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 actual runtimes of a DL training are significantly different from query response times, in a naive implementation, simulators of an asynchronous HPO, e.g. multi-fidelity optimization, must wait for the actual runtimes at each iteration; otherwise, the evaluation order in the simulator does not match with the real experiment. To ease this issue, we develop a Python wrapper to force each worker to wait in order to match the evaluation order with the real experiment and describe the usage. Our implementation reduces the waiting time to 0.01 seconds and it is available at https://github.com/nabenabe0928/mfhpo-simulator/.

本研究开发出Python封装器来解决异步超参数优化（multi-fidelity optimization）中的评估顺序问题，从而加速深度学习（deep learning）的超参数调优（HP optimization），将等待时间减少到0.01秒，使用简单。

无需等待，在 HPO 基准测试中模拟多保真度优化的 Python 封装工具