ubuntu18.04 安装多版本cuda和cudnn安装验证_ubuntu18 cudnn验证

本文不介绍CUDA安装方法，只介绍安装cudnn，验证cuda、cudnn是否安装成功。
实验环境：

• ubuntu18.04
• cuda10.0（原始cuda11.2）
• cudnn 7.6.5

优先 nvidia 官方文档：
cuda安装
cudnn安装
推荐安装博客：
Install CUDA 10.0 and cuDNN 7.5.0 for PyTorch on Ubuntu 18.04 LTS（包含验证cuda和cudnn的命令）
这个cudnn安装，当我执行 sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb 命令时，出现了软链接问题，见后文 Cudnn安装流程。

CUDA 安装验证

cd /usr/local/cuda-10.0/samples
sudo make
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忽略大量关于弃用架构(sm_20和这样古老的gpu)的警告。完成之后，让我们运行两个测试:deviceQuery和matrixMulCUBLAS。首先,尝试deviceQuery:
/usr/local/cuda-10.0/samples/bin/x86_64/linux/release/deviceQuery
期望的返回结果：

/usr/local/cuda-10.0/samples/bin/x86_64/linux/release/deviceQuery
/usr/local/cuda-10.0/samples/bin/x86_64/linux/release/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 1060"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 6078 MBytes (6373572608 bytes)
  (10) Multiprocessors, (128) CUDA Cores/MP:     1280 CUDA Cores
  GPU Max Clock rate:                            1671 MHz (1.67 GHz)
  Memory Clock rate:                             4004 Mhz
  Memory Bus Width:                              192-bit
  L2 Cache Size:                                 1572864 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:               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 2 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
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      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 = 10.0, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS
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Cudnn安装

英文的：
Install CUDA 10.0 and cuDNN 7.5.0 for PyTorch on Ubuntu 18.04 LTS（包含验证cuda和cudnn的命令）
这个cudnn安装，当我执行 sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb 命令时，出现了类似下面的问题（原错误未保存）：

/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8 is not a symbolic link
/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 is not a symbolic link
/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 is not a symbolic link
/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 is not a symbolic link
/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 is not a symbolic link
/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 is not a symbolic link
/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 is not a symbolic link
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解决方案：
网上有介绍可以将上述的链接使用 sudo ln -sf .... 改成软链接，可以解决问题。但我思考了一下，多版本害怕出现问题就没有使用这个办法。

进入正题：换一种cudnn安装方法

Step 1：下载解压文件

我下载的是这个

下载下来的文件是：cudnn-10.0-linux-x64-v7.6.5.32.solitairetheme8 （463M左右，随便放在哪个目录下）

cp cudnn-10.0-linux-x64-v7.6.5.32.solitairetheme8 cudnn-10.0-linux-x64-v7.6.5.32.tgz# 保险起见，先拷贝一份
tar -zxvf cudnn-10.0-linux-x64-v7.6.5.32.tgz # 解压后为一个cuda文件夹
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可使用 tree -L 2 查看文件树结构如下：
(base) username@username:~/Downloads/cuda$ tree -L 2
.
├── include
│ └── cudnn.h
├── lib64
│ ├── libcudnn.so -> libcudnn.so.7
│ ├── libcudnn.so.7 -> libcudnn.so.7.6.5
│ ├── libcudnn.so.7.6.5
│ └── libcudnn_static.a
└── NVIDIA_SLA_cuDNN_Support.txt

Step 2：拷贝 cudnn.h 文件

# 也可以复制到需要的特定版本： `sudo cp cuda/include/cudnn*.h /usr/local/cuda-10.0/include` 
$ sudo cp cuda/include/cudnn*.h /usr/local/cuda/include 
$ sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 
$ sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

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软链接机制：
将文件复制到软链接链接的文件夹 = 复制到软链接源文件夹。
因此假如我目前 /usr/local/cuda 软链接指向的是 /usr/local/cuda-10.0 ，也可将上述 cuda 替换为 cuda-10.0。

Step 3：验证 cudnn 安装

方法1：查看cudnn.h文件

旧版查看版本信息在cudnn.h , 新版本的版本信息在 cudnn_version.h,例如cudnn_8.1.0版本。

# ${CUDNN_H_PATH} 举例：/usr/local/cuda-10.0/include/cudnn.h
cat ${CUDNN_H_PATH} | grep CUDNN_MAJOR -A 2`
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运行结果如下：

#define CUDNN_MAJOR 7
#define CUDNN_MINOR 6
#define CUDNN_PATCHLEVEL 2
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
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那么该cudnn版本就为===>7.6.2
error：cudnn.h 没有那个文件或目录
如果是下面的错误，这是因为安装CUDA和CUDNN时选择的安装包处于 .deb格式 ，这种新的方式不再具有cudnn.h文件！ ！！ ！！ 。 如果您使用的安装包处于“tar.gz”格式，则上述命令就足够了。参考链接：Check CUDA and cuDNN version under Ubuntu

如果选择以“.deb”格式安装，cudnn安装验证方法：
dpkg -l | grep cudnn # 可一键查看cuda &cudnn版本
查看使用CUDNN的所有流程，结果如下：

上面的图像在Ubuntu 16.04上运行，这意味着：安装在此计算机上的CUDA版本为：9.0，CUDNN版本为：7.3.1.20。

方法2：运行samples测试

将cuDNN_samples 复制到一个可写路径
$cp -r /usr/src/cudnn_samples_v8/ $HOME
进入可写路径。
$ cd $HOME/cudnn_samples_v8/mnistCUDNN
编译 mnistCUDNN。
$make clean && make
运行mnistCUDNN样本。
$ ./mnistCUDNN
如果 cuDNN 正确安装并运行在您的Linux系统上，您将看到类似如下的消息:
Test passed!
期待的打印结果：

(base) username@username:/usr/src/cudnn_samples_v7/mnistCUDNN$ ./mnistCUDNN 
cudnnGetVersion() : 7605 , CUDNN_VERSION from cudnn.h : 7605 (7.6.5)
Host compiler version : GCC 7.5.0
There are 1 CUDA capable devices on your machine :
device 0 : sms 36  Capabilities 7.5, SmClock 1620.0 Mhz, MemSize (Mb) 7979, MemClock 7001.0 Mhz, Ecc=0, boardGroupID=0
Using device 0

Testing single precision
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm ...
Fastest algorithm is Algo 0
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.009952 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.018272 time requiring 57600 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.024256 time requiring 3464 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.049152 time requiring 207360 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.061344 time requiring 2057744 memory
Resulting weights from Softmax:
0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000 
Loading image data/three_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000 
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006 

Result of classification: 1 3 5

Test passed!

Testing half precision (math in single precision)
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm ...
Fastest algorithm is Algo 0
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.018016 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.026784 time requiring 28800 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.028576 time requiring 3464 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.047744 time requiring 207360 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.061440 time requiring 2057744 memory
Resulting weights from Softmax:
0.0000001 1.0000000 0.0000001 0.0000000 0.0000563 0.0000001 0.0000012 0.0000017 0.0000010 0.0000001 
Loading image data/three_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000714 0.0000000 0.0000000 0.0000000 0.0000000 
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006 

Result of classification: 1 3 5

Test passed!

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