Python PyTorch cuda 显卡驱动对应关系

第一步 检查驱动版本

输入命令

nvidia-smi

首先要看自己是什么显卡，再选择驱动，比如我现在在用一个2080ti的服务器，驱动是470.256.02，那我就要按照驱动去选别的了。个人不建议为了cuda换驱动，除非万不得已，因为安驱动真的麻烦

第二步  检查驱动支持的cuda

CUDA 12.5 Update 1 Release Notes (nvidia.com)https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

CUDA Toolkit	Toolkit Driver Version
	Linux x86_64 Driver Version	Windows x86_64 Driver Version
CUDA 12.5 Update 1	>=555.42.06	>=555.85
CUDA 12.5 GA	>=555.42.02	>=555.85
CUDA 12.4 Update 1	>=550.54.15	>=551.78
CUDA 12.4 GA	>=550.54.14	>=551.61
CUDA 12.3 Update 1	>=545.23.08	>=546.12
CUDA 12.3 GA	>=545.23.06	>=545.84
CUDA 12.2 Update 2	>=535.104.05	>=537.13
CUDA 12.2 Update 1	>=535.86.09	>=536.67
CUDA 12.2 GA	>=535.54.03	>=536.25
CUDA 12.1 Update 1	>=530.30.02	>=531.14
CUDA 12.1 GA	>=530.30.02	>=531.14
CUDA 12.0 Update 1	>=525.85.12	>=528.33
CUDA 12.0 GA	>=525.60.13	>=527.41
CUDA 11.8 GA	>=520.61.05	>=520.06
CUDA 11.7 Update 1	>=515.48.07	>=516.31
CUDA 11.7 GA	>=515.43.04	>=516.01
CUDA 11.6 Update 2	>=510.47.03	>=511.65
CUDA 11.6 Update 1	>=510.47.03	>=511.65
CUDA 11.6 GA	>=510.39.01	>=511.23
CUDA 11.5 Update 2	>=495.29.05	>=496.13
CUDA 11.5 Update 1	>=495.29.05	>=496.13
CUDA 11.5 GA	>=495.29.05	>=496.04
CUDA 11.4 Update 4	>=470.82.01	>=472.50
CUDA 11.4 Update 3	>=470.82.01	>=472.50
CUDA 11.4 Update 2	>=470.57.02	>=471.41
CUDA 11.4 Update 1	>=470.57.02	>=471.41
CUDA 11.4.0 GA	>=470.42.01	>=471.11
CUDA 11.3.1 Update 1	>=465.19.01	>=465.89
CUDA 11.3.0 GA	>=465.19.01	>=465.89
CUDA 11.2.2 Update 2	>=460.32.03	>=461.33
CUDA 11.2.1 Update 1	>=460.32.03	>=461.09
CUDA 11.2.0 GA	>=460.27.03	>=460.82
CUDA 11.1.1 Update 1	>=455.32	>=456.81
CUDA 11.1 GA	>=455.23	>=456.38
CUDA 11.0.3 Update 1	>= 450.51.06	>= 451.82
CUDA 11.0.2 GA	>= 450.51.05	>= 451.48
CUDA 11.0.1 RC	>= 450.36.06	>= 451.22
CUDA 10.2.89	>= 440.33	>= 441.22
CUDA 10.1 (10.1.105 general release, and updates)	>= 418.39	>= 418.96
CUDA 10.0.130	>= 410.48	>= 411.31
CUDA 9.2 (9.2.148 Update 1)	>= 396.37	>= 398.26
CUDA 9.2 (9.2.88)	>= 396.26	>= 397.44
CUDA 9.1 (9.1.85)	>= 390.46	>= 391.29
CUDA 9.0 (9.0.76)	>= 384.81	>= 385.54
CUDA 8.0 (8.0.61 GA2)	>= 375.26	>= 376.51
CUDA 8.0 (8.0.44)	>= 367.48	>= 369.30
CUDA 7.5 (7.5.16)	>= 352.31	>= 353.66
CUDA 7.0 (7.0.28)	>= 346.46	>= 347.62

比如我的那个，最多安装cuda11.3

第三步 根据cuda找PyTorch

Previous PyTorch Versions | PyTorchhttps://pytorch.org/get-started/previous-versions/这我就不展示了，内容太多了。

比如我用的cuda11.3，我就可以安装pytorch1.11。

第四步 根据PyTorch找Pyhon

Versioning Policy — PyTorch Lightning 2.4.0dev documentationhttps://lightning.ai/docs/pytorch/latest/versioning.html#compatibility-matrix

torch	Python
≥2.1, ≤2.4	≥3.9, ≤3.12
≥2.0, ≤2.3	≥3.8, ≤3.11
≥1.13, ≤2.2	≥3.8, ≤3.11
≥1.12, ≤2.1	≥3.8, ≤3.11
≥1.11, ≤2.0	≥3.8, ≤3.10
≥1.10, ≤1.13	≥3.7, ≤3.10
≥1.10, ≤1.13	≥3.7, ≤3.10
≥1.9, ≤1.12	≥3.7, ≤3.10
≥1.8, ≤1.11	≥3.7, ≤3.9
≥1.7, ≤1.10	≥3.6, ≤3.9
≥1.6, ≤1.9	≥3.6, ≤3.9
≥1.4, ≤1.8	≥3.6, ≤3.9
≥1.4, ≤1.8	≥3.6, ≤3.8
≥1.3, ≤1.8	≥3.6, ≤3.8
≥1.3, ≤1.7	≥3.6, ≤3.8

如果你真的需要更高版本的cuda，那就得尝试升级驱动了