  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.
