# uxlfoundation/scikit-learn-intelex

Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Repository: https://github.com/uxlfoundation/scikit-learn-intelex
Canonical: https://ross.abutalabs.com/products/scikit-learn-intelex
Homepage: https://uxlfoundation.github.io/scikit-learn-intelex/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: oneapi, scikit-learn, machine-learning-algorithms, data-analysis, machine-learning, python, swrepo, ai-machine-learning, big-data, analytics, ai-training, ai-inference, gpu, hacktoberfest
Last push: 2026-08-26T17:46:51+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 75, longevity 100
- inputs: {"age_days": 2948, "days_push": 7, "days_rel": 84, "gap_med": 43.5, "n_releases_24m": 15}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1356, forks 190 (observed 2026-08-28T04:04:29.294442+00:00)

## What it is
Intel's Extension for Scikit-learn is a free AI accelerator that speeds up existing scikit-learn workflows on CPUs and GPUs, claiming up to 100X acceleration. It works as a drop-in patch for scikit-learn across single- and multi-node configurations.

## Use cases
- speed up scikit-learn training
- accelerate machine learning on Intel CPUs
- run scikit-learn algorithms on GPUs
- drop-in replacement for sklearn estimators
- faster data science pipelines
- scale scikit-learn across multiple nodes

## When to choose
- you already use scikit-learn and want faster performance without code changes
- you run on Intel CPUs or Intel GPUs
- you need multi-node scaling for sklearn workloads

## When to avoid
- you need algorithms not covered by the extension's patched estimators
- you are not on Intel hardware and see no speedup
- you need strict bit-for-bit identical results to stock scikit-learn

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, gpu-computing, benchmarking
- domain: machine-learning, data-science, analytics, gpu-computing
- platform: python, windows
- tags: scikit-learn, acceleration, intel, oneapi, drop-in-replacement, cpu-optimization, linux, macos, gpu

## Member repositories
- uxlfoundation/scikit-learn-intelex (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:29.294442+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 2026-08-30T04:41:56.256639+00:00, confidence not recorded.
  - readme: https://github.com/uxlfoundation/scikit-learn-intelex (fetched 2026-08-28T04:04:29.294442+00:00, sha 3434a91cb315)
  - homepage: https://uxlfoundation.github.io/scikit-learn-intelex/ (fetched 2026-08-29T12:00:11.165906+00:00, sha 44136fa355b3)
  - registry_pypi: https://pypi.org/pypi/scikit-learn-intelex/json (fetched 2026-08-29T12:00:11.168399+00:00, sha 2c948d32a35b)
- Data as of 2026-08-30T08:39:29.467469+00:00.