📄 Source: GoogleCloudAiplatformV1Model.php
<?php
/*
* Copyright 2014 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
namespace Google\Service\Aiplatform;
class GoogleCloudAiplatformV1Model extends \Google\Collection
{
protected $collection_key = 'versionAliases';
/**
* Immutable. The path to the directory containing the Model artifact and any
* of its supporting files. Not required for AutoML Models.
*
* @var string
*/
public $artifactUri;
protected $baseModelSourceType = GoogleCloudAiplatformV1ModelBaseModelSource::class;
protected $baseModelSourceDataType = '';
protected $checkpointsType = GoogleCloudAiplatformV1Checkpoint::class;
protected $checkpointsDataType = 'array';
protected $containerSpecType = GoogleCloudAiplatformV1ModelContainerSpec::class;
protected $containerSpecDataType = '';
/**
* Output only. Timestamp when this Model was uploaded into Vertex AI.
*
* @var string
*/
public $createTime;
protected $dataStatsType = GoogleCloudAiplatformV1ModelDataStats::class;
protected $dataStatsDataType = '';
/**
* The default checkpoint id of a model version.
*
* @var string
*/
public $defaultCheckpointId;
protected $deployedModelsType = GoogleCloudAiplatformV1DeployedModelRef::class;
protected $deployedModelsDataType = 'array';
/**
* The description of the Model.
*
* @var string
*/
public $description;
/**
* Required. The display name of the Model. The name can be up to 128
* characters long and can consist of any UTF-8 characters.
*
* @var string
*/
public $displayName;
protected $encryptionSpecType = GoogleCloudAiplatformV1EncryptionSpec::class;
protected $encryptionSpecDataType = '';
/**
* Used to perform consistent read-modify-write updates. If not set, a blind
* "overwrite" update happens.
*
* @var string
*/
public $etag;
protected $explanationSpecType = GoogleCloudAiplatformV1ExplanationSpec::class;
protected $explanationSpecDataType = '';
/**
* 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.
*
* @var string[]
*/
public $labels;
/**
* 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.
*
* @var array
*/
public $metadata;
/**
* 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}`.
*
* @var string
*/
public $metadataArtifact;
/**
* Immutable. Points to a YAML file stored on Google Cloud Storage describing
* additional information about the Model, that is specific to it. Unset if
* the Model does not have any additional information. The schema is defined
* as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-
* Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models
* always have this field populated by Vertex AI, if no additional metadata is
* needed, this field is set to an empty string. Note: The URI given on output
* will be immutable and probably different, including the URI scheme, than
* the one given on input. The output URI will point to a location where the
* user only has a read access.
*
* @var string
*/
public $metadataSchemaUri;
protected $modelSourceInfoType = GoogleCloudAiplatformV1ModelSourceInfo::class;
protected $modelSourceInfoDataType = '';
/**
* Identifier. The resource name of the Model.
*
* @var string
*/
public $name;
protected $originalModelInfoType = GoogleCloudAiplatformV1ModelOriginalModelInfo::class;
protected $originalModelInfoDataType = '';
/**
* Optional. This field is populated if the model is produced by a pipeline
* job.
*
* @var string
*/
public $pipelineJob;
protected $predictSchemataType = GoogleCloudAiplatformV1PredictSchemata::class;
protected $predictSchemataDataType = '';
/**
* Output only. Reserved for future use.
*
* @var bool
*/
public $satisfiesPzi;
/**
* Output only. Reserved for future use.
*
* @var bool
*/
public $satisfiesPzs;
/**
* 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.
*
* @var string[]
*/
public $supportedDeploymentResourcesTypes;
protected $supportedExportFormatsType = GoogleCloudAiplatformV1ModelExportFormat::class;
protected $supportedExportFormatsDataType = '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.
*
* @var string[]
*/
public $supportedInputStorageFormats;
/**
* 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.
*
* @var string[]
*/
public $supportedOutputStorageFormats;
/**
* Output only. The resource name of the TrainingPipeline that uploaded this
* Model, if any.
*
* @var string
*/
public $trainingPipeline;
/**
* Output only. Timestamp when this Model was most recently updated.
*
* @var string
*/
public $updateTime;
/**
* 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{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.
*
* @var string[]
*/
public $versionAliases;
/**
* Output only. Timestamp when this version was created.
*
* @var string
*/
public $versionCreateTime;
/**
* The description of this version.
