Resource: Model
A trained machine learning Model.
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{ "name": string, "versionId": string, "versionAliases": [ string ], "versionCreateTime": string, "versionUpdateTime": string, "displayName": string, "description": string, "versionDescription": string, "predictSchemata": { object ( |
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name |
The resource name of the Model. |
versionId |
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. |
versionAliases[] |
user provided version aliases so that a model version can be referenced via alias (i.e. |
versionCreateTime |
Output only. timestamp when this version was created. A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: |
versionUpdateTime |
Output only. timestamp when this version was most recently updated. A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: |
displayName |
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 |
The description of the Model. |
versionDescription |
The description of this version. |
predictSchemata |
The schemata that describe formats of the Model's predictions and explanations as given and returned via |
metadataSchemaUri |
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 |
Immutable. An additional information about the Model; the schema of the metadata can be found in |
supportedExportFormats[] |
Output only. The formats in which this Model may be exported. If empty, this Model is not available for export. |
trainingPipeline |
Output only. The resource name of the TrainingPipeline that uploaded this Model, if any. |
pipelineJob |
Optional. This field is populated if the model is produced by a pipeline job. |
containerSpec |
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon |
artifactUri |
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models. |
supportedDeploymentResourcesTypes[] |
Output only. When this Model is deployed, its prediction resources are described by the |
supportedInputStorageFormats[] |
Output only. The formats this Model supports in The possible formats are:
If this Model doesn't support any of these formats it means it cannot be used with a |
supportedOutputStorageFormats[] |
Output only. The formats this Model supports in The possible formats are:
If this Model doesn't support any of these formats it means it cannot be used with a |
createTime |
Output only. timestamp when this Model was uploaded into Vertex AI. A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: |
updateTime |
Output only. timestamp when this Model was most recently updated. A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: |
deployedModels[] |
Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to endpoints in different Locations. |
explanationSpec |
The default explanation specification for this Model. The Model can be used for All fields of the explanationSpec can be overridden by If the default explanation specification is not set for this Model, this Model can still be used for |
etag |
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. |
labels |
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. |
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][TrainingPipeline.data_input_config]. |
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. |
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. |
originalModelInfo |
Output only. If this Model is a copy of another Model, this contains info about the original. |
metadataArtifact |
Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is |
baseModelSource |
Optional. user input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models. |
satisfiesPzs |
Output only. reserved for future use. |
satisfiesPzi |
Output only. reserved for future use. |
ExportFormat
Represents export format supported by the Model. All formats export to Google Cloud Storage.
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{
"id": string,
"exportableContents": [
enum ( |
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id |
Output only. The id of the export format. The possible format IDs are:
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exportableContents[] |
Output only. The content of this Model that may be exported. |
ExportableContent
The Model content that can be exported.
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EXPORTABLE_CONTENT_UNSPECIFIED |
Should not be used. |
ARTIFACT |
Model artifact and any of its supported files. Will be exported to the location specified by the artifactDestination field of the ExportModelRequest.output_config object. |
IMAGE |
The container image that is to be used when deploying this Model. Will be exported to the location specified by the imageDestination field of the ExportModelRequest.output_config object. |
DeploymentResourcesType
Identifies a type of Model's prediction resources.
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DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED |
Should not be used. |
DEDICATED_RESOURCES |
Resources that are dedicated to the DeployedModel , and that need a higher degree of manual configuration. |
AUTOMATIC_RESOURCES |
Resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. |
SHARED_RESOURCES |
Resources that can be shared by multiple DeployedModels . A pre-configured DeploymentResourcePool is required. |
DeployedModelRef
Points to a DeployedModel.
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{ "endpoint": string, "deployedModelId": string } |
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endpoint |
Immutable. A resource name of an Endpoint. |
deployedModelId |
Immutable. An id of a DeployedModel in the above Endpoint. |
DataStats
Stats of data used for train or evaluate the Model.
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{ "trainingDataItemsCount": string, "validationDataItemsCount": string, "testDataItemsCount": string, "trainingAnnotationsCount": string, "validationAnnotationsCount": string, "testAnnotationsCount": string } |
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trainingDataItemsCount |
Number of DataItems that were used for training this Model. |
validationDataItemsCount |
Number of DataItems that were used for validating this Model during training. |
testDataItemsCount |
Number of DataItems that were used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test DataItems used by the first evaluation. If the Model is not evaluated, the number is 0. |
trainingAnnotationsCount |
Number of Annotations that are used for training this Model. |
validationAnnotationsCount |
Number of Annotations that are used for validating this Model during training. |
testAnnotationsCount |
Number of Annotations that are used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test Annotations used by the first evaluation. If the Model is not evaluated, the number is 0. |
ModelSourceInfo
Detail description of the source information of the model.
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{
"sourceType": enum ( |
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sourceType |
type of the model source. |
ModelSourceType
Source of the model. Different from objective
field, this ModelSourceType
enum indicates the source from which the model was accessed or obtained, whereas the objective
indicates the overall aim or function of this model.
Enums | |
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MODEL_SOURCE_TYPE_UNSPECIFIED |
Should not be used. |
AUTOML |
The Model is uploaded by automl training pipeline. |
CUSTOM |
The Model is uploaded by user or custom training pipeline. |
BQML |
The Model is registered and sync'ed from BigQuery ML. |
MODEL_GARDEN |
The Model is saved or tuned from Model Garden. |
CUSTOM_TEXT_EMBEDDING |
The Model is uploaded by text embedding finetuning pipeline. |
MARKETPLACE |
The Model is saved or tuned from Marketplace. |
OriginalModelInfo
Contains information about the original Model if this Model is a copy.
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{ "model": string } |
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model |
Output only. The resource name of the Model this Model is a copy of, including the revision. Format: |
BaseModelSource
user input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
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{ // Union field |
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Union field
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modelGardenSource |
Source information of Model Garden models. |
genieSource |
Information about the base model of Genie models. |
ModelGardenSource
Contains information about the source of the models generated from Model Garden.
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{ "publicModelName": string } |
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publicModelName |
Required. The model garden source model resource name. |
GenieSource
Contains information about the source of the models generated from Generative AI Studio.
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{ "baseModelUri": string } |
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baseModelUri |
Required. The public base model URI. |
Methods |
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Copies an already existing Vertex AI Model into the specified Location. |
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Deletes a Model. |
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Deletes a Model version. |
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Exports a trained, exportable Model to a location specified by the user. |
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Gets a Model. |
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Gets the access control policy for a resource. |
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Lists Models in a Location. |
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Lists versions of the specified model. |
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Merges a set of aliases for a Model version. |
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Updates a Model. |
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Sets the access control policy on the specified resource. |
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Returns permissions that a caller has on the specified resource. |
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Incrementally update the dataset used for an examples model. |
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Uploads a Model artifact into Vertex AI. |