# ChainerMN: Scalable Distributed Deep Learning Framework

Source: [ChainerMN: Scalable Distributed Deep Learning Framework](<https://www.sota2.com/research/paper/68fa63c01e209131cea00b47>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:49:10.785Z

Authors: Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

Published: 2017-10-31T07:13:29.000Z

## Abstract

One of the keys for deep learning to have made a breakthrough in various fields was to utilize high computing powers centering around GPUs. Enabling the use of further computing abilities by distributed processing is essential not only to make the deep learning bigger and faster but also to tackle unsolved challenges. We present the design, implementation, and evaluation of ChainerMN, the distributed deep learning framework we have developed. We demonstrate that ChainerMN can scale the learning process of the ResNet-50 model to the ImageNet dataset up to 128 GPUs with the parallel efficiency of 90%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1710.11351>)

[PDF](<https://arxiv.org/pdf/1710.11351.pdf>)