MLXP: A Framework for Conducting Replicable Experiments in Python

التفاصيل البيبلوغرافية
العنوان: MLXP: A Framework for Conducting Replicable Experiments in Python
المؤلفون: Arbel, Michael, Zouaoui, Alexandre
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Software Engineering
الوصف: 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.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2402.13831
رقم الأكسشن: edsarx.2402.13831
قاعدة البيانات: arXiv