mlsanityData to 7 Oct 2026

Tune: A Research Platform for Distributed Model Selection and Training

arXiv 1807.05118 ↗First posted 13 Jul 2018cs.LG cs.DC stat.MLcited by 395

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Abstract (arXiv)

Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have been proposed for improving the efficiency of model selection, however their adaptation to the distributed compute environment is often ad-hoc. We propose Tune, a unified framework for model selection and training that provides a narrow-waist interface between training scripts and search algorithms. We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation. We demonstrate the implementation of several state-of-the-art hyperparameter search algorithms in Tune. Tune is available at http://ray.readthedocs.io/en/latest/tune.html.

Key facts. Cited by 395 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2018.

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Citing papers per year: 2021: 52; 2022: 79; 2023: 90; 2024: 77; 2025: 69; 2026: 28.

395 papers.

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  11. 2023Hyperparameters in Reinforcement Learning and How To Tune Themcited by 58
  12. 2023Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunitycited by 54
38 more
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