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

Result code in 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>

Result on the web

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

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

—

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

Continuities

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

Publication year

2023

Confidentiality

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