PS：时间太久已经找不到原文链接了，若有侵权请联系删除。
运行Pytorch代码的时候遇到：
RuntimeError: cuda runtime error (35) : CUDA driver version is insufficient for CUDA runtime version at /pytorch/aten/src/THC/THCGeneral.cpp:74
可能原因：每一个pytorch版本都有对应的cuda版本，可能是在安装pytorch的时候，选择的pytorch版本所对应的版本cuda版本与本机所安装的cuda版本不相符。
check步骤：
#查看pytorch版本
import torch
torch.__version__
#查看pytorch版本对应的cuda版本
torch.version.cuda
#查看cuda是否可用
torch.cuda.is_available()#查看Linux server安装的cuda版本
#切换到/usr/local/cuda/samples/1_Utilities/deviceQuery然后运行
./deviceQuery出现类似以下信息：
出现类似以下信息：
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX TITAN Black"
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 6080 MBytes (6375407616 bytes)
(15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores
GPU Max Clock rate: 1072 MHz (1.07 GHz)
Memory Clock rate: 3500 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 1572864 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX TITAN Black
Result = PASS比对前后两个cuda版本是否一致，如果不一致，就需要卸载并安装与本机cuda版本相同的pytorch（当然应该也可以改本机的cuda版本，只不过相对比较麻烦）
pip3 uninstall pytorch
pip3 install [pytorch-version-link]打开链接，选择合适版本版本，右键复制链接地址，替换上面的pytorch-version-link，执行命令就行。