高分辨率图像的分布式训练:一种域和空间分解方法

A. Tsaris, Jacob D. Hinkle, D. Lunga, P. Dias
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引用次数: 1

摘要

在这项工作中,我们使用Pytorch RPC接口开发了两个Pytorch库,用于高分辨率图像的分布式深度学习方法。空间分解库允许在非常大的图像上进行分布式训练,否则在单个GPU上是不可能的。通过利用领域分离体系结构,领域并行库允许跨多个领域未标记数据进行分布式训练。这两个库都在橡树岭国家实验室的Summit超级计算机上进行了中等规模的测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Training for High Resolution Images: A Domain and Spatial Decomposition Approach
In this work we developed two Pytorch libraries using the PyTorch RPC interface for distributed deep learning approaches on high resolution images. The spatial decomposition library allows for distributed training on very large images, which otherwise wouldn’t be possible on a single GPU. The domain parallelism library allows for distributed training across multiple domain unlabeled data, by leveraging the domain separation architecture. Both of those libraries where tested on the Summit supercomputer at Oak Ridge National Laboratory at a moderate scale.
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