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02/01/2023
TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation
Model parallelism has become necessary to train large neural networks. H...
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08/30/2022
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
Distributed synchronized GPU training is commonly used for deep learning...
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10/08/2021
M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
Recent expeditious developments in deep learning algorithms, distributed...
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05/31/2021
Exploring Sparse Expert Models and Beyond
Mixture-of-Experts (MoE) models can achieve promising results with outra...
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03/01/2021
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretrainin...
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11/18/2020
Whale: A Unified Distributed Training Framework
Data parallelism (DP) has been a common practice to speed up the trainin...
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07/30/2018
Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes
Synchronized stochastic gradient descent (SGD) optimizers with data para...
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04/16/2018
BigDL: A Distributed Deep Learning Framework for Big Data
In this paper, we present BigDL, a distributed deep learning framework f...
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11/29/2016
