Treffer: PyDTNN: A user-friendly and extensible framework for distributed deep learning

Title:
PyDTNN: A user-friendly and extensible framework for distributed deep learning
Authors:
Barrachina Mir, Sergio, Castelló, Adrián, Catalán Carbó, Mar, Dolz, Manuel F., Mestre Miravet, Jose Ignacio
Publisher Information:
Springer
Publication Year:
2021
Collection:
Repositori Universitat Jaume I (Repositorio UJI)
Subject Terms:
deep neural networks, distributed parallel training, Python
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
15 p.; application/pdf
Language:
English
Relation:
https://www.springer.com/journal/11227; https://hdl.handle.net/10234/193089
DOI:
10.1007/s11227-021-03673-z
Availability:
https://hdl.handle.net/10234/193089
https://doi.org/10.1007/s11227-021-03673-z
Rights:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 ; http://rightsstatements.org/vocab/InC/1.0/
Accession Number:
edsbas.A653ED9C
Database:
BASE

Weitere Informationen

We introduce a framework for training deep neural networks on clusters of computers with the following appealing properties: (1) It is developed in Python, exposing an amiable interface that provides an accessible entry point for the newcomer; (2) it is extensible, offering a customizable tool for the more advanced user in deep learning; (3) it covers the main functionality appearing in convolutional neural networks; and (4) it delivers reasonable inter-node parallel performance exploiting data parallelism by leveraging MPI via MPI4Py for communication and NumPy for the efficient execution of (multithreaded) numerical kernels.