The Beta for Intel® Distribution for Python* 2017 has been available for 1 month and I wanted to share some of our experiences. Our goal is to let you program in Python without having to compromise on performance. The feedback so far has been very encouraging. You love the performance and getting the core packages optimized and tested without having to build the native extensions. We have addressed many of the issues from the tech preview as well as bringing exciting new capabilities to Python.
We have been working closely with Continuum Analytics to bring the capabilities of Anaconda to the Intel Distribution for Python. We include conda, making it easier to install conda packages and create conda environments. You now have easy access to the large and growing set of packages available on Anaconda Cloud*. We have adopted the conda packaging format for the packages we publish, giving you more control managing packages. We are publishing our packages under the Intel channel on Anaconda Cloud* so you now have the choice of using our installer or using conda to install the Intel® Distribution for Python. If you are an Anaconda user, you can be trying out Intel Python in a few minutes without leaving the conda environment. There are still some rough edges that we are working to resolve with the help of Continuum Analytics, so look for more to happen over time.
We have 2 new packages, PyDAAL and TBB. PyDAAL provides pythonic interfaces to the Intel® Data Analytics Acceleration Library (DAAL). Intel DAAL consists of highly optimized building blocks for data analytics, from the same team that brought you Intel® Math Kernel Library. TBB package brings more efficient thread scheduling to Python that lets you take advantage of multiple cores. It is based upon Intel® Threading Building Blocks (TBB) library that has been available to C++ language programmers for 10 years now and is high tuned for many architectures.
It Python TBB package accelerates threads when used with Numpy, Scipy, pyDAAL, Dask, and Joblib.
You asked for OS X* support, and now you have it. We added popular packages like scikit-learn, Numba, and mpi4py. We optimized the FFT in scipy so it can take advantage of multiple cores, and look for more optimizations in the soon to be released Beta update.
Give our Beta a try and let us know what you think.