def GetOptimizerConvergenceState(self, request, context):
with self.exclusive_optimizer(optimizer_id=request.Id) as optimizer:
serialized_convergence_state = serialize_to_bytes_string(
optimizer.get_optimizer_convergence_state())
return OptimizerConvergenceState(
OptimizerHandle=OptimizerHandle(Id=request.Id),
SerializedOptimizerConvergenceState=serialized_convergence_state)
def GetOptimizerInfo(self, request: OptimizerHandle, context):
# TODO: Learn about and leverage gRPC's error handling model for a case
# TODO: when the handle is invalid.
optimizer = self._optimizers_by_id[request.Id]
return OptimizerInfo(
OptimizerHandle=OptimizerHandle(Id=request.Id),
OptimizerConfigJsonString=optimizer.optimizer_config.to_json(),
OptimizationProblem=optimizer.optimization_problem.to_protobuf())
def GetOptimizerInfo(self, request: OptimizerHandle, context):
# TODO: Learn about and leverage gRPC's error handling model for a case
# TODO: when the handle is invalid.
optimizer_id = request.Id
optimizer = self._bayesian_optimizer_store.get_optimizer(optimizer_id)
return OptimizerInfo(
OptimizerHandle=OptimizerHandle(Id=request.Id),
OptimizerConfigJsonString=optimizer.optimizer_config.to_json(),
OptimizationProblem=OptimizerServiceEncoder.
encode_optimization_problem(optimizer.optimization_problem))
def get_optimizer_by_id(self, optimizer_id):
"""Returns a proxy to an optimizer with a specified Id.
:param optimizer_id:
:return:
"""
optimizer_handle = OptimizerHandle(Id=optimizer_id)
optimizer_info = self._optimizer_service_stub.GetOptimizerInfo(optimizer_handle)
optimizer_proxy = self._optimizer_factory.connect_to_existing_remote_optimizer(optimizer_info)
return optimizer_proxy
def ListExistingOptimizers(self, request: Empty, context):
optimizers_info = []
for optimizer_id in self._ordered_ids:
optimizer = self._optimizers_by_id[optimizer_id]
optimizers_info.append(OptimizerInfo(
OptimizerHandle=OptimizerHandle(Id=optimizer_id),
OptimizerConfigJsonString=optimizer.optimizer_config.to_json(),
OptimizationProblem=optimizer.optimization_problem.to_protobuf()
))
return OptimizerList(Optimizers=optimizers_info)
def CreateOptimizer(self, request: CreateOptimizerRequest, context): # pylint: disable=unused-argument
self.logger.info("Creating Optimizer")
optimization_problem = OptimizerServiceDecoder.decode_optimization_problem(
optimization_problem_pb2=request.OptimizationProblem)
optimizer_config_json = request.OptimizerConfig
if optimizer_config_json is not None and len(
optimizer_config_json) > 0:
optimizer_config = Point.from_json(optimizer_config_json)
else:
optimizer_config = bayesian_optimizer_config_store.default
optimizer = BayesianOptimizer(
optimization_problem=optimization_problem,
optimizer_config=optimizer_config)
optimizer_id = self._bayesian_optimizer_store.get_next_optimizer_id()
self._bayesian_optimizer_store.add_optimizer(optimizer_id=optimizer_id,
optimizer=optimizer)
self.logger.info(
f"Created optimizer {optimizer_id} with config: {optimizer.optimizer_config.to_json(indent=2)}"
)
return OptimizerHandle(Id=optimizer_id)