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AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Previous works only address part of the stragglers and could not adaptively solve various stragglers in practice.
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster.
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
Furthermore, our framework provides a simple user interface and allows for the agile allocation of models across devices in a fine-grained manner for various training scenarios, involving models of varying sizes and devices of different scales.
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