![]() So, I ended up benchmarking all three BLAS packages using numpy's and scipy's native. ![]() Installing different versions of BLAS was easy, it literally took just setting a single flag in YAML conda recipes. There might be other versions available via Anaconda, but i didn't really check, since most numerical libs there are linked to Intel's MKL, which doesn't work on macs. First two are non-native, and the latter is optimized by Apple for their processors. I figured that i might as well do everything right, and started the whole ordeal from ground up - by choosing the best possible BLAS library for my M1 machine (in reality i am just super rusty and googling things felt easier than doing derivations by hand).Īt the moment, conda-forge has precompiled packages based on three BLAS implementations: openblas, netlib and accelerate. I've been doing some numerical simulations lately with a lot of 1000x1000 matrices, mostly as a distraction from the madness of past months. Metacademy is a great resource which compiles lesson plans on popular machine learning topics.įor Beginner questions please try /r/LearnMachineLearning, /r/MLQuestions or įor career related questions, visit /r/cscareerquestions/ Please have a look at our FAQ and Link-Collection Rules For Posts + Research + Discussion + Project + News on Twitter Chat with us on Slack Beginners:
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