2024-10-09-watanabe24a.md

title	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks	uisnH6jUDz	While deep learning has celebrated many successes, its results often hinge on the meticulous selection of hyperparameters (HPs). However, the time-consuming nature of deep learning training makes HP optimization (HPO) a costly endeavor, slowing down the development of efficient HPO tools. While zero-cost benchmarks, which provide performance and runtime without actual training, offer a solution for non-parallel setups, they fall short in parallel setups as each worker must communicate its queried runtime to return its evaluation in the exact order. This work addresses this challenge by introducing a user-friendly Python package that facilitates efficient parallel HPO with zero-cost benchmarks. Our approach calculates the exact return order based on the information stored in file system, eliminating the need for long waiting times and enabling much faster HPO evaluations. We first verify the correctness of our approach through extensive testing and the experiments with 6 popular HPO libraries show its applicability to diverse libraries and its ability to achieve over 1000$\times$ speedup compared to a traditional approach. Our package can be installed via pip install mfhpo-simulator.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	watanabe24a	0	Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks	14/1	18	14/1-18	14	false	Watanabe, Shuhei and Mallik, Neeratyoy and Bergman, Edward and Hutter, Frank	given family Shuhei Watanabe given family Neeratyoy Mallik given family Edward Bergman given family Frank Hutter	2024-10-09		Proceedings of the Third International Conference on Automated Machine Learning	256	inproceedings	date-parts 2024 10 9	https://raw.githubusercontent.com/mlresearch/v256/main/assets/watanabe24a/watanabe24a.pdf