Gradient Coding: Avoiding Stragglers in Distributed Learning
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3368-3376, 2017.
We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC2, and show how we compare against baseline approaches in running time and generalization error.