Class: Google::Cloud::AutoML::V1beta1::ImageObjectDetectionModelMetadata
- Inherits:
-
Object
- Object
- Google::Cloud::AutoML::V1beta1::ImageObjectDetectionModelMetadata
- Defined in:
- lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb
Overview
Model metadata specific to image object detection.
Instance Attribute Summary collapse
-
#model_type ⇒ String
Optional.
-
#node_count ⇒ Integer
Output only.
-
#node_qps ⇒ Float
Output only.
-
#stop_reason ⇒ String
Output only.
-
#train_budget_milli_node_hours ⇒ Integer
The train budget of creating this model, expressed in milli node hours i.e.
-
#train_cost_milli_node_hours ⇒ Integer
Output only.
Instance Attribute Details
#model_type ⇒ String
Returns 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.
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# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb', line 146 class ImageObjectDetectionModelMetadata; end |
#node_count ⇒ Integer
Returns 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.
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# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb', line 146 class ImageObjectDetectionModelMetadata; end |
#node_qps ⇒ Float
Returns Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
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# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb', line 146 class ImageObjectDetectionModelMetadata; end |
#stop_reason ⇒ String
Returns Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED
, MODEL_CONVERGED
.
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# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb', line 146 class ImageObjectDetectionModelMetadata; end |
#train_budget_milli_node_hours ⇒ Integer
Returns 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.
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# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb', line 146 class ImageObjectDetectionModelMetadata; end |
#train_cost_milli_node_hours ⇒ Integer
Returns 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.
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# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/image.rb', line 146 class ImageObjectDetectionModelMetadata; end |