Is there a way via pip/conda to compile these to your environment directly? I see most people just pull from repositories and sometimes see wheel discussed.

conda install scikit-learn-intelex -c conda-forge

Thanks Peter. I thought conda-forge was a repository/channel, not a command to compile to local environment.

A few followups: (1) Is this usable for non-intelex packages? (2) What about packages not in conda's channels?

I am busy installing it now. Anaconda should take care of required packages? I am not actually sure. Seems to be working.

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)

Is there a demo that shows this 5000x speedup?

(A jump that large suggests to me they may be fixing issues in the default implementation that could also be fixed for other processors!)

Looks like they are responding to https://github.com/intel/scikit-learn-intelex#-acceleration

I completely agree. I hope some Intel competitor funds a scikit-learn developer to read this code and extract all the portable performance improvements.

The point is that sklernex would bring performance for all X86 architectures, not just Intel. And yes scikit developers already working on generic improvements there

As @jjerphan commented above, there is already ongoing effort to get an improved portable brute-force implementation in vanilla scikit-learn, see:

https://news.ycombinator.com/item?id=29069760