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

Mechanic: A Learning Rate Tuner