win10安装cuda10.2和CUDNN8.2.2

- 日理万妓 2022-09-04 02:43 563阅读 0赞

### win10安装cuda10.2和CUDNN ###

*  一、更新英伟达显卡驱动
 *  二、下载CUDA并安装
 *   *  开始安装cuda
 *  三、下载并安装cudnn

# 一、更新英伟达显卡驱动 #

打开英伟达控制面板——>系统信息——>组件：

![在这里插入图片描述][b1ec55944e4b47c695217a47b573cddf.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70]  
查看是否支持CUDA10.2，大于支持，小于不支持，如果小于，则需要更新驱动，打开设备管理器  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 1]  
自动更新驱动即可。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 2]

# 二、下载CUDA并安装 #

下载地址：  
[https://developer.nvidia.com/cuda-toolkit-archive][https_developer.nvidia.com_cuda-toolkit-archive]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 3]

[windows版本cuda10.2下载地址][windows_cuda10.2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 4]

## 开始安装cuda ##

★如果本机已经有了Visual Studio Integration文件，要取消勾选，避免冲突了。  
★如果本机没有的话，要勾选！！！  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 5]

如果本机的驱动版本(当前版本)小于cuda对应的版本（新版本），则选择，否则不选。如果当前版本小于新版本，并且不覆盖安装，之后电脑会频繁蓝屏或死机

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 6]  
记住安装路径：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 7]  
等待一段时间，结束即可。  
安装两个补丁：选择自定义安装。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 8]

测试是否安装成功：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 9]

主要参考：  
[https://blog.csdn.net/xggbest/article/details/113460935][https_blog.csdn.net_xggbest_article_details_113460935]

# 三、下载并安装cudnn #

下载地址：[CUDNN][]  
找到自己cuda对应的cudnn版本，下载  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 10]  
压缩包解压,下载后发现其实cudnn不是一个exe文件，而是一个压缩包，解压后，有三个文件夹，把三个文件夹拷贝到cuda的安装目录下，如果cuda安装的时候用的是默认路径，则为C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.0，现在大家应该可以理解，cudnn其实就是cuda的一个补丁而已，专为深度学习运算进行优化的。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 11]  
还记得我们前面安装的路径吗？，如果忘了，回去看一下。一般我们是默认安装在下面这个路径下的。

> C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2

当然后面那个v10.2是你自己的版本号。

找到这文件夹后，你需要把cuDNN里面的三个文件夹复制过来，不用担心，一起复制，没有重复文件，不会把里面的某些文件替换掉.  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 12]  
然后再参加环境变量，一般四个：

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin
    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include
    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib
    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\libnvvp

测试是否安装成功，进入如下文件夹

> C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\extras\\demo\_suite>

运行下面命令

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\demo_suite>bandwidthTest.exe

[CUDA Bandwidth Test] - Starting...
    Running on...
    
     Device 0: GeForce RTX 2060
     Quick Mode
    
     Host to Device Bandwidth, 1 Device(s)
     PINNED Memory Transfers
       Transfer Size (Bytes)        Bandwidth(MB/s)
       33554432                     11040.7
    
     Device to Host Bandwidth, 1 Device(s)
     PINNED Memory Transfers
       Transfer Size (Bytes)        Bandwidth(MB/s)
       33554432                     12839.9
    
     Device to Device Bandwidth, 1 Device(s)
     PINNED Memory Transfers
       Transfer Size (Bytes)        Bandwidth(MB/s)
       33554432                     287503.2
    
    Result = PASS
    
    NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\demo_suite>deviceQuery.exe

deviceQuery.exe Starting...
    
     CUDA Device Query (Runtime API) version (CUDART static linking)
    
    Detected 1 CUDA Capable device(s)
    
    Device 0: "GeForce RTX 2060"
      CUDA Driver Version / Runtime Version          11.1 / 10.2
      CUDA Capability Major/Minor version number:    7.5
      Total amount of global memory:                 6144 MBytes (6442450944 bytes)
      (30) Multiprocessors, ( 64) CUDA Cores/MP:     1920 CUDA Cores
      GPU Max Clock rate:                            1710 MHz (1.71 GHz)
      Memory Clock rate:                             7001 Mhz
      Memory Bus Width:                              192-bit
      L2 Cache Size:                                 3145728 bytes
      Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
      Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
      Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
      Total amount of constant memory:               zu bytes
      Total amount of shared memory per block:       zu bytes
      Total number of registers available per block: 65536
      Warp size:                                     32
      Maximum number of threads per multiprocessor:  1024
      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:                          zu bytes
      Texture alignment:                             zu bytes
      Concurrent copy and kernel execution:          Yes with 3 copy engine(s)
      Run time limit on kernels:                     Yes
      Integrated GPU sharing Host Memory:            No
      Support host page-locked memory mapping:       Yes
      Alignment requirement for Surfaces:            Yes
      Device has ECC support:                        Disabled
      CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
      Device supports Unified Addressing (UVA):      Yes
      Device supports Compute Preemption:            Yes
      Supports Cooperative Kernel Launch:            Yes
      Supports MultiDevice Co-op Kernel Launch:      No
      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 = 11.1, CUDA Runtime Version = 10.2, NumDevs = 1, Device0 = GeForce RTX 2060
    Result = PASS

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 13]  
两个demo都出现PASS则说明安装成功。

[b1ec55944e4b47c695217a47b573cddf.png]: /images/20220829/e6ca8b00ac9f4820b2641a9ccc842d15.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70]: /images/20220829/1ca8d64646c04aa98889402f6d285734.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 1]: /images/20220829/190f80a38c5b4635b0b7deaa530ed1b9.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 2]: /images/20220829/52ea1e9adec0402ca19d3793b84ab720.png
[https_developer.nvidia.com_cuda-toolkit-archive]: https://developer.nvidia.com/cuda-toolkit-archive
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 3]: /images/20220829/5686d31ceab24af8b81e613673f3e0da.png
[windows_cuda10.2]: https://developer.nvidia.com/cuda-10.2-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 4]: /images/20220829/a2d7a63508994be8b5c71a4936ba2306.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 5]: /images/20220829/dd937cc64b9747eaa12322a67ab6c51d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 6]: /images/20220829/db46ac7281ec45cd93318d3b229ba286.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 7]: /images/20220829/ae8f88099f394af0844db3dfd3fd9e81.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 8]: /images/20220829/8aff832f85a049cbbf181baef78facd3.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 9]: /images/20220829/93c11257d9cf495599c5c03f60fb0c12.png
[https_blog.csdn.net_xggbest_article_details_113460935]: https://blog.csdn.net/xggbest/article/details/113460935
[CUDNN]: https://developer.nvidia.com/rdp/cudnn-download
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 10]: /images/20220829/4ed3a58c1cca49919190c8520238fef7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 11]: /images/20220829/92474199466c409cb9abe16c4e4d15f0.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 12]: /images/20220829/195e9a0f49494334ae86255d2d706449.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 13]: /images/20220829/8d3cfb679bab48e8b54218ddb96c9143.png