ImageClassificationModelMetadata(
mapping=None, *, ignore_unknown_fields=False, **kwargs
)Model metadata for image classification.
Attributes
| Name | Description |
| base_model_id |
str
Optional. The ID of the ``base`` model. If it is specified, the new model will be created based on the ``base`` model. Otherwise, the new model will be created from scratch. The ``base`` model must be in the same ``project`` and ``location`` as the new model to create, and have the same ``model_type``. |
| train_budget_milli_node_hours |
int
Optional. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual ``train_cost`` will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be ``MODEL_CONVERGED``. Note, node_hour = actual_hour \* number_of_nodes_invovled. For model type ``cloud``\ (default), the train 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. For model type ``mobile-low-latency-1``, ``mobile-versatile-1``, ``mobile-high-accuracy-1``, ``mobile-core-ml-low-latency-1``, ``mobile-core-ml-versatile-1``, ``mobile-core-ml-high-accuracy-1``, the train 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. |
| train_cost_milli_node_hours |
int
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. |
| stop_reason |
str
Output only. The reason that this create model operation stopped, e.g. ``BUDGET_REACHED``, ``MODEL_CONVERGED``. |
| model_type |
str
Optional. Type of the model. The available values are: - ``cloud`` - Model to be used via prediction calls to AutoML API. This is the default value. - ``mobile-low-latency-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. - ``mobile-versatile-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. - ``mobile-high-accuracy-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. - ``mobile-core-ml-low-latency-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models. - ``mobile-core-ml-versatile-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. - ``mobile-core-ml-high-accuracy-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. |
| node_qps |
float
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed. |
| node_count |
int
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field. |