Class Model.Builder (3.41.0)

Version 3.41.0

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public static final class Model.Builder extends GeneratedMessageV3.Builder<Model.Builder> implements ModelOrBuilder

A trained machine learning Model.

Protobuf type google.cloud.aiplatform.v1.Model

Inheritance

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

Implements

ModelOrBuilder

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)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(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

addAllDeployedModels(Iterable<? extends DeployedModelRef> values)

public Model.Builder addAllDeployedModels(Iterable<? extends DeployedModelRef> values)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
values	Iterable<? extends com.google.cloud.aiplatform.v1.DeployedModelRef>

Returns
Type	Description
Model.Builder

addAllSupportedDeploymentResourcesTypes(Iterable<? extends Model.DeploymentResourcesType> values)

public Model.Builder addAllSupportedDeploymentResourcesTypes(Iterable<? extends Model.DeploymentResourcesType> values)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
values	Iterable<? extends com.google.cloud.aiplatform.v1.Model.DeploymentResourcesType> The supportedDeploymentResourcesTypes to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addAllSupportedDeploymentResourcesTypesValue(Iterable<Integer> values)

public Model.Builder addAllSupportedDeploymentResourcesTypesValue(Iterable<Integer> values)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
values	Iterable<Integer> The enum numeric values on the wire for supportedDeploymentResourcesTypes to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addAllSupportedExportFormats(Iterable<? extends Model.ExportFormat> values)

public Model.Builder addAllSupportedExportFormats(Iterable<? extends Model.ExportFormat> values)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
values	Iterable<? extends com.google.cloud.aiplatform.v1.Model.ExportFormat>

Returns
Type	Description
Model.Builder

addAllSupportedInputStorageFormats(Iterable<String> values)

public Model.Builder addAllSupportedInputStorageFormats(Iterable<String> values)

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
values	Iterable<String> The supportedInputStorageFormats to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addAllSupportedOutputStorageFormats(Iterable<String> values)

public Model.Builder addAllSupportedOutputStorageFormats(Iterable<String> values)

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
values	Iterable<String> The supportedOutputStorageFormats to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addAllVersionAliases(Iterable<String> values)

public Model.Builder addAllVersionAliases(Iterable<String> values)

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Parameter
Name	Description
values	Iterable<String> The versionAliases to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addDeployedModels(DeployedModelRef value)

public Model.Builder addDeployedModels(DeployedModelRef value)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	DeployedModelRef

Returns
Type	Description
Model.Builder

addDeployedModels(DeployedModelRef.Builder builderForValue)

public Model.Builder addDeployedModels(DeployedModelRef.Builder builderForValue)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	DeployedModelRef.Builder

Returns
Type	Description
Model.Builder

addDeployedModels(int index, DeployedModelRef value)

public Model.Builder addDeployedModels(int index, DeployedModelRef value)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
value	DeployedModelRef

Returns
Type	Description
Model.Builder

addDeployedModels(int index, DeployedModelRef.Builder builderForValue)

public Model.Builder addDeployedModels(int index, DeployedModelRef.Builder builderForValue)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
builderForValue	DeployedModelRef.Builder

Returns
Type	Description
Model.Builder

addDeployedModelsBuilder()

public DeployedModelRef.Builder addDeployedModelsBuilder()

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
DeployedModelRef.Builder

addDeployedModelsBuilder(int index)

public DeployedModelRef.Builder addDeployedModelsBuilder(int index)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
DeployedModelRef.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

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

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
Model.Builder

Overrides

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

addSupportedDeploymentResourcesTypes(Model.DeploymentResourcesType value)

public Model.Builder addSupportedDeploymentResourcesTypes(Model.DeploymentResourcesType value)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Model.DeploymentResourcesType The supportedDeploymentResourcesTypes to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addSupportedDeploymentResourcesTypesValue(int value)

public Model.Builder addSupportedDeploymentResourcesTypesValue(int value)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	int The enum numeric value on the wire for supportedDeploymentResourcesTypes to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addSupportedExportFormats(Model.ExportFormat value)

public Model.Builder addSupportedExportFormats(Model.ExportFormat value)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Model.ExportFormat

Returns
Type	Description
Model.Builder

addSupportedExportFormats(Model.ExportFormat.Builder builderForValue)

public Model.Builder addSupportedExportFormats(Model.ExportFormat.Builder builderForValue)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	Model.ExportFormat.Builder

Returns
Type	Description
Model.Builder

addSupportedExportFormats(int index, Model.ExportFormat value)

public Model.Builder addSupportedExportFormats(int index, Model.ExportFormat value)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
value	Model.ExportFormat

Returns
Type	Description
Model.Builder

addSupportedExportFormats(int index, Model.ExportFormat.Builder builderForValue)

public Model.Builder addSupportedExportFormats(int index, Model.ExportFormat.Builder builderForValue)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
builderForValue	Model.ExportFormat.Builder

Returns
Type	Description
Model.Builder

addSupportedExportFormatsBuilder()

public Model.ExportFormat.Builder addSupportedExportFormatsBuilder()

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.ExportFormat.Builder

addSupportedExportFormatsBuilder(int index)

public Model.ExportFormat.Builder addSupportedExportFormatsBuilder(int index)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
Model.ExportFormat.Builder

addSupportedInputStorageFormats(String value)

public Model.Builder addSupportedInputStorageFormats(String value)