*
* @var string
*/
public $versionDescription;
/**
* 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.
*
* @var string
*/
public $versionId;
/**
* Output only. Timestamp when this version was most recently updated.
*
* @var string
*/
public $versionUpdateTime;
/**
* Immutable. The path to the directory containing the Model artifact and any
* of its supporting files. Not required for AutoML Models.
*
* @param string $artifactUri
*/
public function setArtifactUri($artifactUri)
{
$this->artifactUri = $artifactUri;
}
/**
* @return string
*/
public function getArtifactUri()
{
return $this->artifactUri;
}
/**
* Optional. User input field to specify the base model source. Currently it
* only supports specifing the Model Garden models and Genie models.
*
* @param GoogleCloudAiplatformV1ModelBaseModelSource $baseModelSource
*/
public function setBaseModelSource(GoogleCloudAiplatformV1ModelBaseModelSource $baseModelSource)
{
$this->baseModelSource = $baseModelSource;
}
/**
* @return GoogleCloudAiplatformV1ModelBaseModelSource
*/
public function getBaseModelSource()
{
return $this->baseModelSource;
}
/**
* Optional. Output only. The checkpoints of the model.
*
* @param GoogleCloudAiplatformV1Checkpoint[] $checkpoints
*/
public function setCheckpoints($checkpoints)
{
$this->checkpoints = $checkpoints;
}
/**
* @return GoogleCloudAiplatformV1Checkpoint[]
*/
public function getCheckpoints()
{
return $this->checkpoints;
}
/**
* 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.
*
* @param GoogleCloudAiplatformV1ModelContainerSpec $containerSpec
*/
public function setContainerSpec(GoogleCloudAiplatformV1ModelContainerSpec $containerSpec)
{
$this->containerSpec = $containerSpec;
}
/**
* @return GoogleCloudAiplatformV1ModelContainerSpec
*/
public function getContainerSpec()
{
return $this->containerSpec;
}
/**
* Output only. Timestamp when this Model was uploaded into Vertex AI.
*
* @param string $createTime
*/
public function setCreateTime($createTime)
{
$this->createTime = $createTime;
}
/**
* @return string
*/
public function getCreateTime()
{
return $this->createTime;
}
/**
* Stats of data used for training or evaluating the Model. Only populated
* when the Model is trained by a TrainingPipeline with data_input_config.
*
* @param GoogleCloudAiplatformV1ModelDataStats $dataStats
*/
public function setDataStats(GoogleCloudAiplatformV1ModelDataStats $dataStats)
{
$this->dataStats = $dataStats;
}
/**
* @return GoogleCloudAiplatformV1ModelDataStats
*/
public function getDataStats()
{
return $this->dataStats;
}
/**
* The default checkpoint id of a model version.
*
* @param string $defaultCheckpointId
*/
public function setDefaultCheckpointId($defaultCheckpointId)
{
$this->defaultCheckpointId = $defaultCheckpointId;
}
/**
* @return string
*/
public function getDefaultCheckpointId()
{
return $this->defaultCheckpointId;
}
/**
* Output only. The pointers to DeployedModels created from this Model. Note
* that Model could have been deployed to Endpoints in different Locations.
*
* @param GoogleCloudAiplatformV1DeployedModelRef[] $deployedModels
*/
public function setDeployedModels($deployedModels)
{
$this->deployedModels = $deployedModels;
}
/**
* @return GoogleCloudAiplatformV1DeployedModelRef[]
*/
public function getDeployedModels()
{
return $this->deployedModels;
}
/**
* The description of the Model.
*
* @param string $description
*/
public function setDescription($description)
{
$this->description = $description;
}
/**
* @return string
*/
public function getDescription()
{
return $this->description;
}
/**
* Required. The display name of the Model. The name can be up to 128
* characters long and can consist of any UTF-8 characters.
*
* @param string $displayName
*/
public function setDisplayName($displayName)
{
$this->displayName = $displayName;
}
/**
* @return string
*/
public function getDisplayName()
{
return $this->displayName;
}
/**
* 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.
*
* @param GoogleCloudAiplatformV1EncryptionSpec $encryptionSpec
*/
public function setEncryptionSpec(GoogleCloudAiplatformV1EncryptionSpec $encryptionSpec)
{
$this->encryptionSpec = $encryptionSpec;
}
/**
* @return GoogleCloudAiplatformV1EncryptionSpec
*/
public function getEncryptionSpec()
{
return $this->encryptionSpec;
}
/**
* Used to perform consistent read-modify-write updates. If not set, a blind
* "overwrite" update happens.
