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In this paper, we propose a method to train DL networks distributed with high efficiency. First, we propose a hierarchical synchronous Stochastic Gradient ...
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In this paper, we propose a method to train DL networks distributed with high efficiency. First, we propose a hierarchical synchronous Stochastic Gradient ...
Scalable Distributed DNN Training Using Commodity GPU Cloud Computing:It introduces a new method for scaling up distributed Stochastic Gradient Descent (SGD) ...
Jan 21, 2022 · There are two main paradigms to distributed training of deep learning models: Data parallelism and Model parallelism. Data parallelism is by far ...
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Apr 12, 2021 · In this work, we propose Optimus, a highly efficient and scalable 2D-partition paradigm of model parallelism that would facilitate the training ...
Toward this end, this paper reviews and evaluates seven popular distributed training algorithms (BSP, ASP, SSP, EASGD, AR-SGD, GoSGD, and AD-PSGD) in terms of ...
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Jan 26, 2024 · In this paper, we adopt two approaches: (1) to ensure high convergence, we utilize dynamic learning rates and local epochs to avoid local optima ...
Jun 8, 2023 · Deep learning models have proven to be capable of understanding and analyzing large quantities of data with high accuracy. However, training ...
Mar 30, 2024 · PipeDream [3] is a deep neural network training system using backpropagation that efficiently combines data, pipeline, and model parallelism by ...
[PDF] projUNN: efficient method for training deep networks with unitary matrices
openreview.net › pdf
In learning with recurrent or very deep feed-forward networks, employing unitary matrices in each layer can be very effective at maintaining long-range ...