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mlf-core: a framework for deterministic machine learning.

Lukas Heumos1,2,3,4, Philipp Ehmele5, Luis Kuhn Cuellar1

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Summary
This summary is machine-generated.

Deterministic machine learning ensures reliable model verification. A new software solution, mlf-core, aids in developing deterministic models across various biomedical applications, enhancing reproducibility.

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Machine Learning

Background:

  • Machine learning (ML) is increasingly used in sensitive applications, necessitating model verification.
  • Ensuring deterministic behavior in ML models is crucial for reliable deployment and validation.
  • Standard ML libraries often use nondeterministic algorithms, posing challenges for reproducibility.

Purpose of the Study:

  • To evaluate deterministic algorithms in ML libraries for their impact on determinism and runtime.
  • To establish requirements for deterministic machine learning.
  • To introduce mlf-core, a software ecosystem designed to facilitate deterministic ML development.

Main Methods:

  • Evaluated deterministic counterparts of nondeterministic algorithms in ML libraries.
  • Formulated requirements for deterministic machine learning based on evaluation results.
  • Developed and applied the mlf-core ecosystem to create deterministic ML models.

Main Results:

  • Deterministic algorithms were assessed for their effect on model determinism and runtime performance.
  • The mlf-core ecosystem was developed to help projects meet determinism requirements.
  • mlf-core was successfully applied to create deterministic models in diverse biomedical fields, including single-cell analysis, medical image segmentation (U-Net), and gene expression-based classification (XGBoost).

Conclusions:

  • Deterministic machine learning is achievable and essential for reliable model deployment.
  • The mlf-core ecosystem provides a practical solution for developing and maintaining deterministic ML models.
  • Reproducible ML models in sensitive biomedical applications can be developed using the mlf-core framework.