Class Model (2.5.3)

public final class Model extends GeneratedMessageV3 implements ModelOrBuilder

A trained machine learning Model.

Protobuf type google.cloud.aiplatform.v1beta1.Model

Implements

ModelOrBuilder

Fields

ARTIFACT_URI_FIELD_NUMBER

public static final int ARTIFACT_URI_FIELD_NUMBER
Field Value
TypeDescription
int

CONTAINER_SPEC_FIELD_NUMBER

public static final int CONTAINER_SPEC_FIELD_NUMBER
Field Value
TypeDescription
int

CREATE_TIME_FIELD_NUMBER

public static final int CREATE_TIME_FIELD_NUMBER
Field Value
TypeDescription
int

DEPLOYED_MODELS_FIELD_NUMBER

public static final int DEPLOYED_MODELS_FIELD_NUMBER
Field Value
TypeDescription
int

DESCRIPTION_FIELD_NUMBER

public static final int DESCRIPTION_FIELD_NUMBER
Field Value
TypeDescription
int

DISPLAY_NAME_FIELD_NUMBER

public static final int DISPLAY_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

ENCRYPTION_SPEC_FIELD_NUMBER

public static final int ENCRYPTION_SPEC_FIELD_NUMBER
Field Value
TypeDescription
int

ETAG_FIELD_NUMBER

public static final int ETAG_FIELD_NUMBER
Field Value
TypeDescription
int

EXPLANATION_SPEC_FIELD_NUMBER

public static final int EXPLANATION_SPEC_FIELD_NUMBER
Field Value
TypeDescription
int

LABELS_FIELD_NUMBER

public static final int LABELS_FIELD_NUMBER
Field Value
TypeDescription
int

METADATA_FIELD_NUMBER

public static final int METADATA_FIELD_NUMBER
Field Value
TypeDescription
int

METADATA_SCHEMA_URI_FIELD_NUMBER

public static final int METADATA_SCHEMA_URI_FIELD_NUMBER
Field Value
TypeDescription
int

NAME_FIELD_NUMBER

public static final int NAME_FIELD_NUMBER
Field Value
TypeDescription
int

PREDICT_SCHEMATA_FIELD_NUMBER

public static final int PREDICT_SCHEMATA_FIELD_NUMBER
Field Value
TypeDescription
int

SUPPORTED_DEPLOYMENT_RESOURCES_TYPES_FIELD_NUMBER

public static final int SUPPORTED_DEPLOYMENT_RESOURCES_TYPES_FIELD_NUMBER
Field Value
TypeDescription
int

SUPPORTED_EXPORT_FORMATS_FIELD_NUMBER

public static final int SUPPORTED_EXPORT_FORMATS_FIELD_NUMBER
Field Value
TypeDescription
int

SUPPORTED_INPUT_STORAGE_FORMATS_FIELD_NUMBER

public static final int SUPPORTED_INPUT_STORAGE_FORMATS_FIELD_NUMBER
Field Value
TypeDescription
int

SUPPORTED_OUTPUT_STORAGE_FORMATS_FIELD_NUMBER

public static final int SUPPORTED_OUTPUT_STORAGE_FORMATS_FIELD_NUMBER
Field Value
TypeDescription
int

TRAINING_PIPELINE_FIELD_NUMBER

public static final int TRAINING_PIPELINE_FIELD_NUMBER
Field Value
TypeDescription
int

UPDATE_TIME_FIELD_NUMBER

public static final int UPDATE_TIME_FIELD_NUMBER
Field Value
TypeDescription
int

Methods

containsLabels(String key)

public boolean containsLabels(String key)

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameter
NameDescription
keyString
Returns
TypeDescription
boolean

equals(Object obj)

public boolean equals(Object obj)
Parameter
NameDescription
objObject
Returns
TypeDescription
boolean
Overrides

getArtifactUri()

public String getArtifactUri()

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

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

Returns
TypeDescription
String

The artifactUri.

getArtifactUriBytes()

public ByteString getArtifactUriBytes()

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

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

Returns
TypeDescription
ByteString

The bytes for artifactUri.

getContainerSpec()

public ModelContainerSpec getContainerSpec()

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

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

Returns
TypeDescription
ModelContainerSpec

The containerSpec.

getContainerSpecOrBuilder()

public ModelContainerSpecOrBuilder getContainerSpecOrBuilder()

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

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

Returns
TypeDescription
ModelContainerSpecOrBuilder

getCreateTime()

public Timestamp getCreateTime()

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

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

Returns
TypeDescription
Timestamp

The createTime.

