Reference documentation and code samples for the Cloud Optimization V1 API class Google::Cloud::Optimization::V1::BatchOptimizeToursRequest::AsyncModelConfig.
Information for solving one optimization model asynchronously.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#display_name
def display_name() -> ::String
Returns
- (::String) — User defined model name, can be used as alias by users to keep track of models.
#display_name=
def display_name=(value) -> ::String
Parameter
- value (::String) — User defined model name, can be used as alias by users to keep track of models.
Returns
- (::String) — User defined model name, can be used as alias by users to keep track of models.
#enable_checkpoints
def enable_checkpoints() -> ::Boolean
Returns
- (::Boolean) — If this is set, the model will be solved in the checkpoint mode. In this mode, the input model can have a deadline longer than 30 mins without the risk of interruption. The model will be solved in multiple short-running stages. Each stage generates an intermediate checkpoint and stores it in the user's Cloud Storage buckets. The checkpoint mode should be preferred over allow_large_deadline_despite_interruption_risk since it prevents the risk of interruption.
#enable_checkpoints=
def enable_checkpoints=(value) -> ::Boolean
Parameter
- value (::Boolean) — If this is set, the model will be solved in the checkpoint mode. In this mode, the input model can have a deadline longer than 30 mins without the risk of interruption. The model will be solved in multiple short-running stages. Each stage generates an intermediate checkpoint and stores it in the user's Cloud Storage buckets. The checkpoint mode should be preferred over allow_large_deadline_despite_interruption_risk since it prevents the risk of interruption.
Returns
- (::Boolean) — If this is set, the model will be solved in the checkpoint mode. In this mode, the input model can have a deadline longer than 30 mins without the risk of interruption. The model will be solved in multiple short-running stages. Each stage generates an intermediate checkpoint and stores it in the user's Cloud Storage buckets. The checkpoint mode should be preferred over allow_large_deadline_despite_interruption_risk since it prevents the risk of interruption.
#input_config
def input_config() -> ::Google::Cloud::Optimization::V1::InputConfig
Returns
- (::Google::Cloud::Optimization::V1::InputConfig) — Required. Information about the input model.
#input_config=
def input_config=(value) -> ::Google::Cloud::Optimization::V1::InputConfig
Parameter
- value (::Google::Cloud::Optimization::V1::InputConfig) — Required. Information about the input model.
Returns
- (::Google::Cloud::Optimization::V1::InputConfig) — Required. Information about the input model.
#output_config
def output_config() -> ::Google::Cloud::Optimization::V1::OutputConfig
Returns
- (::Google::Cloud::Optimization::V1::OutputConfig) — Required. The desired output location information.
#output_config=
def output_config=(value) -> ::Google::Cloud::Optimization::V1::OutputConfig
Parameter
- value (::Google::Cloud::Optimization::V1::OutputConfig) — Required. The desired output location information.
Returns
- (::Google::Cloud::Optimization::V1::OutputConfig) — Required. The desired output location information.