Hi Roberto, As answered previously, there is no multi-threading available for NMF in scikit-learn.
However, if you want to compute *multiple* NMF in parallel, you can use joblib with a threading backend, as the 'cd' solver releases the GIL (through cython code) during a large part of the time. The other main computational cost goes with numpy dot product, which depends on your BLAS configuration. To help choose the best settings for your needs, here is also a quick example for benchmarking multithreading <https://gist.github.com/TomDLT/c1d560a510a41dd80ab6>. Best, Tom 2016-09-30 12:52 GMT+02:00 Roberto Pagliari <roberto.pagli...@asos.com>: > I’m running nmf on a machine with 16 cores. > > > > Is there an option to run nmf with multithreading? I know numpy does, but > all I see is one single process with 100% CPU usage. > > > > > > > > Thanks, > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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