The popular python package ray has a multiprocessing feature that could be used to run optimization-processes in parallel:
from ray.util.multiprocessing import Pool
def f(index):
return index
pool = Pool()
for result in pool.map(f, range(100)):
print(result)
This Pool-API is very similar to the regular multiprocessing. I will look further into if it's possible to integrate Ray so that its features can be used in Hyperactive.
The popular python package ray has a multiprocessing feature that could be used to run optimization-processes in parallel:
This
Pool-API is very similar to the regular multiprocessing. I will look further into if it's possible to integrate Ray so that its features can be used in Hyperactive.