Package com.google.cloud.aiplatform.v1

Interface ModelOrBuilder

  • All Superinterfaces:
    com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
    All Known Implementing Classes:
    Model, Model.Builder
    public interface ModelOrBuilder
extends com.google.protobuf.MessageOrBuilder

    • Method Detail

      • getName

        String getName()
         The resource name of the Model.
        string name = 1;
        Returns:
        The name.

      • getNameBytes

        com.google.protobuf.ByteString getNameBytes()
         The resource name of the Model.
        string name = 1;
        Returns:
        The bytes for name.

      • getVersionId

        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:
        The versionId.

      • getVersionIdBytes

        com.google.protobuf.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:
        The bytes for versionId.

      • getVersionAliasesList

        List<String> 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][a-zA-Z0-9-]{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:
        A list containing the versionAliases.

      • getVersionAliasesCount

        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][a-zA-Z0-9-]{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:
        The count of versionAliases.

      • getVersionAliases

        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][a-zA-Z0-9-]{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:
        index - The index of the element to return.
        Returns:
        The versionAliases at the given index.

      • getVersionAliasesBytes

        com.google.protobuf.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][a-zA-Z0-9-]{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:
        index - The index of the value to return.
        Returns:
        The bytes of the versionAliases at the given index.

      • hasVersionCreateTime

        boolean hasVersionCreateTime()
         Output only. Timestamp when this version was created.
        .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        Whether the versionCreateTime field is set.

      • getVersionCreateTime

        com.google.protobuf.Timestamp getVersionCreateTime()
         Output only. Timestamp when this version was created.
        .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The versionCreateTime.

      • getVersionCreateTimeOrBuilder

        com.google.protobuf.TimestampOrBuilder getVersionCreateTimeOrBuilder()
         Output only. Timestamp when this version was created.
        .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • hasVersionUpdateTime

        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:
        Whether the versionUpdateTime field is set.

      • getVersionUpdateTime

        com.google.protobuf.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:
        The versionUpdateTime.

      • getVersionUpdateTimeOrBuilder

        com.google.protobuf.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];

      • getDisplayName

        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:
        The displayName.

      • getDisplayNameBytes

        com.google.protobuf.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:
        The bytes for displayName.

      • getDescription

        String getDescription()
         The description of the Model.
        string description = 3;
        Returns:
        The description.

      • getDescriptionBytes

        com.google.protobuf.ByteString getDescriptionBytes()
         The description of the Model.
        string description = 3;
        Returns:
        The bytes for description.

      • getVersionDescription

        String getVersionDescription()
         The description of this version.
        string version_description = 30;
        Returns:
        The versionDescription.

      • getVersionDescriptionBytes

        com.google.protobuf.ByteString getVersionDescriptionBytes()
         The description of this version.
        string version_description = 30;
        Returns:
        The bytes for versionDescription.

      • hasPredictSchemata

        boolean hasPredictSchemata()
         The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;
        Returns:
        Whether the predictSchemata field is set.

      • getPredictSchemata

        PredictSchemata getPredictSchemata()
         The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;
        Returns:
        The predictSchemata.

      • getPredictSchemataOrBuilder

        PredictSchemataOrBuilder getPredictSchemataOrBuilder()
         The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

      • getMetadataSchemaUri

        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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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:
        The metadataSchemaUri.

      • getMetadataSchemaUriBytes

        com.google.protobuf.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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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:
        The bytes for metadataSchemaUri.

      • hasMetadata

        boolean hasMetadata()
         Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
        .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];
        Returns:
        Whether the metadata field is set.

      • getMetadata

        com.google.protobuf.Value getMetadata()
         Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
        .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];
        Returns:
        The metadata.

      • getMetadataOrBuilder

        com.google.protobuf.ValueOrBuilder getMetadataOrBuilder()
         Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
        .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

      • getSupportedExportFormatsList

        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];

      • getSupportedExportFormats

        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];

      • getSupportedExportFormatsCount

        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];

      • getSupportedExportFormatsOrBuilderList

        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];

      • getSupportedExportFormatsOrBuilder

        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];

      • getTrainingPipeline

        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:
        The trainingPipeline.

      • getTrainingPipelineBytes

        com.google.protobuf.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:
        The bytes for trainingPipeline.

      • getPipelineJob

        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:
        The pipelineJob.

      • getPipelineJobBytes

        com.google.protobuf.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:
        The bytes for pipelineJob.

      • hasContainerSpec

        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][google.cloud.aiplatform.v1.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:
        Whether the containerSpec field is set.

      • getContainerSpec

        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][google.cloud.aiplatform.v1.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:
        The containerSpec.

      • getContainerSpecOrBuilder

        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][google.cloud.aiplatform.v1.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];

      • getArtifactUri

        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:
        The artifactUri.

      • getArtifactUriBytes

        com.google.protobuf.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:
        The bytes for artifactUri.

