Class ImageClassificationModelMetadata.Builder (2.5.0)

Version 2.5.0

• 2.55.0 (latest)
• 2.54.0
• 2.53.0
• 2.52.0
• 2.51.0
• 2.49.0
• 2.48.0
• 2.47.0
• 2.46.0
• 2.45.0
• 2.44.0
• 2.43.0
• 2.42.0
• 2.41.0
• 2.40.0
• 2.39.0
• 2.37.0
• 2.36.0
• 2.35.0
• 2.34.0
• 2.33.0
• 2.32.0
• 2.31.0
• 2.30.0
• 2.29.0
• 2.28.0
• 2.27.0
• 2.24.0
• 2.23.0
• 2.22.0
• 2.21.0
• 2.20.0
• 2.19.0
• 2.18.0
• 2.17.0
• 2.16.0
• 2.15.0
• 2.14.0
• 2.13.0
• 2.12.0
• 2.11.0
• 2.10.0
• 2.9.0
• 2.8.0
• 2.7.0
• 2.6.0
• 2.5.0
• 2.4.0
• 2.3.18
• 2.2.3
• 2.1.23

public static final class ImageClassificationModelMetadata.Builder extends GeneratedMessageV3.Builder<ImageClassificationModelMetadata.Builder> implements ImageClassificationModelMetadataOrBuilder

Model metadata for image classification.

Protobuf type google.cloud.automl.v1.ImageClassificationModelMetadata

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ImageClassificationModelMetadata.Builder

Implements

ImageClassificationModelMetadataOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns

Type	Description
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ImageClassificationModelMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ImageClassificationModelMetadata build()

Returns

Type	Description
ImageClassificationModelMetadata

buildPartial()

public ImageClassificationModelMetadata buildPartial()

Returns

Type	Description
ImageClassificationModelMetadata

clear()

public ImageClassificationModelMetadata.Builder clear()

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearBaseModelId()

public ImageClassificationModelMetadata.Builder clearBaseModelId()

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.

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

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public ImageClassificationModelMetadata.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearModelType()

public ImageClassificationModelMetadata.Builder clearModelType()

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.

string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clearNodeCount()

public ImageClassificationModelMetadata.Builder clearNodeCount()

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.

int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clearNodeQps()

public ImageClassificationModelMetadata.Builder clearNodeQps()

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 = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ImageClassificationModelMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearStopReason()

public ImageClassificationModelMetadata.Builder clearStopReason()

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

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clearTrainBudgetMilliNodeHours()

public ImageClassificationModelMetadata.Builder clearTrainBudgetMilliNodeHours()

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.

int64 train_budget_milli_node_hours = 16 [(.google.api.field_behavior) = OPTIONAL];

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clearTrainCostMilliNodeHours()

public ImageClassificationModelMetadata.Builder clearTrainCostMilliNodeHours()

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 = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

clone()

public ImageClassificationModelMetadata.Builder clone()

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getBaseModelId()

public String getBaseModelId()

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.

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

Returns

Type	Description
String	The baseModelId.

getBaseModelIdBytes()

public ByteString getBaseModelIdBytes()

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.

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

Returns

Type	Description
ByteString	The bytes for baseModelId.

getDefaultInstanceForType()

public ImageClassificationModelMetadata getDefaultInstanceForType()

Returns

Type	Description
ImageClassificationModelMetadata

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns

Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getModelType()

public String getModelType()

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.

string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns

Type	Description
String	The modelType.

getModelTypeBytes()

public ByteString getModelTypeBytes()

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.

string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns

Type	Description
ByteString	The bytes for modelType.

getNodeCount()

public 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 node_qps field.

int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns

Type	Description
long	The nodeCount.

getNodeQps()

public 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 = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns

Type	Description
double	The nodeQps.

getStopReason()

public 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

Type	Description
String	The stopReason.

getStopReasonBytes()

public 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

Type	Description
ByteString	The bytes for stopReason.

getTrainBudgetMilliNodeHours()

public 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(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.

int64 train_budget_milli_node_hours = 16 [(.google.api.field_behavior) = OPTIONAL];

Returns

Type	Description
long	The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours()

public 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 = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns

Type	Description
long	The trainCostMilliNodeHours.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFrom(ImageClassificationModelMetadata other)

public ImageClassificationModelMetadata.Builder mergeFrom(ImageClassificationModelMetadata other)

Parameter

Name	Description
other	ImageClassificationModelMetadata

Returns

Type	Description
ImageClassificationModelMetadata.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ImageClassificationModelMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public ImageClassificationModelMetadata.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ImageClassificationModelMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setBaseModelId(String value)

public ImageClassificationModelMetadata.Builder setBaseModelId(String value)

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.

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

Parameter

Name	Description
value	String The baseModelId to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setBaseModelIdBytes(ByteString value)

public ImageClassificationModelMetadata.Builder setBaseModelIdBytes(ByteString value)

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.

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

Parameter

Name	Description
value	ByteString The bytes for baseModelId to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public ImageClassificationModelMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setModelType(String value)

public ImageClassificationModelMetadata.Builder setModelType(String value)

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.

string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Parameter

Name	Description
value	String The modelType to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setModelTypeBytes(ByteString value)

public ImageClassificationModelMetadata.Builder setModelTypeBytes(ByteString value)

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.

string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Parameter

Name	Description
value	ByteString The bytes for modelType to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setNodeCount(long value)

public ImageClassificationModelMetadata.Builder setNodeCount(long value)

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.

int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter

Name	Description
value	long The nodeCount to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setNodeQps(double value)

public ImageClassificationModelMetadata.Builder setNodeQps(double value)

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 = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter

Name	Description
value	double The nodeQps to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ImageClassificationModelMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setStopReason(String value)

public ImageClassificationModelMetadata.Builder setStopReason(String value)

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];

Parameter

Name	Description
value	String The stopReason to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setStopReasonBytes(ByteString value)

public ImageClassificationModelMetadata.Builder setStopReasonBytes(ByteString value)

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];

Parameter

Name	Description
value	ByteString The bytes for stopReason to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setTrainBudgetMilliNodeHours(long value)

public ImageClassificationModelMetadata.Builder setTrainBudgetMilliNodeHours(long value)

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.

int64 train_budget_milli_node_hours = 16 [(.google.api.field_behavior) = OPTIONAL];

Parameter

Name	Description
value	long The trainBudgetMilliNodeHours to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setTrainCostMilliNodeHours(long value)

public ImageClassificationModelMetadata.Builder setTrainCostMilliNodeHours(long value)

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 = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter

Name	Description
value	long The trainCostMilliNodeHours to set.

Returns

Type	Description
ImageClassificationModelMetadata.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final ImageClassificationModelMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
ImageClassificationModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)