Intel® Distribution for Python* Release Notes and New Features

This page provides the current Release Notes for the Intel® Distribution for Python*. The notes are categorized by year, from newest to oldest, with individual releases listed within each year.

Click a version to expand it into a summary of new features and changes in that version since the last release, and access the download buttons for the detailed release notes, which include important information, such as pre-requisites, software compatibility, installation instructions, and known issues.

You can copy a link to a specific version's section by clicking the chain icon next to its name.

All files are in PDF format - Adobe Reader* (or compatible) required.
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For questions or technical support, visit Intel® Software Developer Support.

2019

Update 4

Release Notes

  • New distributed model support for "Moments of low order" and "Covariance" algorithms through daal4py package
  • Updated python package versions and their supported platforms
Update 3

Release Notes

  • Extended availability of Intel® DAAL algorithms through daal4py package.
  • Daal4py distributed mode support for scale-out to clusters & support for streaming mode for efficient memory handling.
  • Updated python packages and their supported platforms.
Update 2

Release Notes

  • Intel® Distribution for Python 2019 Update 2 includes functional and security updates. Users should update to the latest version.
Update 1

Release Notes

  • Scikit-learn optimizations for Logistic Regression, Random Forest Regressor & Classifier.
  • New machine learning package (daal4py) with an easy to use API and performance accelerated by Intel® DAAL.
  • Introducing Numba* threading layer abstraction that allows to switch between Intel® TBB (default) and OpenMP* threading layer.
  • Access to MKL runtime settings available through easy-to-use Python control package (mkl-service)
Initial Release

Release Notes

  • Intel® Distribution for Python now integrated into Intel® Parallel Studio XE 2019 installer. Also available as easy command line standalone install.
  • Faster Machine learning with Scikit-learn: Support Vector Machine (SVM) and K-means prediction, accelerated with Intel® DAAL.
  • XGBoost package included in Intel® Distribution for Python (Linux only).
  • Note: The deep learning packages and computer vision packages along with their dependencies will not be included in Intel Distribution for Python, henceforth. However, the packages continue to be available in anaconda/Intel conda channel. Click here to learn more.

2018

Update 3

Release Notes

  • SVM classification for Radial Basis Function (RBF) kernel in scikit-learn accelerated with Intel® Data Analytics Acceleration Library for faster training and prediction
  • Numba enabled with Short Vector Math Library (SVML) by default, to leverage auto-vectorization and parallelization for performance
  • Updated packages include NumPy, SciPy, scikit-learn, Cython, pyDAAL & tbb4py
  • Updated 08/06/2018: For users who‘ve already downloaded this product through the windows standalone installers (w_python*_pu3_2018.3.040.exe), due to an installer issue on user group privileges, we have made new packages available with the fix (w_python*_pu3_2018.3.042.exe). Please visit Intel® Registration Center to install and update your existing product.
Update 2

Release Notes

  • Scikit-learn functions: Support Vector Machine (SVM) binary and multiclass algorithms, and K-means prediction, accelerated with Intel® DAAL
  • Short Vector Math Library (SVML) optimizations enabled by default in Numba, allowing control of accuracy of SVML functions via fast-math argument.
  • XGBoost package included in Intel® Distribution for Python
Update 1

Release Notes

  • mkl_fft and mkl_random have been released as stand-alone packages (originally integrated into Intel's NumPy package)
  • Miscellaneous bug fixes and version bumps
Initial Release

Release Notes

  • Python 3.6 Support
  • Performance accelerations via the latest 2018 Intel® Performance Libraries: Scikit-learn* with Intel® DAAL, FFT in SciPy*, Arithmetic and Transcendental Expressions, and Memory optimizations.
  • Updated with Packages for OpenCV, IPP, MLSL, and MKL-DNN

2017

Update 3

Release Notes

  • Updates to several modules for improved stability and performance
Update 2

Release Notes

New in this release:

  • Includes Intel-optimized Deep Learning frameworks Caffe and Theano, powered by the new Intel® MKL-DNN
  • Select Scikit-learn algorithms now accelerated with Intel® Data Analytics Acceleration Library for ~200X speedup
  • Arithmetic, transcendental and 1D & multi-dimensional FFT functions significantly faster in NumPy and SciPy
Update 1

Release Notes

New in this release:

  • Version updates to the Python packages and bug fixes
Initial Release

Release Notes

New in this release:

  • Announcing the first public release of the performance driven Intel® Distribution for Python* 2017!
  • Achieve near-native speedups with performance libraries such as Intel® Math Kernel Library, threading efficiency with TBB, data analytics with pyDAAL.
  • Seamless interoperability with conda and Anaconda Cloud. Scale easily with mpi4py and Jupyter notebooks.
For more complete information about compiler optimizations, see our Optimization Notice.