This will create a directory named cuda containing include and lib64 subdirectories.
To confirm the installation was successful, check if the cuDNN version is correctly identified in your system files: cudnn-11.2-linux-x64-v8.1.1.33.tgz
: 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. This will create a directory named cuda containing
You should see values representing , Minor 1 , and Patch 1 . Troubleshooting You should see values representing , Minor 1 , and Patch 1
: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
: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.