.. Copyright 2020 Intel Corporation
..
.. Licensed under the Apache License, Version 2.0 (the "License");
.. you may not use this file except in compliance with the License.
.. You may obtain a copy of the License at
..
..     http://www.apache.org/licenses/LICENSE-2.0
..
.. Unless required by applicable law or agreed to in writing, software
.. distributed under the License is distributed on an "AS IS" BASIS,
.. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
.. See the License for the specific language governing permissions and
.. limitations under the License.

.. |intelex_repo| replace:: |intelex| repository
.. _intelex_repo: https://github.com/uxlfoundation/scikit-learn-intelex

.. _index:

#########
|intelex|
#########

Intel(R) Extension for Scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** faster performance for 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.

.. rubric:: Designed for Data Scientists and Framework Designers


Use Intel(R) Extension for Scikit-learn, to:

* Speed up training and inference by up to 100x with the equivalent mathematical accuracy
* Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
* Integrate the extension into your existing Scikit-learn applications without code modifications
* 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>`_.


.. image:: _static/scikit-learn-acceleration.PNG
  :width: 800


These performance charts use benchmarks that you can find in the `scikit-learn bench repository <https://github.com/IntelPython/scikit-learn_bench>`_.


Supported Algorithms
---------------------

See all of the :ref:`sklearn_algorithms`.


Intel(R) Optimizations
----------------------------------

Enable Intel(R) CPU Optimizations
*********************************

::

   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
*********************************
::

   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)



.. toctree::
   :caption: Get Started
   :hidden:
   :maxdepth: 3

   quick-start.rst
   samples.rst
   kaggle.rst

.. toctree::
   :caption: Developer Guide
   :hidden:
   :maxdepth: 2

   algorithms.rst
   oneAPI and GPU support <oneapi-gpu.rst>
   distributed-mode.rst
   non-scikit-algorithms.rst
   input-types.rst
   array_api.rst
   verbose.rst
   preview.rst
   deprecation.rst


.. toctree::
   :caption: Performance
   :hidden:
   :maxdepth: 2

   guide/acceleration.rst

.. toctree::
   :caption: Learn
   :hidden:
   :maxdepth: 2

   Tutorials & Case Studies <tutorials.rst>
   Medium Blogs <blogs.rst>

.. toctree::
   :caption: More
   :hidden:
   :maxdepth: 2

   Support <support.rst>
   contribute.rst
   license.rst