Class Model.Builder (3.32.0) 

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

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

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

clearArtifactUri()

public Model.Builder clearArtifactUri()

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

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

Returns
Type Description
Model.Builder

This builder for chaining.

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 present for AutoML Models or Large 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

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

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 existing Vertex AI Model.

.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

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