Developers are looking to apply AI to an ever expanding range of use cases. As we look to broaden how people can use AI, we’re thrilled to share our recent collaboration between ARM and Facebook to integrate and optimize Caffe2 for ARM’s Mali Graphics Processing Unit (GPU) hardware.
Reinforcement learning (RL) is an area of machine learning focused on teaching agents a complex relationship between its action and behavior, and maximizing a reward after a duration in an environment. The agent can be a game avatar, recommender system, notification bot, or variety of other systems that make decisions. The reward could be points in a game, or more engagement on a website. Facebook uses RL in different ways, with one example being when to let page owners know how their pages are performing.
Today, we are pleased to announce RL_Caffe2 (https://github.com/caffe2/reinforcement-learning-models), a set of RL libraries built on the Caffe2 platform. Sharing an open-source fork of our Caffe2 RL framework allows us to give back to the community and also collaborate with other institutions as RL finds more applications in industry.
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.
After open sourcing Caffe2 at F8 last month, today we are are excited to share our recent work on low precision 16 bit floating point (FP16) training in collaboration with NVIDIA.
Training and deploying AI models is often associated with massive data centers or super computers, with good reason. The ability to continually process, create, and improve models from all kinds of information: images, video, text, and voice, at massive scale, is no small computing feat. Deploying these models on mobile devices so they’re fast and lightweight can be equally daunting. Overcoming these challenges requires a robust, flexible, and portable deep learning framework.
Facebook has been working with others in the open source community to build such a framework. Today, we’re open-sourcing the first production-ready release of Caffe2 - a lightweight and modular deep learning framework emphasizing portability while maintaining scalability and performance.