Composable function transforms can help with a number of use cases that are tricky to do in PyTorch today:

computing per-sample-gradients (or other per-sample quantities)
running ensembles of models on a single machine
efficiently batching together tasks in the inner-loop of MAML
efficiently computing Jacobians and Hessians as well as batched ones

Composing vmap (vectorization), vjp (reverse-mode AD), and jvp (forward-mode AD) transforms allows us to effortlessly express the above without designing a separate library for each.
For more details, please see our documentation, tutorials, and installation instructions.
Distributed Training
(Stable) DDP static graph