Large Scale Distributed Deep Networks.

Jeffrey Dean
Greg S. Corrado
Rajat Monga
Kai Chen
Matthieu Devin
Quoc V. Le
Mark Z. Mao
Marc'Aurelio Ranzato
Andrew Senior
Paul Tucker
Ke Yang
Andrew Y. Ng


Recent work in unsupervised feature learning and deep learning has shown that be- ing able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network train- ing. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k cate- gories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition ser- vice. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm.

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