Document Type
Conference Proceeding
Publication Date
5-14-2023
Publication Title
2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) Proceedings
Pages
2463-2475
Publisher Name
IEEE
Publisher Location
Melbourne, Australia
Abstract
Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.
In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.
Recommended Citation
Jiang, Wenxin; Synovic, Nicholas; Hyatt, Matthew; Schorlemmer, Taylor R.; Sethi, Rohan; Lu, Yung-Hsiang; et al. (2023): An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry. In Proceedings of International Conference on Software Engineering, 2023. https://doi.org/10.6084/m9.figshare.22056872.v1
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
© 2023 IEEE.
Comments
Author Posting © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The definitive work was published in the 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) Proceedings, pp. 2463-2475, www.doi.org/10.1109/ICSE48619.2023.00206