
Updates and additions to profiling and performance for RPC, TorchScript and Stack traces in the autograd profiler
(Beta) Support for NumPy compatible Fast Fourier transforms (FFT) via torch.fft
(Prototype) Support for Nvidia A100 generation GPUs and native TF32 format 
(Prototype) Distributed training on Windows now supported
torchvision
(Stable) Transforms now support Tensor inputs, batch computation, GPU, and TorchScript
(Stable) Native image I/O for JPEG and PNG formats
(Beta) New Video Reader API
torchaudio
(Stable) Added support for speech rec (wav2letter), text to speech (WaveRNN) and source separation (ConvTasNet)

To reiterate, starting PyTorch 1.6, features are now classified as stable, beta and prototype. You can see the detailed announcement here. Note that the prototype features listed in this blog are available as part of this release.