Distributed machine learning (ML) can bring more computational resources to bear than
single-machine learning, thus enabling reductions in training time. Distributed learning …

Distributed machine learning (ML) can bring more computational resources to bear than
single-machine learning, thus enabling reductions in training time. Distributed learning …

As the popularity of deep learning in industry rapidly grows, efficient training of deep neural
networks (DNNs) becomes important. To train a DNN with a large amount of data, distributed …

The interest and demand for training deep neural networks have been experiencing rapid
growth, spanning a wide range of applications in both academia and industry. However …

Decentralized training of deep learning models enables on-device learning over networks,
as well as efficient scaling to large compute clusters. Experiments in earlier works reveal …

The increasing size of machine learning models, especially deep neural network models,
can improve the model generalization capability. However, large models require more …

Recent trends in high-performance computing and deep learning have led to the
proliferation of studies on large-scale deep neural network training. However, the frequent …

We present PyDTNN, a framework for training deep neural networks (DNNs) on clusters of
computers that has been designed as a research-oriented tool with a low learning curve …

To speed up the training of massive deep neural network (DNN) models, distributed training
has been widely studied. In general, a centralized training, a type of distributed training …

In this paper, we consider the parallel implementation of an already-trained deep model on
multiple processing nodes (aka workers). Specifically, we investigate as to how a deep …