# Copy extracted files into specific CUDA directory: sudo cp -v cud */include/cudnn *.h /usr/local/cuda- $/include/cudnn_version. # Or older versions: tar -xzf cudnn-linux-圆4-v.tgz tar.xz package file: # New versions: tar -xf cudnn-linux-x86_64-_ If you're using a command-line interface, just copy the download link from the NVIDIA website and use wget to download.Search and download the required version of CUDA ( cuda_***_n) installer file from this link:.Moreover, if you want to use a different version of CUDA, never install CUDA.DEB file! DEB file! It will overwrite the current default CUDA (if you installed it already) directory (/usr/local/cuda) and also it will overwrite the already installed latest compatible NVIDIA driver version into the old or incompatible driver. RUN file! It can prevent installing incompatible NVIDIA drivers and prevent overwriting the previous /usr/local/cuda (if you installed another CUDA already) symbolic link. Check compatible CUDA versions for required machine learning/deep learning frameworks/libraries you want to use:.Check the compatibility of TensorRT versions with CUDA versions and cuDNN versions:.Check the cuDNN version with compute capability of your GPU model(s):.Check the compute capability of your GPU model(s):.Check system requirements for CUDA versions:.Check the compatibility of the NVIDIA driver version with CUDA versions:.Search and check the latest stable NVIDIA driver version for your GPU model(s): Verify CUDA Installation Verify driver version by looking at: /proc/driver/nvidia/version : Verify the CUDA Toolkit version Verify running CUDA GPU jobs by.Enter fullscreen mode Exit fullscreen mode
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