Class: Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlImageSegmentationInputs

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_segmentation.rb

Defined Under Namespace

Modules: ModelType

Instance Attribute Summary collapse

Instance Attribute Details

#base_model_id ⇒ ::String

Returns The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.

Returns:

  • (::String) —

    The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_segmentation.rb', line 63

class AutoMlImageSegmentationInputs
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A model to be used via prediction calls to uCAIP API. Expected
    # to have a higher latency, but should also have a higher prediction
    # quality than other models.
    CLOUD_HIGH_ACCURACY_1 = 1

    # A model to be used via prediction calls to uCAIP API. Expected
    # to have a lower latency but relatively lower prediction quality.
    CLOUD_LOW_ACCURACY_1 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # model and used on a mobile or edge device afterwards.
    # Expected to have low latency, but may have lower prediction
    # quality than other mobile models.
    MOBILE_TF_LOW_LATENCY_1 = 3
  end
end

#budget_milli_node_hours ⇒ ::Integer

Returns The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. Or actaul_wall_clock_hours = train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For modelType cloud-high-accuracy-1(default), the budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time (1000 milli * 24 hours * 8 nodes).

Returns:

  • (::Integer) —

    The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. Or actaul_wall_clock_hours = train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For modelType cloud-high-accuracy-1(default), the budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time (1000 milli * 24 hours * 8 nodes).



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_segmentation.rb', line 63

class AutoMlImageSegmentationInputs
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A model to be used via prediction calls to uCAIP API. Expected
    # to have a higher latency, but should also have a higher prediction
    # quality than other models.
    CLOUD_HIGH_ACCURACY_1 = 1

    # A model to be used via prediction calls to uCAIP API. Expected
    # to have a lower latency but relatively lower prediction quality.
    CLOUD_LOW_ACCURACY_1 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # model and used on a mobile or edge device afterwards.
    # Expected to have low latency, but may have lower prediction
    # quality than other mobile models.
    MOBILE_TF_LOW_LATENCY_1 = 3
  end
end

#model_type ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlImageSegmentationInputs::ModelType

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_segmentation.rb', line 63

class AutoMlImageSegmentationInputs
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A model to be used via prediction calls to uCAIP API. Expected
    # to have a higher latency, but should also have a higher prediction
    # quality than other models.
    CLOUD_HIGH_ACCURACY_1 = 1

    # A model to be used via prediction calls to uCAIP API. Expected
    # to have a lower latency but relatively lower prediction quality.
    CLOUD_LOW_ACCURACY_1 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # model and used on a mobile or edge device afterwards.
    # Expected to have low latency, but may have lower prediction
    # quality than other mobile models.
    MOBILE_TF_LOW_LATENCY_1 = 3
  end
end