Cuml Machine Learning

This wraps NVIDIA RAPIDS cuML for GPU-accelerated machine learning with a scikit-learn compatible API. You get the same RandomForest, KMeans, PCA, and regression models you already know, but they run on GPU and can handle datasets with 10K+ rows dramatically faster. The skill includes proper initialization boilerplate that smoke-tests GPU availability and falls back to scikit-learn if needed, plus helpers for the cuDF to pandas conversion dance. One thing to remember is cuML wants float32 dense arrays and doesn't support sparse matrices, so you'll need to cast and densify your data first. Good for classification, regression, clustering, and dimensionality reduction when you have real data volume.

skills.sh installs: 247
GitHub stars: 31.2K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add langchain-ai/deepagents --skill cuml-machine-learning --agent claude-code

Installs into .claude/skills of the current project.

Files

cuML Machine Learning Skill

GPU-accelerated machine learning using NVIDIA RAPIDS cuML. cuML provides a scikit-learn-compatible API that runs on NVIDIA GPUs, enabling massive speedups on large datasets.

When to Use This Skill

Use this skill when:

Training classification models (predict categories, detect fraud, classify text)
Training regression models (forecast values, predict prices, estimate quantities)
Clustering data (segment customers, group documents, find patterns)
Dimensionality reduction (visualize high-dimensional data, compress features)
Preprocessing and feature engineering on large datasets
Any ML task on datasets with 10K+ rows where GPU acceleration helps