How To Install Nvidia CUDA, PyTorch, Stable Diffusion & YOLO on Linux
Installing Nvidia CUDA, PyTorch, Stable Diffusion, and YOLO on a Linux machine can be a daunting task for beginners, but with the right guidance, it can be a straightforward process. In this article, we will provide step-by-step instructions on how to install these essential tools for machine learning and computer vision on a Linux system.
Step 1: Install Nvidia CUDA
Nvidia CUDA is a parallel computing platform and programming model that allows developers to use Nvidia GPUs for general-purpose processing. To install Nvidia CUDA, you will need to download the CUDA Toolkit from the Nvidia website and follow the installation instructions provided.
Step 2: Install PyTorch
PyTorch is a popular open-source machine learning library for Python that is developed by Facebook’s AI Research lab. To install PyTorch, you can use the pip package manager to install the appropriate version for your system. Alternatively, you can also build PyTorch from the source if you have specific requirements.
Step 3: Install Stable Diffusion
Stable Diffusion is a library for training and evaluating diffusion models. To install Stable Diffusion, you can use the pip package manager to install the library and its dependencies. Make sure to follow the installation instructions provided by the Stable Diffusion documentation.
Step 4: Install YOLO
YOLO (You Only Look Once) is a real-time object detection system that is known for its speed and accuracy. To install YOLO, you will need to clone the YOLO repository from GitHub and follow the installation instructions provided in the repository’s README file.
Conclusion
By following the steps outlined in this article, you will be able to install Nvidia CUDA, PyTorch, Stable Diffusion, and YOLO on your Linux machine. These tools are essential for developing and deploying machine learning and computer vision applications, and having them installed on your system will enable you to start working on your projects without any further delays.
There are actually multiple ways to install the drivers in Ubuntu. In the video I showed how to install the latest drivers (at the time of making this video) in the command line, but some distros such as Pop OS and Mint have a graphical interface that allows you to easily install the driver version you want.
Another method is to run the following two commands:
sudo ubuntu-drivers devices
sudo ubuntu-drivers autoinstall
Also it might be necessary to create a Python virtual environment before installing packages with PIP. I've updated the written guide to reflect this change.
Hi bro, thanks for your contribution. However, I have some questions
– In 3:45, isn't more general to recommend first to do a "ubuntu-drivers devices" in the terminal, and from there, install the recommended one?
– When installing the driver, it is really odd to me that it works as simple as that. I mean, isn't the default GPU driver being used at that moment for the display? if so, then, how can you replace the default GPU driver with the new one, does the update really happen? don't you need to enter into recovery mode where the GPU driver is not load (or maybe I am wrong here).
– anyways, good video!
Will this fix flickering o Q4 wine running cracked NBA 2K23?
Nice simple instructions seems like people are putting AI in everything except Apple for them its machine learning.