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.

中文翻译：

---

dagger：用于可重复机器学习实验编排的 Python 框架

机器学习的许多研究方向，特别是深度学习，涉及复杂的多阶段实验，通常涉及沿多条执行路径作用于模型的状态变异操作。尽管机器学习框架为定义模型架构和无分支流提供了干净的接口，但研究人员通常会承担跟踪实验来源的负担，即导致最终模型配置并导致多阶段实验的状态树。最初的动机是在神经网络修剪研究的背景下分析再现性，其中多阶段实验管道很常见，我们提出了 dagger，一个促进可再现和可重用实验编排的框架。我们描述了框架的设计原则和示例用法。