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. projects/{project}/locations/{location}/models/{modelId}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{modelId}@{versionId}). The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from versionId. 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. |
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 be 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. |
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 present 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 [requesting explanation][PredictionService.Explain] after being 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 [requesting explanation][PredictionService.Explain] by setting |
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. |
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. |
ExportFormat
Represents export format supported by the Model. All formats export to Google Cloud Storage.
JSON representation |
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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.
Enums | |
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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. |
DeployedModelRef
Points to a DeployedModel.
JSON representation |
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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. |
Methods |
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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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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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Uploads a Model artifact into Vertex AI. |