Comment by vitorsr | Hacker News Reader

Comment by vitorsr | Hacker News Reader

You can easily try it yourself [1]:

    conda create -n intel -c intel intel::intelpython3_core

Or [2]:

    docker pull intelpython/intelpython3_core

Note that it is quite bloated but includes many high-quality libraries.

You can think of it as a recompilation in addition to a collection of patches to make use of their proprietary libraries.

Other useful links to reduce the noise in this thread: [3], [4], [5], [6].

[1] https://software.intel.com/content/www/us/en/develop/article...

[2] https://software.intel.com/content/www/us/en/develop/article...

[3] https://www.nersc.gov/assets/Uploads/IntelPython-NERSC.pdf

[4] https://hub.docker.com/u/intelpython

[5] https://anaconda.org/intel

[6] https://github.com/IntelPython

Any benchmarks comparison data?

   For example:   .... benchmarks with this python is XXX % higher than ... (std python, AMD, ARM)

I haven't done a comparison in a long time, and, even then, it wasn't very thorough, so take this with a grain of salt.

But, 6 years ago, when I was in grad school, just swapping to the Intel build of numpy was an instant ~10x speedup in the machine learning pipeline I was working on at the time.

No idea if that's typical or specific to what I was doing at the time. I don't use MKL anymore because ops doesn't want to deal with it and the standard packages are already plenty good enough for what I'm doing nowadays. If you forced me to guess, I guess I'd have to guess that my experience was atypical.

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