Deep neural networks achieve state-of-the-art performance on many tasks, but require increasingly complex architectures and costly training procedures. Engineers can reduce costs by reusing a pre-trained model (PTM) and fine-tuning it for their own tasks. To facilitate software reuse, engineers collaborate around model hubs, collections of PTMs and datasets organized by problem domain. Although model hubs are now comparable in popularity and size to other software ecosystems, the associated PTM supply chain has not yet been examined from a software engineering perspective.
We present an empirical study of artifacts and security features in 8 model hubs. We indicate the potential threat models and show that the existing defenses are insufficient for ensuring the security of PTMs. We compare PTM and traditional supply chains, and propose directions for further measurements and tools to increase the reliability of the PTM supply chain.
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This content will become publicly available on May 1, 2024
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
Deep Neural Networks (DNNs) are being adopted
as components in software systems. Creating and specializing
DNNs from scratch has grown increasingly difficult as stateof-
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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- NSF-PAR ID:
- 10427473
- Date Published:
- Journal Name:
- Proceedings of the International Conference on Software Engineering
- ISSN:
- 1819-3781
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as "model hubs" support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult - there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.more » « less
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