dagger：一个用于可再现机器学习实验演示的Python框架
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration
摘要：
机器学习的许多研究方向，特别是在深度学习中，涉及复杂的多阶段实验，通常涉及沿着多个执行路径作用于模型的状态突变操作。虽然机器学习框架提供了清晰的接口来定义模型架构和无分支的流程，但研究人员通常需要跟踪实验起源，即导致最终模型配置并导致多阶段实验的状态树。最初出于分析再现性的背景下，神经网络修剪研究，多阶段的实验管道是常见的，我们提出匕首，一个框架，以促进可重复和可重用的实验编排。我们描述了框架的设计原则和示例使用。
英文摘要：
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.
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。
