  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.
