PyTorch: An Imperative Style, High-Performance Deep Learning Library

NeurIPS 2019 Adam PaszkeSam GrossFrancisco MassaAdam LererJames BradburyGregory ChananTrevor KilleenZeming LinNatalia GimelsheinLuca AntigaAlban DesmaisonAndreas KopfEdward YangZachary DevitoMartin RaisonAlykhan TejaniSasank ChilamkurthyBenoit SteinerLu FangJunjie BaiSoumith Chintala

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... (read more)

PDF Abstract


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet