Benchmarking Python Deep Learning Frameworks for Language Modeling on GPUs

Neuralnetworksareomnipresentinnaturallanguageprocessing(NLP). We benchmark three popular Python frameworks (DyNet, TensorFlow, and Theano) on the standard NLP task of language modeling, and find that DyNet is significantly faster on this task. We also discuss other bottlenecks beyond performance, such as ease of use, that may impact the selection of a neural network framework.

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Publication Details

Journal
Kalpa publications in computing
Published
2024-10-17
DOI
https://doi.org/10.29007/ptjm
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
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article

Benchmarking Python Deep Learning Frameworks for Language Modeling on GPUs

Vijaya Laxmi Pachva
Kalpa publications in computing
Natural Language Processing Techniques
article

Benchmarking Python Deep Learning Frameworks for Language Modeling on GPUs

Vijaya Laxmi Pachva
article en

Abstract

Neuralnetworksareomnipresentinnaturallanguageprocessing(NLP). We benchmark three popular Python frameworks (DyNet, TensorFlow, and Theano) on the standard NLP task of language modeling, and find that DyNet is significantly faster on this task. We also discuss other bottlenecks beyond performance, such as ease of use, that may impact the selection of a neural network framework.

Kalpa publications in computingVol. 20
Openalex Percentile: Top 84%
Natural Language Processing Techniques
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Benchmarking Python Deep Learning Frameworks for Language Modeling on GPUs — Vijaya Laxmi Pachva · Kalpa publications in computing (2024) | TGRS Research Map | TGRS