2 citations · 7 across the 12 of their papers we have counts for
4 papers · 1 filter
FailBench: Evaluating Fault Tolerance Across Distributed Training Architectures
Khaled Aljbab, Amine Barrak
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 w…
ML in a Box: Analyzing Containerization Practices in Open Source ML Projects
Faten Jebari, Emna Ksontini, Amine Barrak +1
Containerization has become increasingly essential in the machine learning (ML) domain, providing reproducibility, portability, and environment consistency. While prior studies hav…
A Large-Scale Dataset of MCP Implementations on GitHub
Benny Toeppe, Amine Barrak, Emna Ksontini
The rapid emergence of the Model Context Protocol (MCP) has introduced a new standard for connecting large language models to external tools and services. Despite its rapid adoptio…
Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
Amine Barrak
Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clients across aggregators, but eve…