论文标题
匕首：可再现机器学习实验编排的Python框架
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration
论文作者
论文摘要
机器学习中的许多研究方向，尤其是在深度学习中，都涉及复杂的多阶段实验，通常涉及沿多个执行路径上作用于模型的状态 - 突击操作。尽管机器学习框架为定义模型体系结构和未分支流提供了干净的接口，但研究人员通常会为跟踪实验来源（即导致最终模型配置并导致多阶段实验的状态树）承担负担。最初是由分析在神经网络修剪研究的背景下重现性的动机，在多阶段实验管道很常见，我们提出了匕首，这是一个促进可重复可重复可重复使用的实验编排的框架。我们描述了框架的设计原理和示例用法。
Many research directions in machine learning, particularly in deep learning, involve complex, multi-stage experiments, commonly involving state-mutating operations acting on models along multiple paths of execution. Although machine learning frameworks provide clean interfaces for defining model architectures and unbranched flows, burden is often placed on the researcher to track experimental provenance, that is, the state tree that leads to a final model configuration and result in a multi-stage experiment. Originally motivated by analysis reproducibility in the context of neural network pruning research, where multi-stage experiment pipelines are common, we present dagger, a framework to facilitate reproducible and reusable experiment orchestration. We describe the design principles of the framework and example usage.
