Machine learning systems are bloated and vulnerable
Paper i proceeding, 2024

Today's software is bloated with both code and features that are not used by most users. This bloat is prevalent across the entire software stack, from operating systems and applications to containers. Containers are lightweight virtualization technologies used to package code and dependencies, providing portable, reproducible and isolated environments. For their ease of use, data scientists often utilize machine learning containers to simplify their workflow. However, this convenience comes at a cost: containers are often bloated with unnecessary code and dependencies, resulting in very large sizes. In this paper, we analyze and quantify bloat in machine learning containers. We develop MMLB, a framework for analyzing bloat in software systems, focusing on machine learning containers. MMLB measures the amount of bloat at both the container and package levels, quantifying the sources of bloat. In addition, MMLB integrates with vulnerability analysis tools and performs package dependency analysis to evaluate the impact of bloat on container vulnerabilities. Through experimentation with 15 machine learning containers from TensorFlow, PyTorch, and Nvidia, we show that bloat accounts for up to 80% of machine learning container sizes, increasing container provisioning times by up to 370% and exacerbating vulnerabilities by up to 99%. For more detail, see the full paper, Huaifeng Zhang, Mohannad Alhanahnah, Fahmi Abdulqadir Ahmed, Dyako Fatih, Philipp Leitner, and Ahmed Ali-Eldin. 2024. Machine Learning Systems are Bloated and Vulnerable. Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 8, 1 (2024), 1--30. io .

Application programs

Codes (symbols)

software debloating

Machine learning

machine learning systems


Huaifeng Zhang

Nätverk och System

Mohannad Alhanahnah

University of Wisconsin Madison

Fahmi Abdulqadir Ahmed

Student vid Chalmers

Dyako Fatih

Student vid Chalmers

Philipp Leitner

Software Engineering 2

Ahmed Ali-Eldin Hassan

Nätverk och System

SIGMETRICS/PERFORMANCE 2024 - Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

979-840070624-0 (ISBN)

2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2024
Venice, Italy,





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