: Look for the version definition in cudnn_version.h :

: Ensure /usr/local/cuda/lib64 is in your LD_LIBRARY_PATH environment variable so your software can find the libraries.

: This specific build is for CUDA 11.x. While cuDNN 8.x is generally compatible across CUDA 11.x versions, using the exact matching CUDA 11.2 toolkit is recommended for stability with frameworks like TensorFlow 2.6.

: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

:If you don't have it yet, you can typically find it in the NVIDIA cuDNN Archive . Note that you must be logged into an NVIDIA Developer account to access these files.

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