
High level API: Extend mobile native APIs to cover common preprocessing and integration tasks needed for incorporating ML in mobile applications. e.g. Computer vision and NLP

Learn more or get started on Android or iOS here.
New tools for model interpretability and privacy
Captum
As models become ever more complex, it is increasingly important to develop new methods for model interpretability. To help address this need, we’re launching Captum, a tool to help developers working in PyTorch understand why their model generates a specific output. Captum provides state-of-the-art tools to understand how the importance of specific neurons and layers and affect predictions made by the models. Captum’s algorithms include integrated gradients, conductance, SmoothGrad and VarGrad, and DeepLift.
The example below shows how to apply model interpretability algorithms on a pretrained ResNet model and then visualize the attributions for each pixel by overlaying them on the image.