Mechanic: A Learning Rate Tuner

Ashok Cutkosky; Aaron Defazio; Harsh Mehta

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arXiv · 2306.00144

Mechanic: A Learning Rate Tuner

Ashok Cutkosky·Aaron Defazio·Harsh Mehta

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Abstract

We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call \textsc{mechanic}. Our method provides a practical realization of recent theoretical reductions for accomplishing a similar goal in online convex optimization. We rigorously evaluate \textsc{mechanic} on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms. These experiments demonstrate that depending on the problem, \textsc{mechanic} either comes very close to, matches or even improves upon manual tuning of learning rates.

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Ashok Cutkosky, Aaron Defazio, Harsh Mehta. 2023-05-31. Mechanic: A Learning Rate Tuner. https://arxiv.org/abs/2306.00144

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