Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::DeployedModel (v0.6.0)

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

A deployment of a Model. Endpoints contain one or more DeployedModels.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#automatic_resources

def automatic_resources() -> ::Google::Cloud::AIPlatform::V1::AutomaticResources

Returns

• (::Google::Cloud::AIPlatform::V1::AutomaticResources) — A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

#automatic_resources=

def automatic_resources=(value) -> ::Google::Cloud::AIPlatform::V1::AutomaticResources

Parameter

• value (::Google::Cloud::AIPlatform::V1::AutomaticResources) — A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

Returns

• (::Google::Cloud::AIPlatform::V1::AutomaticResources) — A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

#create_time

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

Returns

• (::Google::Protobuf::Timestamp) — Output only. Timestamp when the DeployedModel was created.

#dedicated_resources

def dedicated_resources() -> ::Google::Cloud::AIPlatform::V1::DedicatedResources

Returns

• (::Google::Cloud::AIPlatform::V1::DedicatedResources) — A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

#dedicated_resources=

def dedicated_resources=(value) -> ::Google::Cloud::AIPlatform::V1::DedicatedResources

Parameter

• value (::Google::Cloud::AIPlatform::V1::DedicatedResources) — A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

Returns

• (::Google::Cloud::AIPlatform::V1::DedicatedResources) — A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

#disable_container_logging

def disable_container_logging() -> ::Boolean

Returns

• (::Boolean) — For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.

  User can disable container logging by setting this flag to true.

#disable_container_logging=

def disable_container_logging=(value) -> ::Boolean

Parameter

• value (::Boolean) — For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.

  User can disable container logging by setting this flag to true.

Returns

• (::Boolean) — For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.

  User can disable container logging by setting this flag to true.

#display_name

def display_name() -> ::String

Returns

• (::String) — The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.

#display_name=

def display_name=(value) -> ::String

Parameter

• value (::String) — The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.

Returns

• (::String) — The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.

#enable_access_logging

def enable_access_logging() -> ::Boolean

Returns

• (::Boolean) — These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.

  Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

#enable_access_logging=

def enable_access_logging=(value) -> ::Boolean

Parameter

• value (::Boolean) — These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.

  Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

Returns

• (::Boolean) — These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.

  Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

#explanation_spec

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

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — Explanation configuration for this DeployedModel.

  When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.

#explanation_spec=

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

Parameter

• value (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — Explanation configuration for this DeployedModel.

  When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — Explanation configuration for this DeployedModel.

  When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.

#id

def id() -> ::String

Returns

• (::String) — Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID.

  This value should be 1-10 characters, and valid characters are /[0-9]/.

#id=

def id=(value) -> ::String

Parameter

• value (::String) — Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID.

  This value should be 1-10 characters, and valid characters are /[0-9]/.

Returns

• (::String) — Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID.

  This value should be 1-10 characters, and valid characters are /[0-9]/.

#model

def model() -> ::String

Returns

• (::String) — Required. The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

  The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be deployed.

#model=

def model=(value) -> ::String

Parameter

• value (::String) — Required. The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

  The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be deployed.

Returns

• (::String) — Required. The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

  The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be deployed.

#private_endpoints

def private_endpoints() -> ::Google::Cloud::AIPlatform::V1::PrivateEndpoints

Returns

• (::Google::Cloud::AIPlatform::V1::PrivateEndpoints) — Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.

#service_account

def service_account() -> ::String

Returns

• (::String) — The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project.

  Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

#service_account=

def service_account=(value) -> ::String

Parameter

• value (::String) — The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project.

  Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

Returns

• (::String) — The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project.

  Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.