def main_master(opt_prob, num_workers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/mpiexample_mpi.log"):
os.remove("./logfiles/mpiexample_mpi.log")
logging.basicConfig(filename="./logfiles/mpiexample_mpi.log",
level=logging.INFO)
max_evals = 500
rbf = RBFInterpolant(dim=opt_prob.dim, kernel=CubicKernel(),
tail=LinearTail(opt_prob.dim))
slhd = SymmetricLatinHypercube(
dim=opt_prob.dim, num_pts=2*(opt_prob.dim+1))
# Create a strategy and a controller
strategy = SRBFStrategy(
max_evals=max_evals, opt_prob=opt_prob, exp_design=slhd,
surrogate=rbf, asynchronous=True, batch_size=num_workers)
controller = MPIController(strategy)
print("Number of workers: {}".format(num_workers))
print("Maximum number of evaluations: {}".format(max_evals))
print("Strategy: {}".format(controller.strategy.__class__.__name__))
print("Experimental design: {}".format(slhd.__class__.__name__))
print("Surrogate: {}".format(rbf.__class__.__name__))
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0], max_line_width=np.inf,
precision=5, suppress_small=True)))
def main_master(num_workers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_subprocess_mpi.log"):
os.remove("./logfiles/test_subprocess_mpi.log")
logging.basicConfig(filename="./logfiles/test_subprocess_mpi.log",
level=logging.INFO)
print("\nTesting the POAP MPI controller with {0} workers".format(
num_workers))
print("Maximum number of evaluations: 200")
print("Search strategy: Candidate DYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
assert os.path.isfile(path), "You need to build sphere_ext"
max_evals = 200
sphere = Sphere(dim=10)
rbf = RBFInterpolant(dim=sphere.dim,
kernel=CubicKernel(),
tail=LinearTail(sphere.dim))
slhd = SymmetricLatinHypercube(dim=sphere.dim,
num_pts=2 * (sphere.dim + 1))
# Create a strategy and a controller
strategy = SRBFStrategy(max_evals=max_evals,
opt_prob=sphere,
exp_design=slhd,
surrogate=rbf,
asynchronous=True,
batch_size=num_workers)
controller = MPIController(strategy)
print("Number of threads: {}".format(num_workers))
print("Maximum number of evaluations: {}".format(max_evals))
print("Strategy: {}".format(controller.strategy.__class__.__name__))
print("Experimental design: {}".format(slhd.__class__.__name__))
print("Surrogate: {}".format(rbf.__class__.__name__))
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def main():
"Controller process main."
logging.basicConfig(format="%(name)-18s: %(levelname)-8s %(message)s",
filename='test_mpi_serve.log-{0}'.format(rank),
level=logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s")
ch.setFormatter(formatter)
logging.getLogger('').addHandler(ch)
strategy = FixedSampleStrategy([1, 2, 3, 4, 5])
c = MPIController(strategy)
result = c.run()
print("Final: {0:.3e} @ {1}".format(result.value, result.params))
def main():
"Controller process main."
logging.basicConfig(format="%(name)-18s: %(levelname)-8s %(message)s",
filename='test_mpi_serve.log-{0}'.format(rank),
level=logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s")
ch.setFormatter(formatter)
logging.getLogger('').addHandler(ch)
strategy = FixedSampleStrategy([1, 2, 3, 4, 5])
c = MPIController(strategy)
result = c.run()
print(("Final: {0:.3e} @ {1}".format(result.value, result.params)))
def main_master(num_workers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_subprocess_mpi.log"):
os.remove("./logfiles/test_subprocess_mpi.log")
logging.basicConfig(filename="./logfiles/test_subprocess_mpi.log",
level=logging.INFO)
print("\nTesting the POAP MPI controller with {0} workers".format(
num_workers))
print("Maximum number of evaluations: 200")
print("Search strategy: Candidate DYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
assert os.path.isfile(path), "You need to build sphere_ext"
max_evals = 200
sphere = Sphere(dim=10)
rbf = RBFInterpolant(dim=sphere.dim, kernel=CubicKernel(),
tail=LinearTail(sphere.dim))
slhd = SymmetricLatinHypercube(
dim=sphere.dim, num_pts=2*(sphere.dim+1))
# Create a strategy and a controller
strategy = SRBFStrategy(
max_evals=max_evals, opt_prob=sphere, exp_design=slhd,
surrogate=rbf, asynchronous=True, batch_size=num_workers)
controller = MPIController(strategy)
print("Number of threads: {}".format(num_workers))
print("Maximum number of evaluations: {}".format(max_evals))
print("Strategy: {}".format(controller.strategy.__class__.__name__))
print("Experimental design: {}".format(slhd.__class__.__name__))
