PyDTNN: A user-friendly and extensible framework for distributed deep learning
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Otros documentos de la autoría: Barrachina Mir, Sergio; Castelló, Adrián; Catalán Carbó, Mar; Dolz, Manuel F.; Mestre Miravet, Jose Ignacio
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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INVESTIGACIONMetadatos
Título
PyDTNN: A user-friendly and extensible framework for distributed deep learningAutoría
Fecha de publicación
2021-02-22Editor
SpringerISSN
0920-8542; 1573-0484Cita bibliográfica
Barrachina, S., Castelló, A., Catalán, M. et al. PyDTNN: A user-friendly and extensible framework for distributed deep learning. J Supercomput (2021). https://doi.org/10.1007/s11227-021-03673-zTipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.springer.com/journal/11227Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades (Spain) | Generalitat Valenciana
Código del proyecto o subvención
TIN2017-82972-R | CDEIGENT/2018/014
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021
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info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
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