_______  __________________________________________ ___________________________   ________  
\      \ \_   _____/\__    ___/\______   \______   \\_____  \__    ___/\_____  \  \_____  \ 
/   |   \ |    __)_   |    |    |     ___/|       _/ /   |   \|    |    /   |   \  /  ____/ 
/    |    \|        \  |    |    |    |    |    |   \/    |    \    |   /    |    \/       \ 
\____|__  /_______  /  |____|    |____|    |____|_  /\_______  /____|   \_______  /\_______ \
       \/        \/                              \/         \/                 \/         \/ 

AI Training Architecture Prototype

This repository contains a prototype implementation for AI training architectures. The goal is to provide a flexible and scalable framework for experimenting with machine learning models and workflows.

---

Requirements

Environment

• Python 13.13.1 (ensure you have this version installed for compatibility)

Dependencies

The project uses several Python packages to facilitate data processing, model training, and evaluation. These are listed in the requirements.txt file.

---

Setup

Step 1: Clone the Repository

git clone https://github.com/your-repo/ai-training-prototype.git
cd ai-training-prototype

Step 2: Install Python 13.13.1

Use your preferred version manager (e.g., pyenv) to install Python 13.13.1:

pyenv install 13.13.1
pyenv local 13.13.1

Ensure the correct version is active:

python --version

Output should be:

Python 13.13.1

Step 3: Create a Virtual Environment

Create and activate a virtual environment to isolate dependencies:

python -m venv venv
source venv/bin/activate  # For Linux/macOS
venv\Scripts\activate   # For Windows

Step 4: Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

---

How to Run the Project

Training a Model

To train a model using the provided architecture:

python train_model.py

Evaluate the Model

To evaluate a trained model:

python evaluate_model.py

Configurations

Model configurations, training parameters, and dataset paths can be modified in the config.json file.

Running Tests

To ensure the project is functioning as expected, run the unit tests:

python -m unittest discover tests/

---

Project Structure

.
├── train_model.py        # Main script for training models
├── evaluate_model.py     # Script for evaluating trained models
├── requirements.txt      # List of Python dependencies
├── config.json           # Configuration file for model parameters
├── data/                 # Folder to store datasets
├── models/               # Folder to save trained models
├── tests/                # Unit tests for the project
└── README.md             # Project documentation (this file)

---

Notes

• Ensure that your system supports Python 13.13.1. If you encounter issues, verify your Python installation.
• Before running any scripts, always activate your virtual environment.
• Keep your config.json file updated with the appropriate paths and parameters for your experiments.

---

Contributing

Contributions are welcome! Feel free to submit issues or pull requests to enhance the prototype.

---

Project Owner

Even Berge Sandvik

License

This project is licensed under the MIT License. See the LICENSE file for more details.