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	String The supportedInputStorageFormats to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addSupportedInputStorageFormatsBytes(ByteString value)

public Model.Builder addSupportedInputStorageFormatsBytes(ByteString value)

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	ByteString The bytes of the supportedInputStorageFormats to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addSupportedOutputStorageFormats(String value)

public Model.Builder addSupportedOutputStorageFormats(String value)

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	String The supportedOutputStorageFormats to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addSupportedOutputStorageFormatsBytes(ByteString value)

public Model.Builder addSupportedOutputStorageFormatsBytes(ByteString value)

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	ByteString The bytes of the supportedOutputStorageFormats to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addVersionAliases(String value)

public Model.Builder addVersionAliases(String value)

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Parameter
Name	Description
value	String The versionAliases to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

addVersionAliasesBytes(ByteString value)

public Model.Builder addVersionAliasesBytes(ByteString value)

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Parameter
Name	Description
value	ByteString The bytes of the versionAliases to add.

Returns
Type	Description
Model.Builder	This builder for chaining.

build()

public Model build()

Returns
Type	Description
Model

buildPartial()

public Model buildPartial()

Returns
Type	Description
Model

clear()

public Model.Builder clear()

Returns
Type	Description
Model.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearArtifactUri()

public Model.Builder clearArtifactUri()

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearBaseModelSource()

public Model.Builder clearBaseModelSource()

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
Model.Builder

clearContainerSpec()

public Model.Builder clearContainerSpec()

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
Model.Builder

clearCreateTime()

public Model.Builder clearCreateTime()

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Returns
Type	Description
Model.Builder

clearDataStats()

public Model.Builder clearDataStats()

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Returns
Type	Description
Model.Builder

clearDeployedModels()

public Model.Builder clearDeployedModels()

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clearDescription()

public Model.Builder clearDescription()

The description of the Model.

string description = 3;

Returns
Type	Description
Model.Builder	This builder for chaining.

clearDisplayName()

public Model.Builder clearDisplayName()

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearEncryptionSpec()

public Model.Builder clearEncryptionSpec()

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
Model.Builder

clearEtag()

public Model.Builder clearEtag()

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

string etag = 16;

Returns
Type	Description
Model.Builder	This builder for chaining.

clearExplanationSpec()

public Model.Builder clearExplanationSpec()

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
Model.Builder

clearField(Descriptors.FieldDescriptor field)

public Model.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
Model.Builder

Overrides

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

clearLabels()

public Model.Builder clearLabels()

Returns
Type	Description
Model.Builder

clearMetadata()

public Model.Builder clearMetadata()

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
Model.Builder

clearMetadataArtifact()

public Model.Builder clearMetadataArtifact()

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.

string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearMetadataSchemaUri()

public Model.Builder clearMetadataSchemaUri()

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearModelSourceInfo()

public Model.Builder clearModelSourceInfo()

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clearName()

public Model.Builder clearName()

The resource name of the Model.

string name = 1;

Returns
Type	Description
Model.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public Model.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
Model.Builder

Overrides

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

clearOriginalModelInfo()

public Model.Builder clearOriginalModelInfo()

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clearPipelineJob()

public Model.Builder clearPipelineJob()

Optional. This field is populated if the model is produced by a pipeline job.

string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
Model.Builder	This builder for chaining.

clearPredictSchemata()

public Model.Builder clearPredictSchemata()

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
Model.Builder

clearSupportedDeploymentResourcesTypes()

public Model.Builder clearSupportedDeploymentResourcesTypes()

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearSupportedExportFormats()

public Model.Builder clearSupportedExportFormats()

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clearSupportedInputStorageFormats()

public Model.Builder clearSupportedInputStorageFormats()

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearSupportedOutputStorageFormats()

public Model.Builder clearSupportedOutputStorageFormats()

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearTrainingPipeline()

public Model.Builder clearTrainingPipeline()

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }

Returns
Type	Description
Model.Builder	This builder for chaining.

clearUpdateTime()

public Model.Builder clearUpdateTime()

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clearVersionAliases()

public Model.Builder clearVersionAliases()

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Returns
Type	Description
Model.Builder	This builder for chaining.

clearVersionCreateTime()

public Model.Builder clearVersionCreateTime()

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clearVersionDescription()

public Model.Builder clearVersionDescription()

The description of this version.

string version_description = 30;

Returns
Type	Description
Model.Builder	This builder for chaining.

clearVersionId()

public Model.Builder clearVersionId()

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder	This builder for chaining.

clearVersionUpdateTime()

public Model.Builder clearVersionUpdateTime()

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.Builder

clone()

public Model.Builder clone()

Returns
Type	Description
Model.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

containsLabels(String key)

public boolean containsLabels(String key)

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameter
Name	Description
key	String

Returns
Type	Description
boolean

getArtifactUri()

public String getArtifactUri()

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
String	The artifactUri.

getArtifactUriBytes()

public ByteString getArtifactUriBytes()

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
ByteString	The bytes for artifactUri.

getBaseModelSource()

public Model.BaseModelSource getBaseModelSource()

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
Model.BaseModelSource	The baseModelSource.

