How to Install TensorFlow on Ubuntu 22.04: A Complete Guide
TensorFlow is a powerhouse in the world of machine learning and artificial intelligence. Developed by Google, this open-source library provides a comprehensive ecosystem of tools and resources for building and deploying ML-powered applications. Whether you’re a student, researcher, or developer, getting TensorFlow running on your system is the first step toward building incredible projects.
This guide provides a clear, step-by-step process for installing TensorFlow on Ubuntu 22.04 LTS (Jammy Jellyfish). We will cover the recommended installation method using a Python virtual environment to ensure a clean and manageable setup.
Prerequisites
Before you begin, make sure you have the following:
- An installed version of Ubuntu 22.04.
- Access to a terminal or command line.
- Sudo or root privileges for installing system packages.
Step 1: Update Your System
First, it’s always a best practice to ensure your system’s package list and installed packages are up to date. This prevents potential conflicts and ensures you have the latest security patches.
Open your terminal and run the following commands:
sudo apt update
sudo apt upgrade -y
Step 2: Install Python and Pip
TensorFlow is a Python library, so you’ll need Python and its package manager, Pip, to install it. Ubuntu 22.04 comes with Python 3 pre-installed, but you need to install the package manager (pip) and the module for creating virtual environments (venv).
Install these essential packages with the following command:
sudo apt install python3-pip python3-venv -y
Step 3: Create and Activate a Python Virtual Environment
Using a virtual environment is a crucial best practice for any Python project. It creates an isolated space for your project’s dependencies, preventing conflicts between different projects that might require different versions of the same library.
First, create a directory for your TensorFlow project and navigate into it.
mkdir my_tensorflow_project cd my_tensorflow_projectNext, create a virtual environment inside this directory. We’ll name it
tf_env.python3 -m venv tf_envNow, you must activate the virtual environment. This step modifies your shell’s prompt to indicate that you are now working inside the isolated environment.
source tf_env/bin/activate
You will know the environment is active because your command prompt will be prefixed with (tf_env). Any Python packages you install from now on will be placed inside this environment, leaving your global Python installation untouched.
Step 4: Install TensorFlow
With your virtual environment active, you’re ready to install TensorFlow. It’s a good idea to first upgrade pip to its latest version within the environment to ensure a smooth installation process.
pip install --upgrade pip
Now, install TensorFlow using a single pip command. This will download and install the latest stable version of the library and its required dependencies.
pip install tensorflow
The installation may take a few minutes, depending on your internet connection speed.
Step 5: Verify the TensorFlow Installation
Once the installation is complete, you should verify that TensorFlow was installed correctly and is accessible from within your environment.
Run the following command to start a Python interpreter and print the installed TensorFlow version:
python -c "import tensorflow as tf; print(tf.__version__)"
If the installation was successful, the terminal will output the installed version number (e.g., 2.11.0), confirming that you are ready to start building models.
Enabling GPU Support (For NVIDIA Users)
For significant performance gains in model training, you’ll want to use your NVIDIA GPU. This requires installing the TensorFlow build with GPU support and configuring the necessary NVIDIA drivers and libraries.
Install NVIDIA Drivers: First and foremost, you must have the proprietary NVIDIA drivers installed. The easiest way to do this on Ubuntu is using the “Software & Updates” application. Navigate to the “Additional Drivers” tab, select the recommended proprietary driver, and apply the changes.
Install CUDA and cuDNN: TensorFlow relies on NVIDIA’s CUDA Toolkit and cuDNN library for GPU acceleration. You must install versions of these libraries that are compatible with the version of TensorFlow you installed. It is highly recommended to follow the official TensorFlow documentation for the specific CUDA and cuDNN version requirements, as these can change between TensorFlow releases.
Install GPU-Ready TensorFlow: The standard
pip install tensorflowcommand now includes GPU support by default. TensorFlow will automatically detect and use a compatible GPU if the drivers and CUDA libraries are correctly configured.
After setting up your GPU environment, you can verify that TensorFlow detects your GPU with this Python command:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
If the output shows your GPU device, your setup is complete and ready for accelerated machine learning tasks.
Source: https://kifarunix.com/install-tensorflow-ubuntu-22-04/
