关于Pytorch框架下报错CUDA驱动版本不满足CUDA运行版本——一种可能的原因及解决办法

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：

./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。

这样应该就可以解决了。