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Poster
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke · Sam Gross · Francisco Massa · Adam Lerer · James Bradbury · Gregory Chanan · Trevor Killeen · Zeming Lin · Natalia Gimelshein · Luca Antiga · Alban Desmaison · Andreas Kopf · Edward Yang · Zachary DeVito · Martin Raison · Alykhan Tejani · Sasank Chilamkurthy · Benoit Steiner · Lu Fang · Junjie Bai · Soumith Chintala

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #182
in Data, Challenges, Implementations, and Software -- Software Toolkits »

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.

Keywords: Hardware and Systems · Software Toolkits · Applications · Data, Challenges, Implementations, and Software
[ Paper]  [ Poster]  [ Slides] 

Author Information

Adam Paszke (University of Warsaw)
Sam Gross (Facebook)
Francisco Massa (Facebook AI Research)
Adam Lerer (Facebook AI Research)
James Bradbury (Google Research)
Gregory Chanan (Facebook)
Trevor Killeen (Self Employed)
Zeming Lin (Facebook AI Reseach)
Natalia Gimelshein (NVIDIA)
Luca Antiga (Orobix)
Alban Desmaison (Oxford University)
Andreas Kopf (Xamla)
Edward Yang (Facebook)
Zachary DeVito (Facebook AI Research)
Martin Raison (Nabla)
Alykhan Tejani (Twitter, Inc.)
Sasank Chilamkurthy (Qure.ai)
Benoit Steiner (Facebook AI Research)
Lu Fang (Facebook)
Junjie Bai (Facebook)
Soumith Chintala (Facebook AI Research)

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