拥抱脸深度学习模型注册中预训练模型重用的实证研究
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
摘要：
深度神经网络（DNN）正被用作软件系统中的组件。随着最先进的架构变得越来越复杂，从头开始创建和专门化DNN变得越来越困难。遵循传统软件工程的道路，机器学习工程师已经开始重用大规模预训练模型（PTM），并为下游任务微调这些模型。以前的作品已经研究了传统软件包的重用实践，以指导软件工程师更好地维护软件包和依赖管理。我们缺乏类似的知识基础来指导预先训练的模型生态系统中的行为。在这项工作中，我们提出了第一个PTM重用的实证调查。我们采访了来自最受欢迎的PTM生态系统Hugging Face的12位从业者，以了解PTM重用的实践和挑战。从这些数据中，我们为PTM重用的决策过程建模。基于确定的做法，我们描述了有用的属性模型重用，包括出处，再现性和可移植性。PTM重用的三个挑战是缺少属性、声称的性能与实际性能之间的差异以及模型风险。我们证实了这些确定的挑战与系统的测量拥抱脸生态系统。我们的工作通过自动测量有用的属性和潜在的攻击，为优化深度学习生态系统提供了未来的方向，并设想了未来对模型注册表的基础设施和标准化的研究。
英文摘要：
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.
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