Performance of Distributed Deep Learning using ChainerMN

At Deep Learning Summit 2017 in San Francisco on this January, PFN announced advancements on distributed deep learning using Chainer in multi-node environment. In this post, I would like to explain the detail of the announcement. (This post is translated from the original post written by Takuya Akiba.) Despite the performance of GPU continuously improves, the number of parameters in neural network models and the computational cost for training them are also increasing to realize higher accuracy using larger training dataset. Some use-cases can take more than one week to train on a single GPU using frameworks including Chainer. In order to handle larger size of training dataset and to make iterative trial-and-error more efficient, it is paramount to accelerate the training process by coordinating multiple GPUs. Therefore, PFN developed…

Link to Full Article: Performance of Distributed Deep Learning using ChainerMN

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