We are now running simple tests with CuPy to gauge its ability to replace our NumPy-based pipeline. Unfortunately, it appears that initializing an array takes at least 10x longer with CuPy than with Numpy. For example: $ python3.9 -m tim...
empty arrays should keep their type, but they always come back as double from array import array from numpy import array as ndarray x = array('B',range(10)) print x.typecode # 'B' x = ndarray(x) print x.dtype.char # 'l' x = array('B',[])...
since it can degrade performance or cause incorrect results.The best thing todois to ensure that only a single OpenMP runtime is linked into the process,e.g.by avoidingstaticlinking
Checklist I added a descriptive title. I searched through existing issues and couldn't find a solution or duplicate issue. I searched on the web (e.g. Google) and didn't find any helpful information. I searched the Anaconda documentation...