Interface ModelOrBuilder (3.17.0)

public interface ModelOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

containsLabels(String key)

public abstract 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 abstract String getArtifactUri()

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
`String`	The artifactUri.

getArtifactUriBytes()

public abstract ByteString getArtifactUriBytes()

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
`ByteString`	The bytes for artifactUri.

getContainerSpec()

public abstract 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 present for AutoML Models or Large Models.

.google.cloud.aiplatform.v1beta1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
`ModelContainerSpec`	The containerSpec.

getContainerSpecOrBuilder()

public abstract ModelContainerSpecOrBuilder getContainerSpecOrBuilder()

.google.cloud.aiplatform.v1beta1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Returns
Type	Description
`ModelContainerSpecOrBuilder`

getCreateTime()

public abstract 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.

getCreateTimeOrBuilder()

public abstract 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`

getDeployedModels(int index)

public abstract 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.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`DeployedModelRef`

getDeployedModelsCount()

public abstract 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.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`int`

getDeployedModelsList()

public abstract 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.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`List<DeployedModelRef>`

getDeployedModelsOrBuilder(int index)

public abstract 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.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`DeployedModelRefOrBuilder`

getDeployedModelsOrBuilderList()

public abstract 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.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`List<? extends com.google.cloud.aiplatform.v1beta1.DeployedModelRefOrBuilder>`

getDescription()

public abstract String getDescription()

The description of the Model.

string description = 3;

Returns
Type	Description
`String`	The description.

getDescriptionBytes()

public abstract ByteString getDescriptionBytes()

The description of the Model.

string description = 3;

Returns
Type	Description
`ByteString`	The bytes for description.

getDisplayName()

public abstract 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 abstract 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 abstract 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.v1beta1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
`EncryptionSpec`	The encryptionSpec.

getEncryptionSpecOrBuilder()

public abstract 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.v1beta1.EncryptionSpec encryption_spec = 24;

Returns
Type	Description
`EncryptionSpecOrBuilder`

getEtag()

public abstract 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 abstract 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 abstract 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.v1beta1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
`ExplanationSpec`	The explanationSpec.

getExplanationSpecOrBuilder()

public abstract ExplanationSpecOrBuilder getExplanationSpecOrBuilder()

.google.cloud.aiplatform.v1beta1.ExplanationSpec explanation_spec = 23;

Returns
Type	Description
`ExplanationSpecOrBuilder`

getLabels()

public abstract Map<String,String> getLabels()

Use #getLabelsMap() instead.

Returns
Type	Description
`Map<String,String>`

getLabelsCount()

public abstract int getLabelsCount()

map<string, string> labels = 17;

Returns
Type	Description
`int`

getLabelsMap()

public abstract Map<String,String> getLabelsMap()

map<string, string> labels = 17;

Returns
Type	Description
`Map<String,String>`

getLabelsOrDefault(String key, String defaultValue)

public abstract String getLabelsOrDefault(String key, String defaultValue)

map<string, string> labels = 17;

Parameters
Name	Description
`key`	`String`
`defaultValue`	`String`

Returns
Type	Description
`String`

getLabelsOrThrow(String key)

public abstract String getLabelsOrThrow(String key)

map<string, string> labels = 17;

Parameter
Name	Description
`key`	`String`

Returns
Type	Description
`String`

getMetadata()

public abstract 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 abstract 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 abstract ByteString getMetadataArtifactBytes()

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

Returns
Type	Description
`ByteString`	The bytes for metadataArtifact.

getMetadataOrBuilder()

public abstract 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 abstract 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 abstract ByteString getMetadataSchemaUriBytes()

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

Returns
Type	Description
`ByteString`	The bytes for metadataSchemaUri.

getModelSourceInfo()

public abstract ModelSourceInfo getModelSourceInfo()

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.v1beta1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`ModelSourceInfo`	The modelSourceInfo.

getModelSourceInfoOrBuilder()

public abstract ModelSourceInfoOrBuilder getModelSourceInfoOrBuilder()

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.v1beta1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`ModelSourceInfoOrBuilder`

getName()

public abstract String getName()

The resource name of the Model.

string name = 1;

Returns
Type	Description
`String`	The name.

getNameBytes()

public abstract ByteString getNameBytes()

The resource name of the Model.

string name = 1;

Returns
Type	Description
`ByteString`	The bytes for name.

getOriginalModelInfo()

public abstract Model.OriginalModelInfo getOriginalModelInfo()

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

.google.cloud.aiplatform.v1beta1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Model.OriginalModelInfo`	The originalModelInfo.

getOriginalModelInfoOrBuilder()

public abstract Model.OriginalModelInfoOrBuilder getOriginalModelInfoOrBuilder()

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

.google.cloud.aiplatform.v1beta1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Model.OriginalModelInfoOrBuilder`

getPredictSchemata()

public abstract 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.v1beta1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
`PredictSchemata`	The predictSchemata.

getPredictSchemataOrBuilder()

public abstract 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.v1beta1.PredictSchemata predict_schemata = 4;

Returns
Type	Description
`PredictSchemataOrBuilder`

getSupportedDeploymentResourcesTypes(int index)

public abstract 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.