Introduction to ADeus

ADeus is an open-source framework designed to facilitate building and managing machine learning projects in Python with a particular focus on JAX integration. It provides a robust and modular infrastructure that allows AI researchers and developers to accelerate experimentation and development while keeping their workflows clean and reproducible. This project is ideal for those looking for scalable ML infrastructure that blends simplicity with powerful features.

What Are the Key Features of ADeus?

• Modular and Flexible Design: Easily customize components like data loading, training loops, and model management tailored to different ML needs.
• Seamless JAX Integration: Full support for JAX, offering high-performance numerical computing features essential for advanced machine learning workflows.
• Configurable Experiment Management: Organize experiments efficiently, track hyperparameters, and manage reproducibility.
• Lightweight Core: Minimal dependencies ensure fast installation and speed without compromising extensibility.
• Extensive Documentation and Examples: Includes comprehensive guides and sample code to help users get started quickly.
• CLI Tools: Command-line interface to manage and run experiments smoothly.

How to Install ADeus?

Installing ADeus is straightforward and requires Python 3.8 or higher. The recommended way is using pip:

pip install adeus

Alternatively, to get the latest version directly from GitHub, you can clone the repository and install dependencies manually:

git clone https://github.com/adamcohenhillel/ADeus.git
cd ADeus
pip install -r requirements.txt

Ensure that JAX is installed in your environment for GPU support as needed by following instructions from the official JAX website.

How Can You Use ADeus in Your Projects?

After installation, you can quickly bootstrap ML experiments using ADeus’ modular components. The framework offers customizable training loops, dataset loaders, and configurable models allowing seamless experimentation.

Start by importing core modules and setting up an experiment configuration:

from adeus import ExperimentConfig, Trainer
config = ExperimentConfig(model='your_model', dataset='your_dataset')
trainer = Trainer(config)
trainer.run()

This simple approach abstracts the complex boilerplate needed for training machine learning models, letting you focus more on experimentation and less on setup.

What Are Sample Code Snippets from ADeus?

# Sample: Define training configuration
config = {
  'model': 'resnet50',
  'dataset': 'cifar10',
  'batch_size': 64,
  'learning_rate': 0.001,
}

# Initialize and run trainer
trainer = Trainer(ExperimentConfig(**config))
trainer.run()

This snippet demonstrates the simplicity of configuring and running an experiment using ADeus’ clean API.

How to Contribute to ADeus?

The project welcomes contributions to improve functionality, fix bugs, or enhance documentation. To contribute:

• Fork the repository and create a feature branch.
• Follow the coding standards and add tests for new features.
• Submit a pull request with a clear description of changes.
• Refer to the contribution guidelines on GitHub for detailed steps.

A code of conduct is also maintained to ensure a friendly and collaborative community environment.

How to Access Community and Support?

Community support is mainly available via GitHub Issues where you can report bugs or request features. The repository’s README links to discussion forums and provides contact points for direct assistance.

Stay updated with the project’s latest trends by watching the GitHub repo or joining social media channels linked on the repo page.

Conclusion

ADeus offers a powerful and flexible infrastructure for machine learning projects especially for developers seeking JAX-compatible tooling. It streamlines experimentation with modular components, making advanced AI workflows accessible and easier to manage.

Whether you are a researcher or developer, ADeus simplifies complex ML pipelines with an intuitive API backed by a supportive community.

Additional Resources

• Official GitHub Repository
• Complete README Documentation
• Contribution Guidelines