Piper: A Programmable Distributed Training System

Piper, a new programmable distributed training system, has been introduced to enhance large-scale model training by allowing users to define high-level parallelism strategies through minimal model annotations and scheduling directives. It utilizes an intermediate representation (IR) to compile execution plans, maintaining performance parity with established strategies like ZeRO while enabling improved efficiency through advanced scheduling techniques like DeepSeek-V3's DualPipe. This flexibility is significant for practitioners as it simplifies the integration of state-of-the-art parallelism strategies and optimizations into their training workflows.