YAMLE: Yet Another Machine Learning Environment
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 9 Feb 2024.
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References · 12
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2018Albumentations: fast and flexible image augmentationscited by 558
- 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
- 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
- 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
- 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 287
- 2016Theano: A Python framework for fast computation of mathematical expressionscited by 64
- 2019Ludwig: a type-based declarative deep learning toolboxcited by 24
- 2018Simple, Distributed, and Accelerated Probabilistic Programmingcited by 18
- 2023Reproducibility in Machine Learning-Driven Researchcited by 15
- 2023Fortuna: A Library for Uncertainty Quantification in Deep Learningcited by 14
- 2022An Empirical Study of Library Usage and Dependency in Deep Learning Frameworkscited by 2
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