What is the Intel Distribution for Python and how can it improve data analysis?

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Discussion Overview

The discussion revolves around the Intel Distribution for Python, particularly its implications for numerical and data analysis. Participants explore what a distribution means in this context, how it compares to other distributions like Anaconda, and how to utilize the Math Kernel Library (MKL) within Anaconda.

Discussion Character

  • Exploratory
  • Technical explanation
  • Homework-related

Main Points Raised

  • One participant expresses a desire to understand the Intel Distribution for Python and its potential benefits for data analysis, noting their current hardware setup.
  • Another participant clarifies that a "distribution" refers to the installation package of software, indicating that various companies provide their own versions of Python.
  • A participant mentions that Anaconda is a distribution from Continuum Analytics and questions whether it is analogous to the Intel Distribution.
  • There are inquiries about how to acquire the Math Kernel Library (MKL) via Anaconda, with one participant suggesting that MKL may already be installed with Anaconda.
  • One participant seeks clarification on whether simply importing MKL in a Python script would lead to performance improvements.

Areas of Agreement / Disagreement

Participants generally agree on the definition of a distribution and its role in software installation. However, there is uncertainty regarding the specific functionalities and performance enhancements provided by the Intel Distribution compared to Anaconda.

Contextual Notes

Participants have not fully explored the implications of using the Intel Distribution versus Anaconda, and there are unresolved questions about the installation and configuration of the Math Kernel Library within Anaconda.

Who May Find This Useful

This discussion may be useful for individuals new to Python who are interested in data analysis, as well as those exploring different Python distributions and their respective libraries.

Jamison Lahman
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Hello,
I am relatively new to Python. I would really like to use Python for numerical and data analysis. I recently stumbled upon something called Intel Distribution from this article: http://www.infoworld.com/article/31...n-distribution-turbocharges-data-science.html
I don't really know what a distribution is in this context. It sounds promising and I would like to hear if anyone has more knowledge on this. I have an AMD CPU and MSI GPU on my tower which I hope to replace soon for an Intel chip and Nvidia GPU. Will I be able to utilize this Intel Distribution in the meantime?

Thanks

Edit: I already have anaconda installed (though I'm not sure I have it configured correctly.
when I run $ python, it gives me "Python 3.6.1 |Continuum Analytics, Inc.| (default, May 11 2017, 13:08:54)" so I imagine it's working. I am not yet familiar with applications like SciPy or NumPy so I haven't tested those. How should I go about checking if I have this Intel Distribution or installing via conda?
 
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A "distribution" is just the installation package. It's how a company distributes its software, or in this case it's version of a software type. Lots of companies distribute Python.
 
The quantecon website has some lectures on Python and the anaconda distro from Comtinuum

https://lectures.quantecon.org/py/getting_started.html
 
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phinds said:
A "distribution" is just the installation package. It's how a company distributes its software, or in this case it's version of a software type. Lots of companies distribute Python.
o0)
So Anaconda is a distribution from Continuum Analytics and is analogous to this Intel Distribution?
I am sure the answer will be in Jedi's link which I will read in a second, but how might I go about acquiring this Math Kernel Library via anaconda?
 
Generally you acquire a distribution by going to the site and clicking on "download".
 
phinds said:
Generally you acquire a distribution by going to the site and clicking on "download".
Haha I meant within anaconda but thanks. It seems that MKL is already installed with anaconda https://www.continuum.io/blog/developer-blog/anaconda-25-release-now-mkl-optimizations

If I write a python script, all I do is start with "import mkl" and it runs faster?
 

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