## Overview Intel(R) 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 for all upcoming Intel(R) platforms at launch time. With Intel(R) Extension for Scikit-learn, you can: * Speed up training and inference by up to 100x with the equivalent mathematical accuracy * Benefit from performance improvements across different Intel(R) hardware 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 Intel(R) Extension for Scikit-learn is also a part of [Intel(R) AI Tools](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html). ## Acceleration  [Benchmarks code](https://github.com/IntelPython/scikit-learn_bench) ## Intel(R) Optimizations - **Enable Intel(R) CPU optimizations** ```py 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 Intel(R) GPU optimizations** ```py import numpy as np import dpctl 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/intel/scikit-learn-intelex/tree/master/examples/notebooks) for more examples. ## Installation To install Intel(R) Extension for Scikit-learn, run: ``` pip install scikit-learn-intelex ``` See all installation instructions in the [Installation Guide](https://intel.github.io/scikit-learn-intelex/latest/installation.html). ## Integration The software acceleration is achieved through patching. It means, replacing the stock scikit-learn algorithms with their optimized versions provided by the extension. The patching only affects [supported algorithms and their parameters](https://intel.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://intel.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: ``` python -m sklearnex my_application.py ``` * Add the following lines to the script: ``` from sklearnex import patch_sklearn patch_sklearn() ``` :eyes: Read about [other ways to patch scikit-learn](https://intel.github.io/scikit-learn-intelex/index.html#usage). ## Documentation * [Quick Start](https://intel.github.io/scikit-learn-intelex/latest/quick-start.html) * [Documentation and Tutorials](https://intel.github.io/scikit-learn-intelex/latest/index.html) * [Release Notes](https://github.com/intel/scikit-learn-intelex/releases) * [Medium Blogs](https://intel.github.io/scikit-learn-intelex/latest/blogs.html) * [Code of Conduct](https://github.com/intel/scikit-learn-intelex/blob/master/CODE_OF_CONDUCT.md) ### daal4py and oneDAL The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Learn more: - [About Intel(R) oneAPI Data Analytics Library](https://github.com/oneapi-src/oneDAL) - [About daal4py](https://github.com/intel/scikit-learn-intelex/tree/main/daal4py) ## Samples & Examples * [Examples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks) * [Samples](https://intel.github.io/scikit-learn-intelex/latest/samples.html) * [Kaggle Kernels](https://intel.github.io/scikit-learn-intelex/latest/kaggle.html) ## How to Contribute We welcome community contributions, check our [Contributing Guidelines](https://github.com/intel/scikit-learn-intelex/blob/master/CONTRIBUTING.md) 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.