POAP provides an event-driven framework for building and combining asynchronous optimization strategies. A typical optimization code written with POAP might look like:
from poap.strategy import FixedSampleStrategy
from poap.strategy import CheckWorkStrategy
from poap.controller import ThreadController
from poap.controller import BasicWorkerThread
# samples = list of sample points ...
controller = ThreadController()
sampler = FixedSampleStrategy(samples)
controller.strategy = CheckWorkerStrategy(controller, sampler)
for i in range(NUM_WORKERS):
t = BasicWorkerThread(controller, objective)
controller.launch_worker(t)
result = controller.run()
print 'Best result: {0} at {1}'.format(result.value, result.params)The basic ingredients are a controller capable of asking workers to run function evaluations and a strategy for choosing where to sample. The strategies send the controller proposed actions, which the controller then accepts or rejects; the controller, in turn, informs the strategies of relevant events through callback functions.
Most users will probably want to provide their own strategies, controllers, or both.
If you use POAP, please cite the following paper: David Eriksson, David Bindel, Christine A. Shoemaker. pySOT and POAP: An event-driven asynchronous framework for surrogate optimization. arXiv preprint arXiv:1908.00420, 2019
@misc{pysot_poap,
Author = {David Eriksson and David Bindel and Christine A. Shoemaker},
Title = {pySOT and POAP: An event-driven asynchronous framework for surrogate optimization},
Year = {2019},
Eprint = {arXiv:1908.00420},
}Build Status:
