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Accelerating End-to-End Machine Learning Using the Intel® oneAPI Toolkit and Intel® Flex Series GPU
Executive summary Introduction Evaluation of oneAPI compatible ETL libraries Optimized End-to-End ML Pipeline vs. Serial Pipeline Evaluation of ML Pipeline with Varying Dataset Sizes Conclusions Appendix: Reproducibility
Appendix: Reproducibility
References

Appendix: Reproducibility

Home > AI Solutions > Artificial Intelligence > White Papers > Accelerating End-to-End Machine Learning Using the Intel® oneAPI Toolkit and Intel® Flex Series GPU > Appendix: Reproducibility

Appendix: Reproducibility

  • Software
    • Python 3.7
    • pandas 1.2.3
    • XGBoost 1.4.2
    • Intel oneAPI AI Analytics Toolkit
    • Intel Modin 0.9.1
    • Intel daal4py 2021.4.0

    Data

    Dataset 1: NYC Taxi

    Dataset 2: Higgs

    Code Repository

    Models:

    • NYC Taxi: XGBoost regression
    • Higgs: XGBoost classification

    E2EMLpipeline code repository:

    • (Link to be determined)

    Hardware

    Platform: Intel DevCloud

    • CPU model name: Intel® Xeon® Ice Lake-SP
    • CPU cores: 96
    • CPU Sockets: 2

    GPU: ATS-M

    • GPU Model: 150W