MLXP: A framework for conducting replicable Machine Learning eXperiments
  in Python
Michael Arbel Alexandre Zouaoui
Replicability in machine learning (ML) research is increasingly concerning
due to the utilization of complex non-deterministic algorithms and the
dependence on numerous hyper-parameter choices, such as model architecture and
training datasets. Ensuring reproducible and replicable results is crucial for
advancing the field, yet often requires significant technical effort to conduct
systematic and well-organized experiments that yield robust conclusions.
Several tools have been developed to facilitate experiment management and
enhance reproducibility; however, they often introduce complexity that hinders
adoption within the research community, despite being well-handled in
industrial settings. To address the challenge of low adoption, we propose MLXP,
an open-source, simple, and lightweight experiment management tool based on
Python, available at https://github.com/inria-thoth/mlxp . MLXP streamlines the
experimental process with minimal practitioner overhead while ensuring a high
level of reproducibility.