tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7301--2019.5.12

安裝的cudnn的版本是7.1.0.3，而要求的cudnn版本是7.3.0.0。

將tensorflow版本從1.5換成1.8，順利運行程序（升級tensorflow版本來解決）

 

ll 命令查看 連接  /usr/local/cuda/lib64下 把對應的 libcudnn.so.7,3,1 連到 libcudnn.so.7 在連到libcudnn.so

sudo ln -sf libcudnn.so.7.3.1 libcudnn.so.7

sudo ln -sf libcudnn.so.7 libcudnn.so

sudo ldconfig

 

然後升級一下tensorflow-gpu即可。

使用 nvidia-smi 命令

$ nvidia-smi

但是這個命令只能顯示一次，如果要實時顯示，配合watch命令, 讓一秒刷新一次

$ watch -n 1 nvidia-smi

Ubuntu16.04下安裝多版本cuda和cudnn

2018年06月28日 16:33:58 tiankong_hut 閱讀數：517

 i7-7700k + TITAN X + 16G DDR4 2400 + 256G SSD

ubuntu16.0.4+ anaconda3+ tensorflow-gpu(0.12.1)   

電腦已經安裝CUDA9+ cuDNN7.1， 本次安裝CUDA8.0.44 + cuDNN5.1

相關命令：

查看cuda版本 ：      nvcc -V

查看位置  ：            which nvcc

查看NVIDIA動態使用情況：  watch -n 1 nvidia-smi  

cuda 版本    ：    cat /usr/local/cuda/version.txt

cudnn 版本  ：    cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

NVIDIA 驅動版本  ：  cat /proc/driver/nvidia/version

查看環境變量  :           env

LD_DEBUG=all cat

卸載cuda ：                 sudo  /usr/local/cuda-8.0/bin/uninstall_cuda_8.0.pl

卸載NVIDIA Driver ：   sudo  /usr/bin/nvidia-uninstall

多版本CUDA切換:

sudo  rm  -rf  /usr/local/cuda                   

sudo  ln  -s    /usr/local/cuda-8.0   /usr/local/cuda  

sudo  ln  -s    /usr/local/cuda-9.1   /usr/local/cuda   

查看目錄屬性:    ls -l 目錄名

具有管理員權限的文件管理器, 比如移動文件夾 :   sudo nautilus 

加入-R 參數，將權限傳遞給子文件夾 :   chmod -R  777  /home/mypackage

**********************************************************************************************************

GitHub上下了個程序，tensorflow-gpu=0.12，gpu下跑報錯，應該是CUDA版本高了

1. E tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7103 (compatibility version 7100) but source was compiled with 5105 (compatibility version 5100). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.

2. E tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7103 (compatibility version 7100) but source was compiled with 5105 (compatibility version 5100). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.

3. F tensorflow/core/kernels/conv_ops.cc:532] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)

4. F tensorflow/core/kernels/conv_ops.cc:532] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)

https://blog.csdn.net/tunhuzhuang1836/article/details/79545625    ：Ubuntu16.04下安裝多版本cuda和cudnn

https://blog.csdn.net/Mr_KkTian/article/details/78632756                ：Ubuntu16.04下同時安裝CUDA8.0和CUDA7.0

https://blog.csdn.net/maple2014/article/details/78574275                ：安裝多版本 cuda ，多版本之間切換

https://blog.csdn.net/mumoDM/article/details/79462604                 ：多版本CUDA問題

https://blog.csdn.net/liangyihuai/article/details/78688228                ：windows下tensorflow-gpu安裝

      0、 Tensorflow gpu 官方安裝指南：

           https://www.tensorflow.org/install/install_windows 

1. 下載CUDA並安裝： 
   各個版本的CUDA ：https://developer.nvidia.com/cuda-toolkit-archive

2. 下載CUDNN  (要註冊)
   CUDNN庫下載地址：https://developer.nvidia.com/cudnn   

Installation Guide for Linux :  cuda_8.0.44(官方安裝說明) cuda_8.0.44

安裝CUDA8.0和cuDNN5.1：

下載好後直接命令行解壓然後複製 lib64 和 include 文件夾到  usr/local/cuda-8.0，命令如下：

# Installing from a Tar File

1. tar -zxvf 壓縮文件名.tar.gz

2. sudo cp cuda/include/cudnn.h /usr/local/cuda-8.0/include

3. sudo cp cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64

4. sudo chmod a+r /usr/local/cuda-9.1/include/cudnn.h /usr/local/cuda-8.0/lib64/libcudnn*

cuda版本切換

gedit ~/.bashrc             #更改 ~/.bashrc 文件，添加兩行

1. export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}

2. export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

下面的不行：

1. export PATH="$PATH:/usr/local/cuda/bin"

2. export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64/"

sudo /etc/profile    #必須更改/etc/profile 文件, 而且更改後必須重啓計算機纔有效 (source /etc/profile 不能生效)

1. export PATH=/usr/local/cuda-9.1/bin:$PATH

2. export LD_LIBRARY_PATH=/usr/local/cuda-9.1/lib64:$LD_LIBRARY_PATH

改爲:

