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INSTALL.md

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Installation

To install Intel(R) Extension for Scikit-learn*, use one of the following scenarios:

NOTE: Intel(R) Extension for Scikit-learn* is also available as a part of Intel® AI Tools. If you already have it installed, you do not need to separately install the extension.

Before You Begin

Check System and Memory Requirements.

Supported Configurations

OS / Python version Python 3.9 Python 3.10 Python 3.11 Python 3.12
Linux [CPU, GPU] [CPU, GPU] [CPU, GPU] [CPU, GPU]
Windows [CPU, GPU] [CPU, GPU] [CPU, GPU] [CPU, GPU]

Applicable for:

  • PyPI
  • Anaconda Cloud from Conda-Forge Channel
  • Anaconda Cloud from Intel Channel

Install via PIP

To prevent version conflicts, create and activate a new environment:

  • On Linux:

    python -m venv env
source env/bin/activate

  • On Windows:

    python -m venv env
.\env\Scripts\activate

Install from PyPI Channel (recommended by default)

Install scikit-learn-intelex:

pip install scikit-learn-intelex

Install from Anaconda Cloud

To prevent version conflicts, we recommend to create and activate a new environment.

Install via Anaconda Cloud from Conda-Forge Channel

  • Install into a newly created environment (recommended):

    conda config --add channels conda-forge
conda config --set channel_priority strict
conda create -n env python=3.10 scikit-learn-intelex

NOTE: If you do not specify the Python version, the latest one is downloaded.

  • Install into your current environment:

    conda config --add channels conda-forge
conda config --set channel_priority strict
conda install scikit-learn-intelex

Install via Anaconda Cloud from Intel Channel

We recommend this installation for the users of Intel® Distribution for Python.

  • Install into a newly created environment (recommended):

    conda config --add channels https://software.repos.intel.com/python/conda/
conda config --set channel_priority strict
conda create -n env python=3.10 scikit-learn-intelex

NOTE: If you do not specify the Python version, the latest one is downloaded.

  • Install into your current environment:

    conda config --add channels https://software.repos.intel.com/python/conda/
conda config --set channel_priority strict
conda install scikit-learn-intelex

NOTE: If you do not specify the version of Python, the latest one is downloaded.

  • Install into your current environment:

    conda install scikit-learn-intelex

Build from Sources

Intel(R) Extension for Scikit-learn* is easily built from the sources with the majority of the necessary prerequisites available with conda or pip.

The package is available for Windows* OS, Linux* OS, and macOS*.

Multi-node (distributed) and streaming support can be disabled if needed.

The build-process (using setup.py) happens in 4 stages:

  1. Creating C++ and Cython sources from oneDAL C++ headers
  2. Building oneDAL Python interfaces via cmake and pybind11
  3. Running Cython on generated sources
  4. Compiling and linking them

Prerequisites

  • Python version >= 3.9, <= 3.12
  • Jinja2
  • Cython
  • Numpy
  • cmake and pybind11
  • A C++ compiler with C++11 support
  • Clang-Format
  • Intel® oneAPI Data Analytics Library (oneDAL) version 2021.1 or later
    • You can use the pre-built dal-devel conda package from conda-forge channel
  • MPI (optional, needed for distributed mode)
    • You can use the pre-built impi-devel conda package from conda-forge channel
  • A DPC++ compiler (optional, needed for DPC++ interfaces)

Configure the Build with Environment Variables

  • SKLEARNEX_VERSION: sets the package version
  • DALROOT: sets the oneAPI Data Analytics Library path
  • NO_DIST: set to '1', 'yes' or alike to build without support for distributed mode
  • NO_STREAM: set to '1', 'yes' or alike to build without support for streaming mode
  • NO_DPC: set to '1', 'yes' or alike to build without support of oneDAL DPC++ interfaces
  • OFF_ONEDAL_IFACE: set to '1' to build without the support of oneDAL interfaces

Build Intel(R) Extension for Scikit-learn

  • To install the package:

    cd <checkout-dir>
python setup.py install

  • To install the package in the development mode:

    cd <checkout-dir>
python setup.py develop

  • To install scikit-learn-intelex without checking for dependencies:

    cd <checkout-dir>
python setup.py install --single-version-externally-managed --record=record.txt
    cd <checkout-dir>
python setup.py develop --no-deps

  • To build the python module without installing it:

    cd <checkout-dir>
python setup.py build_ext --inplace --force
python setup.py build

Where:

  • Keys --single-version-externally-managed and --no-deps are required to not download daal4py after the installation of Intel(R) Extension for Scikit-learn.
  • The develop mode does not install the package but creates a .egg-link in the deployment directory back to the project source-code directory. That way, you can edit the source code and see the changes without reinstalling the package after a small change.
  • --single-version-externally-managed is an option for Python packages instructing the setup tools module to create a package the host's package manager can easily manage.

Build from Sources with conda-build

Intel(R) Extension for Scikit-learn* is easily built from the sources using only one command and conda-build utility.

Prerequisites for conda-build

  • any conda distribution (miniforge is recommended)
  • conda-build and conda-verify installed in a conda environment
  • (Windows only) Microsoft Visual Studio*
  • (optional) Intel(R) oneAPI DPC++/C++ Compiler

conda-build config requires 2022 version of Microsoft Visual Studio* by default, you can specify another version in conda-recipe/conda_build_config.yaml if needed.

In order to enable DPC++ interfaces support on Windows, you need to set DPCPPROOT environment variable pointing to DPC++/C++ Compiler distribution. Conda-forge distribution of DPC++ compiler is used by default on Linux, but you still can set your own distribution via DPCPPROOT variable.

Build Intel(R) Extension for Scikit-learn with conda-build

Create and verify scikit-learn-intelex conda package with next command executed from root of sklearnex repo:

conda build .

Next Steps