Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::Model (v0.22.0)

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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::Model.

A trained machine learning Model.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#artifact_uri

def artifact_uri() -> ::String

Returns

• (::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.

#artifact_uri=

def artifact_uri=(value) -> ::String

Parameter

• value (::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.

Returns

• (::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.

#container_spec

def container_spec() -> ::Google::Cloud::AIPlatform::V1::ModelContainerSpec

Returns

• (::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.

#container_spec=

def container_spec=(value) -> ::Google::Cloud::AIPlatform::V1::ModelContainerSpec

Parameter

• value (::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.

Returns

• (::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.

#create_time

def create_time() -> ::Google::Protobuf::Timestamp

Returns

• (::Google::Protobuf::Timestamp) — Output only. Timestamp when this Model was uploaded into Vertex AI.

#deployed_models

def deployed_models() -> ::Array<::Google::Cloud::AIPlatform::V1::DeployedModelRef>

Returns

• (::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.

#description

def description() -> ::String

Returns

• (::String) — The description of the Model.

#description=

def description=(value) -> ::String

Parameter

• value (::String) — The description of the Model.

Returns

• (::String) — The description of the Model.

#display_name

def display_name() -> ::String

Returns

• (::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.

#display_name=

def display_name=(value) -> ::String

Parameter

• value (::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.

Returns

• (::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.

#encryption_spec

def encryption_spec() -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Returns

• (::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.

#encryption_spec=

def encryption_spec=(value) -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Parameter

• value (::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.

Returns

• (::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.

#etag

def etag() -> ::String

Returns

• (::String) — Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

#etag=

def etag=(value) -> ::String

Parameter

• value (::String) — Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

Returns

• (::String) — Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

#explanation_spec

def explanation_spec() -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — The default explanation specification for this Model.

  The Model can be used for [requesting explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after being deployed if it is populated. The Model can be used for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_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][google.cloud.aiplatform.v1.PredictionService.Explain] by setting explanation_spec of DeployModelRequest.deployed_model and for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation] by setting explanation_spec of BatchPredictionJob.

#explanation_spec=

def explanation_spec=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec

Parameter

• value (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — The default explanation specification for this Model.

  The Model can be used for [requesting explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after being deployed if it is populated. The Model can be used for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_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][google.cloud.aiplatform.v1.PredictionService.Explain] by setting explanation_spec of DeployModelRequest.deployed_model and for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation] by setting explanation_spec of BatchPredictionJob.

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — The default explanation specification for this Model.

  The Model can be used for [requesting explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after being deployed if it is populated. The Model can be used for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_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 specificati