# scikit-learn-intelex

**Repository Path**: llxingkongxia/scikit-learn-intelex

## Basic Information

- **Project Name**: scikit-learn-intelex
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: dev/eglaser-jupyter-fix2
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-30
- **Last Updated**: 2025-07-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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<div align="center">


# Extension for Scikit-learn*

<h3> 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)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Documentation](https://uxlfoundation.github.io/scikit-learn-intelex/)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Examples](https://github.com/uxlfoundation/scikit-learn-intelex/tree/master/examples/notebooks)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Support](SUPPORT.md)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;[License](https://github.com/uxlfoundation/scikit-learn-intelex/blob/master/LICENSE)&nbsp;&nbsp;&nbsp;


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---
</h3>

<div align="left">

## Overview

Extension for Scikit-learn is a **free software AI accelerator** designed to deliver over **10-100X** acceleration to your existing scikit-learn code.
The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations.


With Extension for Scikit-learn, you can:

* Speed up training and inference by up to 100x with equivalent mathematical accuracy
* Benefit from performance improvements across different CPU hardware configurations, including GPUs and multi-GPU configurations
* Integrate the extension into your existing Scikit-learn applications without code modifications
* Continue to use the open-source scikit-learn API
* Enable and disable the extension with a couple of lines of code or at the command line

## Acceleration

![](https://raw.githubusercontent.com/uxlfoundation/scikit-learn-intelex/master/doc/sources/_static/scikit-learn-acceleration.PNG)

[Benchmarks code](https://github.com/IntelPython/scikit-learn_bench)

## Optimizations

Easiest way to benefit from accelerations from the extension is by patching scikit-learn 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: 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://github.com/uxlfoundation/scikit-learn-intelex/blob/main/INSTALL.md).

## Integration

The easiest way of accelerating scikit-learn workflows with the extension is through through [patching](https://uxlfoundation.github.io/scikit-learn-intelex/latest/quick-start.html#patching), which replaces the stock scikit-learn algorithms with their optimized versions provided by the extension using the same namespaces in the same modules as scikit-learn.

The patching only affects [supported algorithms and their parameters](https://uxlfoundation.github.io/scikit-learn-intelex/latest/algorithms.html).
You can still use not supported ones in your code, the package simply fallbacks into the stock version of scikit-learn.

> **_TIP:_** Enable [verbose mode](https://uxlfoundation.github.io/scikit-learn-intelex/latest/verbose.html) to see which implementation of the algorithm is currently used.

To patch scikit-learn, you can:
* Use the following command-line flag:
  ```shell
  python -m sklearnex my_application.py
  ```
* Add the following lines to the script:
  ```python
  from sklearnex import patch_sklearn
  patch_sklearn()
  ```

:eyes: Read about [other ways to patch scikit-learn](https://uxlfoundation.github.io/scikit-learn-intelex/latest/quick-start.html#patching).

As an alternative, accelerated classes from the extension can also be imported directly without patching, thereby allowing to keep them separate from stock scikit-learn ones - for example:

```python
from sklearnex.cluster import DBSCAN as exDBSCAN
from sklearn.cluster import DBSCAN as stockDBSCAN

# ...
```

## Documentation

* [Quick Start](https://uxlfoundation.github.io/scikit-learn-intelex/latest/quick-start.html)
* [Documentation and Tutorials](https://uxlfoundation.github.io/scikit-learn-intelex/latest/index.html)
* [Release Notes](https://github.com/uxlfoundation/scikit-learn-intelex/releases)
* [Medium Blogs](https://uxlfoundation.github.io/scikit-learn-intelex/latest/blogs.html)
* [Code of Conduct](https://github.com/uxlfoundation/scikit-learn-intelex/blob/master/CODE_OF_CONDUCT.md)

### Extension and oneDAL

Acceleration in patched scikit-learn classes is achieved by replacing calls to scikit-learn with calls to oneDAL (oneAPI Data Analytics Library) behind the scenes:
- [oneAPI Data Analytics Library](https://github.com/uxlfoundation/oneDAL)

## Samples & Examples

* [Examples](https://github.com/uxlfoundation/scikit-learn-intelex/tree/master/examples/notebooks)
* [Samples](https://uxlfoundation.github.io/scikit-learn-intelex/latest/samples.html)
* [Kaggle Kernels](https://uxlfoundation.github.io/scikit-learn-intelex/latest/kaggle.html)


## How to Contribute

We welcome community contributions, check our [Contributing Guidelines](https://github.com/uxlfoundation/scikit-learn-intelex/blob/master/CONTRIBUTING.md) to learn more.

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