PyTorch tries keeping up with research interest in 1.3 release • DEVCLASS

PyTorch has debuted a slew of experimental features in its just-released version 1.3 as support for the TensorFlow competitor broadens, and new tools to tackle challenges like privacy appear. PyTorch 1.3 seems to be right on trend with its new capabilities, adding, for example, previews of implementations for model quantisation and on-device machine learning. The latter is heavily looked into these days, as interest in privacy-focused approaches soars. Mobile support is one of the building blocks to, for example, realise federated learning, a technique which allows training data to be spread between clients, meaning that data doesn't have to leave a device anymore to be included in the training of a centralised model. In its first iteration, mobile support comes down to prebuilt LibTorch libraries for Android and iOS, optimised implementations for certain operators, modules making sure that TorchScript inference is possible and forward operations can be executed on mobile CPUs.