Class AutoMlImageSegmentationInputs (1.50.0)

Version 1.50.0

• 1.51.0 (latest)
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

AutoMlImageSegmentationInputs(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

Attributes
Name	Description
budget_milli_node_hours	int 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).
base_model_id	str 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.

Classes

ModelType

ModelType(value)

Enum values:

MODEL_TYPE_UNSPECIFIED (0):
    Should not be set.
CLOUD_HIGH_ACCURACY_1 (1):
    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_LOW_ACCURACY_1 (2):
    A model to be used via prediction calls to
    uCAIP API. Expected to have a lower latency but
    relatively lower prediction quality.
MOBILE_TF_LOW_LATENCY_1 (3):
    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.

Methods

AutoMlImageSegmentationInputs

AutoMlImageSegmentationInputs(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

AutoMlImageSegmentationInputs

AutoMlImageSegmentationInputs(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)