Installing cuDNN on Linux

Published 2018-07-16 00:29
Tagged on ubuntu cudnn cuda deep-learning

### 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 `<cudnnpath>` ### 3. Installing from a Tar File (In may case: cudnn-9.0-linux-x64-v7.tgz) - Navigate to your `<cudnnpath>` 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

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