Cloud Optimization V1 API - Class Google::Cloud::Optimization::V1::BatchOptimizeToursRequest::AsyncModelConfig (v0.2.1)

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

#input_config=

def input_config=(value) -> ::Google::Cloud::Optimization::V1::InputConfig
Parameter
Returns

#output_config

def output_config() -> ::Google::Cloud::Optimization::V1::OutputConfig
Returns

#output_config=

def output_config=(value) -> ::Google::Cloud::Optimization::V1::OutputConfig
Parameter
Returns