v0.0.1 Release Notes

We are excited to announce the first official release of YAMLE - Yet Another Machine Learning Environment! This version marks the first milestone in providing a flexible, open-source framework designed to streamline machine learning research and development. YAMLE facilitates rapid prototyping, experimentation, and reproducibility across a wide range of machine learning projects.

🌟 Features

• Modular Design: Customize and extend data, models, and methods components with ease.
• Command-Line Interface: A user-friendly CLI for managing experiments, including training, testing, and hyperparameter optimization.
• Integrated with PyTorch: Leverage the power of PyTorch for ML model development.
• Hyperparameter Optimization: Built-in support for hyperparameter tuning to find the best model configurations.
• Logging and Visualization: Integrated with TensorBoard for tracking experiments and visualizing performance metrics.

🛠️ Improvements
Initial release: As this is the first version, every feature is new and designed with the community's feedback in mind. Future releases will include detailed improvements based on user contributions and insights.

📚 Getting Started
To begin using YAMLE, please follow these steps:

Clone the repository: git clone https://github.com/martinferianc/yamle.git
Install dependencies: pip install -e .
Explore the documentation for guides on using and extending YAMLE.

🤝 How to Contribute
Any contributions you make are greatly appreciated. Here's how you can get involved:

Submit bugs and feature requests: Help us improve YAMLE by reporting bugs and suggesting features.
Pull requests: Want to contribute directly to the codebase? Check out the open issues or start a discussion with your ideas.