getCreateTimeOrBuilder()

public TimestampOrBuilder getCreateTimeOrBuilder()

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

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

Returns
TypeDescription
TimestampOrBuilder

getDefaultInstance()

public static Model getDefaultInstance()
Returns
TypeDescription
Model

getDefaultInstanceForType()

public Model getDefaultInstanceForType()
Returns
TypeDescription
Model

getDeployedModels(int index)

public DeployedModelRef getDeployedModels(int index)

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

repeated .google.cloud.aiplatform.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
DeployedModelRef

getDeployedModelsCount()

public int getDeployedModelsCount()

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

repeated .google.cloud.aiplatform.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
int

getDeployedModelsList()

public List<DeployedModelRef> getDeployedModelsList()

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

repeated .google.cloud.aiplatform.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<DeployedModelRef>

getDeployedModelsOrBuilder(int index)

public DeployedModelRefOrBuilder getDeployedModelsOrBuilder(int index)

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

repeated .google.cloud.aiplatform.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
DeployedModelRefOrBuilder

getDeployedModelsOrBuilderList()

public List<? extends DeployedModelRefOrBuilder> getDeployedModelsOrBuilderList()

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

repeated .google.cloud.aiplatform.v1beta1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<? extends com.google.cloud.aiplatform.v1beta1.DeployedModelRefOrBuilder>

getDescription()

public String getDescription()

The description of the Model.

string description = 3;

Returns
TypeDescription
String

The description.

getDescriptionBytes()

public ByteString getDescriptionBytes()

The description of the Model.

string description = 3;

Returns
TypeDescription
ByteString

The bytes for description.

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

getDisplayName()

public String getDisplayName()

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

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

Returns
TypeDescription
String

The displayName.

getDisplayNameBytes()

public ByteString getDisplayNameBytes()

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

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

Returns
TypeDescription
ByteString

The bytes for displayName.

getEncryptionSpec()

public EncryptionSpec getEncryptionSpec()

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

.google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 24;

Returns
TypeDescription
EncryptionSpec

The encryptionSpec.

getEncryptionSpecOrBuilder()

public EncryptionSpecOrBuilder getEncryptionSpecOrBuilder()

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

.google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 24;

Returns
TypeDescription
EncryptionSpecOrBuilder

getEtag()

public String getEtag()

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

string etag = 16;

Returns
TypeDescription
String

The etag.

getEtagBytes()

public ByteString getEtagBytes()

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

string etag = 16;

Returns
TypeDescription
ByteString

The bytes for etag.

getExplanationSpec()

public ExplanationSpec getExplanationSpec()

The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

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

Returns
TypeDescription
ExplanationSpec

The explanationSpec.

getExplanationSpecOrBuilder()

public ExplanationSpecOrBuilder getExplanationSpecOrBuilder()

The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

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

Returns
TypeDescription
ExplanationSpecOrBuilder

getLabels()

public Map<String,String> getLabels()

Use #getLabelsMap() instead.

Returns
TypeDescription
Map<String,String>

getLabelsCount()

public int getLabelsCount()

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Returns
TypeDescription
int

getLabelsMap()

public Map<String,String> getLabelsMap()

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Returns
TypeDescription
Map<String,String>

getLabelsOrDefault(String key, String defaultValue)

public String getLabelsOrDefault(String key, String defaultValue)

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameters
NameDescription
keyString
defaultValueString
Returns
TypeDescription
String

getLabelsOrThrow(String key)

public String getLabelsOrThrow(String key)

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

map<string, string> labels = 17;

Parameter
NameDescription
keyString
Returns
TypeDescription
String

getMetadata()

public Value getMetadata()

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

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

Returns
TypeDescription
Value

The metadata.

getMetadataOrBuilder()

public ValueOrBuilder getMetadataOrBuilder()

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

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

Returns
TypeDescription
ValueOrBuilder

getMetadataSchemaUri()

public String getMetadataSchemaUri()

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

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

Returns
TypeDescription
String

The metadataSchemaUri.

getMetadataSchemaUriBytes()

public ByteString getMetadataSchemaUriBytes()

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

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

Returns
TypeDescription
ByteString

The bytes for metadataSchemaUri.

getName()

public String getName()

The resource name of the Model.

string name = 1;

Returns
TypeDescription
String

The name.

getNameBytes()

public ByteString getNameBytes()

The resource name of the Model.

string name = 1;

Returns
TypeDescription
ByteString

The bytes for name.

getParserForType()

public Parser<Model> getParserForType()
Returns
TypeDescription
Parser<Model>
Overrides

getPredictSchemata()

public PredictSchemata getPredictSchemata()

