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
@Generated public interface ModelOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • 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.

    • getDefaultCheckpointId

      String getDefaultCheckpointId()
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Returns:
      The defaultCheckpointId.

    • getDefaultCheckpointIdBytes

      com.google.protobuf.ByteString getDefaultCheckpointIdBytes()
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Returns:
      The bytes for defaultCheckpointId.

    • 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 required for AutoML 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 required for AutoML 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 required for AutoML 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 required for AutoML 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 required for AutoML 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;

    • getLabels

      @Deprecated Map<String,String> getLabels()
      Deprecated.
      Use getLabelsMap() instead.

    • 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;

    • hasDataStats

      boolean hasDataStats()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;
      Returns:
      Whether the dataStats field is set.

    • getDataStats

      Model.DataStats getDataStats()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;
      Returns:
      The dataStats.

    • getDataStatsOrBuilder

      Model.DataStatsOrBuilder getDataStatsOrBuilder()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

    • 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 saved and tuned from Genie or
 Model Garden.
      .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 saved and tuned from Genie or
 Model Garden.
      .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 saved and tuned from Genie or
 Model Garden.
      .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.

    • hasBaseModelSource

      boolean hasBaseModelSource()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      Whether the baseModelSource field is set.

    • getBaseModelSource

      Model.BaseModelSource getBaseModelSource()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The baseModelSource.

    • getBaseModelSourceOrBuilder

      Model.BaseModelSourceOrBuilder getBaseModelSourceOrBuilder()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

    • getSatisfiesPzs

      boolean getSatisfiesPzs()
       Output only. Reserved for future use.
      bool satisfies_pzs = 51 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The satisfiesPzs.

    • getSatisfiesPzi

      boolean getSatisfiesPzi()
       Output only. Reserved for future use.
      bool satisfies_pzi = 52 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The satisfiesPzi.

    • getCheckpointsList

      List<Checkpoint> getCheckpointsList()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpoints

      Checkpoint getCheckpoints(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpointsCount

      int getCheckpointsCount()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpointsOrBuilderList

      List<? extends CheckpointOrBuilder> getCheckpointsOrBuilderList()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpointsOrBuilder

      CheckpointOrBuilder getCheckpointsOrBuilder(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];