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Sure, here’s an informative tutorial on how to install CUDA on Linux for Python, specifically targeting the NVIDIA GPU environment. This tutorial assumes that you have a compatible NVIDIA GPU and a Linux system. The example code will use Python with the TensorFlow library, which often benefits from GPU acceleration.
Before installing CUDA, make sure your GPU is compatible. You can check the NVIDIA CUDA GPU support list on the official NVIDIA website.
Ensure that you have the latest NVIDIA GPU drivers installed. You can usually install them using your Linux distribution’s package manager.
Replace version with the appropriate driver version for your GPU.
Visit the official NVIDIA CUDA Toolkit download page and download the appropriate version for your system. Make sure to select the deb (local) option for Debian-based systems.
Replace your-distribution and version with your Linux distribution and the CUDA Toolkit version you downloaded.
Add the CUDA installation path to your PATH and LD_LIBRARY_PATH environment variables. You can do this by adding the following lines to your shell profile file (e.g., .bashrc or .zshrc).
For additional performance, you can install cuDNN, a GPU-accelerated library for deep neural networks.
Finally, you can install TensorFlow with GPU support using pip.
Now, you have successfully installed CUDA on Linux and configured your system for GPU-accelerated Python development using TensorFlow. You can adapt this tutorial for other deep learning frameworks that support GPU acceleration on CUDA, such as PyTorch.
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