https://github.com/uxlfoundation/scikit-learn-intelex
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
https://github.com/uxlfoundation/scikit-learn-intelex
ai-inference ai-machine-learning ai-training analytics big-data data-analysis gpu hacktoberfest machine-learning machine-learning-algorithms oneapi python scikit-learn swrepo
Last synced: 15 days ago
JSON representation
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
- Host: GitHub
- URL: https://github.com/uxlfoundation/scikit-learn-intelex
- Owner: uxlfoundation
- License: apache-2.0
- Created: 2018-08-07T06:45:41.000Z (about 8 years ago)
- Default Branch: main
- Last Pushed: 2026-10-16T12:41:10.000Z (18 days ago)
- Last Synced: 2026-10-17T10:41:07.645Z (18 days ago)
- Topics: ai-inference, ai-machine-learning, ai-training, analytics, big-data, data-analysis, gpu, hacktoberfest, machine-learning, machine-learning-algorithms, oneapi, python, scikit-learn, swrepo
- Language: Python
- Homepage: https://uxlfoundation.github.io/scikit-learn-intelex/
- Size: 45.5 MB
- Stars: 1,355
- Watchers: 24
- Forks: 190
- Open Issues: 91
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
- Agents: AGENTS.md
Awesome Lists containing this project
README
# Extension for scikit-learn*
Speed up your scikit-learn applications for CPUs and GPUs across single- and multi-node configurations
[Releases](https://github.com/uxlfoundation/scikit-learn-intelex/releases) | [Documentation](https://uxlfoundation.github.io/scikit-learn-intelex/) | [Examples](https://uxlfoundation.github.io/scikit-learn-intelex/latest/samples.html) | [Support](https://uxlfoundation.github.io/scikit-learn-intelex/latest/support.html) | [License](https://github.com/uxlfoundation/scikit-learn-intelex/blob/master/LICENSE)
[](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=main)
[](https://scan.coverity.com/projects/daal4py)
[](https://securityscorecards.dev/viewer/?uri=github.com/uxlfoundation/scikit-learn-intelex)
[](https://github.com/uxlfoundation/scikit-learn-intelex/discussions)
[](https://pypi.org/project/scikit-learn-intelex/)
[](https://anaconda.org/conda-forge/scikit-learn-intelex)
[](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14-blue)
[](https://img.shields.io/badge/sklearn-1.6%20%7C%201.7%20%7C%201.8%20%7C%201.9-blue)
---
## Overview
Extension for scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** acceleration to existing workflows from [scikit-learn](https://scikit-learn.org) (e.g. logistic regression models, random forests, support vector machines, among others), the most widely-used Python library for machine learning on tabular data.
This software acceleration is achieved through SIMD instructions, multi-threading, hardware-specific cache optimizations, and algorithmic optimizations.
Features:
* Average speed up of 8.5x on both training and inference over [scikit-learn](https://scikit-learn.org), with equivalent mathematical accuracy.
* Support for different hardware configurations, including [GPUs](https://uxlfoundation.github.io/scikit-learn-intelex/latest/oneapi-gpu.html) and [multi-GPU](https://uxlfoundation.github.io/scikit-learn-intelex/latest/distributed-mode.html) setups.
* Easy to integrate into existing [scikit-learn](https://scikit-learn.org) workflows without code modifications through [monkeypatching](https://uxlfoundation.github.io/scikit-learn-intelex/latest/patching.html), or as an API-compatible module `sklearnex`.
* Can produce [much faster predictions](https://uxlfoundation.github.io/scikit-learn-intelex/latest/model_builders.html) from decision-tree models generated by other libraries such as [XGBoost](https://xgboost.ai/) or [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html).
## Acceleration
[Benchmarks code](https://github.com/IntelPython/scikit-learn_bench)
## Optimizations
Easiest way to benefit from accelerations from the extension is by patching [scikit-learn](https://scikit-learn.org) with it:
- **Enable CPU optimizations**
```python
import numpy as np
from sklearnex import patch_sklearn
patch_sklearn()
from sklearn.cluster import DBSCAN
X = np.array([[1., 2.], [2., 2.], [2., 3.],
[8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
clustering = DBSCAN(eps=3, min_samples=2).fit(X)
```
- **Enable GPU optimizations**
_Note: executing on GPU has [additional system software requirements](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html) - see [details](https://uxlfoundation.github.io/scikit-learn-intelex/latest/oneapi-gpu.html)._
```python
import numpy as np
from sklearnex import patch_sklearn, config_context
patch_sklearn()
from sklearn.cluster import DBSCAN
X = np.array([[1., 2.], [2., 2.], [2., 3.],
[8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
with config_context(target_offload="gpu:0"):
clustering = DBSCAN(eps=3, min_samples=2).fit(X)
```
:eyes: Read about [other ways to patch scikit-learn](https://uxlfoundation.github.io/scikit-learn-intelex/latest/patching.html).
:eyes: Check out available [notebooks](https://github.com/uxlfoundation/scikit-learn-intelex/tree/master/examples/notebooks) for more examples.
### Usage without patching
Alternatively, all functionalities are also available under a separate module which can be imported directly, without involving any patching.
* To run on CPU:
```python
import numpy as np
from sklearnex.cluster import DBSCAN
X = np.array([[1., 2.], [2., 2.], [2., 3.],
[8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
clustering = DBSCAN(eps=3, min_samples=2).fit(X)
```
* To run on GPU:
```python
import numpy as np
from sklearnex import config_context
from sklearnex.cluster import DBSCAN
X = np.array([[1., 2.], [2., 2.], [2., 3.],
[8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
with config_context(target_offload="gpu:0"):
clustering = DBSCAN(eps=3, min_samples=2).fit(X)
```
## Installation
To install Extension for scikit-learn, run:
```shell
pip install scikit-learn-intelex
```
Package is also offered through other channels such as conda-forge. See all installation instructions in the [Installation Guide](https://uxlfoundation.github.io/scikit-learn-intelex/latest/installation.html).
## Documentation
* [Technical documentation](https://uxlfoundation.github.io/scikit-learn-intelex/latest/index.html)
* [Release Notes](https://github.com/uxlfoundation/scikit-learn-intelex/releases)
* [Medium Blog Posts](https://uxlfoundation.github.io/scikit-learn-intelex/latest/blogs.html)
### Extension and oneDAL
Acceleration in patched scikit-learn classes is achieved by replacing calls to [scikit-learn](https://scikit-learn.org) with calls to oneDAL (oneAPI Data Analytics Library) behind the scenes:
- [oneAPI Data Analytics Library](https://github.com/uxlfoundation/oneDAL)
## How to Contribute
We welcome community contributions, check our [Contributing Guidelines](https://uxlfoundation.github.io/scikit-learn-intelex/latest/contribute.html) to learn more.
------------------------------------------------------------------------
\* The Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.