An open source deep learning framework built to be flexible and modular for research, with the stability and support needed for production deployment. It enables fast, flexible experimentation through a tape-based autograd system designed for immediate and python-like execution.Overview
Provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments
Extends the PyTorch API to cover common preprocessing and integration tasks needed for incorporating ML in mobile applications
Multimodal framework (MMF)
Our open source, modular deep learning framework for vision and language multimodal research
An open source framework that simplifies the development of complex applications. Its dynamic approach to configuration will accelerate the development of complex Python applications.
An open source framework for automatic differentiation with a powerful, expressive type of graph called weighted finite-state transducers (WFSTs).
A resource for training, evaluating and analyzing NLP models on Knowledge Intensive Language Tasks.
A ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.
SSL framework for hyperparameter tuning that uses time series features as inputs and accurately produces optimal hyperparameters in 6-20x less time
Helping researchers, public health experts, and organizations better understand the spread of COVID-19
Models and Libraries
Our open-sourced libraries and models for those taking our AI learnings further through software and app development
Large-scale datasets and benchmarks for training, evaluating, and testing models to measure and advance AI progress
Our demos for anyone wanting to experience our latest research breakthroughs first hand
Multiple machine learning (ML) models that help people understand the intention, impact and limitations of our AI systems
Our library of published papers to learn about our latest AI breakthroughs and innovations