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cuml-machine-learning

Train and evaluate cuML classification, regression, clustering, and dimensionality reduction models on tabular datasets.

3|Updated Mar 16, 2026
Search Tags:#regression#dimensionality reduction#clustering#cuml#gpu machine learning#tabular modeling
One-click install
npx skills add https://github.com/bogware/bog-agents --skill cuml-machine-learning-bogware
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuml-machine-learning
Source: https://github.com/bogware/bog-agents/tree/main/examples/nvidia_deep_agent/skills/cuml-machine-learning
Command: npx skills add https://github.com/bogware/bog-agents --skill cuml-machine-learning-bogware

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves slow, CPU-bound machine learning training and experimentation on large tabular datasets by running common ML algorithms on NVIDIA GPUs via cuML.

Core Features & Use Cases

  • GPU-accelerated ML for tabular data: Uses NVIDIA RAPIDS cuML for scalable training and inference.
  • Multiple task types: Supports classification, regression, clustering, dimensionality reduction, and preprocessing/feature engineering.
  • Practical CPU fallback: Automatically falls back to scikit-learn when cuML/cuDF are unavailable, enabling the workflow to keep running.

Quick Start

Train a cuML RandomForestClassifier or CPU scikit-learn equivalent on your dataset by prompting the assistant to run cuML with GPU smoke-test, perform the train/test split, and print accuracy plus key outputs for the specified features and target.

Frequently Asked Questions about cuml-machine-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train machine learning models on large tabular datasets using GPUs?▼

GPU machine learning accelerates training on large tabular datasets by using NVIDIA RAPIDS cuML to run classification, regression, and clustering models, enabling fast experimentation on datasets with 10,000 or more rows.

What is the best way to run clustering and dimensionality reduction on GPU?▼

Running clustering and dimensionality reduction on GPU uses NVIDIA RAPIDS cuML to apply accelerated algorithms to dense float32 tabular inputs, delivering scalable data processing and feature engineering.

Do I need an NVIDIA GPU to run GPU-accelerated tabular modeling?▼

An NVIDIA GPU is not strictly required for GPU-accelerated tabular modeling because the workflow automatically falls back to CPU-based scikit-learn when cuML or cuDF are unavailable on the system.

Can I use sparse matrices for GPU machine learning with cuML?▼

Sparse matrices cannot be used for GPU machine learning with cuML in this workflow, as the implementation requires GPU-friendly dense float32 feature matrices to properly execute training and preprocessing operations.

How do I run a GPU smoke-test before training a cuML classification model?▼

Running a GPU smoke-test before training a cuML classification model involves initializing cuML and cuDF, casting features to dense float32 matrices, and splitting data to print accuracy and key outputs.

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