daal4py - A Convenient Python API to the Intel® Data Analytics Acceleration Library (Intel® DAAL)

A simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for either direct usage or integration into one's own framework.

• Documentation
• Source Code
• About Intel® DAAL

Running full scikit-learn test suite with daal4p's optimization patches

• when applied to scikit-learn from PyPi
• when applied to build from master branch

With this daal4py API, your Python programs can use Intel® DAAL algorithms in just one line:

kmeans_init(data, 10, t_method="plusPlusDense")

You can even run this on a cluster by simple adding a keyword-parameter

kmeans_init(data, 10, t_method="plusPlusDense", distributed=True)

Getting Started

daal4py is easily built from source with the majority of the necessary prerequisites available on conda. The instructions below detail how to gather the prerequisites, setting one's build environment, and finally building and installing the completed package. daal4py can be built for all three major platforms (Windows, Linux, macOS). Multi-node (distributed) and streaming support can be disabled if desired.

The build-process (using setup.py) happens in 3 stages:

1. Creating C++ and cython sources from DAAL C++ headers
2. Running cython on generated source
3. Compiling and linking

Building daal4py using conda-build

The easiest way to build daal4py is using the conda-build with the provided recipe.

Prerequisites

• Python version 2.7 or >= 3.6
• conda-build version >= 3
• C++ compiler with C++11 support

Building daal4py

cd <checkout-dir>
conda build conda-recipe -c intel -c conda-forge

This will build the conda package and tell you where to find it (.../daal4py*.tar.bz2).

Installing the built daal4py conda package

conda install <path-to-conda-package-as-built-above>

To actually use your daal4py, dependent packages need to be installed. To ensure, do

Linux and OsX:

conda install -c intel -c conda-forge mpich tbb daal numpy

Windows:

conda install -c intel mpi_rt tbb daal numpy

Building daal4py without conda-build

Without conda-build you have to manually setup your environment before building daal4py.

Prerequisites

• Python version 2.7 or >= 3.6
• Jinja2
• Cython
• Numpy
• A C++ compiler with C++11 support
• Intel(R) Threading Building Blocks (Intel® TBB) version 2018.0.4 or later (https://www.threadingbuildingblocks.org/)
  • You can use the pre-built conda package from Intel's channel or conda-forge channel on anaconda.org (see below)
  • Needed for distributed mode. You can disable support for distributed mode by setting NO_DIST to '1' or 'yes'
• Intel® Data Analytics Acceleration Library (Intel® DAAL) version 2019 or later (https://github.com/01org/daal)
  • You can use the pre-built conda package from Intel channel on anaconda.org (see below)
• MPI
  • You can use the pre-built conda package intel or conda-forge channel on anaconda.org (see below)
  • Needed for distributed mode. You can disable support for distributed mode by setting NO_DIST to '1' or 'yes'

Setting up a build environment

The easiest path for getting cython, DAAL, TBB, MPI etc. is by creating a conda environment and setting environment variables:

conda create -n DAAL4PY python=3.6 impi-devel tbb-devel daal daal-include cython jinja2 numpy clang-tools -c intel -c conda-forge
conda activate DAAL4PY
export TBBROOT=$CONDA_PREFIX
export DAALROOT=$CONDA_PREFIX
export MPIROOT=$CONDA_PREFIX

Configuring the build with environment variables

• DAAL4PY_VERSION: sets package version
• 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

Notes on building for macOS

If building in High Sierra or higher, one may have to run into C++ build errors related to platform targets. Utilize export MACOSX_DEPLOYMENT_TARGET="10.9" if running into platform target issues.

Building daal4py

Requires Intel® DAAL, Intel® TBB and MPI being properly setup, e.g. DAALROOT, TBBROOT and MPIROOT being set.

cd <checkout-dir>
python setup.py build_ext

Installing daal4py

Requires Intel® DAAL, Intel® TBB and MPI being properly setup, e.g. DAALROOT, TBBROOT and MPIROOT being set.

cd <checkout-dir>
python setup.py install

Building documentation for daal4py

Prerequisites for creating documentation

• sphinx
• sphinx_rtd_theme

Building documentation

1. Install daal4py into your python environment
2. cd doc && make html
3. The documentation will be in doc/_build/html