2022/12/16 by Huaifeng Zhang, Zhang, Huaifeng, Fahmi Abdulqadir Ahmed +11 · 1 voice · 1 citation
Computer Science · Decision Sciences · #Cloud Computing and Resource Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software System Performance and Reliability #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2212.09437
openalex publication_date 2022/12/16 · arxiv published 2022/12/16 · arxiv updated 2024/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.