There is no consensus yet on the question whether adaptive gradient methods like Adam are easier to use than non-adaptive optimization methods like SGD. In this work, we fill in the important, yet ambiguous concept of ‘ease-of-use’ by defining an optimizer’s tunability:  How easy is it to find good hyperparameter configurations using automatic random hyperparameter search? We propose a practical and universal quantitative measure for optimizer tunability that can form the basis for a fair optimizer benchmark.   Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, we find  that Adam is the most tunable for the majority of problems, especially with a low budget for hyperparameter tuning.
We provide a method to benchmark optimizers that is cognizant to the hyperparameter tuning process. Introduction of a novel metric to capture the tunability of an optimizer, and a comprehensive empirical comparison of deep learning optimizers under different amounts of hyper-parameter tuning.  This paper introduces a simple measure of tunability that allows to compare optimizers under resource constraints, finding that tuning Adam optimizers' learning rate is easiest to find well-performing hyperparameter configurations.