Gossip training for deep learning
2016-11-29Code Available0· sign in to hype
Michael Blot, David Picard, Matthieu Cord, Nicolas Thome
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Abstract
We address the issue of speeding up the training of convolutional networks. Here we study a distributed method adapted to stochastic gradient descent (SGD). The parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way to share information between different threads inspired by gossip algorithms and showing good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized. We compared our method to the recent EASGD in elastic on CIFAR-10 show encouraging results.