def connect_to_existing_remote_optimizer(self, optimizer_info: OptimizerInfo) -> BayesianOptimizerProxy:
"""Connects to an existing optimizer.
Parameters
----------
optimizer_info : OptimizerInfo
Returns
-------
BayesianOptimizerProxy
"""
return BayesianOptimizerProxy(
grpc_channel=self._grpc_channel,
optimization_problem=OptimizerServiceDecoder.decode_optimization_problem(optimizer_info.OptimizationProblem),
optimizer_config=Point.from_json(optimizer_info.OptimizerConfigJsonString),
id=optimizer_info.OptimizerHandle.Id,
logger=self.logger
)
def create_remote_optimizer(
self,
optimization_problem: OptimizationProblem,
optimizer_config: Point = None) -> BayesianOptimizerProxy:
"""Creates a remote optimizer over a given problem with a given config.
Parameters
----------
optimization_problem : OptimizationProblem
Optimization problem for the new remote optimizer.
optimizer_config : Point
Configuration for the new remote optimizer.
Returns
-------
BayesianOptimizerProxy
"""
assert self._optimizer_service_stub is not None
if optimizer_config is None:
optimizer_config = bayesian_optimizer_config_store.default
create_optimizer_request = CreateOptimizerRequest(
OptimizationProblem=optimization_problem.to_protobuf(),
OptimizerConfigName='', # TODO: add this functionality
OptimizerConfig=optimizer_config.to_json())
optimizer_handle = self._optimizer_service_stub.CreateOptimizer(
create_optimizer_request)
return BayesianOptimizerProxy(
grpc_channel=self._grpc_channel,
optimization_problem=optimization_problem,
optimizer_config=optimizer_config,
id=optimizer_handle.Id,
logger=self.logger)