@inproceedings{wohlwend-etal-2019-flambe,
    title = "{F}lamb{\'e}: A Customizable Framework for Machine Learning Experiments",
    author = "Wohlwend, Jeremy  and
      Matthews, Nicholas  and
      Itzcovich, Ivan",
    editor = "Costa-juss{\`a}, Marta R.  and
      Alfonseca, Enrique",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P19-3029/",
    doi = "10.18653/v1/P19-3029",
    pages = "181--188",
    abstract = "Flamb{\'e} is a machine learning experimentation framework built to accelerate the entire research life cycle. Flamb{\'e}{'}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. Flamb{\'e} 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."
}