Latest revision as of 01:47, 2 February 2021

Abstract

Flambe is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambe’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. Flambe 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.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.18653/v1/p19-3029 under the license https://creativecommons.org/licenses/by

• https://www.aclweb.org/anthology/P19-3029.pdf

• https://www.aclweb.org/anthology/P19-3029.pdf,

https://www.aclweb.org/anthology/P19-3029,

https://dblp.uni-trier.de/db/conf/acl/acl2019-3.html#WohlwendMI19,

https://academic.microsoft.com/#/detail/2966832372 under the license cc-by