【親測】centos 7 下安裝cuDNN

親測centos 7 下安裝cuDNN
參考

http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html

文件准備

cuda_8.0.61_375.26_linux.run

cudnn-8.0-linux-x64-v5.1.tgz

步驟

切換到壓縮包所在位置，解壓 $ tar -xzvf cudnn-8.0-linux-x64-v5.1.tgz

複制相關文件到cuda特定目錄下(我的cuda安裝目錄為/usr/local/cuda/)

$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64

修改文件權限 $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

使用 這個時候編譯caffe可以將Makefile.config中的USE_CUDNN不注釋，從而使用cuDNN加速 # cuDNN acceleration switch (uncomment to build with cuDNN). USE_CUDNN := 1

附錄
來源： http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html
2.3. Installing cuDNN on Linux The following steps describe how to build a cuDNN dependent program. Choose the installation method that meets your environment needs. For example, the tar file installation applies to all Linux platforms. The debian installation package applies to Ubuntu 14.04 and 16.04. In the following sections:

> your CUDA directory path is referred to as /usr/local/cuda/
> your cuDNN download path is referred to as <cudnnpath>

2.3.1. Installing from a Tar File

Navigate to your directory containing the cuDNN Tar file.

Unzip the cuDNN package. $ tar -xzvf cudnn-9.0-linux-x64-v7.tgz

Copy the following files into the CUDA Toolkit directory.

$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h
/usr/local/cuda/lib64/libcudnn*

2.3.2. Installing from a Debian File

1. Navigate to your <cudnnpath> directory containing cuDNN Debian file.




2. Install the runtime library, for example:
 sudo dpkg -i libcudnn7_7.0.3.11-1+cuda9.0_amd64.deb
3. Install the developer library, for example:
 sudo dpkg -i libcudnn7-dev_7.0.3.11-1+cuda9.0_amd64.deb
4. Install the code samples and the cuDNN Library User Guide, for example:
sudo dpkg -i libcudnn7-doc_7.0.3.11-1+cuda9.0_amd64.deb

2.4. Verifying To verify that cuDNN is installed and is running properly, compile the mnistCUDNN sample located in the /usr/src/cudnn_samples_v7 directory in the debian file.

Copy the cuDNN sample to a writable path. $ cp -r /usr/src/cudnn_samples_v7/ $HOME

Go to the writable path. $ cd $HOME/cudnn_samples_v7/mnistCUDNN

Compile the mnistCUDNN sample. $make clean && make

Run the mnistCUDNN sample. $ ./mnistCUDNN

If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following: Test passed!
Read more at: http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ixzz4y6gZeDaA Follow us: @GPUComputing on Twitter | NVIDIA on Facebook