print("Surrogate: {}".format(rbf.__class__.__name__))
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0], max_line_width=np.inf,
precision=5, suppress_small=True)))
def main_master(opt_prob, num_workers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/mpiexample_mpi.log"):
os.remove("./logfiles/mpiexample_mpi.log")
logging.basicConfig(filename="./logfiles/mpiexample_mpi.log",
level=logging.INFO)
max_evals = 500
rbf = RBFInterpolant(dim=opt_prob.dim,
lb=opt_prob.lb,
ub=opt_prob.ub,
kernel=CubicKernel(),
tail=LinearTail(opt_prob.dim))
slhd = SymmetricLatinHypercube(dim=opt_prob.dim,
num_pts=2 * (opt_prob.dim + 1))
# Create a strategy and a controller
strategy = SRBFStrategy(
max_evals=max_evals,
opt_prob=opt_prob,
exp_design=slhd,
surrogate=rbf,
asynchronous=True,
batch_size=num_workers,
)
controller = MPIController(strategy)
print("Number of workers: {}".format(num_workers))
print("Maximum number of evaluations: {}".format(max_evals))
print("Strategy: {}".format(controller.strategy.__class__.__name__))
print("Experimental design: {}".format(slhd.__class__.__name__))
print("Surrogate: {}".format(rbf.__class__.__name__))
result = controller.run()
print("Best value found: {0}".format(result.value))
print("Best solution found: {0}\n".format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def main_master(nworkers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_subprocess_mpi.log"):
os.remove("./logfiles/test_subprocess_mpi.log")
logging.basicConfig(filename="./logfiles/test_subprocess_mpi.log",
level=logging.INFO)
print(
"\nTesting the POAP MPI controller with {0} workers".format(nworkers))
print("Maximum number of evaluations: 200")
print("Search strategy: Candidate DYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
assert os.path.isfile("./sphere_ext"), "You need to build sphere_ext"
maxeval = 200
data = Sphere(dim=10)
print(data.info)
# Create a strategy and a controller
strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nworkers,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval))
controller = MPIController(strategy)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def main():
"Testing routine."
# Log at DEBUG level to file, higher level to console
logging.basicConfig(format="%(name)-18s: %(levelname)-8s %(message)s",
filename='test_mpi_pw.log-{0}'.format(rank),
level=logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s")
ch.setFormatter(formatter)
logging.getLogger('').addHandler(ch)
samples = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
controller = MPIController()
strategy = FixedSampleStrategy(samples)
strategy = CheckWorkerStrategy(controller, strategy)
strategy = AddArgStrategy(strategy, extra_args='./dummy_sim')
strategy = ChaosMonkeyStrategy(controller, strategy, mtbf=3)
controller.strategy = strategy
result = controller.run()
print("Final: {0:.3e} @ {1}".format(result.value, result.params))
def main():
"Testing routine."
# Log at DEBUG level to file, higher level to console
logging.basicConfig(format="%(name)-18s: %(levelname)-8s %(message)s",
filename='test_mpi_pw.log-{0}'.format(rank),
level=logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s")
ch.setFormatter(formatter)
logging.getLogger('').addHandler(ch)
samples = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
controller = MPIController()
strategy = FixedSampleStrategy(samples)
strategy = CheckWorkerStrategy(controller, strategy)
strategy = AddArgStrategy(strategy, extra_args='./dummy_sim')
strategy = ChaosMonkeyStrategy(controller, strategy, mtbf=3)
controller.strategy = strategy
result = controller.run()
print("Final: {0:.3e} @ {1}".format(result.value, result.params))
def main_master(data, nworkers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_simple_mpi.log"):
os.remove("./logfiles/test_simple_mpi.log")
logging.basicConfig(filename="./logfiles/test_simple_mpi.log",
level=logging.INFO)
print(
"\nTesting the POAP MPI controller with {0} workers".format(nworkers))
print("Maximum number of evaluations: 500")
print("Sampling method: CandidateDYCORS, with weight 0.5")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF, domain scaled to unit box")
maxeval = 500
print(data.info)
# Create a strategy and a controller
strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nworkers,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim, weights=[0.5]))
controller = MPIController(strategy)
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))