Data Science & Analytics

cuML

Use when writing, running, debugging or upgrading Python code that uses NVIDIA cuML (GPU machine learning), cuml.accel (zero-code-change GPU acceleration of scikit-learn, umap-learn and hdbscan), or GPU UMAP/HDBSCAN/RandomForest/KMeans/SVC, including through libraries that wrap cuML (BERTopic, rapids-singlecell). Covers APIs removed or renamed in 2025-2026 releases, verifying code really ran on the GPU, large-data UMAP/HDBSCAN memory recipes, and exporting trained models. Not for cuDF-only dataframe work, PyTorch or XGBoost.

Publisher / source

NVIDIA Skills (Verified)

Upstream record

NVIDIA/skills

Version

Version 1

Published

2026-11-03

Publication review

Approved for publication

Content fingerprint

cd69f1293b5c80533274d0a3fa50cac705c12e515f0bfbc02383728fcef48e01