PyText: A Seamless Path from NLP research to production
This addresses the problem of bridging research and production for NLP practitioners, though it is incremental as it builds on existing tools like PyTorch and Caffe2.
The authors tackled the conflict between rapid experimentation and scalable production in NLP by introducing PyText, a deep learning framework built on PyTorch, which enabled faster iteration on modeling ideas and seamless deployment at industrial scale.
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.