YAMLE: Yet Another Machine Learning Environment
This addresses the need for streamlined ML development and reproducibility, but it is incremental as it builds on existing PyTorch-based libraries.
The paper tackles the problem of repetitive work and poor reproducibility in machine learning research by introducing YAMLE, an open-source framework that facilitates rapid prototyping and experimentation, aiming to grow into a shared ecosystem for researchers and practitioners.
YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning (ML) models and methods. The key motivation is to reduce repetitive work when implementing new approaches and improve reproducibility in ML research. YAMLE includes a command-line interface and integrations with popular and well-maintained PyTorch-based libraries to streamline training, hyperparameter optimisation, and logging. The ambition for YAMLE is to grow into a shared ecosystem where researchers and practitioners can quickly build on and compare existing implementations. Find it at: https://github.com/martinferianc/yamle.