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Installing TensorFlow on Rocky Linux

Benhcmark Administrator

10/09/2025

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A Comprehensive Guide to Installing TensorFlow on Rocky Linux

Unleash the power of machine learning on your server by installing TensorFlow, the world-class open-source platform developed by Google. Rocky Linux, known for its stability and enterprise-grade performance, provides a robust foundation for building and deploying complex AI models. This guide will walk you through the entire process, ensuring a clean and effective installation.

Whether you are a data scientist, a developer, or a system administrator, setting up your environment correctly is the first step toward success. We will cover the essential prerequisites, the installation process using a virtual environment, and how to verify that everything is working perfectly.

Before You Begin: Essential Prerequisites

Before diving into the TensorFlow installation, it’s crucial to ensure your Rocky Linux system is prepared. A few preliminary steps will prevent common issues and create a smooth setup experience.

1. Update Your System: Always start with a fully updated system. This ensures you have the latest security patches and package versions. Open your terminal and run the following command:

   sudo dnf update -y
   

2. Install Python and pip: TensorFlow is a Python library, so you need Python and its package installer, pip, installed. Rocky Linux typically comes with Python 3, but it’s wise to confirm and install pip if it’s missing.
   bash
sudo dnf install python3 python3-pip -y

   You can verify your Python version by running python3 --version.

Step 1: Create a Python Virtual Environment

Working within a virtual environment is a critical best practice for any Python project, especially in machine learning. It creates an isolated space for your project’s dependencies, preventing conflicts with system-wide packages or other projects.

1. Install the venv module: First, ensure you have the necessary package to create virtual environments.

   sudo dnf install python3-venv -y
   

2. Create the environment: Choose a directory for your project and create a virtual environment inside it. A common name is tf_env or simply venv.

   mkdir my_tf_project
   cd my_tf_project
   python3 -m venv tf_env
   

3. Activate the environment: To start using the environment, you must activate it.
   bash
source tf_env/bin/activate

   You’ll know it’s active because your command prompt will be prefixed with (tf_env). All subsequent commands in this guide should be run inside this active environment.

Step 2: Install TensorFlow Using Pip

With your isolated environment active, you can now safely install TensorFlow.

1. Upgrade Pip: It’s a good habit to ensure you’re using the latest version of pip within your new environment.

   pip install --upgrade pip
   

2. Install TensorFlow: Now, use pip to install the main TensorFlow package. This command will download and install the latest stable CPU-only version of TensorFlow along with all of its required dependencies.
   bash
pip install tensorflow

   This process may take a few minutes, depending on your internet connection and system speed.

Step 3: Verify Your TensorFlow Installation

After the installation completes, it’s essential to verify that TensorFlow was installed correctly and is accessible to Python.

A simple way to do this is to run a short Python command that imports TensorFlow and prints its version.

python3 -c "import tensorflow as tf; print('TensorFlow Version:', tf.__version__)"

If the installation was successful, you will see output similar to this, confirming the installed version:

TensorFlow Version: 2.15.0

Congratulations, you have successfully installed TensorFlow on Rocky Linux!

Enabling GPU Support for Accelerated Performance

For serious machine learning tasks, training models on a CPU can be incredibly slow. To dramatically speed up computations, you can leverage a compatible NVIDIA GPU.

Installing TensorFlow with GPU support is more complex and requires several additional components:

1. NVIDIA GPU: Your system must have a CUDA-enabled NVIDIA graphics card.
2. NVIDIA Drivers: You must install the appropriate proprietary NVIDIA drivers for your card on Rocky Linux.
3. CUDA Toolkit: This is a parallel computing platform and API model created by NVIDIA.
4. cuDNN SDK: The NVIDIA CUDA Deep Neural Network library is a GPU-accelerated library of primitives for deep neural networks.

Important Security Tip: Always ensure that the versions of your NVIDIA drivers, CUDA Toolkit, and cuDNN are compatible with the version of TensorFlow you intend to install. The official TensorFlow documentation provides a compatibility matrix that is essential to consult before you begin.

Once the NVIDIA prerequisites are correctly installed and configured, you can install the GPU-compatible version of TensorFlow within your virtual environment using a similar pip command. The modern versions of TensorFlow bundle the CPU and GPU capabilities into the same package, and will automatically detect and use a configured GPU.

When you are finished with your work, you can leave the virtual environment by simply typing:

deactivate

Source: https://kifarunix.com/install-tensorflow-on-rocky-linux/