PyText: A Seamless Path from NLP research to production

Ahmed Aly; Kushal Lakhotia; Shicong Zhao; Mrinal Mohit; Barlas Oguz; Abhinav Arora; Sonal Gupta; Christopher Dewan; Stef Nelson-Lindall; Rushin Shah

arXiv · 1812.08729

PyText: A Seamless Path from NLP research to production

Ahmed Aly·Kushal Lakhotia·Shicong Zhao·Mrinal Mohit·Barlas Oguz·Abhinav Arora·Sonal Gupta·Christopher Dewan·Stef Nelson-Lindall·Rushin Shah

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Abstract

We introduce PyText - a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces for model components, and by using PyTorch's capabilities of exporting models for inference via the optimized Caffe2 execution engine. We report our own experience of migrating experimentation and production workflows to PyText, which enabled us to iterate faster on novel modeling ideas and then seamlessly ship them at industrial scale.

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Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah. 2018-12-12. PyText: A Seamless Path from NLP research to production. https://arxiv.org/abs/1812.08729

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