*
* @param string $etag
*/
public function setEtag($etag)
{
$this->etag = $etag;
}
/**
* @return string
*/
public function getEtag()
{
return $this->etag;
}
/**
* 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.
*
* @param GoogleCloudAiplatformV1ExplanationSpec $explanationSpec
*/
public function setExplanationSpec(GoogleCloudAiplatformV1ExplanationSpec $explanationSpec)
{
$this->explanationSpec = $explanationSpec;
}
/**
* @return GoogleCloudAiplatformV1ExplanationSpec
*/
public function getExplanationSpec()
{
return $this->explanationSpec;
}
/**
* 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.
*
* @param string[] $labels
*/
public function setLabels($labels)
{
$this->labels = $labels;
}
/**
* @return string[]
*/
public function getLabels()
{
return $this->labels;
}
/**
* 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.
*
* @param array $metadata
*/
public function setMetadata($metadata)
{
$this->metadata = $metadata;
}
/**
* @return array
*/
public function getMetadata()
{
return $this->metadata;
}
/**
* 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}`.
*
* @param string $metadataArtifact
*/
public function setMetadataArtifact($metadataArtifact)
{
$this->metadataArtifact = $metadataArtifact;
}
/**
* @return string
*/
public function getMetadataArtifact()
{
return $this->metadataArtifact;
}
/**
* Immutable. Points to a YAML file stored on Google Cloud Storage describing
* additional information about the Model, that is specific to it. Unset if
* the Model does not have any additional information. The schema is defined
* as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-
* Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models
* always have this field populated by Vertex AI, if no additional metadata is
* needed, this field is set to an empty string. Note: The URI given on output
* will be immutable and probably different, including the URI scheme, than
* the one given on input. The output URI will point to a location where the
* user only has a read access.
*
* @param string $metadataSchemaUri
*/
public function setMetadataSchemaUri($metadataSchemaUri)
{
$this->metadataSchemaUri = $metadataSchemaUri;
}
/**
* @return string
*/
public function getMetadataSchemaUri()
{
return $this->metadataSchemaUri;
}
/**
* 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.
*
* @param GoogleCloudAiplatformV1ModelSourceInfo $modelSourceInfo
*/
public function setModelSourceInfo(GoogleCloudAiplatformV1ModelSourceInfo $modelSourceInfo)
{
$this->modelSourceInfo = $modelSourceInfo;
}
/**
* @return GoogleCloudAiplatformV1ModelSourceInfo
*/
public function getModelSourceInfo()
{
return $this->modelSourceInfo;
}
/**
* Identifier. The resource name of the Model.
*
* @param string $name
*/
public function setName($name)
{
$this->name = $name;
}
/**
* @return string
*/
public function getName()
{
return $this->name;
}
/**
* Output only. If this Model is a copy of another Model, this contains info
* about the original.
*
* @param GoogleCloudAiplatformV1ModelOriginalModelInfo $originalModelInfo
*/
public function setOriginalModelInfo(GoogleCloudAiplatformV1ModelOriginalModelInfo $originalModelInfo)
{
$this->originalModelInfo = $originalModelInfo;
}
/**
* @return GoogleCloudAiplatformV1ModelOriginalModelInfo
*/
public function getOriginalModelInfo()
{
return $this->originalModelInfo;
}
/**
* Optional. This field is populated if the model is produced by a pipeline
* job.
*
* @param string $pipelineJob
*/
public function setPipelineJob($pipelineJob)
{
$this->pipelineJob = $pipelineJob;
}
/**
* @return string
*/
public function getPipelineJob()
{
return $this->pipelineJob;
}
/**
* The schemata that describe formats of the Model's predictions and
* explanations as given and returned via PredictionService.Predict and
* PredictionService.Explain.
*
* @param GoogleCloudAiplatformV1PredictSchemata $predictSchemata
*/
public function setPredictSchemata(GoogleCloudAiplatformV1PredictSchemata $predictSchemata)
{
$this->predictSchemata = $predictSchemata;
}
/**
* @return GoogleCloudAiplatformV1PredictSchemata
*/
public function getPredictSchemata()
{
return $this->predictSchemata;
}
/**
* Output only. Reserved for future use.
