11-8 Package: cuda-toolkit-11-8-config-common Package: cuda-toolkit-11-config-common Package: cuda-toolkit-config-common Package: cuda-tools-11-8 Package: cuda-visual-tools-11-8 Package: gds-tools-11-8 Package: libcublas-11-8 Package: libcublas-dev-11-8 Package: libcuda1 Package: libcuda1 Package: libcuda1-i386 Package: libcudadebugger1 Package: libcufft-11-8 Package: libcufft-dev-11-8 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 Package: libnpp-11-8 Package: libnpp-dev-11-8 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-520 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: libnvjpeg-11-8 Package: libnvjpeg-dev-11-8 Package: libnvoptix1 Package: libxnvctrl0 Package: libxnvctrl-dev Package: nsight-compute-2022.3.0 Package: nsight-systems-2022.4.2 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-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 查看是否存在 GPU (一般都有，可忽略)·
3.2 安装最新版 GCC (一般都有，可忽略)·
直接安装 GCC 11 即可。
1 sudo apt update ; sudo apt install gcc g++ gdb
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 。
1 sudo apt update ; sudo apt install linux-headers-$(uname -r)
3.4.1 在线安装（推荐）·
首先信任 NVIDIA 的上游库。
1 2 3 4 wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb
随后即可通过 apt 安装
1 2 sudo apt update sudo apt install cuda
3.4.2 本地安装 （不推荐!!!）· 1 2 3 4 5 6 7 wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-debian11-11-8-local_11.8.0-520.61.05-1_amd64.deb sudo dpkg -i cuda-repo-debian11-11-8-local_11.8.0-520.61.05-1_amd64.deb sudo cp /var/cuda-repo-debian11-11-8-local /cuda-*-keyring.gpg /usr/share/keyrings/ sudo add-apt-repository contrib sudo apt-get update sudo apt-get -y install cuda
随后 reboot 即可。
4. 安装 cuDNN·
4.1 安装 zlib·
1 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 Driver、cuDNN 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：
1 2 sudo apt update sudo apt install libcudnn8 libcudnn8-dev
4.3 测试 cuDNN·
直接上测试代码：
1 2 3 4 5 6 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
5. 安装 PyTorch·
5.1 查看 CUDA 版本·
去如下官网下载合适的 PyTorch
1 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。
1 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。
1 conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
5.3 测试·
1 2 3 import torchtorch.cuda.is_available()
6. 安装 NVIDIA-Docker 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-docker-runtime .
[nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit)：允许用户创建并执行支持 GPU 加速的容器，它包含
容器运行时库
自动化小工具：使得容器能够用上 GPU
[nvidia-container-runtime](https://github.com/NVIDIA/nvidia-container-runtime)：
6.1 安装 Docker 引擎 (一般已经安装好了)·
6.2 安装 NVIDIA-docker·
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：
1 2 3 4 5 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/$distribution /libnvidia-container.list | \ sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
随后，即可安装：
1 2 3 sudo apt update sudo apt install nvidia-container-toolkit
User Guide
仅仅安装 nvidia-container-toolkit 并不能直接生效，还需要修改 docker 运行时。这里我们以 docker-ce 引擎为例。其他容器技术，如 containerd、LXC、cri-o 等，请自行研究。
首先创建 /etc/systemd/system/docker.service.d/override.conf 文件，修改 ExecStart 为 dockerd 所在目录。
1 2 3 4 5 6 7 sudo mkdir -p /etc/systemd/system/docker.service.d sudo tee c <<EOF [Service] ExecStart= ExecStart=/usr/sbin/dockerd EOF
随后，注入 runtimes 表项到 /etc/docker/daemon.json 文件：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 { "ipv6" : true , "fixed-cidr-v6" : "fd00::/80" , "experimental" : true , "ip6tables" : true , "runtimes" : { "nvidia" : { "path" : "nvidia-container-runtime" , "runtimeArgs" : [ ] } } , "hosts" : [ "tcp://10.2.4.21:2375" , "unix:///var/run/docker.sock" ] }
最后，重载服务并重启 docker 服务：
1 2 sudo systemctl daemon-reload sudo systemctl restart docker
现在，我们就可以在容器里使用 GPU 了，不妨做个简单测试：
1 2 3 4 sudo systemctl restart docker # 测试：拉取 cuda 容器，执行 `nvidia-smi` 命令查看能否使用 GPU sudo docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu20.04 nvidia-smi
安装 FreeImage 测试集
1 sudo apt-get install libfreeimage3 libfreeimage-dev
dpkg·
1 2 3 4 5 6 7 8 9 10 11 12 deb [signed-by=/usr/share/keyrings/nvidia-drivers.gpg] https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/ / deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/debian10/$(ARCH) / deb [signed-by=/usr/share/keyrings/cuda-CFB56B08-keyring.gpg] file:///var/cuda-repo-debian11-11-8-local / deb [signed-by=/usr/share/keyrings/cudnn-local-BD0DA317-keyring.gpg] file:///var/cudnn-local-repo-debian11-8.7.0.84 /
1 2 sudo systemctl set-default multi-user.target sudo systemctl set-default graphical.target
docker-compose·
1 export PATH =/home/newton /Applications/bin : $PATH
1 wget https: //github.com/docker /compose/releases /download/v 2.15.1 /docker-compose-linux-x86_64
