While deep learning has celebrated many successes, its results often hinge on
the meticulous selection of hyperparameters (HPs). However, the time-consuming
nature of deep learning training makes HP optimization (HPO) a costly endeavor,
slowing down the development of efficient HPO tools. While zero-cost
benchmarks, which provide performance and runtime without actual training,
offer a solution for non-parallel setups, they fall short in parallel setups as
each worker must communicate its queried runtime to return its evaluation in
the exact order. This work addresses this challenge by introducing a
user-friendly Python package that facilitates efficient parallel HPO with
zero-cost benchmarks. Our approach calculates the exact return order based on
the information stored in file system, eliminating the need for long waiting
times and enabling much faster HPO evaluations. We first verify the correctness
of our approach through extensive testing and the experiments with 6 popular
HPO libraries show its applicability to diverse libraries and its ability to
achieve over 1000x speedup compared to a traditional approach. Our package can
be installed via pip install mfhpo-simulator.

深度学习研究中，超参数选择对结果产生重要影响，然而深度学习训练的耗时性质使得超参数优化变得昂贵，本文提供了一个用户友好的 Python 包，利用零成本基准测试实现了高效并行的超参数优化，通过文件系统中的信息计算返回顺序，从而消除了长时间等待，实现了比传统方法快 1000 倍以上的加速。