Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility

TL;DR

This paper introduces MLEModernizer, an LLM-based framework that modernizes machine learning notebooks to restore their reproducibility despite evolving hardware and software environments, thereby facilitating reuse and scientific progress.

Contribution

The paper presents MLEModernizer, a novel LLM-driven system that automatically updates notebooks to ensure reproducibility in modern environments, addressing environment erosion issues.

Findings

Restored reproducibility in 74.2% of non-reproducible notebooks

Environment backporting does not improve reproducibility and causes failures

Only 35.4% of Kaggle notebooks remain reproducible today

Abstract

Interactive computational notebooks (e.g., Jupyter notebooks) are widely used in machine learning engineering (MLE) to program and share end-to-end pipelines, from data preparation to model training and evaluation. However, environment erosion-the rapid evolution of hardware and software ecosystems for machine learning-has rendered many published MLE notebooks non-reproducible in contemporary environments, hindering code reuse and scientific progress. To quantify this gap, we study 12,720 notebooks mined from 79 popular Kaggle competitions: only 35.4% remain reproducible today. Crucially, we find that environment backporting, i.e., downgrading dependencies to match the submission time, does not improve reproducibility but rather introduces additional failure modes. To address environment erosion, we design and implement MLEModernizer, an LLM-driven agentic framework that treats the contemporary environment as a fixed constraint and modernizes notebook code to restore reproducibility. MLEModernizer iteratively executes notebooks, collects execution feedback, and applies targeted fixes in three types: error-repair, runtime-reduction, and score-calibration. Evaluated on 7,402 notebooks that are non-reproducible under the baseline environment, MLEModernizer makes 5,492 (74.2%) reproducible. MLEModernizer enables practitioners to validate, reuse, and maintain MLE artifacts as the hardware and software ecosystems continue to evolve.