Hi.
My name is Sergey Maidanov.
In this video, we'll be talking about Python and how
it can help accelerate technical computing and machine learning.
I will also highlight some key features of Intel distribution
for Python.
Stay with me to learn more.
Python is known as a popular and powerful language
used across various application domains.
Being an interpreted language, it
has inherited performance constraints
limiting its usage to environments not very demanding
for performance.
Python's low efficiency in production environments
creates an organizational challenge
when companies and institutions need
to have two distinct things.
The one that prototypes in numerical model in Python
and the other, that it writes it in a different language
to deploy it in production.
Our team's mission at Intel is to bring Python performance up
when a prototype numerical or machine learning
model can be deployed in production without the need
to rewrite it in a different programming language.
Since our target customers [INAUDIBLE] with development
productivity, we aim to build performance on Intel
architecture out-of-the-box with relatively small effort
on the user side.
Let me briefly outline what Intel Python is
and how it brings performance efficiency.
We deliver pre-built Python along with the most
popular packages for numerical computing and data science,
such as NumPy, SciPy, and Scikit-learn
All are linked with Intel's performance
libraries such as MKL and DAAL for near-to-native code speeds.
Intel Python is also accompanied with productivity tools
such as Jupyter notebooks and [INAUDIBLE]..
It also shipped with Conda and PIP
package managers that allow you to seamlessly install
any other package available in the community.
For machine learning, our distribution
comes with optimized deep software, Caffe and Theano,
as well as classic machines learning libraries
like, Scikit-learn and pyDAAL.
We also package Cython and Numba for tuning performance hotspots
to native speeds.
And for [INAUDIBLE] performance, we
ship MPI for Py accelerated with Intel MPI.
Python distribution is available in a variety of options,
so don't forget to follow the links below to access it.
Let me illustrate the out-of-the-box performance
on the example of Black-Scholes formal application being run
in prototype environment on Intel Core-based processor
and in production on Intel Xeon and Xeon Phi servers.
The bars show performance that we
can attain with the stock NumPy, illustrated
by the dark blue bars, and with NumPy
shipped with Intel Python, represented by the light bars.
You can see that Intel's NumPy delivers
significantly better performance on Intel Core-based system.
But it scales on relatively small problem sizes
shown on the horizontal axis as the total number of options
to price.
This is typical for prototype environment.
You build and test your model on relatively small problem
first, and then deploy in production
to run it in full scale on powerful CPUs.
This graph shows how the same application
scales in production on the Intel Xeon-based server.
You can see that Intel Python delivers
much better performance and scales really well
to large problems.
Next, this graph shows how the same application scales
on Intel Xeon Phi-based system.
You can see that Intel Python delivers
even better performance on these highly parallel workload that
scales well for large enough problems.
Besides, Intel Python engineering,
we work with all major Python vendors and the open source
community to make these optimizations broadly
accessible.
And we encourage you to take advantage of Intel Python's
exceptional performance in your own numerical and machine
learning projects.
Every option to get Python is free
for academic and commercial use, so don't
forget to follow the links to access it.
And thanks for watching.