Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351170" target="_blank" >RIV/68407700:21230/21:00351170 - isvavai.cz</a>

Result on the web

<a href="https://arxiv.org/abs/1905.11845" target="_blank" >https://arxiv.org/abs/1905.11845</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees

Original language description

Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. As training most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex objectives, there is a pressing need for such convergence guarantees. In this paper, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared model is asynchronously updated by concurrent processes. To this end, we use a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes. Under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum. From a practical perspective, one issue with the family of algorithms that we consider is that they are not efficiently supported by machine learning frameworks, which mostly focus on distributed data-parallel strategies. To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks for a partitioned but shared model in single- and multi-GPU settings. Based on this implementation, we achieve on average about 1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks, without compromising accuracy.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-866-4

ISSN

2159-5399

e-ISSN

2374-3468

Number of pages

8

Pages from-to

8209-8216

Publisher name

Association for the Advancement of Artificial Intelligence (AAAI)

Place of publication

Palo Alto, California

Event location

Virtual

Event date

Feb 2, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000680423508037