[ML][Python] Optimize eager loading setup by removing redundant RDF triggers #21202
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siliataider
changed the title
Test Results 22 files 22 suites 3d 19h 45m 31s ⏱️ Results for commit 45f183f. ♻️ This comment has been updated with latest results. |
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This Pull request:
Changes or fixes:
This PR optimizes the setup time of the ROOT dataloader in case of eager loading with undersampling.
Previously, the Python
BaseGeneratorvalidated the sampling ratio in the Python layer by callingRDataFrame::Count()on both datasets, triggering the RDataFrame computation graph twice already during generator construction.With this change we delay the sampling ratio validation inside the
ROOT::ML::RSamplerwhere the required entry counts are already known.Micro-benchmarks measuring the generator creation time in eager mode show a
~3–6%reduction in setup time, depending on the dataset size and graph complexity.Checklist: