def compute(self, config, budget, **kwargs):
"""
Simple example for a compute function
The loss is just a the config + some noise (that decreases with the budget)
For dramatization, the function can sleep for a given interval to emphasizes
the speed ups achievable with parallel workers.
Args:
config: dictionary containing the sampled configurations by the optimizer
budget: (float) amount of time/epochs/etc. the model can use to train
Returns:
dictionary with mandatory fields:
'loss' (scalar)
'info' (dict)
"""
params = []
for k, v in config.items():
param = PCSBasedComponentParameter_pb2.PCSBasedParameterProto(
key=k, value=str(v))
params.append(param)
channel = grpc.insecure_channel("localhost:" + str(self.gRPC_port))
stub = PCSBasedComponentParameter_pb2_grpc.PCSBasedOptimizerServiceStub(
channel)
cmp = PCSBasedComponentParameter_pb2.PCSBasedComponentProto(
name=self.component_name, parameters=params)
response = stub.Evaluate(cmp)
channel.close()
# res = numpy.clip(config['x'] + numpy.random.randn() / budget, config['x'] / 2, 1.5 * config['x'])
# config['weka.classifiers.bayes.BayesNet.Q']
# time.sleep(self.sleep_interval)
print(response.result)
return ({
'loss': float(response.result
), # this is the a mandatory field to run hyperband
'info': response.
result # can be used for any user-defined information - also mandatory
})
def compute(self, config, budget, **kwargs):
"""
Simple example for a compute function
The loss is just a the config + some noise (that decreases with the budget)
For dramatization, the function can sleep for a given interval to emphasizes
the speed ups achievable with parallel workers.
Args:
config: dictionary containing the sampled configurations by the optimizer
budget: (float) amount of time/epochs/etc. the model can use to train
Returns:
dictionary with mandatory fields:
'loss' (scalar)
'info' (dict)
"""
with open("worker.log", "a") as file:
file.write("Budget " + str(budget) + "\n")
params = []
for k, v in config.items():
params.append(
PCSBasedComponentParameter_pb2.PCSBasedParameterProto(
key=k, value=str(v)))
cmp = PCSBasedComponentParameter_pb2.PCSBasedComponentProto(
name=str(math.ceil(budget / 1000)), parameters=params)
channel = grpc.insecure_channel("localhost:" + str(self.gRPC_port))
stub = PCSBasedComponentParameter_pb2_grpc.PCSBasedOptimizerServiceStub(
channel)
response = stub.Evaluate(cmp)
channel.close()
if (response.result < 0):
return ({'loss': float(10000), 'info': 'crashed'})
else:
return ({'loss': float(response.result), 'info': 'succeeded'})
import PCSBasedComponentParameter_pb2_grpc
if __name__ == '__main__':
# find parameterfile pcs in scenario
f = open("scenario.txt", "r")
lines = f.readlines()
for line in lines:
if line.startswith("paramfile"):
componentName = line.replace("paramfile = ", "")[:-5]
if line.startswith("gRPC_port"):
gRPC_port = line.replace("gRPC_port = ", "").strip()
params = []
for i in range(6, len(sys.argv) - 1, 2):
param = PCSBasedComponentParameter_pb2.PCSBasedParameterProto(
key=sys.argv[i][1:].replace("-", ""), value=sys.argv[i + 1])
params.append(param)
channel = grpc.insecure_channel("localhost:" + gRPC_port)
stub = PCSBasedComponentParameter_pb2_grpc.PCSBasedOptimizerServiceStub(
channel)
cmp = PCSBasedComponentParameter_pb2.PCSBasedComponentProto(
name=componentName, parameters=params)
response = stub.Evaluate(cmp)
channel.close()
f = open("testout.txt", "a")
f.write("### start run ###\n")
for arg in sys.argv: