论文标题
PHS：用于并行超参数搜索的工具箱
PHS: A Toolbox for Parallel Hyperparameter Search
论文作者
论文摘要
我们引入了一个名为PHS的开源Python框架 - 并行超参数搜索，以在任何任意Python函数的许多计算实例上启用超参数优化。这是通过目标函数内部最小修改来实现的。可能的应用似乎是昂贵的，以评估很大程度上取决于高参数（例如机器学习）的数值计算。选择贝叶斯优化作为样本有效方法，以提出下一个查询的参数集。
We introduce an open source python framework named PHS - Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.
