In deep learning (DL) and machine learning (ML) communities, fragmentation in the frameworks have been a long-standing problem that comes with significant costs. Because of the fragmentation, resources are spread across different stacks to focus on competition which distracts the communities from innovation, and users and developers are forced to be locked into a side. As deep learning frameworks gradually mature and converge to similar design choices, opportunities arise to standardize the API to address the costly fragmentation by fostering collaboration across frameworks. To this end, Apache MXNet 2.0 adopts Python array API standard and Open Neural Network Exchange (ONNX), the two complementing standards for machine learning and deep learning. In this talk, I will share about these standardization efforts and MXNet’s participation in them, and highlight the exciting new features that helps Apache MXNet as a framework that surfaces the innovation.
Sheng Zha: Sheng Zha is a senior applied scientist at Amazon AI. He s also a committer and PPMC member of Apache MXNet (Incubating), a steering committee member of ONNX in LF Data & AI Foundation, and a member on the Consortium for Python Data API Standard. In his research, Sheng focuses on the intersection between deep learning-based natural language processing and computing systems, with the aim of enabling large-scale model learning on language data and making it accessible.