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.