#7 · Primary category： Classical Machine Learning Frameworks
cuml
NVIDIA cuML: GPU-Accelerated Machine Learning
Project last updated：08/28/26
GitHub Stars
5.3K
Forks
658
Contributors
206
License
Apache-2.0
Why we included this project
cuML is a good fit when your team's tabular workloads are hitting CPU limits and you'd rather not rewrite everything. Its GPU-native estimators follow the familiar scikit-learn fit-predict-transform pattern, so clustering, regression, classification, and dimensionality reduction run on CUDA without leaving the API you already know. The cuml.accel module is the more interesting part for existing code: point it at a scikit-learn, UMAP, or HDBSCAN script and supported operations move to the GPU, falling back to CPU automatically when an estimator isn't covered. That gives you a low-risk path to speed up legacy pipelines, while the distributed API handles datasets that outgrow a single GPU.
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