1. Downloading cuDNN

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  • Go to: NVIDIA cuDNN home page. (https://developer.nvidia.com/rdp/cudnn-download)
  • Join/Signup and complete the short survey and click Submit.
  • Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  • Select the cuDNN version you want to install. A list of available resources displays. (I prefer cuDNN v7.0 Library for Linux)

2. Before we begin, let's assume that -

  • your CUDA directory path is referred to as /usr/local/cuda/
  • your cuDNN download path is referred to as

3. Installing from a Tar File (In may case: cudnn-9.0-linux-x64-v7.tgz)

  • 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*

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. If there is no such samples, download cuDNN v7.0 Code Samples and User Guide for Ubuntu16.04 (Deb) from the same list mentioned above.

  • 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#ixzz4vJYBjUpe