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Fix and optimize cuml.accel TargetEncoder and StandardScaler GPU acceleration - #7812

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dantegd:fix-opt-te-accel
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Fix and optimize cuml.accel TargetEncoder and StandardScaler GPU acceleration#7812
dantegd wants to merge 4 commits into
NVIDIA:mainfrom
dantegd:fix-opt-te-accel

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@dantegd

@dantegd dantegd commented Feb 17, 2026

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Fix cuml.accel TargetEncoder and StandardScaler GPU acceleration.

  • Vectorizes the category encoding extraction in _fit_transform_independent, replacing an O(n_unique) Python for-loop with a single cudf merge
  • Adds DataFrame column dtype validation to StandardScaler (was only checking array .dtype, missing DataFrame .dtypes).
  • Removes the overly broad object-dtype guard that blocked string categorical inputs from reaching the GPU TargetEncoder.
  • Fixes GPU persistence after fit by removing redundant attribute syncing that inadvertently cleared self._gpu via __setattr__, causing all subsequent transform calls to fall back to CPU.

…tandardScaler DataFrame checks, TargetEncoder string support and GPU persistence
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@github-actions github-actions Bot added the Cython / Python Cython or Python issue label Feb 17, 2026
@dantegd dantegd changed the title ENH Vectorize TargetEncoder category loop and fix cuml.accel proxy: S… Fix and optimize cuml.accel TargetEncoder and StandardScaler GPU acceleration Feb 17, 2026
@dantegd

dantegd commented Feb 18, 2026

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/ok to test fe9e34a

@dantegd

dantegd commented Feb 18, 2026

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/ok to test 09c58e9

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dantegd commented Feb 24, 2026

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/ok to test 607ad2f

@csadorf

csadorf commented May 19, 2026

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Superseded by #8075.

@csadorf csadorf closed this May 19, 2026
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