Flambe: A Customizable Framework for Machine Learning Experiments

被引:0
|
作者
Wohlwend, Jeremy [1 ]
Matthews, Nicholas [1 ]
Itzcovich, Ivan [1 ]
机构
[1] ASAPP Inc, New York, NY 10007 USA
来源
PROCEEDINGS OF THE 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, (ACL 2019) | 2019年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.(1)
引用
收藏
页码:181 / 188
页数:8
相关论文
共 50 条
  • [21] Statistical Machine Learning - A Unified Framework
    Liu, Xiao
    JOURNAL OF QUALITY TECHNOLOGY, 2022, 54 (05) : 605 - 605
  • [22] A smooth extreme learning machine framework
    Yang, Liming
    Zhang, Siyun
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2017, 33 (06) : 3373 - 3381
  • [23] A machine learning framework for gaze guidance
    Vig, E.
    Dorr, M.
    Gegenfurtner, K. R.
    Barth, E.
    PERCEPTION, 2009, 38 : 46 - 46
  • [24] Statistical Machine Learning: A Unified Framework
    Liu, Shuangzhe
    INTERNATIONAL STATISTICAL REVIEW, 2021, 89 (01) : 210 - 212
  • [25] Statistical Machine Learning: A Unified Framework
    Liu, Shuangzhe
    INTERNATIONAL STATISTICAL REVIEW, 2021,
  • [26] A Framework for Machine Learning with Ambiguous Objects
    Zhou, Zhi-Hua
    ACTIVE MEDIA TECHNOLOGY, PROCEEDINGS, 2009, 5820 : 6 - 6
  • [27] Negative correlation learning in the extreme learning machine framework
    Perales-Gonzalez, Carlos
    Carbonero-Ruz, Mariano
    Perez-Rodriguez, Javier
    Becerra-Alonso, David
    Fernandez-Navarro, Francisco
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (17): : 13805 - 13823
  • [28] A Machine Learning Based Framework for Adaptive Mobile Learning
    Al-Hmouz, Ahmed
    Shen, Jun
    Yan, Jun
    ADVANCES IN WEB BASED LEARNING - ICWL 2009, 2009, 5686 : 34 - 43
  • [29] Negative correlation learning in the extreme learning machine framework
    Carlos Perales-González
    Mariano Carbonero-Ruz
    Javier Pérez-Rodríguez
    David Becerra-Alonso
    Francisco Fernández-Navarro
    Neural Computing and Applications, 2020, 32 : 13805 - 13823
  • [30] Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy
    Lei, Benjamin
    Bissonnette, Justine R.
    Hogan, Una E.
    Bec, Avery E.
    Feng, Xinyi
    Smith, Rodney D. L.
    ANALYTICAL CHEMISTRY, 2022, 94 (49) : 17011 - 17019