How to Install TensorFlow on Ubuntu 24.04 and Other Debian-based Distro 2025

TensorFlow, a leading open-source library crafted by Google, stands as a cornerstone in the realms of machine learning and deep learning. Its versatility and power are harnessed globally by researchers, developers, and data scientists to construct and refine sophisticated machine learning models.

For those working on an Ubuntu system and seeking to leverage TensorFlow, this guide provides a structured walkthrough for installing and setting up TensorFlow on your machine.

Before you begin the installation, ensure your Ubuntu system meets these prerequisites:

• Python: TensorFlow is compatible with Python versions 3.7 through 3.10.
• Pip: Pip, the Python package installer, is essential for managing Python libraries.
• Hardware: While TensorFlow operates on CPUs, utilizing a compatible GPU can significantly accelerate the training process for complex models.

Step 1: Installing Python and Pip on Ubuntu

Initiate the process by updating your Ubuntu package repositories and upgrading existing packages to their latest versions. This practice ensures a stable and current system environment.

Bash

sudo apt update
sudo apt upgrade

Next, install Python and Pip, the package manager for Python, which is used to install and manage Python packages.

Bash

sudo apt install python3
sudo apt install python3-pip

After installation, it's prudent to verify the installed versions of both Python and Pip before proceeding with TensorFlow installation.

Bash

python3 --version
pip3 --version

Step 2: Setting up a Virtual Environment (Recommended)

Creating a virtual environment is an optional yet highly recommended step. It isolates your TensorFlow installation, preventing conflicts with other Python projects and maintaining a clean project environment.

Begin by creating a dedicated directory for your TensorFlow project and navigate into it:

Bash

mkdir tensorflow_project
cd tensorflow_project

Within your project directory, create a virtual environment named tensorflow_env and activate it:

Bash

python3 -m venv tensorflow_env
source tensorflow_env/bin/activate

Upon successful activation, your terminal prompt will be prefixed with the virtual environment's name (tensorflow_env), indicating that the environment is active.

Step 3: Installing TensorFlow

With your virtual environment active, you can now install the latest stable release of TensorFlow and its dependencies using pip. First, ensure pip is up-to-date:

Bash

pip install --upgrade pip

Then, proceed to install TensorFlow:

Bash

pip install tensorflow

Step 4: Verifying the TensorFlow Installation

To confirm a successful installation, launch the Python shell within your virtual environment:

Bash

python3

Inside the Python shell, import TensorFlow and check its version.

Python

import tensorflow as tf
print(tf.__version__)

If the installation was successful, the command will output the installed TensorFlow version without errors. For a basic functionality test, execute the following:

Python

hello = tf.constant('Hello, TensorFlow!')
print(hello)

This should produce an output similar to:

tf.Tensor(b'Hello, TensorFlow!', shape=(), dtype=string)

Step 5: Installing TensorFlow with GPU Support (Optional)

For users with a compatible NVIDIA GPU seeking accelerated computation, TensorFlow with GPU support can be installed. First, verify your NVIDIA GPU with the following command:

Bash

lspci | grep -i nvidia

Install the recommended NVIDIA drivers for your system. In this example, driver version 535 is used:

Bash

sudo apt install nvidia-driver-535 -y
sudo reboot

After rebooting, install the GPU-enabled version of TensorFlow:

Bash

pip install tensorflow-gpu

Verify GPU support by checking for the number of GPUs TensorFlow detects:

Python

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

Conclusion

Congratulations! You have successfully installed TensorFlow on Ubuntu 24.04. You are now equipped to embark on machine learning endeavors, from simple projects to intricate deep learning models, leveraging the robust tools TensorFlow offers. Remember to maintain organized project environments using virtual environments and consider GPU support for computationally intensive tasks.