Seeking exemplary ML projects that showcase clear abstractions for models, datasets, and metrics, along with straightforward configuration setups. Please share recommendations and libraries to avoid.
hey evryone, i really apprcated how huggingface’s repo shows a neat split betwen models, data, and config. its kinda rad to see such design clarity. anyone else found cool projects with similar decoupling?
i think pytorch lightning offers a neat mix of modularity and simplicity. its design excellently splts the model training from data management, making it a solid choice for clean, scalable projects.
In my experience, projects that maintain clear separations between model definitions, data handling, and training logic offer tremendous insights into software design. For instance, TensorFlow’s official models illustrate exemplary modular design that not only simplifies debugging but also facilitates scaling. The practical approach to configuration management and logging in these codebases ensures that experiments remain reproducible even as complexity grows. Adopting and adapting these design principles has improved my own projects by encouraging clean abstractions and maintainable code organization.