GoogleCloudAiplatformV1beta1DeployedModelResponse class

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

Constructors

GoogleCloudAiplatformV1beta1DeployedModelResponse({required Input<GoogleCloudAiplatformV1beta1AutomaticResourcesResponse> automaticResources, required Input<String> createTime, required Input<GoogleCloudAiplatformV1beta1DedicatedResourcesResponse> dedicatedResources, required Input<bool> disableExplanations, required Input<String> displayName, required Input<bool> enableAccessLogging, required Input<bool> enableContainerLogging, required Input<GoogleCloudAiplatformV1beta1ExplanationSpecResponse> explanationSpec, required Input<String> model, required Input<String> modelVersionId, required Input<GoogleCloudAiplatformV1beta1PrivateEndpointsResponse> privateEndpoints, required Input<String> serviceAccount, required Input<String> sharedResources})
Creates a new GoogleCloudAiplatformV1beta1DeployedModelResponse. automaticResources A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. createTime Timestamp when the DeployedModel was created. dedicatedResources A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. disableExplanations If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec. displayName The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. enableAccessLogging If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that 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. enableContainerLogging If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models. 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. model 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. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. modelVersionId The version ID of the model that is deployed. privateEndpoints 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. serviceAccount 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. sharedResources The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
const
GoogleCloudAiplatformV1beta1DeployedModelResponse.fromMap(Map<String, dynamic> map)
factory

Properties

automaticResources → Input<GoogleCloudAiplatformV1beta1AutomaticResourcesResponse>
A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
final
createTime → Input<String>
Timestamp when the DeployedModel was created.
final
dedicatedResources → Input<GoogleCloudAiplatformV1beta1DedicatedResourcesResponse>
A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
final
disableExplanations → Input<bool>
If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
final
displayName → Input<String>
The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
final
enableAccessLogging → Input<bool>
If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that 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.
final
enableContainerLogging → Input<bool>
If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
final
explanationSpec → Input<GoogleCloudAiplatformV1beta1ExplanationSpecResponse>
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.
final
hashCode → int
The hash code for this object.
no setterinherited
model → Input<String>
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. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
final
modelVersionId → Input<String>
The version ID of the model that is deployed.
final
privateEndpoints → Input<GoogleCloudAiplatformV1beta1PrivateEndpointsResponse>
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.
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
serviceAccount → Input<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.
final
sharedResources → Input<String>
The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited