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I just managed to set up an Anaconda Environment to run this example from MinPy.

Now as I understand it, the parallelization is in the training and predicting part.

However, for my specific use case I want to go one level higher: Since I have one very large data set that is split up in fairly small ones I want to do many multinomial regressions for each of the subsets.

Is there a way I can parallelize at this high of a level? Since I am very new to the topic I simply do not know how to approach this problem.

Thanks in advance for any advice :)

talonmies
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betaigeuze
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  • If each individual subset uses all available CPUs in parallel, my understand is that there is no gain in running several of those subsets in parallel. – James Phillips Jan 06 '20 at 00:02

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