7 papers
Learngene Search Across Multiple Datasets for Building Variable-Sized Models
Boyu Shi, Junbo Zhou, Chang Liu +3
Deep learning methods are widely used under diverse resource constraints, resulting in models of varying sizes, such as the Vision Transformer (ViT) series. Deploying these models…
AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
Siyuan Li, Youshao Xiao, Fanzhuang Meng +4
Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amoun…
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Youshao Xiao, Lin Ju, Zhenglei Zhou +8
Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers…
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
Zhongshu Zhu, Bin Jing, Xiaopei Wan +3
As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult…
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
Youshao Xiao, Zhenglei Zhou, Fagui Mao +6
Recently, ChatGPT or InstructGPT like large language models (LLM) has made a significant impact in the AI world. Many works have attempted to reproduce the complex InstructGPT's tr…
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
Chan Wu, Hanxiao Zhang, Lin Ju +8
Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory co…