def mpi_main(args):
DEBUG_FLAG = args.debug
ea = EA(1)
ea.load_config(args.config)
reseedPeriod = int(args.reseed)
taskNum = int(args.task_num)
np.random.seed(1)
seed = np.random.randint(0, 2**32 - 1, size=(taskNum), dtype=np.uint32)
seed = seed.tolist()
print(seed)
for i in range(int(args.generation)):
if ((reseedPeriod > 0) and (i % reseedPeriod == 0)):
for j in range(taskNum):
seed[j] = random.randint(0, 2**32 - 1)
ea_time = time.time()
pop = ea.ask()
ea_time = time.time() - ea_time
fitnesses = []
workloads = []
num_workers = int(args.num_workers) - 1
gc.collect()
prep_time = time.time()
for j in range(len(pop)):
workloads.append((pop[j], args.task, seed, args.debug))
prep_time = time.time() - prep_time
eval_time = time.time()
success = False
while (success is False):
try:
with MPICommExecutor(MPI.COMM_WORLD, root=0) as executor:
if executor is not None:
results = executor.map(eval, workloads)
success = True
except OverflowError:
success = False
eval_time = time.time() - eval_time
reducedResult = EvalSummary()
reducedResult.reduce(results, 'pfit')
ea.tell(reducedResult, args.task, seed)
ea.write_history(args.output)
#print(ea.fitnesses)
print(
'iter: {0} fit: {1}, pfit:{7} Q: {2}, ea_time: {3}, prep_time: {4}, eval_time: {5}, max_depth:{6}'
.format(i, ea.fitnesses[0], np.mean(reducedResult.get_res('Q')[0]),
ea_time, prep_time, eval_time, ea.pop[0].maxDepth,
np.mean(reducedResult.get_res('pfit')[0])))
def main(args):
ea = EA(1)
ea.load_config(args.config)
reseedPeriod = int(args.reseed)
taskNum = int(args.task_num)
np.random.seed(0)
seed = np.random.randint(0, 2**32 - 1, size=(taskNum), dtype=np.uint32)
seed = seed.tolist()
print(seed)
for i in range(int(args.generation)):
if ((reseedPeriod > 0) and (i % reseedPeriod == 0)):
for j in range(taskNum):
seed[j] = random.randint(0, 2**32 - 1)
ea_time = time.time()
pop = ea.ask()
ea_time = time.time() - ea_time
fitnesses = []
workloads = []
num_workers = int(args.num_workers) - 1
gc.collect()
prep_time = time.time()
for j in range(len(pop)):
workloads.append((pop[j], args.task, seed))
prep_time = time.time() - prep_time
eval_time = time.time()
if (num_workers > 1):
with mp.Pool(num_workers) as pool:
results = pool.map(eval, workloads)
else:
results = []
for w in workloads:
results.append(eval(w))
eval_time = time.time() - eval_time
ea.tell(results, args.task, seed)
ea.write_history(args.output)
print(
'iter: {0} fit: {1}, Q: {2}, ea_time: {3}, prep_time: {4}, eval_time: {5}, max_depth:{6}'
.format(i, ea.fitnesses[0], np.mean(ea.Q), ea_time, prep_time,
eval_time, ea.pop[0].maxDepth))