*
* @param bool $satisfiesPzi
*/
public function setSatisfiesPzi($satisfiesPzi)
{
$this->satisfiesPzi = $satisfiesPzi;
}
/**
* @return bool
*/
public function getSatisfiesPzi()
{
return $this->satisfiesPzi;
}
/**
* Output only. Reserved for future use.
*
* @param bool $satisfiesPzs
*/
public function setSatisfiesPzs($satisfiesPzs)
{
$this->satisfiesPzs = $satisfiesPzs;
}
/**
* @return bool
*/
public function getSatisfiesPzs()
{
return $this->satisfiesPzs;
}
/**
* 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.
*
* @param string[] $supportedDeploymentResourcesTypes
*/
public function setSupportedDeploymentResourcesTypes($supportedDeploymentResourcesTypes)
{
$this->supportedDeploymentResourcesTypes = $supportedDeploymentResourcesTypes;
}
/**
* @return string[]
*/
public function getSupportedDeploymentResourcesTypes()
{
return $this->supportedDeploymentResourcesTypes;
}
/**
* Output only. The formats in which this Model may be exported. If empty,
* this Model is not available for export.
*
* @param GoogleCloudAiplatformV1ModelExportFormat[] $supportedExportFormats
*/
public function setSupportedExportFormats($supportedExportFormats)
{
$this->supportedExportFormats = $supportedExportFormats;
}
/**
* @return GoogleCloudAiplatformV1ModelExportFormat[]
*/
public function getSupportedExportFormats()
{
return $this->supportedExportFormats;
}
/**
* 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.
*
* @param string[] $supportedInputStorageFormats
*/
public function setSupportedInputStorageFormats($supportedInputStorageFormats)
{
$this->supportedInputStorageFormats = $supportedInputStorageFormats;
}
/**
* @return string[]
*/
public function getSupportedInputStorageFormats()
{
return $this->supportedInputStorageFormats;
}
/**
* 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.
*
* @param string[] $supportedOutputStorageFormats
*/
public function setSupportedOutputStorageFormats($supportedOutputStorageFormats)
{
$this->supportedOutputStorageFormats = $supportedOutputStorageFormats;
}
/**
* @return string[]
*/
public function getSupportedOutputStorageFormats()
{
return $this->supportedOutputStorageFormats;
}
/**
* Output only. The resource name of the TrainingPipeline that uploaded this
* Model, if any.
*
* @param string $trainingPipeline
*/
public function setTrainingPipeline($trainingPipeline)
{
$this->trainingPipeline = $trainingPipeline;
}
/**
* @return string
*/
public function getTrainingPipeline()
{
return $this->trainingPipeline;
}
/**
* Output only. Timestamp when this Model was most recently updated.
*
* @param string $updateTime
*/
public function setUpdateTime($updateTime)
{
$this->updateTime = $updateTime;
}
/**
* @return string
*/
public function getUpdateTime()
{
return $this->updateTime;
}
/**
* 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{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.
*
* @param string[] $versionAliases
*/
public function setVersionAliases($versionAliases)
{
$this->versionAliases = $versionAliases;
}
/**
* @return string[]
*/
public function getVersionAliases()
{
return $this->versionAliases;
}
/**
* Output only. Timestamp when this version was created.
*
* @param string $versionCreateTime
*/
public function setVersionCreateTime($versionCreateTime)
{
$this->versionCreateTime = $versionCreateTime;
}
/**
* @return string
*/
public function getVersionCreateTime()
{
return $this->versionCreateTime;
}
/**
* The description of this version.
*
* @param string $versionDescription
*/
public function setVersionDescription($versionDescription)
{
$this->versionDescription = $versionDescription;
}
/**
* @return string
*/
public function getVersionDescription()
{
return $this->versionDescription;
}
/**
* 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.
*
* @param string $versionId
*/
public function setVersionId($versionId)
{
$this->versionId = $versionId;
}
/**
* @return string
*/
public function getVersionId()
{
return $this->versionId;
}
/**
* Output only. Timestamp when this version was most recently updated.
*
* @param string $versionUpdateTime
*/
public function setVersionUpdateTime($versionUpdateTime)
{
$this->versionUpdateTime = $versionUpdateTime;
}
/**
* @return string
*/
public function getVersionUpdateTime()
{
return $this->versionUpdateTime;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1Model::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1Model');
← Back