Hosting AI/ML Workloads on Linux

Hosting AI/ML workloads on Linux involves setting up an environment that supports the efficient execution of machine learning algorithms and models. This lesson covers the essential steps to set up a Linux server for AI/ML workloads, including software requirements, installation, and configuration.

Before setting up your Linux environment for AI/ML workloads, ensure you have the following:

• Linux distribution (Ubuntu, CentOS, etc.)
• Python 3.x installed
• Package manager (e.g., `apt` for Ubuntu, `yum` for CentOS)
• Virtual environment tool (e.g., `venv` or `conda`)
• GPU (optional but recommended for deep learning tasks)

Follow these steps to install necessary packages and libraries:

1. Update your package list:

sudo apt update

2. Install Python and pip:

sudo apt install python3 python3-pip

3. Install virtual environment tools:

sudo pip3 install virtualenv

4. Create a new virtual environment for your project:

virtualenv my_ml_env

5. Activate the virtual environment:

source my_ml_env/bin/activate

After installing the necessary packages, configure your environment:

1. Install AI/ML libraries:

pip install numpy pandas scikit-learn tensorflow keras

2. Set up your project structure:

mkdir my_project && cd my_project

3. Prepare your data and scripts within the project directory.

Follow these best practices for optimal performance:

• Use virtual environments to manage dependencies.
• Utilize version control (e.g., Git) for your codebase.
• Document your code and processes thoroughly.
• Regularly back up your data and models.
• Monitor system performance and resource usage.