Model metadata specific to image object detection.
Attributes
Name
Description
model_type
str
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.
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.
