Google Cloud Ai Platform V1 Client - Class Model (0.13.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class Model.

A trained machine learning Model.

Generated from protobuf message google.cloud.aiplatform.v1.Model

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ name`	`string` The resource name of the Model.
`↳ version_id`	`string` 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.
`↳ version_aliases`	`array` 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.
`↳ version_create_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this version was created.
`↳ version_update_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this version was most recently updated.
`↳ display_name`	`string` Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
`↳ description`	`string` The description of the Model.
`↳ version_description`	`string` The description of this version.
`↳ predict_schemata`	`Google\Cloud\AIPlatform\V1\PredictSchemata` The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
`↳ metadata_schema_uri`	`string` 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.
`↳ metadata`	`Google\Protobuf\Value` 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.
`↳ supported_export_formats`	`array<Google\Cloud\AIPlatform\V1\Model\ExportFormat>` Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.
`↳ training_pipeline`	`string` Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.
`↳ container_spec`	`Google\Cloud\AIPlatform\V1\ModelContainerSpec` Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
`↳ artifact_uri`	`string` Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
`↳ supported_deployment_resources_types`	`array` 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.
`↳ supported_input_storage_formats`	`array` 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.
`↳ supported_output_storage_formats`	`array` 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.
`↳ create_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this Model was uploaded into Vertex AI.
`↳ update_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this Model was most recently updated.
`↳ deployed_models`	`array<Google\Cloud\AIPlatform\V1\DeployedModelRef>` Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
`↳ explanation_spec`	`Google\Cloud\AIPlatform\V1\ExplanationSpec` 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.
`↳ etag`	`string` Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
`↳ labels`	`array\|Google\Protobuf\Internal\MapField` 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.
`↳ encryption_spec`	`Google\Cloud\AIPlatform\V1\EncryptionSpec` 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.
`↳ model_source_info`	`Google\Cloud\AIPlatform\V1\ModelSourceInfo` Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
`↳ original_model_info`	`Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo` Output only. If this Model is a copy of another Model, this contains info about the original.
`↳ metadata_artifact`	`string` 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}`.

getName

The resource name of the Model.

Returns
Type	Description
`string`

setName

The resource name of the Model.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`string`

setVersionId

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.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getVersionAliases

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.

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setVersionAliases

User provided version aliases so that a model version can be referenced via alias (i.e.

Parameter
Name	Description
`var`	`string[]`

Returns
Type	Description
`$this`

getVersionCreateTime

Output only. Timestamp when this version was created.

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasVersionCreateTime

clearVersionCreateTime

setVersionCreateTime

Output only. Timestamp when this version was created.

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getVersionUpdateTime

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

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasVersionUpdateTime

clearVersionUpdateTime

setVersionUpdateTime

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

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`string`

setDisplayName

Required. The display name of the Model.

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

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getDescription

The description of the Model.

Returns
Type	Description
`string`

setDescription

The description of the Model.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getVersionDescription

The description of this version.

Returns
Type	Description
`string`

setVersionDescription

The description of this version.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getPredictSchemata

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

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\PredictSchemata\|null`

hasPredictSchemata

clearPredictSchemata

setPredictSchemata

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

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\PredictSchemata`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`string`

setMetadataSchemaUri

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Protobuf\Value\|null`

hasMetadata

clearMetadata

setMetadata

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.

Parameter
Name	Description
`var`	`Google\Protobuf\Value`

Returns
Type	Description
`$this`

getSupportedExportFormats

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

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setSupportedExportFormats

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

Parameter
Name	Description
`var`	`array<Google\Cloud\AIPlatform\V1\Model\ExportFormat>`

Returns
Type	Description
`$this`

getTrainingPipeline

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

Returns
Type	Description
`string`

setTrainingPipeline

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

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getContainerSpec

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI.

Not present for AutoML Models or Large Models.

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\ModelContainerSpec\|null`

hasContainerSpec

clearContainerSpec

setContainerSpec

Not present for AutoML Models or Large Models.

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\ModelContainerSpec`

Returns
Type	Description
`$this`

getArtifactUri

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

Returns
Type	Description
`string`

setArtifactUri

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

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getSupportedDeploymentResourcesTypes

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.

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setSupportedDeploymentResourcesTypes

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.

Parameter
Name	Description
`var`	`int[]`

Returns
Type	Description
`$this`

getSupportedInputStorageFormats

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.

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setSupportedInputStorageFormats

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.

Parameter
Name	Description
`var`	`string[]`

Returns
Type	Description
`$this`

getSupportedOutputStorageFormats

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.

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setSupportedOutputStorageFormats

Output only. The formats this Model supports in BatchPredictionJob.output_config.

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.

Parameter
Name	Description
`var`	`string[]`

Returns
Type	Description
`$this`

getCreateTime

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

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasCreateTime

clearCreateTime

setCreateTime

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

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getUpdateTime

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

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasUpdateTime

clearUpdateTime

setUpdateTime

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

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getDeployedModels

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

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setDeployedModels

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

Parameter
Name	Description
`var`	`array<Google\Cloud\AIPlatform\V1\DeployedModelRef>`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\ExplanationSpec\|null`

hasExplanationSpec

clearExplanationSpec

setExplanationSpec

The default explanation specification for this Model.

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\ExplanationSpec`

Returns
Type	Description
`$this`

getEtag

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

Returns
Type	Description
`string`

setEtag

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

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getLabels

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.

Returns
Type	Description
`Google\Protobuf\Internal\MapField`

setLabels

The labels with user-defined metadata to organize your Models.

Parameter
Name	Description
`var`	`array\|Google\Protobuf\Internal\MapField`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\EncryptionSpec\|null`

hasEncryptionSpec

clearEncryptionSpec

setEncryptionSpec

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.

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\EncryptionSpec`

Returns
Type	Description
`$this`

getModelSourceInfo

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\ModelSourceInfo\|null`

hasModelSourceInfo

clearModelSourceInfo

setModelSourceInfo

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\ModelSourceInfo`

Returns
Type	Description
`$this`

getOriginalModelInfo

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

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo\|null`

hasOriginalModelInfo

clearOriginalModelInfo

setOriginalModelInfo

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

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo`

Returns
Type	Description
`$this`

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}.

Returns
Type	Description
`string`

setMetadataArtifact

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`