getBaseModelSourceBuilder()

public Model.BaseModelSource.Builder getBaseModelSourceBuilder()

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
Model.BaseModelSource.Builder

getBaseModelSourceOrBuilder()

public Model.BaseModelSourceOrBuilder getBaseModelSourceOrBuilder()

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
Model.BaseModelSourceOrBuilder

getContainerSpec()

public ModelContainerSpec getContainerSpec()

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
ModelContainerSpec	The containerSpec.

getContainerSpecBuilder()

public ModelContainerSpec.Builder getContainerSpecBuilder()

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
ModelContainerSpec.Builder

getContainerSpecOrBuilder()

public ModelContainerSpecOrBuilder getContainerSpecOrBuilder()

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
ModelContainerSpecOrBuilder

getCreateTime()

public Timestamp getCreateTime()

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Returns
Type	Description
Timestamp	The createTime.

getCreateTimeBuilder()

public Timestamp.Builder getCreateTimeBuilder()

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Returns
Type	Description
Builder

getCreateTimeOrBuilder()

public TimestampOrBuilder getCreateTimeOrBuilder()

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Returns
Type	Description
TimestampOrBuilder

getDataStats()

public Model.DataStats getDataStats()

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Returns
Type	Description
Model.DataStats	The dataStats.

getDataStatsBuilder()

public Model.DataStats.Builder getDataStatsBuilder()

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Returns
Type	Description
Model.DataStats.Builder

getDataStatsOrBuilder()

public Model.DataStatsOrBuilder getDataStatsOrBuilder()

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Returns
Type	Description
Model.DataStatsOrBuilder

getDefaultInstanceForType()

public Model getDefaultInstanceForType()

Returns
Type	Description
Model

getDeployedModels(int index)

public DeployedModelRef getDeployedModels(int index)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
DeployedModelRef

getDeployedModelsBuilder(int index)

public DeployedModelRef.Builder getDeployedModelsBuilder(int index)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
DeployedModelRef.Builder

getDeployedModelsBuilderList()

public List<DeployedModelRef.Builder> getDeployedModelsBuilderList()

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<Builder>

getDeployedModelsCount()

public int getDeployedModelsCount()

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
int

getDeployedModelsList()

public List<DeployedModelRef> getDeployedModelsList()

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<DeployedModelRef>

getDeployedModelsOrBuilder(int index)

public DeployedModelRefOrBuilder getDeployedModelsOrBuilder(int index)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
DeployedModelRefOrBuilder

getDeployedModelsOrBuilderList()

public List<? extends DeployedModelRefOrBuilder> getDeployedModelsOrBuilderList()

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<? extends com.google.cloud.aiplatform.v1.DeployedModelRefOrBuilder>

getDescription()

public String getDescription()

The description of the Model.

string description = 3;

Returns
Type	Description
String	The description.

getDescriptionBytes()

public ByteString getDescriptionBytes()

The description of the Model.

string description = 3;

Returns
Type	Description
ByteString	The bytes for description.

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getDisplayName()

public String getDisplayName()

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
String	The displayName.

getDisplayNameBytes()

public ByteString getDisplayNameBytes()

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
ByteString	The bytes for displayName.

getEncryptionSpec()

public EncryptionSpec getEncryptionSpec()

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
EncryptionSpec	The encryptionSpec.

getEncryptionSpecBuilder()

public EncryptionSpec.Builder getEncryptionSpecBuilder()

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
EncryptionSpec.Builder

getEncryptionSpecOrBuilder()

public EncryptionSpecOrBuilder getEncryptionSpecOrBuilder()

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
EncryptionSpecOrBuilder

getEtag()

public String getEtag()

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

string etag = 16;

Returns
Type	Description
String	The etag.

getEtagBytes()

public ByteString getEtagBytes()

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

string etag = 16;

Returns
Type	Description
ByteString	The bytes for etag.

getExplanationSpec()

public ExplanationSpec getExplanationSpec()

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
ExplanationSpec	The explanationSpec.

getExplanationSpecBuilder()

public ExplanationSpec.Builder getExplanationSpecBuilder()

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
ExplanationSpec.Builder

getExplanationSpecOrBuilder()

public ExplanationSpecOrBuilder getExplanationSpecOrBuilder()

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
ExplanationSpecOrBuilder

getLabels() (deprecated)

public Map<String,String> getLabels()

Use #getLabelsMap() instead.

Returns
Type	Description
Map<String,String>

getLabelsCount()

public int getLabelsCount()

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Returns
Type	Description
int

getLabelsMap()

public Map<String,String> getLabelsMap()

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Returns
Type	Description
Map<String,String>

getLabelsOrDefault(String key, String defaultValue)

public String getLabelsOrDefault(String key, String defaultValue)

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameters
Name	Description
key	String
defaultValue	String

Returns
Type	Description
String

getLabelsOrThrow(String key)

public String getLabelsOrThrow(String key)

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameter
Name	Description
key	String

Returns
Type	Description
String

getMetadata()

public Value getMetadata()

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
Value	The metadata.

getMetadataArtifact()

public String getMetadataArtifact()

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.

string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
String	The metadataArtifact.

getMetadataArtifactBytes()

public ByteString getMetadataArtifactBytes()

