Hi, I'm Frank Schlimbach.
I'm going to talk about how Intel makes your Scikit-Learn
faster with the Intel Math Kernel Library and Intel Data
Analytics Acceleration Library.
Stay here to learn more.
Also, follow the links below for more information.
[MUSIC PLAYING]
With Intel Distribution for Python,
we provide performance optimized Python packages.
You know our latest release, Scikit-Learn
got another performance boost by our highly optimized
compute engine, Intel DAAL.
Previous versions of Intel Scikit-Learn
already show decent speed-ups over standard versions,
such as packages delivered by [INAUDIBLE] Pythons.
Scikit-Learn uses NumPy and ScyPi for its compute kernels
and by accelerating NumPy, we were
able to achieve significant performance
gains in Scikit-Learn without even touching its code.
Our version of NumPy uses Intel MKL internally
so it gets best in class performance.
The speed-ups achievable with accelerated NumPy
range from a few percent to factors up to eight.
In our latest release, we further
optimized selected kernels from Scikit-Learn
by using Intel DAAL, which is also a specialized performance
library.
Intel DAAL provides highly optimized building blocks
needed to build your analytics pipeline and machine learning
algorithms.
It not only covers the core functionality
like analysis, decision making, and modeling, but also IO,
and data manipulation.
The algorithms we currently support now
show extreme speed-ups over the previous version.
The performance is now close to native DAAL performance, which
can be considered as best in class.
Scikit-Learn is a mature Python package
with hundreds of algorithms with different configuration
parameters each.
DAAL has a different set of algorithms
and sometimes implementations use slightly different variants
of the algorithm.
To make sure the use of optimized DAAL
gives valid results, we make sure that only
mathematically equivalent implementations
are used from DAAL.
Configurations without an equivalent in DAAL
will fall back to Scikit-Learn's only limitation.
It.
Additionally, we allow easy, on the fly enabling and disabling
these DAAL optimizations.
This is done by simply calling enable or disable,
and can be applied to each algorithm individually.
Last, but not least, I'd like to mention that DAAL also
comes with its own Python API, which lets you utilize
its full power directly.
It operates with other Python packages through NumPy arrays.
So you can easily combine it with anything that
also works with NumPy arrays.
Of course, Scikit-Learn is one of these.
Thanks for watching.
To learn more, or access anything
discussed in this video, follow the links below.