Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F23%3A10253673" target="_blank" >RIV/61989100:27740/23:10253673 - isvavai.cz</a>

Výsledek na webu

<a href="https://events.it4i.cz/event/195/" target="_blank" >https://events.it4i.cz/event/195/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

Popis výsledku v původním jazyce

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during training makes possible an incredible wealth of new applications that utilize deep learning.Effectively using systems with multiple GPUs also reduces training time, allowing for faster application development and much faster iteration cycles. Teams who can train with multiple GPUs have an edge, building models trained on more data in shorter periods and with greater engineer productivity.This workshop taught techniques for data-parallel deep learning training on multiple GPUs to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, participants learned how to decrease model training time by distributing data to multiple GPUs while retaining the accuracy of training on a single GPU.

Název v anglickém jazyce

Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

Popis výsledku anglicky

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during training makes possible an incredible wealth of new applications that utilize deep learning.Effectively using systems with multiple GPUs also reduces training time, allowing for faster application development and much faster iteration cycles. Teams who can train with multiple GPUs have an edge, building models trained on more data in shorter periods and with greater engineer productivity.This workshop taught techniques for data-parallel deep learning training on multiple GPUs to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, participants learned how to decrease model training time by distributing data to multiple GPUs while retaining the accuracy of training on a single GPU.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/MC2301" target="_blank" >MC2301: National Competence Centres in the framework of EuroHPC Phase 2 - EUROCC 2</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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