ML in a Box: Analyzing Containerization Practices in Open Source ML Projects
Containerization has become increasingly essential in the machine learning (ML) domain, providing reproducibility, portability, and environment consistency. While prior studies have analyzed Dockerfile structures and best practices, none have examined ML projects in depth to reveal how the iterative nature of ML workflows influences container footprint, build performance, and caching behavior. We present the first large scale empirical study of 1,993 ML related Dockerfiles, combining quantitative analysis of container roles in ML projects and build dynamics with a qualitative investigation of refactoring practices. Results show that containers serve distinct roles across training, inferen...
| Authors | Faten Jebari, Emna Ksontini, Amine Barrak et al. |
|---|---|
| Primary category | cs.SE |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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