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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)
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
)