Part of Proceedings of Machine Learning and Systems 3 (MLSys 2021)
Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol, Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramakrishnan, Ishan Bhatt, Kohen Chia, Neelesh Dodda, Zhihan Li, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan, Jeffrey Geevarghese, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi
Manual debugging is a common productivity drain in the machine learning (ML) lifecycle. Identifying underperforming training jobs requires constant developer attention and deep domain expertise. As state-of-the-art models grow in size and complexity, debugging becomes increasingly difficult. Just as unit tests boost traditional software development, an automated ML debugging library can save time and money. We present Amazon SageMaker Debugger, a machine learning feature that automatically identifies and stops underperforming training jobs. Debugger is a new feature of Amazon SageMaker that automatically captures relevant data during training and evaluation and presents it for online and offline inspection. Debugger helps users define a set of conditions, in the form of built-in or custom rules, that are applied to this data, thereby enabling users to catch training issues as well as monitor and debug ML model training in real-time. These rules save time and money by alerting the developer and terminating a problematic training job early.