Accelerated Estimator Support#

cuml.accel accelerates the estimators listed below. Unsupported estimators and operations continue to use their original CPU implementations. Click an estimator name to display its full list of limitations.

General Behavior#

Compatibility: The accelerator is tested with scikit-learn versions 1.6 through 1.9, umap-learn versions 0.5.7 through 0.5.12, and hdbscan versions 0.8.39 through 0.8.44. When cuml.accel detects a version outside these ranges, it issues a runtime warning and continues. The untested version will likely still work, but it has not been validated and may have subtle compatibility issues. Some estimators use scikit-learn’s experimental Array API support and require scikit-learn 1.8 or newer for GPU acceleration.
CPU fallback: Estimators not listed below use their original CPU implementations. Listed estimators may also fall back for particular methods, hyperparameters, input types, or dependency versions. These fallbacks should be transparent and preserve the original workflow. Logging and Profiling explains how to identify which operations ran on the GPU and why others fell back to the CPU.
Results: GPU and CPU implementations should provide comparable model quality, but they are not guaranteed to be numerically identical. Parallel floating-point operations may run in a different order, and some GPU algorithms use implementations designed for highly parallel hardware. cuml.accel also does not always generate the full set of fitted attributes that scikit-learn does. Missing attributes are typically inspection aids, such as n_iters_, rather than values required for inference. Compare appropriate model-quality metrics, such as model.score or accuracy, instead of comparing fitted coefficients directly.
Performance: Performance depends on the estimator and workload. Larger datasets generally benefit most. On small inputs, initialization and data-transfer costs can dominate, limiting the benefit of acceleration or even making execution slower. Some GPU operations use runtime-compiled kernels, so their first invocation may include compilation overhead. Warm up the operation before timing it, and compare end-to-end runtime with and without cuml.accel. See Benchmarks for representative results.
Errors and warnings: Error and warning messages may differ from scikit-learn, and some errors may include C++ stack traces. Exceptions, failures, or measurably worse model quality are bugs and should be reported by opening an issue.

scikit-learn#

sklearn.cluster#

The algorithms used in cuML differ from those in scikit-learn. As such, you shouldn’t expect the fitted attributes (e.g. labels_) to numerically match an estimator fitted without cuml.accel.

To compare results between estimators, we recommend comparing scores like sklearn.metrics.adjusted_rand_score or sklearn.metrics.adjusted_mutual_info_score. For low-dimensional data, you can also visually inspect the resulting cluster assignments.

sklearn.covariance#

sklearn.decomposition#

The sklearn.decomposition implementations used by cuml.accel use different SVD solvers than the ones in scikit-learn, which may result in numeric differences in the components_ and explained_variance_ values. These differences should be small for most algorithms, but may be larger for randomized or less-numerically-stable solvers like "randomized" or "covariance_eigh".

sklearn.ensemble#

The random forest implementation used by cuml.accel differs algorithmically from the one in scikit-learn. As such, you shouldn’t expect the fitted attributes (e.g. estimators_) to numerically match an estimator fitted without cuml.accel.

To compare results between estimators, we recommend comparing scores like sklearn.metrics.root_mean_squared_error (for regression) or sklearn.metrics.log_loss (for classification).

sklearn.kernel_ridge#

sklearn.linear_model#

The linear model solvers used by cuml.accel differ from those used in scikit-learn. As such, you shouldn’t expect the fitted attributes (e.g. coef_) to numerically match an estimator fitted without cuml.accel. For some estimators (e.g. LinearRegression) you might get a close match, but for others there may be larger numeric differences.

To compare results between estimators, we recommend comparing model quality scores like sklearn.metrics.r2_score (for regression) or sklearn.metrics.accuracy_score (for classification).

sklearn.manifold#

sklearn.neighbors#

sklearn.preprocessing#

sklearn.svm#

The SVM implementations used by cuml.accel differ from those in scikit-learn. As such, you shouldn’t expect the fitted attributes (e.g. coef_ or support_vectors_) to numerically match an estimator fitted without cuml.accel.