Optimize scikit-learn Model Inference Using ONNX and Fix Missing Datasets in xgb_cpu_main_config.json for Benchmarking #181
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Error handling (invalid JSON, missing files) Automatic fixes (correct dataset paths if names mismatch) Clear logging with warnings and actions
Compares ONNX vs. TVM performance on a scikit-learn model Demonstrates ONNX conversion from sklearn Uses TVM to optimize the model for inference Benchmarks inference times for performance analysis
Create refactoring_of_benchmarks.py
Create compilation_frameworks.py
napetrov
requested review from
icfaust,
ahuber21,
razdoburdin,
Alexsandruss,
david-cortes-intel and
ethanglaser
|
Hi @JaimeAdanCuevas, it looks like there may be some additions needed before review. Could you please add a description and lint and let us know if you have any questions or need any support? |
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Description
Add a comprehensive description of proposed changes
List associated issue number(s) if exist(s): #6 (for example)
Documentation PR (if needed): #1340 (for example)
PR should start as a draft, then move to ready for review state after CI is passed and all applicable checkboxes are closed.
This approach ensures that reviewers don't spend extra time asking for regular requirements.
You can remove a checkbox as not applicable only if it doesn't relate to this PR in any way.
Checklist to comply with before moving PR from draft:
PR completeness and readability
Testing