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parallel.py
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parallel.py
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"""
Runs evaluation functions in parallel subprocesses
in order to evaluate multiple genomes at once.
"""
from multiprocessing import Pool
class ParallelEvaluator(object):
def __init__(self, num_workers, eval_function, timeout=None, maxtasksperchild=None):
"""
eval_function should take one argument, a tuple of (genome object, config object),
and return a single float (the genome's fitness).
"""
self.eval_function = eval_function
self.timeout = timeout
self.pool = Pool(processes=num_workers, maxtasksperchild=maxtasksperchild)
def __del__(self):
self.pool.close()
self.pool.join()
self.pool.terminate()
def evaluate(self, genomes, config):
jobs = []
for ignored_genome_id, genome in genomes:
jobs.append(self.pool.apply_async(self.eval_function, (genome, config)))
# assign the fitness back to each genome
for job, (ignored_genome_id, genome) in zip(jobs, genomes):
genome.fitness = job.get(timeout=self.timeout)