Distributed deep learning relies on data, pipeline, tensor, and hybrid parallelism, yet fault-tolerance mechanisms are typically evaluated only on the architecture for which they were designed. This leaves practitioners with little guidance when choosing mechanisms across architectures. FailBench provides a unified eva...
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 workfl...
Faten Jebari, Emna Ksontini, Amine Barrak et al.· IEEE Working Conference on M...· 0 citations
This study presents the first large-scale, evidence-based dataset of real-world MCP implementation collected directly from GitHub and shows that Python and TypeScript dominate MCP development, with hybrid architectures emerging as the most common design pattern.
Benny Toeppe, Amine Barrak, Emna Ksontini· IEEE Working Conference on M...· 1 citation· ⚡1
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