Cudnn-11.2-linux-x64-v8.1.1.33.tgz Guide

:Open your terminal and navigate to the download folder. Use the following command to extract the .tgz file: tar -xzvf cudnn-11.2-linux-x64-v8.1.1.33.tgz Use code with caution. Copied to clipboard

: Your GPU drivers must support CUDA 11.2. Check this with the nvidia-smi command. Step-by-Step Installation Guide

:Ensure the files are readable by all users to avoid permission errors during model training: cudnn-11.2-linux-x64-v8.1.1.33.tgz

This will create a directory named cuda containing include and lib64 subdirectories.

:You need to move the header and library files into your system's CUDA installation (usually located at /usr/local/cuda-11.2/ ). Run these commands with sudo : :Open your terminal and navigate to the download folder

cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 Use code with caution. Copied to clipboard

You should see values representing , Minor 1 , and Patch 1 . Troubleshooting Check this with the nvidia-smi command

Do you need help to a specific framework like TensorFlow or PyTorch? Installing cuDNN Backend on Windows