Flambé: A Customizable Framework for Machine Learning Experiments

Jeremy Wohlwend, Nicholas Matthews, Ivan Itzcovich


Abstract
Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambé achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.
Anthology ID:
P19-3029
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
181–188
Language:
URL:
https://aclanthology.org/P19-3029
DOI:
10.18653/v1/P19-3029
Bibkey:
Cite (ACL):
Jeremy Wohlwend, Nicholas Matthews, and Ivan Itzcovich. 2019. Flambé: A Customizable Framework for Machine Learning Experiments. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 181–188, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Flambé: A Customizable Framework for Machine Learning Experiments (Wohlwend et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-3029.pdf
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