# dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration

arXiv: https://arxiv.org/abs/2006.07484 · HTML: https://mlsanity.com/paper/2006.07484/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [What Do Machine Learning Researchers Mean by "Reproducible"?](https://mlsanity.com/paper/2412.03854/) (2024)
- [A Siren Song of Open Source Reproducibility](https://mlsanity.com/paper/2204.04372/) (2022)
- [Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance](https://mlsanity.com/paper/2105.10051/) (2021)
- [TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance](https://mlsanity.com/paper/2312.13632/) (2023)

## Abstract (arXiv)

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.