[2406.18701] Fast Optimizer Benchmark
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Computer Science > Machine Learning

arXiv:2406.18701 (cs)
[Submitted on 26 Jun 2024]

Title:Fast Optimizer Benchmark

Authors:Simon Blauth, Tobias Bürger, Zacharias Häringer, Jörg Franke, Frank Hutter
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Abstract:In this paper, we present the Fast Optimizer Benchmark (FOB), a tool designed for evaluating deep learning optimizers during their development. The benchmark supports tasks from multiple domains such as computer vision, natural language processing, and graph learning. The focus is on convenient usage, featuring human-readable YAML configurations, SLURM integration, and plotting utilities. FOB can be used together with existing hyperparameter optimization (HPO) tools as it handles training and resuming of runs. The modular design enables integration into custom pipelines, using it simply as a collection of tasks. We showcase an optimizer comparison as a usage example of our tool. FOB can be found on GitHub: this https URL.
Comments: 5 pages + 12 appendix pages, submitted to AutoML Conf 2024 Workshop Track
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2406.18701 [cs.LG]
  (or arXiv:2406.18701v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2406.18701

Submission history

From: Simon Blauth [view email]
[v1] Wed, 26 Jun 2024 19:10:34 UTC (3,301 KB)
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