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Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters.

Y. Xiang, Z. Wu, W. Gong, S. Ding, X. Mo, Y. Liu, S. Wang, P. Liu, Y. Hou, L. Li, B. Wang, S. Shi, Y. Han, Y. Yu, G. Li, Y. Sun, Y. Ma, and D. Yu. CoRR, (2022)

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Lei Shi

Huili Shi

Zhanjun Shi

Renhu Shi

Jian Shi

Other publications of authors with the same name

MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms.S. Shi, and X. Chu. CoRR, (2018)Mixed Precision Method for GPU-based FFT.S. Qi, X. Wang, and S. Shi. CSE, page 580-586. IEEE Computer Society, (2011)Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs.S. Shi, Q. Wang, X. Chu, B. Li, Y. Qin, R. Liu, and X. Zhao. INFOCOM, page 406-415. IEEE, (2020)Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining.Y. Wang, S. Shi, X. He, Z. Tang, X. Pan, Y. Zheng, X. Wu, A. Zhou, B. He, and X. Chu. CoRR, (2023)Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters.S. Shi, X. Zhou, S. Song, X. Wang, Z. Zhu, X. Huang, X. Jiang, F. Zhou, Z. Guo, L. Xie and 14 other author(s). MLSys, mlsys.org, (2021)Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters.Y. Xiang, Z. Wu, W. Gong, S. Ding, X. Mo, Y. Liu, S. Wang, P. Liu, Y. Hou, L. Li and 8 other author(s). CoRR, (2022)Communication-Efficient Distributed Deep Learning: A Comprehensive Survey.Z. Tang, S. Shi, X. Chu, W. Wang, and B. Li. CoRR, (2020)Layer-Wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees.S. Shi, Z. Tang, Q. Wang, K. Zhao, and X. Chu. ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 1467-1474. IOS Press, (2020)EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching.Q. Wang, S. Shi, K. Zhao, and X. Chu. ECCV (32), volume 13692 of Lecture Notes in Computer Science, page 437-453. Springer, (2022)DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining.L. Zhang, S. Shi, X. Chu, W. Wang, B. Li, and C. Liu. ICDCS, page 142-153. IEEE, (2023)

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Disambiguation of "Shi, Shaohuai"

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Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters.

Please choose a person to relate this publication to

Lei Shi

Huili Shi

Zhanjun Shi

Renhu Shi

Jian Shi

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Shi, Shaohuai"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters.

Please choose a person to relate this publication to

Lei Shi

Huili Shi

Zhanjun Shi

Renhu Shi

Jian Shi

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
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Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters.