Yeah this kind of stuff drove us crazy. Numpy uses things like SIMD everywhere with no option to turn it off, which is good for performance but makes life reall

Yeah this kind of stuff drove us crazy. Numpy uses things like SIMD everywhere with no option to turn it off, which is good for performance but makes life really hard when you want consistently reproducible results across different machines. Before switching to Julia we eventually standardized on numpy.std with ddof=1, and with some error tolerances in our tests.