  Hyperparameter (HP) optimization of deep learning (DL) is essential for high
performance. As DL often requires several hours to days for its training, HP
optimization (HPO) of DL is often prohibitively expensive. This boosted the
emergence of tabular or surrogate benchmarks, which enable querying the
(predictive) performance of DL with a specific HP configuration in a fraction.
However, since the actual runtime of a DL training is significantly different
from its query response time, simulators of an asynchronous HPO, e.g.
multi-fidelity optimization, must wait for the actual runtime at each iteration
in a na\"ive implementation; otherwise, the evaluation order during simulation
does not match with the real experiment. To ease this issue, we developed a
Python wrapper and describe its usage. This wrapper forces each worker to wait
so that we yield exactly the same evaluation order as in the real experiment
with only $10^{-2}$ seconds of waiting instead of waiting several hours. Our
implementation is available at
https://github.com/nabenabe0928/mfhpo-simulator/.
