  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.

Software Engineering;Machine Learning