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.

string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ByteString	The bytes for metadataArtifact.

getMetadataBuilder()

public Value.Builder getMetadataBuilder()

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
Builder

getMetadataOrBuilder()

public ValueOrBuilder getMetadataOrBuilder()

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
ValueOrBuilder

getMetadataSchemaUri()

public String getMetadataSchemaUri()

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
String	The metadataSchemaUri.

getMetadataSchemaUriBytes()

public ByteString getMetadataSchemaUriBytes()

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
ByteString	The bytes for metadataSchemaUri.

getModelSourceInfo()

public ModelSourceInfo getModelSourceInfo()

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ModelSourceInfo	The modelSourceInfo.

getModelSourceInfoBuilder()

public ModelSourceInfo.Builder getModelSourceInfoBuilder()

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ModelSourceInfo.Builder

getModelSourceInfoOrBuilder()

public ModelSourceInfoOrBuilder getModelSourceInfoOrBuilder()

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ModelSourceInfoOrBuilder

getMutableLabels() (deprecated)

public Map<String,String> getMutableLabels()

Use alternate mutation accessors instead.

Returns
Type	Description
Map<String,String>

getName()

public String getName()

The resource name of the Model.

string name = 1;

Returns
Type	Description
String	The name.

getNameBytes()

public ByteString getNameBytes()

The resource name of the Model.

string name = 1;

Returns
Type	Description
ByteString	The bytes for name.

getOriginalModelInfo()

public Model.OriginalModelInfo getOriginalModelInfo()

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.OriginalModelInfo	The originalModelInfo.

getOriginalModelInfoBuilder()

public Model.OriginalModelInfo.Builder getOriginalModelInfoBuilder()

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.OriginalModelInfo.Builder

getOriginalModelInfoOrBuilder()

public Model.OriginalModelInfoOrBuilder getOriginalModelInfoOrBuilder()

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Model.OriginalModelInfoOrBuilder

getPipelineJob()

public String getPipelineJob()

Optional. This field is populated if the model is produced by a pipeline job.

string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
String	The pipelineJob.

getPipelineJobBytes()

public ByteString getPipelineJobBytes()

Optional. This field is populated if the model is produced by a pipeline job.

string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
ByteString	The bytes for pipelineJob.

getPredictSchemata()

public PredictSchemata getPredictSchemata()

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
PredictSchemata	The predictSchemata.

getPredictSchemataBuilder()

public PredictSchemata.Builder getPredictSchemataBuilder()

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
PredictSchemata.Builder

getPredictSchemataOrBuilder()

public PredictSchemataOrBuilder getPredictSchemataOrBuilder()

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
PredictSchemataOrBuilder

getSupportedDeploymentResourcesTypes(int index)

public Model.DeploymentResourcesType getSupportedDeploymentResourcesTypes(int index)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
Model.DeploymentResourcesType	The supportedDeploymentResourcesTypes at the given index.

getSupportedDeploymentResourcesTypesCount()

public int getSupportedDeploymentResourcesTypesCount()

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
int	The count of supportedDeploymentResourcesTypes.

getSupportedDeploymentResourcesTypesList()

public List<Model.DeploymentResourcesType> getSupportedDeploymentResourcesTypesList()

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<DeploymentResourcesType>	A list containing the supportedDeploymentResourcesTypes.

getSupportedDeploymentResourcesTypesValue(int index)

public int getSupportedDeploymentResourcesTypesValue(int index)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
int	The enum numeric value on the wire of supportedDeploymentResourcesTypes at the given index.

getSupportedDeploymentResourcesTypesValueList()

public List<Integer> getSupportedDeploymentResourcesTypesValueList()

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<Integer>	A list containing the enum numeric values on the wire for supportedDeploymentResourcesTypes.

getSupportedExportFormats(int index)

public Model.ExportFormat getSupportedExportFormats(int index)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
Model.ExportFormat

getSupportedExportFormatsBuilder(int index)

public Model.ExportFormat.Builder getSupportedExportFormatsBuilder(int index)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
Model.ExportFormat.Builder

getSupportedExportFormatsBuilderList()

public List<Model.ExportFormat.Builder> getSupportedExportFormatsBuilderList()

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<Builder>

getSupportedExportFormatsCount()

public int getSupportedExportFormatsCount()

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
int

getSupportedExportFormatsList()

public List<Model.ExportFormat> getSupportedExportFormatsList()

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<ExportFormat>

getSupportedExportFormatsOrBuilder(int index)

public Model.ExportFormatOrBuilder getSupportedExportFormatsOrBuilder(int index)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
Model.ExportFormatOrBuilder

getSupportedExportFormatsOrBuilderList()

public List<? extends Model.ExportFormatOrBuilder> getSupportedExportFormatsOrBuilderList()

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
List<? extends com.google.cloud.aiplatform.v1.Model.ExportFormatOrBuilder>

getSupportedInputStorageFormats(int index)

public String getSupportedInputStorageFormats(int index)

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The supportedInputStorageFormats at the given index.

getSupportedInputStorageFormatsBytes(int index)

public ByteString getSupportedInputStorageFormatsBytes(int index)

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the supportedInputStorageFormats at the given index.

