论文标题
Sherpa：用于机器学习的强大超参数优化
Sherpa: Robust Hyperparameter Optimization for Machine Learning
论文作者
论文摘要
Sherpa是用于机器学习模型的超参数优化库。它是专门针对计算昂贵，迭代功能评估的问题的设计，例如深神经网络的高参数调整。使用夏尔巴省，科学家可以使用各种功能强大且可互换的算法快速优化超参数。 Sherpa可以在一台计算机上或在集群上并行运行。最后，交互式仪表板使用户可以在训练中查看模型的进度，取消试验并探索哪些超参数组合最有效。 Sherpa通过自动化模型调整的更繁琐的方面来赋予机器学习从业人员。它的源代码和文档可在https://github.com/sherpa-ai/sherpa上获得。
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Sherpa can be run on either a single machine or in parallel on a cluster. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning practitioners by automating the more tedious aspects of model tuning. Its source code and documentation are available at https://github.com/sherpa-ai/sherpa.
