sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* Use code with caution. Copied to clipboard Verification
: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.
: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 : cudnn-11.2-linux-x64-v8.1.1.33.tgz
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. sudo chmod a+r /usr/local/cuda/include/cudnn*
: 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.
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 Use code with caution. Copied to clipboard :You need to move the header and library
You should see values representing , Minor 1 , and Patch 1 . Troubleshooting