      • getSupportedDeploymentResourcesTypesList

        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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
        repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        A list containing the supportedDeploymentResourcesTypes.

      • getSupportedDeploymentResourcesTypesCount

        int getSupportedDeploymentResourcesTypesCount()
         Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
        repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The count of supportedDeploymentResourcesTypes.

      • getSupportedDeploymentResourcesTypes

        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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
        repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Parameters:
        index - The index of the element to return.
        Returns:
        The supportedDeploymentResourcesTypes at the given index.

      • getSupportedDeploymentResourcesTypesValueList

        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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
        repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        A list containing the enum numeric values on the wire for supportedDeploymentResourcesTypes.

      • getSupportedDeploymentResourcesTypesValue

        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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
        repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Parameters:
        index - The index of the value to return.
        Returns:
        The enum numeric value on the wire of supportedDeploymentResourcesTypes at the given index.

      • getSupportedInputStorageFormatsList

        List<String> getSupportedInputStorageFormatsList()
         Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        A list containing the supportedInputStorageFormats.

      • getSupportedInputStorageFormatsCount

        int getSupportedInputStorageFormatsCount()
         Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The count of supportedInputStorageFormats.

      • getSupportedInputStorageFormats

        String getSupportedInputStorageFormats​(int index)
         Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Parameters:
        index - The index of the element to return.
        Returns:
        The supportedInputStorageFormats at the given index.

      • getSupportedInputStorageFormatsBytes

        com.google.protobuf.ByteString getSupportedInputStorageFormatsBytes​(int index)
         Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Parameters:
        index - The index of the value to return.
        Returns:
        The bytes of the supportedInputStorageFormats at the given index.

      • getSupportedOutputStorageFormatsList

        List<String> getSupportedOutputStorageFormatsList()
         Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        A list containing the supportedOutputStorageFormats.

      • getSupportedOutputStorageFormatsCount

        int getSupportedOutputStorageFormatsCount()
         Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The count of supportedOutputStorageFormats.

      • getSupportedOutputStorageFormats

        String getSupportedOutputStorageFormats​(int index)
         Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Parameters:
        index - The index of the element to return.
        Returns:
        The supportedOutputStorageFormats at the given index.

      • getSupportedOutputStorageFormatsBytes

        com.google.protobuf.ByteString getSupportedOutputStorageFormatsBytes​(int index)
         Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
        repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Parameters:
        index - The index of the value to return.
        Returns:
        The bytes of the supportedOutputStorageFormats at the given index.

      • hasCreateTime

        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:
        Whether the createTime field is set.

      • getCreateTime

        com.google.protobuf.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:
        The createTime.

      • getCreateTimeOrBuilder

        com.google.protobuf.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];

      • hasUpdateTime

        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:
        Whether the updateTime field is set.

      • getUpdateTime

        com.google.protobuf.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:
        The updateTime.

      • getUpdateTimeOrBuilder

        com.google.protobuf.TimestampOrBuilder getUpdateTimeOrBuilder()
         Output only. Timestamp when this Model was most recently updated.
        .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • getDeployedModelsList

        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];

      • getDeployedModels

        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];

      • getDeployedModelsCount

        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];

      • getDeployedModelsOrBuilderList

        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];

      • getDeployedModelsOrBuilder

        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];

      • hasExplanationSpec

        boolean hasExplanationSpec()
         The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
        .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;
        Returns:
        Whether the explanationSpec field is set.

      • getExplanationSpec

        ExplanationSpec getExplanationSpec()
         The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
        .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;
        Returns:
        The explanationSpec.

      • getExplanationSpecOrBuilder

        ExplanationSpecOrBuilder getExplanationSpecOrBuilder()
         The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
        .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

      • getEtag

        String getEtag()
         Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
        string etag = 16;
        Returns:
        The etag.

      • getEtagBytes

        com.google.protobuf.ByteString getEtagBytes()
         Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
        string etag = 16;
        Returns:
        The bytes for etag.

      • getLabelsCount

        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;

      • containsLabels

        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;

      • getLabelsMap

        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;

      • getLabelsOrDefault

        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;

      • getLabelsOrThrow

        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;

      • hasEncryptionSpec

        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:
        Whether the encryptionSpec field is set.

      • getEncryptionSpec

        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:
        The encryptionSpec.

      • getEncryptionSpecOrBuilder

        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;

      • hasModelSourceInfo

        boolean hasModelSourceInfo()
         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:
        Whether the modelSourceInfo field is set.

      • getModelSourceInfo

        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.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The modelSourceInfo.

      • getModelSourceInfoOrBuilder

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

      • hasOriginalModelInfo

        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:
        Whether the originalModelInfo field is set.

      • getOriginalModelInfo

        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:
        The originalModelInfo.

      • getOriginalModelInfoOrBuilder

        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];

      • getMetadataArtifact

        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:
        The metadataArtifact.

      • getMetadataArtifactBytes

        com.google.protobuf.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:
        The bytes for metadataArtifact.