getSupportedInputStorageFormatsCount()

public int getSupportedInputStorageFormatsCount()

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
int	The count of supportedInputStorageFormats.

getSupportedInputStorageFormatsList()

public ProtocolStringList getSupportedInputStorageFormatsList()

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ProtocolStringList	A list containing the supportedInputStorageFormats.

getSupportedOutputStorageFormats(int index)

public String getSupportedOutputStorageFormats(int index)

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The supportedOutputStorageFormats at the given index.

getSupportedOutputStorageFormatsBytes(int index)

public ByteString getSupportedOutputStorageFormatsBytes(int index)

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the supportedOutputStorageFormats at the given index.

getSupportedOutputStorageFormatsCount()

public int getSupportedOutputStorageFormatsCount()

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
int	The count of supportedOutputStorageFormats.

getSupportedOutputStorageFormatsList()

public ProtocolStringList getSupportedOutputStorageFormatsList()

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ProtocolStringList	A list containing the supportedOutputStorageFormats.

getTrainingPipeline()

public String getTrainingPipeline()

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }

Returns
Type	Description
String	The trainingPipeline.

getTrainingPipelineBytes()

public ByteString getTrainingPipelineBytes()

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }

Returns
Type	Description
ByteString	The bytes for trainingPipeline.

getUpdateTime()

public Timestamp getUpdateTime()

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Timestamp	The updateTime.

getUpdateTimeBuilder()

public Timestamp.Builder getUpdateTimeBuilder()

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Builder

getUpdateTimeOrBuilder()

public TimestampOrBuilder getUpdateTimeOrBuilder()

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
TimestampOrBuilder

getVersionAliases(int index)

public String getVersionAliases(int index)

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The versionAliases at the given index.

getVersionAliasesBytes(int index)

public ByteString getVersionAliasesBytes(int index)

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the versionAliases at the given index.

getVersionAliasesCount()

public int getVersionAliasesCount()

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Returns
Type	Description
int	The count of versionAliases.

getVersionAliasesList()

public ProtocolStringList getVersionAliasesList()

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Returns
Type	Description
ProtocolStringList	A list containing the versionAliases.

getVersionCreateTime()

public Timestamp getVersionCreateTime()

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Timestamp	The versionCreateTime.

getVersionCreateTimeBuilder()

public Timestamp.Builder getVersionCreateTimeBuilder()

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Builder

getVersionCreateTimeOrBuilder()

public TimestampOrBuilder getVersionCreateTimeOrBuilder()

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
TimestampOrBuilder

getVersionDescription()

public String getVersionDescription()

The description of this version.

string version_description = 30;

Returns
Type	Description
String	The versionDescription.

getVersionDescriptionBytes()

public ByteString getVersionDescriptionBytes()

The description of this version.

string version_description = 30;

Returns
Type	Description
ByteString	The bytes for versionDescription.

getVersionId()

public String getVersionId()

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
String	The versionId.

getVersionIdBytes()

public ByteString getVersionIdBytes()

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
ByteString	The bytes for versionId.

getVersionUpdateTime()

public Timestamp getVersionUpdateTime()

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Timestamp	The versionUpdateTime.

getVersionUpdateTimeBuilder()

public Timestamp.Builder getVersionUpdateTimeBuilder()

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
Builder

getVersionUpdateTimeOrBuilder()

public TimestampOrBuilder getVersionUpdateTimeOrBuilder()

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
TimestampOrBuilder

hasBaseModelSource()

public boolean hasBaseModelSource()

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the baseModelSource field is set.

hasContainerSpec()

public boolean hasContainerSpec()

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
boolean	Whether the containerSpec field is set.

hasCreateTime()

public boolean hasCreateTime()

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Returns
Type	Description
boolean	Whether the createTime field is set.

hasDataStats()

public boolean hasDataStats()

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Returns
Type	Description
boolean	Whether the dataStats field is set.

hasEncryptionSpec()

public boolean hasEncryptionSpec()

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
boolean	Whether the encryptionSpec field is set.

hasExplanationSpec()

public boolean hasExplanationSpec()

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
boolean	Whether the explanationSpec field is set.

hasMetadata()

public boolean hasMetadata()

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type	Description
boolean	Whether the metadata field is set.

hasModelSourceInfo()

public boolean hasModelSourceInfo()

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
boolean	Whether the modelSourceInfo field is set.

hasOriginalModelInfo()

public boolean hasOriginalModelInfo()

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
boolean	Whether the originalModelInfo field is set.

hasPredictSchemata()

public boolean hasPredictSchemata()

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
boolean	Whether the predictSchemata field is set.

hasUpdateTime()

public boolean hasUpdateTime()

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
boolean	Whether the updateTime field is set.

hasVersionCreateTime()

public boolean hasVersionCreateTime()

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
boolean	Whether the versionCreateTime field is set.

hasVersionUpdateTime()

public boolean hasVersionUpdateTime()

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
boolean	Whether the versionUpdateTime field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

internalGetMapFieldReflection(int number)

protected MapFieldReflectionAccessor internalGetMapFieldReflection(int number)

Parameter
Name	Description
number	int

Returns
Type	Description
com.google.protobuf.MapFieldReflectionAccessor

Overrides

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

internalGetMutableMapFieldReflection(int number)

protected MapFieldReflectionAccessor internalGetMutableMapFieldReflection(int number)

