Learn how to install CUDA and cuDNN to enable GPU acceleration, enhancing performance in deep learning, scientific computing, and machine learning tasks. This guide provides step-by-step instructions for both Windows and Ubuntu systems, including compatibility checks, troubleshooting tips, and environment variable configurations. By following these instructions, you can optimize your system for frameworks like TensorFlow, PyTorch, and MXNet, leveraging the full capabilities of NVIDIA GPUs for faster computation.
Table of contents
IntroductionPrerequisitesWhat are CUDA and cuDNNSystem Requirements and PreparationsInstalling CUDA and cuDNN on WindowsInstalling CUDA on UbuntuInstalling cuDNN on UbuntuVersion Compatibility and Framework IntegrationUsing CUDA and cuDNN with Popular FrameworksInstalling cuDNN with Python Wheels via pipTroubleshooting Common IssuesFAQsConclusionReferencesSort: