PhD Position F/M Frugal Distributed Training with Volatile Resources

Contexte et atouts du poste

This PhD thesis is part of the Inria–Hivenet Challenge Cupseli: Collaborative Unified Platform for a Scalable and Efficient Learning Infrastructure. Hivenet, which will soon become Antimatter, aims to develop scalable, efficient, and secure solutions for running AI training and inference workloads on distributed, heterogeneous, and volatile computing resources.

The PhD candidate will be hired by Hivenet and hosted mostly by the NEO project-team at the Inria Centre at Université Côte d’Azur, in Sophia Antipolis. The thesis will be carried out in close collaboration with the Inria ARGO project-team and with Hivenet engineers. The work will focus on the design of new distributed training algorithms tailored to environments where participants share computing and storage resources only for limited periods of time.

The research activity will be supervised by:

Giovanni Neglia, Inria NEO, Lau...