Mechanic: A Learning Rate Tuner

• URL: http://arxiv.org/abs/2306.00144v2
• Date: Fri, 2 Jun 2023 01:09:47 GMT
• Title: Mechanic: A Learning Rate Tuner
• Authors: Ashok Cutkosky, Aaron Defazio, Harsh Mehta
• Abstract summary: We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call textscmechanic. We rigorously evaluate textscmechanic on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms.
• Score: 52.4242550204696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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