架构：在超参数优化期间实现并行性的基础设施
Orchestrate: Infrastructure for Enabling Parallelism during Hyperparameter Optimization
摘要：
两个关键因素主导了有效的生产级机器学习模型的开发。首先，它需要本地软件实现和迭代过程。其次，它需要分布式基础设施来有效地进行训练和超参数优化。虽然现代机器学习框架在前者非常有效，但实践者通常会为后者构建临时框架。我们提出了SigOpt架构，一个在云环境中进行同步培训的库。我们描述的激励因素和由此产生的设计，这个图书馆，从最初的测试反馈，和未来的目标。
英文摘要：
Two key factors dominate the development of effective production grade machine learning models. First, it requires a local software implementation and iteration process. Second, it requires distributed infrastructure to efficiently conduct training and hyperparameter optimization. While modern machine learning frameworks are very effective at the former, practitioners are often left building ad hoc frameworks for the latter. We present SigOpt Orchestrate, a library for such simultaneous training in a cloud environment. We describe the motivating factors and resulting design of this library, feedback from initial testing, and future goals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
