Frameworks and tools

Sharing frameworks and tools for research, collaboration and production deployment.

Featured tool


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.




Provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments

Mobile (Experimental)

Extends the PyTorch API to cover common preprocessing and integration tasks needed for incorporating ML in mobile applications


A research platform for dynamic data collection and benchmarking

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).


An open source neural-based decompiler framework that converts to readable code with greater accuracy than traditional systems.


A platform that streamlines the process of researching, training, and evaluating conversational models across multiple tasks.


KILT Benchmarking

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.

Large-scale forecasting

SSL framework for hyperparameter tuning that uses time series features as inputs and accurately produces optimal hyperparameters in 6-20x less time

COVID-19 Forecasting

Helping researchers, public health experts, and organizations better understand the spread of COVID-19