  Modern deep learning (DL) applications are built using DL libraries and
frameworks such as TensorFlow and PyTorch. These frameworks have complex
parameters and tuning them to obtain good training and inference performance is
challenging for typical users, such as DL developers and data scientists.
Manual tuning requires deep knowledge of the user-controllable parameters of DL
frameworks as well as the underlying hardware. It is a slow and tedious
process, and it typically delivers sub-optimal solutions.
  In this paper, we treat the problem of tuning parameters of DL frameworks to
improve training and inference performance as a black-box optimization problem.
We then investigate applicability and effectiveness of Bayesian optimization
(BO), genetic algorithm (GA), and Nelder-Mead simplex (NMS) to tune the
parameters of TensorFlow's CPU backend. While prior work has already
investigated the use of Nelder-Mead simplex for a similar problem, it does not
provide insights into the applicability of other more popular algorithms.
Towards that end, we provide a systematic comparative analysis of all three
algorithms in tuning TensorFlow's CPU backend on a variety of DL models. Our
findings reveal that Bayesian optimization performs the best on the majority of
models. There are, however, cases where it does not deliver the best results.
