com.google.cloud.automl.v1

Interface ImageObjectDetectionModelMetadataOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ImageObjectDetectionModelMetadata, ImageObjectDetectionModelMetadata.Builder

public interface ImageObjectDetectionModelMetadataOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
String	getModelType() Optional.
com.google.protobuf.ByteString	getModelTypeBytes() Optional.
long	getNodeCount() Output only.
double	getNodeQps() Output only.
String	getStopReason() Output only.
com.google.protobuf.ByteString	getStopReasonBytes() Output only.
long	getTrainBudgetMilliNodeHours() Optional.
long	getTrainCostMilliNodeHours() Output only.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

getModelType

String getModelType()

 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][google.cloud.automl.v1.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][google.cloud.automl.v1.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][google.cloud.automl.v1.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.
 

string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];

Returns:

The modelType.

getModelTypeBytes

com.google.protobuf.ByteString getModelTypeBytes()

 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][google.cloud.automl.v1.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][google.cloud.automl.v1.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][google.cloud.automl.v1.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.
 

string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];

Returns:

The bytes for modelType.

getNodeCount

long getNodeCount()

 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.
 

int64 node_count = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The nodeCount.

getNodeQps

double getNodeQps()

 Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.
 

double node_qps = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The nodeQps.

getStopReason

String getStopReason()

 Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
 

string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The stopReason.

getStopReasonBytes

com.google.protobuf.ByteString getStopReasonBytes()

 Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
 

string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The bytes for stopReason.

getTrainBudgetMilliNodeHours

long getTrainBudgetMilliNodeHours()

 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-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.
 

int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns:

The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours

long getTrainCostMilliNodeHours()

 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.
 

int64 train_cost_milli_node_hours = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The trainCostMilliNodeHours.