研究人员开发了Piper，一个新颖的分布式训练系统，旨在简化大规模模型训练中组合各种并行策略的复杂过程。该系统将策略声明与运行时实现分离，允许用户通过模型注解和调度指令来定义训练方法。Piper随后将这些指令编译成执行计划，在保持与现有方法相当的性能的同时，通过计算和通信的联合调度实现新的效率。 AI
影响 简化了复杂的分布式训练设置，有望加速大型模型的研究和部署。
排序理由 该集群包含一篇详细介绍新的分布式训练系统的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.11169v1 Announce Type: cross Abstract: Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO. Deployed systems for foundation mode…
Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO. Deployed systems for foundation model pretraining often rely on human experts to manua…