How to Install PyTorch on AlmaLinux

PyTorch is one of the most popular open-source machine learning frameworks developed by Facebook’s AI Research lab. It is widely used for deep learning applications, including computer vision and natural language processing. If you’re using AlmaLinux, a stable and reliable RHEL-based Linux distribution, this guide will show you how to install PyTorch quickly and efficiently.

Prerequisites

Before you begin, ensure that:

• You have a running instance of AlmaLinux 8 or 9

• You have root or sudo privileges

• Python 3.7 or higher is installed

Step 1: Update Your System

Open your terminal and run the following command to update all system packages:

Optionally, install EPEL for access to additional packages:

Step 2: Install Python and pip

Check if Python is already installed:

If it’s not installed, you can install it with:

Step 3: Create a Virtual Environment (Optional)

To keep your Python environment clean and isolated, it’s a good idea to create a virtual environment:

Step 4: Install PyTorch Using pip

To install PyTorch with CPU-only support:

If you want GPU acceleration and have a supported NVIDIA GPU along with proper drivers and CUDA installed, use the appropriate command. For example, to install PyTorch with CUDA 11.8 support:

You can find the latest installation options at the official PyTorch website:
https://pytorch.org/get-started/locally

Step 5: Verify the Installation

To verify that PyTorch has been installed correctly, run:

Then enter the following in the Python shell:

If everything is working, you will see the installed version of PyTorch and whether CUDA is available.

Troubleshooting Tips

• If you encounter issues with pip or SSL certificates, update pip:

• If using CUDA, make sure the NVIDIA drivers and CUDA toolkit are correctly installed and compatible with the version of PyTorch you’re installing.

Conclusion

You have successfully installed PyTorch on AlmaLinux. Whether you’re developing deep learning models or running experiments, PyTorch provides a flexible and powerful framework for machine learning on Linux systems. For more advanced setups, consider integrating with Jupyter Notebook or using Docker containers.