We are excited to share our recent work on supporting a recurrent neural network (RNN).
We did not support RNN models at our open source launch in April. So, over the last several months, we have developed state-of-the-art RNN building blocks to support RNN use cases (machine translation and speech recognition, for example).
Using Caffe2, we significantly improved the efficiency and quality of machine translation systems at Facebook. We got an efficiency boost of 2.5x, which allows us to deploy neural machine translation models into production. As a result, all machine translation models at Facebook have been transitioned from phrase-based systems to neural models for all languages. In addition, several product teams at Facebook, including speech recognition and ads ranking, have started using Caffe2 to train RNN models.
We invite machine learning engineers and researchers to experience Caffe2’s RNN capability. More details about what we implemented and open-sourced for RNN support are outlined below.