Accelerate StandardScaler, MinMaxScaler, and LabelEncoder via sklearn's array API dispatch - #8013
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csadorf wants to merge 13 commits into
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Accelerate StandardScaler, MinMaxScaler, and LabelEncoder via sklearn's array API dispatch#8013csadorf wants to merge 13 commits into
StandardScaler, MinMaxScaler, and LabelEncoder via sklearn's array API dispatch#8013csadorf wants to merge 13 commits into
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Use ensure_host(y) instead of np.asanyarray(y) so that cupy arrays passed in (e.g. from Pipeline transformations) are moved to host before np.unique runs. Made-with: Cursor
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csadorf
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/ok to test 8ca9c0f |
betatim
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Apr 28, 2026
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I thought we had agreed to leave the preprocessors in cuml proper alone for now?
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Adds GPU acceleration for
sklearn.preprocessing.StandardScaler,MinMaxScaler, andLabelEncoderincuml.accelby routing theirfit,transform,fit_transform,inverse_transform, andpartial_fitmethods through sklearn's built-in array API dispatch path with CuPy arrays.Rather than using
ProxyBase(which requires a separate cuML GPU implementation), these estimators are monkey-patched in place. When the input is accelerable (numeric dtype, non-sparse, non-DataFrame), the patch converts the input to a CuPy array, temporarily promotes fitted attributes to the device, enablesarray_api_dispatch=True, and calls the original sklearn method. Fitted attributes are stored as NumPy after fit for user-visible consistency; they are promoted to CuPy only during accelerated calls.I decided to not support dataframe-input for now since it made the patch path significantly more complex.
Modeled partially after #7843
Closes #8014 #8015