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

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_classification.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_classification.rb', line 77

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

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A Model best tailored to be used within Google Cloud, and which cannot
    # be exported.
    # Default.
    CLOUD = 1

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML 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 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device with afterwards.
    MOBILE_TF_VERSATILE_1 = 3

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device afterwards.
    # Expected to have a higher latency, but should also have a higher
    # prediction quality than other mobile models.
    MOBILE_TF_HIGH_ACCURACY_1 = 4
  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. For modelType cloud(default), the budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time, considering 8 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.

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. For modelType cloud(default), the budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time, considering 8 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_classification.rb', line 77

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

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A Model best tailored to be used within Google Cloud, and which cannot
    # be exported.
    # Default.
    CLOUD = 1

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML 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 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device with afterwards.
    MOBILE_TF_VERSATILE_1 = 3

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device afterwards.
    # Expected to have a higher latency, but should also have a higher
    # prediction quality than other mobile models.
    MOBILE_TF_HIGH_ACCURACY_1 = 4
  end
end

#disable_early_stopping ⇒ ::Boolean

Returns Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.

Returns:

  • (::Boolean) —

    Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_classification.rb', line 77

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

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A Model best tailored to be used within Google Cloud, and which cannot
    # be exported.
    # Default.
    CLOUD = 1

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML 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 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device with afterwards.
    MOBILE_TF_VERSATILE_1 = 3

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device afterwards.
    # Expected to have a higher latency, but should also have a higher
    # prediction quality than other mobile models.
    MOBILE_TF_HIGH_ACCURACY_1 = 4
  end
end

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

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_classification.rb', line 77

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

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A Model best tailored to be used within Google Cloud, and which cannot
    # be exported.
    # Default.
    CLOUD = 1

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML 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 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device with afterwards.
    MOBILE_TF_VERSATILE_1 = 3

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device afterwards.
    # Expected to have a higher latency, but should also have a higher
    # prediction quality than other mobile models.
    MOBILE_TF_HIGH_ACCURACY_1 = 4
  end
end

#multi_label ⇒ ::Boolean

Returns If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).

Returns:

  • (::Boolean) —

    If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).



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# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_classification.rb', line 77

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

  module ModelType
    # Should not be set.
    MODEL_TYPE_UNSPECIFIED = 0

    # A Model best tailored to be used within Google Cloud, and which cannot
    # be exported.
    # Default.
    CLOUD = 1

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML 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 = 2

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device with afterwards.
    MOBILE_TF_VERSATILE_1 = 3

    # A model that, in addition to being available within Google
    # Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
    # or Core ML model and used on a mobile or edge device afterwards.
    # Expected to have a higher latency, but should also have a higher
    # prediction quality than other mobile models.
    MOBILE_TF_HIGH_ACCURACY_1 = 4
  end
end