bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders
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 resul...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025001293 |
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| author | George Stoica Mihaela Elena Breabăn |
| author_facet | George Stoica Mihaela Elena Breabăn |
| author_sort | George Stoica |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a312605764ca4e9c8234c7d8316a8283 |
| institution | DOAJ |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-a312605764ca4e9c8234c7d8316a82832025-08-20T03:12:16ZengElsevierSoftwareX2352-71102025-05-013010216210.1016/j.softx.2025.102162bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoadersGeorge Stoica0Mihaela Elena Breabăn1Corresponding author.; Alexandru Ioan Cuza University, Faculty of Computer Science, Iasi, RomaniaAlexandru Ioan Cuza University, Faculty of Computer Science, Iasi, RomaniaDeep 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.http://www.sciencedirect.com/science/article/pii/S2352711025001293Deep learningLearning rateBatch sizeSchedulers |
| spellingShingle | George Stoica Mihaela Elena Breabăn bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders SoftwareX Deep learning Learning rate Batch size Schedulers |
| title | bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders |
| title_full | bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders |
| title_fullStr | bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders |
| title_full_unstemmed | bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders |
| title_short | bs-scheduler: A Batch Size Scheduler library compatible with PyTorch DataLoaders |
| title_sort | bs scheduler a batch size scheduler library compatible with pytorch dataloaders |
| topic | Deep learning Learning rate Batch size Schedulers |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025001293 |
| work_keys_str_mv | AT georgestoica bsschedulerabatchsizeschedulerlibrarycompatiblewithpytorchdataloaders AT mihaelaelenabreaban bsschedulerabatchsizeschedulerlibrarycompatiblewithpytorchdataloaders |