araras 1.2.0
A collection of tools for Machine Learning and Data Science
Requires Python: >=3.7

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This is a python module that provides a set of tools for working with machine learning models. It includes utilities for neural architecture search using optuna, builders and helpers for keras/tensorflow, a monitoring system for the kernel, and several other features. The module is designed to be easy to use and flexible, allowing users to customize their machine learning workflows.

## Table of Contents

- [Table of Contents](#table-of-contents)
- [Features](#features)
- [⚙️ Installation Instructions](#️-installation-instructions)
  - [📌 Prerequisites](#-prerequisites)
  - [🪜 Steps](#-steps)
- [🤝 Contributing](#-contributing)
- [📜 License](#-license)
- [🤝 Collaborators](#-collaborators)

## Features

- **Neural Architecture Search**: Leverage Optuna for efficient hyperparameter optimization.
- Custom Keras callbacks (e.g., NaN loss pruner for Optuna trials)
- **Keras/TensorFlow Utilities**: Simplify model building with custom builders and helpers.
- **Custom Callbacks**: Includes features like NaN loss pruning for Optuna trials.
- **Hyperparameter Management**: Fine-tune activations, regularizers, optimizers, and scalers.
- **Model Complexity Regularization**: Penalize loss based on FLOPs or parameter count.
- **System Resource Inspection**: Monitor GPU and system resources effortlessly.
- **Experiment Tracking**: Manage directories and files for seamless experiment organization.
- **Kernel Monitoring**: Receive email alerts for crashes or terminations.
- **Email Notifications**: Get notified for successes, warnings, or custom events.
- **Optuna Analysis Tools**: Visualize and analyze Optuna studies with ease.
- **Logging Utilities**: Track exceptions and resource usage effectively.
- **Modular Design**: Build extensible and customizable machine learning workflows.

## ⚙️ Installation Instructions

To set up the development environment, follow these steps:

### 📌 Prerequisites

- Optuna
- Tensorflow
- Keras
- pandas
- Git

### 🪜 Steps

1. **Clone the repository:**

   ```bash
   git clone https://github.com/MatheusFS-dev/araras.git
   ```

2. **Import the modules as you need**

## 🤝 Contributing

Contributions are what make the open-source community amazing. To contribute:

1. Fork the project.
2. Create a feature branch (`git checkout -b feature/new-feature`).
3. Commit your changes (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/new-feature`).
5. Open a Pull Request.

## 📜 License

This project is licensed under the **[General Public License](LICENSE)**.

## 🤝 Collaborators

We thank the following people who contributed to this project:

<table>
  <tr>
    <td align="center">
      <a href="https://github.com/MatheusFS-dev" title="Matheus Ferreira">
        <img src="https://avatars.githubusercontent.com/u/99222557" width="100px;" alt="Foto do Matheus Ferreira no GitHub"/><br>
        <sub>
          <b>Matheus Ferreira</b>
        </sub>
      </a>
    </td>
  </tr>
</table>