Parameter
Name	Description
number	int

Returns
Type	Description
com.google.protobuf.MapFieldReflectionAccessor

Overrides

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeBaseModelSource(Model.BaseModelSource value)

public Model.Builder mergeBaseModelSource(Model.BaseModelSource value)

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	Model.BaseModelSource

Returns
Type	Description
Model.Builder

mergeContainerSpec(ModelContainerSpec value)

public Model.Builder mergeContainerSpec(ModelContainerSpec value)

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Parameter
Name	Description
value	ModelContainerSpec

Returns
Type	Description
Model.Builder

mergeCreateTime(Timestamp value)

public Model.Builder mergeCreateTime(Timestamp value)

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

mergeDataStats(Model.DataStats value)

public Model.Builder mergeDataStats(Model.DataStats value)

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Parameter
Name	Description
value	Model.DataStats

Returns
Type	Description
Model.Builder

mergeEncryptionSpec(EncryptionSpec value)

public Model.Builder mergeEncryptionSpec(EncryptionSpec value)

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Parameter
Name	Description
value	EncryptionSpec

Returns
Type	Description
Model.Builder

mergeExplanationSpec(ExplanationSpec value)

public Model.Builder mergeExplanationSpec(ExplanationSpec value)

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Parameter
Name	Description
value	ExplanationSpec

Returns
Type	Description
Model.Builder

mergeFrom(Model other)

public Model.Builder mergeFrom(Model other)

Parameter
Name	Description
other	Model

Returns
Type	Description
Model.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

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

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
Model.Builder

Overrides

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

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public Model.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
Model.Builder

Overrides

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

mergeMetadata(Value value)

public Model.Builder mergeMetadata(Value value)

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
value	Value

Returns
Type	Description
Model.Builder

mergeModelSourceInfo(ModelSourceInfo value)

public Model.Builder mergeModelSourceInfo(ModelSourceInfo value)

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	ModelSourceInfo

Returns
Type	Description
Model.Builder

mergeOriginalModelInfo(Model.OriginalModelInfo value)

public Model.Builder mergeOriginalModelInfo(Model.OriginalModelInfo value)

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Model.OriginalModelInfo

Returns
Type	Description
Model.Builder

mergePredictSchemata(PredictSchemata value)

public Model.Builder mergePredictSchemata(PredictSchemata value)

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Parameter
Name	Description
value	PredictSchemata

Returns
Type	Description
Model.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final Model.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
Model.Builder

Overrides

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

mergeUpdateTime(Timestamp value)

public Model.Builder mergeUpdateTime(Timestamp value)

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

mergeVersionCreateTime(Timestamp value)

public Model.Builder mergeVersionCreateTime(Timestamp value)

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

mergeVersionUpdateTime(Timestamp value)

public Model.Builder mergeVersionUpdateTime(Timestamp value)

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

putAllLabels(Map<String,String> values)

public Model.Builder putAllLabels(Map<String,String> values)

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameter
Name	Description
values	Map<String,String>

Returns
Type	Description
Model.Builder

putLabels(String key, String value)

public Model.Builder putLabels(String key, String value)

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameters
Name	Description
key	String
value	String

Returns
Type	Description
Model.Builder

removeDeployedModels(int index)

public Model.Builder removeDeployedModels(int index)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
Model.Builder

removeLabels(String key)

public Model.Builder removeLabels(String key)

The labels with user-defined metadata to organize your Models.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameter
Name	Description
key	String

Returns
Type	Description
Model.Builder

removeSupportedExportFormats(int index)

public Model.Builder removeSupportedExportFormats(int index)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
index	int

Returns
Type	Description
Model.Builder

setArtifactUri(String value)

public Model.Builder setArtifactUri(String value)

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
value	String The artifactUri to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setArtifactUriBytes(ByteString value)

public Model.Builder setArtifactUriBytes(ByteString value)

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
value	ByteString The bytes for artifactUri to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setBaseModelSource(Model.BaseModelSource value)

public Model.Builder setBaseModelSource(Model.BaseModelSource value)

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	Model.BaseModelSource

Returns
Type	Description
Model.Builder

setBaseModelSource(Model.BaseModelSource.Builder builderForValue)

public Model.Builder setBaseModelSource(Model.BaseModelSource.Builder builderForValue)

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
builderForValue	Model.BaseModelSource.Builder

Returns
Type	Description
Model.Builder

setContainerSpec(ModelContainerSpec value)

public Model.Builder setContainerSpec(ModelContainerSpec value)

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Parameter
Name	Description
value	ModelContainerSpec

Returns
Type	Description
Model.Builder

setContainerSpec(ModelContainerSpec.Builder builderForValue)

public Model.Builder setContainerSpec(ModelContainerSpec.Builder builderForValue)

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Parameter
Name	Description
builderForValue	ModelContainerSpec.Builder

Returns
Type	Description
Model.Builder

setCreateTime(Timestamp value)

public Model.Builder setCreateTime(Timestamp value)

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

setCreateTime(Timestamp.Builder builderForValue)

public Model.Builder setCreateTime(Timestamp.Builder builderForValue)

Output only. Timestamp when this Model was uploaded into Vertex AI.

