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.