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

.google.cloud.aiplatform.v1beta1.PredictSchemata predict_schemata = 4;

Returns
TypeDescription
PredictSchemata

The predictSchemata.

getPredictSchemataOrBuilder()

public PredictSchemataOrBuilder getPredictSchemataOrBuilder()

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

.google.cloud.aiplatform.v1beta1.PredictSchemata predict_schemata = 4;

Returns
TypeDescription
PredictSchemataOrBuilder

getSerializedSize()

public int getSerializedSize()
Returns
TypeDescription
int
Overrides

getSupportedDeploymentResourcesTypes(int index)

public Model.DeploymentResourcesType getSupportedDeploymentResourcesTypes(int index)

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

repeated .google.cloud.aiplatform.v1beta1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
Model.DeploymentResourcesType

The supportedDeploymentResourcesTypes at the given index.

getSupportedDeploymentResourcesTypesCount()

public int getSupportedDeploymentResourcesTypesCount()

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

repeated .google.cloud.aiplatform.v1beta1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
int

The count of supportedDeploymentResourcesTypes.

getSupportedDeploymentResourcesTypesList()

public List<Model.DeploymentResourcesType> getSupportedDeploymentResourcesTypesList()

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

repeated .google.cloud.aiplatform.v1beta1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<DeploymentResourcesType>

A list containing the supportedDeploymentResourcesTypes.

getSupportedDeploymentResourcesTypesValue(int index)

public int getSupportedDeploymentResourcesTypesValue(int index)

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

repeated .google.cloud.aiplatform.v1beta1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
int

The enum numeric value on the wire of supportedDeploymentResourcesTypes at the given index.

getSupportedDeploymentResourcesTypesValueList()

public List<Integer> getSupportedDeploymentResourcesTypesValueList()

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

repeated .google.cloud.aiplatform.v1beta1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<Integer>

A list containing the enum numeric values on the wire for supportedDeploymentResourcesTypes.

getSupportedExportFormats(int index)

public Model.ExportFormat getSupportedExportFormats(int index)

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

repeated .google.cloud.aiplatform.v1beta1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
Model.ExportFormat

getSupportedExportFormatsCount()

public int getSupportedExportFormatsCount()

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

repeated .google.cloud.aiplatform.v1beta1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
int

getSupportedExportFormatsList()

public List<Model.ExportFormat> getSupportedExportFormatsList()

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

repeated .google.cloud.aiplatform.v1beta1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<ExportFormat>

getSupportedExportFormatsOrBuilder(int index)

public Model.ExportFormatOrBuilder getSupportedExportFormatsOrBuilder(int index)

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

repeated .google.cloud.aiplatform.v1beta1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
Model.ExportFormatOrBuilder

getSupportedExportFormatsOrBuilderList()

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

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

repeated .google.cloud.aiplatform.v1beta1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<? extends com.google.cloud.aiplatform.v1beta1.Model.ExportFormatOrBuilder>

getSupportedInputStorageFormats(int index)

public String getSupportedInputStorageFormats(int index)

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

  • jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.
  • csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.
  • tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.
  • tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.
  • bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.
  • file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Parameter
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
String

The supportedInputStorageFormats at the given index.

getSupportedInputStorageFormatsBytes(int index)

public ByteString getSupportedInputStorageFormatsBytes(int index)

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

  • jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.
  • csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.
  • tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.
  • tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.
  • bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.
  • file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Parameter
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
ByteString

The bytes of the supportedInputStorageFormats at the given index.

getSupportedInputStorageFormatsCount()

public int getSupportedInputStorageFormatsCount()

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

  • jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.
  • csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.
  • tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.
  • tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.
  • bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.
  • file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Returns
TypeDescription
int

The count of supportedInputStorageFormats.

getSupportedInputStorageFormatsList()

public ProtocolStringList getSupportedInputStorageFormatsList()

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

  • jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.
  • csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.
  • tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.
  • tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.
  • bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.
  • file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Returns
TypeDescription
ProtocolStringList

A list containing the supportedInputStorageFormats.

getSupportedOutputStorageFormats(int index)

public String getSupportedOutputStorageFormats(int index)

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

  • jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.
  • csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.
  • bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Parameter
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
String

The supportedOutputStorageFormats at the given index.

getSupportedOutputStorageFormatsBytes(int index)

public ByteString getSupportedOutputStorageFormatsBytes(int index)

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

  • jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.
  • csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.
  • bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Parameter
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
ByteString

The bytes of the supportedOutputStorageFormats at the given index.

getSupportedOutputStorageFormatsCount()

public int getSupportedOutputStorageFormatsCount()

