PyText: A Seamless Path from NLP research to production

12/12/2018
by   Ahmed Aly, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/15/2022

Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale

We introduce Saga, a next-generation knowledge construction and serving ...
research
01/11/2021

Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization

Designing deep learning-based solutions is becoming a race for training ...
research
07/12/2022

Sockeye 3: Fast Neural Machine Translation with PyTorch

Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machin...
research
08/16/2019

CFO: A Framework for Building Production NLP Systems

This paper introduces a novel orchestration framework, called CFO (COMPU...
research
06/11/2019

Improving Reproducible Deep Learning Workflows with DeepDIVA

The field of deep learning is experiencing a trend towards producing rep...
research
11/16/2021

STAMP 4 NLP – An Agile Framework for Rapid Quality-Driven NLP Applications Development

The progress in natural language processing (NLP) research over the last...
research
10/09/2019

Engineering for a Science-Centric Experimentation Platform

Netflix is an internet entertainment service that routinely employs expe...

Please sign up or login with your details

Forgot password? Click here to reset