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.

翻译：机器学习研究的可复现性问题日益受到关注，其原因在于复杂非确定性算法的使用以及对模型架构与训练数据集等大量超参数选择的依赖。确保结果的可复现与可再现对于推动领域发展至关重要，但通常需要投入大量技术精力来开展系统化、结构化的实验，从而得出稳健的结论。尽管已开发出多种工具以促进实验管理并增强可复现性，但这些工具往往因引入复杂性而阻碍了研究社区的采纳——即便在工业环境中能够良好运行。为应对低采纳率的挑战，我们提出MLXP，这是一个基于Python的开源、简单且轻量级的实验管理工具，其代码托管于https://github.com/inria-thoth/mlxp。MLXP在最小化研究人员额外工作量的前提下，精简实验流程，同时确保高水平的可复现性。