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

  • jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.
  • csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.
  • bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Returns
TypeDescription
int

The count of supportedOutputStorageFormats.

getSupportedOutputStorageFormatsList()

public ProtocolStringList getSupportedOutputStorageFormatsList()

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

  • jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.
  • csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.
  • bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

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

Returns
TypeDescription
ProtocolStringList

A list containing the supportedOutputStorageFormats.

getTrainingPipeline()

public String getTrainingPipeline()

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

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

Returns
TypeDescription
String

The trainingPipeline.

getTrainingPipelineBytes()

public ByteString getTrainingPipelineBytes()

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

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

Returns
TypeDescription
ByteString

The bytes for trainingPipeline.

getUnknownFields()

public final UnknownFieldSet getUnknownFields()
Returns
TypeDescription
UnknownFieldSet
Overrides

getUpdateTime()

public Timestamp getUpdateTime()

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

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

Returns
TypeDescription
Timestamp

The updateTime.

getUpdateTimeOrBuilder()

public TimestampOrBuilder getUpdateTimeOrBuilder()

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

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

Returns
TypeDescription
TimestampOrBuilder

hasContainerSpec()

public boolean hasContainerSpec()

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

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

Returns
TypeDescription
boolean

Whether the containerSpec field is set.

hasCreateTime()

public boolean hasCreateTime()

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

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

Returns
TypeDescription
boolean

Whether the createTime field is set.

hasEncryptionSpec()

public boolean hasEncryptionSpec()

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

.google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 24;

Returns
TypeDescription
boolean

Whether the encryptionSpec field is set.

hasExplanationSpec()

public boolean hasExplanationSpec()

The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

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

Returns
TypeDescription
boolean

Whether the explanationSpec field is set.

hasMetadata()

public boolean hasMetadata()

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

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

Returns
TypeDescription
boolean

Whether the metadata field is set.

hasPredictSchemata()

public boolean hasPredictSchemata()

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

.google.cloud.aiplatform.v1beta1.PredictSchemata predict_schemata = 4;

Returns
TypeDescription
boolean

Whether the predictSchemata field is set.

hasUpdateTime()

public boolean hasUpdateTime()

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

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

Returns
TypeDescription
boolean

Whether the updateTime field is set.

hashCode()

public int hashCode()
Returns
TypeDescription
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

internalGetMapField(int number)

protected MapField internalGetMapField(int number)
Parameter
NameDescription
numberint
Returns
TypeDescription
MapField
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

newBuilder()

public static Model.Builder newBuilder()
Returns
TypeDescription
Model.Builder

newBuilder(Model prototype)

public static Model.Builder newBuilder(Model prototype)
Parameter
NameDescription
prototypeModel
Returns
TypeDescription
Model.Builder

newBuilderForType()

public Model.Builder newBuilderForType()
Returns
TypeDescription
Model.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected Model.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
NameDescription
parentBuilderParent
Returns
TypeDescription
Model.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
NameDescription
unusedUnusedPrivateParameter
Returns
TypeDescription
Object
Overrides

parseDelimitedFrom(InputStream input)

public static Model parseDelimitedFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
Model
Exceptions
TypeDescription
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static Model parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
Model
Exceptions
TypeDescription
IOException

parseFrom(byte[] data)

public static Model parseFrom(byte[] data)
Parameter
NameDescription
databyte[]
Returns
TypeDescription
Model
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static Model parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
databyte[]
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
Model
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data)

public static Model parseFrom(ByteString data)
Parameter
NameDescription
dataByteString
Returns
TypeDescription
Model
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static Model parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteString
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
Model
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static Model parseFrom(CodedInputStream input)
Parameter
NameDescription
inputCodedInputStream
Returns
TypeDescription
Model
Exceptions
TypeDescription
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static Model parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
Model
Exceptions
TypeDescription
IOException

parseFrom(InputStream input)

public static Model parseFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
Model
Exceptions
TypeDescription
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static Model parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
Model
Exceptions
TypeDescription
IOException

parseFrom(ByteBuffer data)

public static Model parseFrom(ByteBuffer data)
Parameter
NameDescription
dataByteBuffer
Returns
TypeDescription
Model
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static Model parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteBuffer
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
Model
Exceptions
TypeDescription
InvalidProtocolBufferException

parser()

public static Parser<Model> parser()
Returns
TypeDescription
Parser<Model>

toBuilder()

public Model.Builder toBuilder()
Returns
TypeDescription
Model.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
NameDescription
outputCodedOutputStream
Overrides Exceptions
TypeDescription
IOException