: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.
cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 Use code with caution. Copied to clipboard cudnn-11.2-linux-x64-v8.1.1.33.tgz
: Look for the version definition in cudnn_version.h : :If you don't have it yet, you can
: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 Copied to clipboard : Look for the version
This will create a directory named cuda containing include and lib64 subdirectories.
: Ensure you have the matching CUDA version installed. You can verify this by running nvcc --version in your terminal.
: 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.