I'm using the skopt (scikit-optimize) package, using the ask-tell syntax. I'm using python 3.7, on a windows machine The ask function call takes a long time (first call ~1 minute, then increases 1 minute for each iteration, so ultimately as much as 20 minutes). Is there a way to parallelize this call (not the cost function evaluation, which I've already parallelized, but the ask function)? I have a 40 core machine, and also a good GPU, could use either.
thanks Mark
Reply to Jerome: Thanks for taking a look, sorry it wasn't clear. I realize that CPU code cannot be run on a GPU, but if there is a method to do either, that would be an option. I'm doing a pretty straightforward optimization (currently using GP, but would also like to use RF and GBRF). I'm using the ask-tell syntax. Basic code is:
from skopt import Optimizer
opt = Optimizer(20,n_jobs = 5, base_estimator='GP')
# the ask step below is what I'd like to parallelize,
# if possible, I tried setting n_jobs above, didn't help
suggested = opt.ask(n_points = 20)
# calculate fitness - this part is parallelized, works nicely
fitnesses = calc_fitness(suggested)
# the tell step is fast, no need to parallelize
res = opt.tell( suggested, fitnesses)
Any suggestions would be appreciated. thanks