tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility v

时间：2024-03-15 11:08:09

安装的cudnn的版本是7.1.0.3，而要求的cudnn版本是7.3.0.0。

将tensorflow版本从1.5换成1.8，顺利运行程序（升级tensorflow版本来解决）

 

ll 命令查看 连接  /usr/local/cuda/lib64下 把对应的 libcudnn.so.7,4,2 连到 libcudnn.so.7 在连到libcudnn.so

sudo ln -sf libcudnn.so.5.1.10 libcudnn.so.5

sudo ln -sf libcudnn.so.5 libcudnn.so

sudo ldconfig

 

 

使用 nvidia-smi 命令

$ nvidia-smi

但是这个命令只能显示一次，如果要实时显示，配合watch命令, 让一秒刷新一次

$ watch -n 1 nvidia-smi

Ubuntu16.04下安装多版本cuda和cudnn

2018年06月28日 16:33:58 tiankong_hut 阅读数：517

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

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

电脑已经安装CUDA9+ cuDNN7.1， 本次安装CUDA8.0.44 + cuDNN5.1

相关命令：

查看cuda版本 ：      nvcc -V

查看位置  ：            which nvcc

查看NVIDIA动态使用情况：  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 驱动版本  ：  cat /proc/driver/nvidia/version

查看环境变量  :           env

LD_DEBUG=all cat

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

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

多版本CUDA切换:

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   

查看目录属性:    ls -l 目录名

具有管理员权限的文件管理器, 比如移动文件夹 :   sudo nautilus 

加入-R 参数，将权限传递给子文件夹 :   chmod -R  777  /home/mypackage

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

GitHub上下了个程序，tensorflow-gpu=0.12，gpu下跑报错，应该是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下安装多版本cuda和cudnn

https://blog.csdn.net/Mr_KkTian/article/details/78632756                ：Ubuntu16.04下同时安装CUDA8.0和CUDA7.0

https://blog.csdn.net/maple2014/article/details/78574275                ：安装多版本 cuda ，多版本之间切换

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

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

      0、 Tensorflow gpu 官方安装指南：

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

  1. 下载CUDA并安装： 
    各个版本的CUDA ：https://developer.nvidia.com/cuda-toolkit-archive

  2. 下载CUDNN  (要注册)
    CUDNN库下载地址：https://developer.nvidia.com/cudnn   

Installation Guide for Linux :  cuda_8.0.44(官方安装说明) cuda_8.0.44

tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility v

安装CUDA8.0和cuDNN5.1：

下载好后直接命令行解压然后复制 lib64 和 include 文件夹到  usr/local/cuda-8.0，命令如下：

# Installing from a Tar File

  1. tar -zxvf 压缩文件名.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版本切换

gedit ~/.bashrc             #更改 ~/.bashrc 文件，添加两行

 

  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    #必须更改/etc/profile 文件, 而且更改后必须重启计算机才有效 (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

改为:

 

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

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

 

从cuda9.1切换到cuda8.0：

  1. sudo rm -rf /usr/local/cuda                    #删除之前创建的软链接

  2. sudo ln -s /usr/local/cuda-8.0 /usr/local/cuda #创建新 cuda 的软链接

 

从cuda8.0切换到cuda9.0：sudo rm -rf /usr/local/cuda                       #删除之前创建的软链接sudo ln -s /usr/local/cuda-9.1  /usr/local/cuda   #创建新 cuda 的软链接

 

可以用命令来查看cuda是否切换完成：

 

  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 未完全安装：


 

  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

tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility v

 

#编译并测试设备 deviceQuery：  
切换到例子存放的路径，默认路径是 ~/NVIDIA_CUDA-7.5_Samples ，切换到相应路径
然后终端输入：$  make
运行编译生成的二进制文件
编译后的二进制文件, 默认存放在 ~/NVIDIA_CUDA-7.5_Samples/bin 
切换路径 ：$  cd  /NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release 
终端输入 ：$  ./deviceQuery

 #编译并测试带宽 bandwidthTest：  
     cd ../bandwidthTest  
     sudo make  
     ./bandwidthTest  
如果这两个测试的最后结果都是Result = PASS，说明CUDA安装成功

 

 

 

 

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

 

  1. [email protected]:~/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': 没有那个文件或目录

  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: 没有那个文件或目录

  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 编译测试正常：

tensorflow/stream_executor/cuda/cuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility v

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

 

 

 

 

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