On Fri, 8 Apr 2022 at 11:17, Stefan Blumentrath <stefan.blumentr...@nina.no> wrote: > > Ciao Luca, >
Ciao Stefan > Yes, you could also consider looping over e.g. rows (maybe in combination > with "np.apply_along_axis") so you could put results easier back together to > a map if needed at a later stage. > > In addition, since you use multiprocessing.Manager, you may try to use > multiprocessing.Array: > https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array > > E.g. here: > https://github.com/lucadelu/grass-addons/blob/5ca56bdb8b3394ebeed23aa5b3240bf6690e51bf/src/raster/r.raoq.area/r.raoq.area.py#L81 > > According to the post here: > https://medium.com/analytics-vidhya/using-numpy-efficiently-between-processes-1bee17dcb01 > multiprocessing.Array is needed to put the numpy array into shared memory and > avoid pickling. > > I have not tried or investigated myself, but maybe worth a try... > Yes I saw it but I didn't try before. I tried last days but I didn't get any improvements, I will try in the coming days > Cheers > Stefan > -- ciao Luca www.lucadelu.org _______________________________________________ grass-dev mailing list grass-dev@lists.osgeo.org https://lists.osgeo.org/mailman/listinfo/grass-dev