1. export PATH=/usr/local/cuda/bin:$PATH

2. export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

 

從cuda9.1切換到cuda8.0：

1. sudo rm -rf /usr/local/cuda                    #刪除之前創建的軟鏈接

2. sudo ln -s /usr/local/cuda-8.0 /usr/local/cuda #創建新 cuda 的軟鏈接

 

從cuda8.0切換到cuda9.0：sudo rm -rf /usr/local/cuda                       #刪除之前創建的軟鏈接sudo ln -s /usr/local/cuda-9.1  /usr/local/cuda   #創建新 cuda 的軟鏈接

 

可以用命令來查看cuda是否切換完成：

1. $ nvcc --version

2. nvcc: NVIDIA (R) Cuda compiler driver

3. Copyright (c) 2005-2017 NVIDIA Corporation

4. Built on Fri_Sep__1_21:08:03_CDT_2017

5. Cuda compilation tools, release 9.0, V9.0.176

which nvcc ：查看nvcc位置

CUDA8.0+cuDNN5.1 未完全安裝：

1. Installing the CUDA Toolkit in /usr/local/cuda-8.0 ...

2. Installing the CUDA Samples in /home/human-machine ...

3. Copying samples to /home/human-machine/NVIDIA_CUDA-8.0_Samples now...

4. Finished copying samples.

5.  

6. ===========

7. = Summary =

8. ===========

9.  

10. Driver: Not Selected

11. Toolkit: Installed in /usr/local/cuda-8.0

12. Samples: Installed in /home/human-machine

13.  

14. Please make sure that

15. - PATH includes /usr/local/cuda-8.0/bin

16. - LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root

17.  

18. To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin

19.  

20. Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.

21.  

22. ***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.

23. To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:

24. sudo <CudaInstaller>.run -silent -driver

25.  

26. Logfile is /tmp/cuda_install_1534.log

 

#編譯並測試設備 deviceQuery：  
切換到例子存放的路徑，默認路徑是 ~/NVIDIA_CUDA-7.5_Samples ，切換到相應路徑
然後終端輸入：$  make
運行編譯生成的二進制文件
編譯後的二進制文件, 默認存放在 ~/NVIDIA_CUDA-7.5_Samples/bin 
切換路徑 ：$  cd  /NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release 
終端輸入 ：$  ./deviceQuery

 #編譯並測試帶寬 bandwidthTest：  
     cd ../bandwidthTest  
     sudo make  
     ./bandwidthTest  
如果這兩個測試的最後結果都是Result = PASS，說明CUDA安裝成功

 

 

 

 

CUDA8.0+cuDNN5.1報錯，但tensorflow-gpu可以跑，tensorboard也可以用。

1. human-machine@humanmachine-System-Product-Name:~/NVIDIA_CUDA-8.0_Samples$ make

2. make[1]: Entering directory '/home/human-machine/NVIDIA_CUDA-8.0_Samples/0_Simple/simpleVoteIntrinsics_nvrtc'

3. find: `/usr/local/cuda-8.0/lib64/stubs': 沒有那個文件或目錄

4. >>> WARNING - libcuda.so not found, CUDA Driver is not installed. Please re-install the driver. <<<

5. [@] g++ -I../../common/inc -I/usr/local/cuda-8.0/include -o simpleVoteIntrinsics.o -c simpleVoteIntrinsics.cpp

6. [@] g++ -L/usr/local/cuda-8.0/lib64 -L/usr/local/cuda-8.0/lib64/stubs -o simpleVoteIntrinsics_nvrtc simpleVoteIntrinsics.o -lcuda -lnvrtc

7. [@] mkdir -p ../../bin/x86_64/linux/release

8. [@] cp simpleVoteIntrinsics_nvrtc ../../bin/x86_64/linux/release

9. make[1]: Leaving directory '/home/human-machine/NVIDIA_CUDA-8.0_Samples/0_Simple/simpleVoteIntrinsics_nvrtc'

10. make[1]: Entering directory '/home/human-machine/NVIDIA_CUDA-8.0_Samples/0_Simple/matrixMul'

11. "/usr/local/cuda-8.0"/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_20,code=sm_20 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_60,code=compute_60 -o matrixMul.o -c matrixMul.cu

12. nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).

13. cc1plus: fatal error: cuda_runtime.h: 沒有那個文件或目錄

14. compilation terminated.

15. Makefile:250: recipe for target 'matrixMul.o' failed

16. make[1]: *** [matrixMul.o] Error 1

17. make[1]: Leaving directory '/home/human-machine/NVIDIA_CUDA-8.0_Samples/0_Simple/matrixMul'

18. Makefile:52: recipe for target '0_Simple/matrixMul/Makefile.ph_build' failed

19. make: *** [0_Simple/matrixMul/Makefile.ph_build] Error 2

CUDA9.1+cuDNN7.1 編譯測試正常：

https://www.linuxidc.com/Linux/2017-08/146391.htm       :參考官方文檔，乾貨
https://blog.csdn.net/weixin_32820767/article/details/80421913
http://www.sohu.com/a/225953058_491081