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.

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2019

Beta Release

Release Notes

  • Intel® Distribution for Python 2019 standalone installer now supports command line installations on Windows, Linux, MacOS.
  • Intel® Distribution for Python 2019 is now available to download as part of Intel® Parallel Studio XE installer.
  • Short Vector Math Library (SVML) optimizations enabled by default in Numba, allowing control of accuracy of SVML functions via fast-math argument.

2018

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.