Minerva

Welcome to Minerva, a comprehensive framework designed to enhance the experience of researchers training machine learning models. Minerva allows you to effortlessly create, train, and evaluate models using a diverse set of tools and architectures.

Featuring a robust command-line interface (CLI), Minerva streamlines the process of training and evaluating models. Additionally, it offers a versioning and configuration system for experiments, ensuring reproducibility and facilitating comparison of results within the community.

Description

This project aims to provide a robust and flexible framework for researchers working on machine learning projects. It includes various utilities and modules for data transformation, model creation, and analysis metrics.

Features

Minerva offers a wide range of features to help you with your machine learning projects:

• Model Creation: Minerva offers a variety of models and architectures to choose from.
• Training and Evaluation: Minerva provides tools to train and evaluate your models, including loss functions, optimizers, and evaluation metrics.
• Data Transformation: Minerva provides tools to preprocess and transform your data, including data loaders, data augmentation, and data normalization.
• Command-Line Interface (CLI): Minerva offers a CLI to streamline the process of training and evaluating models.
• Modular Design: Minerva is designed to be modular and extensible, allowing you to easily add new features and functionalities.
• Reproducibility: Minerva ensures reproducibility by providing tools for versioning, configuration, and logging of experiments.
• Experiment Management: Minerva allows you to manage your experiments, including versioning, configuration, and logging.
• SSL Support: Minerva supports SSL (Semi-Supervised Learning) for training models with limited labeled data.

Near Future Features

• Hyperparameter Optimization: Minerva will offer tools for hyperparameter optimization powered by Ray Tune.
• PyPI Package: Minerva will be available as a PyPI package for easy installation.
• Pre-trained Models: Minerva will offer pre-trained models for common tasks and datasets.

Installation

Install Locally

To install Minerva, you can use pip:

pip install .

Get container from Docker Hub

docker pull gabrielbg0/minerva:latest

Usage

You can ether use Minerva's modules directly or use the command line interface (CLI) to train and evaluate models.

CLI

To train a model using the CLI, you can use any of the available pipelines. For example, to train a simple model using the Lightning module, you can run the following command:

python minerva/pipelines/simple_lightning_pipeline.py --config config.yaml

Modules

You can also use Minerva's modules directly in your code. Just import the module you want to use and call the desired functions.

License

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

Contact

For any questions or concerns, please open an issue on our GitHub issue tracker.

Contribute

If you want to contribute to this project make sure to read our Code of Conduct and Contributing pages.

Acknowledgements

This project is maintained by Gabriel Gutierrez, Otávio Napoli, Fernando Gubitoso Marques, and Edson Borin.