PyTorch lights up version 1.6, follows competition down the profiling route • DEVCLASS

Just one day after TensorFlow hit version 2.3, Facebook's challenger project PyTorch was updated to 1.6, sporting support for automatic mixed precision training and a changed classification scheme for new features. The classification system will fall into one of three categories: stable, beta, or prototype. Beta corresponds to what had been known as experimental features, meaning there is a proven added value, but the API could still change or there are performance or coverage issues yet to tackle. Examples for features in this category include custom C classes, named tensors, and PyTorch Mobile. Prototypes are meant for getting "high bandwidth" feedback on the utility of a proposed new feature in order to either commit to getting it to beta or let it fall by the wayside. Prototypes aren't part of a binary and only available for those building from source or using nightlies or the associated compiler flag, which is why a couple of neat additions such as a profiler for distributed training or graph mode quantisation are a bit trickier to access.