bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders

IF 2.4 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
George Stoica, Mihaela Elena Breabăn
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引用次数: 0

Abstract

Deep learning models involve computationally intensive training experiments. Increasing the batch size improves the training speed and hardware efficiency by enabling deep neural networks to ingest and process more data in parallel. Inspired by learning rate adaptation policies that yield good results, methods that gradually adjust the batch size have been developed. These methods enhance hardware efficiency without compromising generalization performance. Despite their potential, such methods have not gained widespread popularity or adoption: unlike widely used learning rate policies, for which there is built-in support in most of the deep learning frameworks, the use of batch size adaptation policies requires custom implementations. We introduce an open-source package that implements batch size adaptation policies, which can be seamlessly integrated into deep learning training pipelines. This facilitates more efficient experimentation and accelerates research workflows.
bs-scheduler:一个与PyTorch dataloader兼容的批处理大小调度程序库
深度学习模型涉及计算密集型训练实验。增加批处理大小提高了训练速度和硬件效率,使深度神经网络能够并行地摄取和处理更多的数据。受学习率适应策略的启发,逐渐调整批量大小的方法得到了发展。这些方法在不影响泛化性能的情况下提高了硬件效率。尽管它们具有潜力,但这些方法并没有得到广泛的普及或采用:与广泛使用的学习率策略不同,大多数深度学习框架都有内置支持,批量大小适应策略的使用需要自定义实现。我们引入了一个实现批量大小适应策略的开源包,可以无缝集成到深度学习训练管道中。这有助于更有效的实验和加速研究工作流程。
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来源期刊
SoftwareX
SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.50
自引率
2.90%
发文量
184
审稿时长
9 weeks
期刊介绍: SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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