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

Parameter
Name	Description
builderForValue	Builder

Returns
Type	Description
Model.Builder

setDataStats(Model.DataStats value)

public Model.Builder setDataStats(Model.DataStats value)

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Parameter
Name	Description
value	Model.DataStats

Returns
Type	Description
Model.Builder

setDataStats(Model.DataStats.Builder builderForValue)

public Model.Builder setDataStats(Model.DataStats.Builder builderForValue)

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config.

.google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

Parameter
Name	Description
builderForValue	Model.DataStats.Builder

Returns
Type	Description
Model.Builder

setDeployedModels(int index, DeployedModelRef value)

public Model.Builder setDeployedModels(int index, DeployedModelRef value)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
value	DeployedModelRef

Returns
Type	Description
Model.Builder

setDeployedModels(int index, DeployedModelRef.Builder builderForValue)

public Model.Builder setDeployedModels(int index, DeployedModelRef.Builder builderForValue)

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
builderForValue	DeployedModelRef.Builder

Returns
Type	Description
Model.Builder

setDescription(String value)

public Model.Builder setDescription(String value)

The description of the Model.

string description = 3;

Parameter
Name	Description
value	String The description to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setDescriptionBytes(ByteString value)

public Model.Builder setDescriptionBytes(ByteString value)

The description of the Model.

string description = 3;

Parameter
Name	Description
value	ByteString The bytes for description to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setDisplayName(String value)

public Model.Builder setDisplayName(String value)

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Parameter
Name	Description
value	String The displayName to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setDisplayNameBytes(ByteString value)

public Model.Builder setDisplayNameBytes(ByteString value)

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Parameter
Name	Description
value	ByteString The bytes for displayName to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setEncryptionSpec(EncryptionSpec value)

public Model.Builder setEncryptionSpec(EncryptionSpec value)

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Parameter
Name	Description
value	EncryptionSpec

Returns
Type	Description
Model.Builder

setEncryptionSpec(EncryptionSpec.Builder builderForValue)

public Model.Builder setEncryptionSpec(EncryptionSpec.Builder builderForValue)

Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Parameter
Name	Description
builderForValue	EncryptionSpec.Builder

Returns
Type	Description
Model.Builder

setEtag(String value)

public Model.Builder setEtag(String value)

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

string etag = 16;

Parameter
Name	Description
value	String The etag to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setEtagBytes(ByteString value)

public Model.Builder setEtagBytes(ByteString value)

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

string etag = 16;

Parameter
Name	Description
value	ByteString The bytes for etag to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setExplanationSpec(ExplanationSpec value)

public Model.Builder setExplanationSpec(ExplanationSpec value)

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Parameter
Name	Description
value	ExplanationSpec

Returns
Type	Description
Model.Builder

setExplanationSpec(ExplanationSpec.Builder builderForValue)

public Model.Builder setExplanationSpec(ExplanationSpec.Builder builderForValue)

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Parameter
Name	Description
builderForValue	ExplanationSpec.Builder

Returns
Type	Description
Model.Builder

setField(Descriptors.FieldDescriptor field, Object value)

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

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
Model.Builder

Overrides

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

setMetadata(Value value)

public Model.Builder setMetadata(Value value)

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
value	Value

Returns
Type	Description
Model.Builder

setMetadata(Value.Builder builderForValue)

public Model.Builder setMetadata(Value.Builder builderForValue)

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
builderForValue	Builder

Returns
Type	Description
Model.Builder

setMetadataArtifact(String value)

public Model.Builder setMetadataArtifact(String value)

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.

string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	String The metadataArtifact to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setMetadataArtifactBytes(ByteString value)

public Model.Builder setMetadataArtifactBytes(ByteString value)

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.

string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	ByteString The bytes for metadataArtifact to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setMetadataSchemaUri(String value)

public Model.Builder setMetadataSchemaUri(String value)

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
value	String The metadataSchemaUri to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setMetadataSchemaUriBytes(ByteString value)

public Model.Builder setMetadataSchemaUriBytes(ByteString value)

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name	Description
value	ByteString The bytes for metadataSchemaUri to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setModelSourceInfo(ModelSourceInfo value)

public Model.Builder setModelSourceInfo(ModelSourceInfo value)

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	ModelSourceInfo

Returns
Type	Description
Model.Builder

setModelSourceInfo(ModelSourceInfo.Builder builderForValue)

public Model.Builder setModelSourceInfo(ModelSourceInfo.Builder builderForValue)

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	ModelSourceInfo.Builder

Returns
Type	Description
Model.Builder

setName(String value)

public Model.Builder setName(String value)

The resource name of the Model.

string name = 1;

Parameter
Name	Description
value	String The name to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setNameBytes(ByteString value)

public Model.Builder setNameBytes(ByteString value)

The resource name of the Model.

string name = 1;

Parameter
Name	Description
value	ByteString The bytes for name to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setOriginalModelInfo(Model.OriginalModelInfo value)

public Model.Builder setOriginalModelInfo(Model.OriginalModelInfo value)

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Model.OriginalModelInfo

Returns
Type	Description
Model.Builder

setOriginalModelInfo(Model.OriginalModelInfo.Builder builderForValue)

public Model.Builder setOriginalModelInfo(Model.OriginalModelInfo.Builder builderForValue)

Output only. If this Model is a copy of another Model, this contains info about the original.

