718 citations · 1.4k across the 11 of their papers we have counts for
7 papers · 1 filter
ByteComp: Revisiting Gradient Compression in Distributed Training
Zhuang Wang, Haibin Lin, Yibo Zhu +1
Gradient compression (GC) is a promising approach to addressing the communication bottleneck in distributed deep learning (DDL). However, it is challenging to find the optimal comp…
CSER: Communication-efficient SGD with Error Reset
Cong Xie, Shuai Zheng, Oluwasanmi Koyejo +3
The scalability of Distributed Stochastic Gradient Descent (SGD) is today limited by communication bottlenecks. We propose a novel SGD variant: Communication-efficient SGD with Err…
Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes
Shuai Zheng, Haibin Lin, Sheng Zha +1
BERT has recently attracted a lot of attention in natural language understanding (NLU) and achieved state-of-the-art results in various NLU tasks. However, its success requires lar…
Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta +1
When scaling distributed training, the communication overhead is often the bottleneck. In this paper, we propose a novel SGD variant with reduced communication and adaptive learnin…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
Haibin Lin, Hang Zhang, Yifei Ma +4
With an increasing demand for training powers for deep learning algorithms and the rapid growth of computation resources in data centers, it is desirable to dynamically schedule di…