In this tutorial, we’ll walk through the steps of creating a powerful Machine Learning (ML) development environment on Ubuntu. This set-up includes the installation of pivotal frameworks such as TensorFlow. By the end, you’ll have all the tools you need to start your ML project.
Applications Needed for the Machine Learning Environment on Ubuntu
Install Python
Ubuntu comes with Python pre-installed. However, to ensure you have the latest version, use the following commands:
sudo apt-get update
sudo apt-get install python3.8
Install Pip
Pip is a package manager for Python. It’s used to install and manage Python packages. Install pip using the following command:
sudo apt-get install -y python3-pip
Install Virtualenv
Virtualenv is a tool to create isolated Python environments. It’s good practice to create a virtual environment for each project to avoid dependency conflicts.
pip3 install virtualenv
Install TensorFlow
TensorFlow is one of the most widely used libraries in machine learning. You can install it within your virtual environment using pip:
pip install tensorflow
Install Other Useful Libraries
There are other useful libraries for machine learning such as numpy, pandas, and matplotlib. You can install them using pip:
pip install numpy pandas matplotlib
Install Jupyter Notebook
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. Install it with pip:
pip install jupyter
Create and Activate a Virtual Environment
Create a new directory for your project and navigate into it:
mkdir my_ml_project
cd my_ml_project
Deploy a new virtual environment inside your project folder:
python3 -m venv env
Activate the virtual environment:
source env/bin/activate
Conclusion on Creating a Machine Learning Environment
You now have a powerful, robust machine learning development environment on Ubuntu. This will aid in developing high-quality ML projects. Happy coding!
Related Articles