.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	Model.OriginalModelInfo.Builder

Returns
Type	Description
Model.Builder

setPipelineJob(String value)

public Model.Builder setPipelineJob(String value)

Optional. This field is populated if the model is produced by a pipeline job.

string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Parameter
Name	Description
value	String The pipelineJob to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setPipelineJobBytes(ByteString value)

public Model.Builder setPipelineJobBytes(ByteString value)

Optional. This field is populated if the model is produced by a pipeline job.

string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Parameter
Name	Description
value	ByteString The bytes for pipelineJob to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setPredictSchemata(PredictSchemata value)

public Model.Builder setPredictSchemata(PredictSchemata value)

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Parameter
Name	Description
value	PredictSchemata

Returns
Type	Description
Model.Builder

setPredictSchemata(PredictSchemata.Builder builderForValue)

public Model.Builder setPredictSchemata(PredictSchemata.Builder builderForValue)

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

Parameter
Name	Description
builderForValue	PredictSchemata.Builder

Returns
Type	Description
Model.Builder

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

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

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
Model.Builder

Overrides

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

setSupportedDeploymentResourcesTypes(int index, Model.DeploymentResourcesType value)

public Model.Builder setSupportedDeploymentResourcesTypes(int index, Model.DeploymentResourcesType value)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int The index to set the value at.
value	Model.DeploymentResourcesType The supportedDeploymentResourcesTypes to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setSupportedDeploymentResourcesTypesValue(int index, int value)

public Model.Builder setSupportedDeploymentResourcesTypesValue(int index, int value)

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int The index to set the value at.
value	int The enum numeric value on the wire for supportedDeploymentResourcesTypes to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setSupportedExportFormats(int index, Model.ExportFormat value)

public Model.Builder setSupportedExportFormats(int index, Model.ExportFormat value)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
value	Model.ExportFormat

Returns
Type	Description
Model.Builder

setSupportedExportFormats(int index, Model.ExportFormat.Builder builderForValue)

public Model.Builder setSupportedExportFormats(int index, Model.ExportFormat.Builder builderForValue)

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int
builderForValue	Model.ExportFormat.Builder

Returns
Type	Description
Model.Builder

setSupportedInputStorageFormats(int index, String value)

public Model.Builder setSupportedInputStorageFormats(int index, String value)

Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int The index to set the value at.
value	String The supportedInputStorageFormats to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setSupportedOutputStorageFormats(int index, String value)

public Model.Builder setSupportedOutputStorageFormats(int index, String value)

Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
Name	Description
index	int The index to set the value at.
value	String The supportedOutputStorageFormats to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setTrainingPipeline(String value)

public Model.Builder setTrainingPipeline(String value)

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }

Parameter
Name	Description
value	String The trainingPipeline to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setTrainingPipelineBytes(ByteString value)

public Model.Builder setTrainingPipelineBytes(ByteString value)

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }

Parameter
Name	Description
value	ByteString The bytes for trainingPipeline to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final Model.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
Model.Builder

Overrides

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

setUpdateTime(Timestamp value)

public Model.Builder setUpdateTime(Timestamp value)

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

setUpdateTime(Timestamp.Builder builderForValue)

public Model.Builder setUpdateTime(Timestamp.Builder builderForValue)

Output only. Timestamp when this Model was most recently updated.

.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	Builder

Returns
Type	Description
Model.Builder

setVersionAliases(int index, String value)

public Model.Builder setVersionAliases(int index, String value)

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

repeated string version_aliases = 29;

Parameters
Name	Description
index	int The index to set the value at.
value	String The versionAliases to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setVersionCreateTime(Timestamp value)

public Model.Builder setVersionCreateTime(Timestamp value)

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

setVersionCreateTime(Timestamp.Builder builderForValue)

public Model.Builder setVersionCreateTime(Timestamp.Builder builderForValue)

Output only. Timestamp when this version was created.

.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	Builder

Returns
Type	Description
Model.Builder

setVersionDescription(String value)

public Model.Builder setVersionDescription(String value)

The description of this version.

string version_description = 30;

Parameter
Name	Description
value	String The versionDescription to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setVersionDescriptionBytes(ByteString value)

public Model.Builder setVersionDescriptionBytes(ByteString value)

The description of this version.

string version_description = 30;

Parameter
Name	Description
value	ByteString The bytes for versionDescription to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setVersionId(String value)

public Model.Builder setVersionId(String value)

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	String The versionId to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setVersionIdBytes(ByteString value)

public Model.Builder setVersionIdBytes(ByteString value)

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	ByteString The bytes for versionId to set.

Returns
Type	Description
Model.Builder	This builder for chaining.

setVersionUpdateTime(Timestamp value)

public Model.Builder setVersionUpdateTime(Timestamp value)

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
value	Timestamp

Returns
Type	Description
Model.Builder

setVersionUpdateTime(Timestamp.Builder builderForValue)

public Model.Builder setVersionUpdateTime(Timestamp.Builder builderForValue)

Output only. Timestamp when this version was most recently updated.

.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
builderForValue	Builder

Returns
Type	Description
Model.Builder