# NVIDIA/cuml

NVIDIA cuML: GPU-Accelerated Machine Learning

Repository: https://github.com/NVIDIA/cuml
Canonical: https://ross.abutalabs.com/products/cuml
Homepage: https://docs.nvidia.com/cuml/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning-algorithms, machine-learning, cuda, gpu, nvidia
Last push: 2026-08-26T23:07:18+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 2883, "days_push": 7, "days_rel": 28, "gap_med": 62, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5264, forks 655 (observed 2026-08-28T04:09:13.871534+00:00)

## What it is
NVIDIA cuML is a GPU-accelerated machine learning library offering scikit-learn-style estimators that run on NVIDIA GPUs via CUDA. It also provides cuml.accel, a drop-in accelerator for existing scikit-learn, UMAP, and HDBSCAN code, plus multi-GPU support through Dask.

## Use cases
- speed up scikit-learn workflows on gpu
- run clustering and regression on nvidia gpus
- accelerate umap and hdbscan without changing code
- scale machine learning across multiple gpus
- gpu-accelerated nearest neighbor search
- drop-in replacement for sklearn estimators

## When to choose
- you have nvidia gpus and want large speedups on sklearn-style workloads
- you want to accelerate existing scikit-learn code with minimal changes
- you need multi-gpu or multi-node distributed machine learning with dask

## When to avoid
- you have no nvidia gpu hardware
- you rely on estimators not yet supported by cuml.accel and need guaranteed cpu fallback behavior
- you need a pure-cpu portable solution

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, gpu-computing
- domain: machine-learning, data-science, gpu-computing
- platform: python
- tags: cuda, scikit-learn, rapids, dask, acceleration, gpu, linux, docker

## Member repositories
- NVIDIA/cuml (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:13.871534+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:59:30.127754+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/cuml (fetched 2026-08-28T04:09:13.871534+00:00, sha 3b19a1187fa7)
  - homepage: https://docs.nvidia.com/cuml/ (fetched 2026-08-29T08:54:19.943898+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.