Year: 2023
Type: preprint
Abstract: Production Machine Learning involves continuous training: hosting multiple versions of models over time, often with many model versions running at once. When model performance does not meet expectations, Machine Learning Engineers (MLEs) debug issues by exploring and analyzing numerous prior versions of code and training data to identify root causes and mitigate problems. Traditional debugging and logging tools often fall short in managing this experimental, multi-version context. FlorDB introduces Multiversion Hindsight Logging, which allows engineers to use the most recent version's logging statements to query past versions, even when older versions logged different data. Log statement propagation enables consistent injection of logging statements into past code versions, regardless of changes to the codebase. Once log statements are propagated across code versions, the remaining challenge in Multiversion Hindsight Logging is to efficiently replay the new log statements based on checkpoints from previous runs. Finally, a coherent user experience is required to help MLEs debug across all versions of code and data. To this end, FlorDB presents a unified relational model for efficient handling of historical queries, offering a comprehensive view of the log history to simplify the exploration of past code iterations. We present a performance evaluation on diverse benchmarks confirming its scalability and the ability to deliver real-time query responses, leveraging query-based filtering and checkpoint-based parallelism for efficient replay.
Source: arXiv (Cornell University)
Institution: University of California, Berkeley
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: III: Medium: Collaborative Research: Reasoning about Optimizers for Data-Intensive Systems, USING VERIFIED LIFTING TO OPTIMIZE LEGACY STENCIL CODES, CAREER: Generating Application-Specific Database Management Systems, Secure, Real-Time Decisions on Live Data, G2859789661, G5944197847, G8876996369
OpenAlex ID: W4387634941