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
Namespace
Google \ Cloud \ AIPlatform \ V1Methods
__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. |
↳ 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 |
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<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. |
↳ pipeline_job |
string
Optional. This field is populated if the model is produced by a pipeline job. |
↳ container_spec |
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 required for AutoML Models. |
↳ artifact_uri |
string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models. |
↳ supported_deployment_resources_types |
array
Output only. When this Model is deployed, its prediction resources are described by the |
↳ 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: * * |
↳ 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: * * |
↳ 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<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 |
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. |
↳ data_stats |
Model\DataStats
Stats of data used for training or evaluating the Model. Only populated when the Model is trained by a TrainingPipeline with data_input_config. |
↳ encryption_spec |
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 |
ModelSourceInfo
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. |
↳ original_model_info |
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 |
↳ base_model_source |
Model\BaseModelSource
Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models. |
↳ satisfies_pzs |
bool
Output only. Reserved for future use. |
↳ satisfies_pzi |
bool
Output only. Reserved for future use. |
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.
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.
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 |
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 |
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
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.
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<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 |
getPipelineJob
Optional. This field is populated if the model is produced by a pipeline job.
Returns | |
---|---|
Type | Description |
string |
setPipelineJob
Optional. This field is populated if the model is produced by a pipeline job.
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 required for AutoML Models.
Returns | |
---|---|
Type | Description |
ModelContainerSpec|null |
hasContainerSpec
clearContainerSpec
setContainerSpec
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 required for AutoML Models.
Parameter | |
---|---|
Name | Description |
var |
ModelContainerSpec
|
Returns | |
---|---|
Type | Description |
$this |
getArtifactUri
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.
Returns | |
---|---|
Type | Description |
string |
setArtifactUri
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML 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
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.
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 totf-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, usesgcs_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 totf-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, usesgcs_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.
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.
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<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 |
ExplanationSpec|null |
hasExplanationSpec
clearExplanationSpec
setExplanationSpec
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.
Parameter | |
---|---|
Name | Description |
var |
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.
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.
Parameter | |
---|---|
Name | Description |
var |
array|Google\Protobuf\Internal\MapField
|
Returns | |
---|---|
Type | Description |
$this |
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.
Returns | |
---|---|
Type | Description |
Model\DataStats|null |
hasDataStats
clearDataStats
setDataStats
Stats of data used for training or evaluating the Model.
Only populated when the Model is trained by a TrainingPipeline with data_input_config.
Parameter | |
---|---|
Name | Description |
var |
Model\DataStats
|
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 |
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 |
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 saved and tuned from Genie or Model Garden.
Returns | |
---|---|
Type | Description |
ModelSourceInfo|null |
hasModelSourceInfo
clearModelSourceInfo
setModelSourceInfo
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.
Parameter | |
---|---|
Name | Description |
var |
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 |
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 |
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
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}
.
Parameter | |
---|---|
Name | Description |
var |
string
|
Returns | |
---|---|
Type | Description |
$this |
getBaseModelSource
Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
Returns | |
---|---|
Type | Description |
Model\BaseModelSource|null |
hasBaseModelSource
clearBaseModelSource
setBaseModelSource
Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
Parameter | |
---|---|
Name | Description |
var |
Model\BaseModelSource
|
Returns | |
---|---|
Type | Description |
$this |
getSatisfiesPzs
Output only. Reserved for future use.
Returns | |
---|---|
Type | Description |
bool |
setSatisfiesPzs
Output only. Reserved for future use.
Parameter | |
---|---|
Name | Description |
var |
bool
|
Returns | |
---|---|
Type | Description |
$this |
getSatisfiesPzi
Output only. Reserved for future use.
Returns | |
---|---|
Type | Description |
bool |
setSatisfiesPzi
Output only. Reserved for future use.
Parameter | |
---|---|
Name | Description |
var |
bool
|
Returns | |
---|---|
Type | Description |
$this |