What's New in PyTorch 2.0? torch.compile - PyImageSearch

Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). The success of PyTorch is attributed to its simplicity, first-class Python integration, and imperative style of programming. Since the launch of PyTorch in 2017, it has strived for high performance and eager execution. It has provided some of the best abstractions for distributed training, data loading, and automatic differentiation. With continuous innovation from the PyTorch team, PyTorch has moved from version 1.0 to the most recent version, 1.13. However, over all these years, hardware accelerators like GPUs have become 15x and 2x faster in compute and memory access, respectively. Thus, to leverage these resources and deliver high-performance eager execution, the team moved substantial parts of PyTorch internals to C .