ImageObjectDetectionModelMetadata(
mapping=None, *, ignore_unknown_fields=False, **kwargs
)Model metadata specific to image object detection. .. attribute:: model_type
Optional. Type of the model. The available values are:
cloud-high-accuracy-1- (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.cloud-low-latency-1- A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.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.:type: str
Attributes
| Name | Description |
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 qps_per_node field. |
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. |
stop_reason |
str
Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
|
train_budget_milli_node_hours |
int
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-high-accuracy-1\ (default) and
cloud-low-latency-1, the train budget must be between
20,000 and 900,000 milli node hours, inclusive. The default
value is 216, 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. |