Carl Case

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2018

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OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models
Oleksii Kuchaiev | Boris Ginsburg | Igor Gitman | Vitaly Lavrukhin | Carl Case | Paulius Micikevicius
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

We present OpenSeq2Seq – an open-source toolkit for training sequence-to-sequence models. The main goal of our toolkit is to allow researchers to most effectively explore different sequence-to-sequence architectures. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq provides building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.
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