Struct gapi_grpc::google::cloud::aiplatform::v1::DeployedModel[][src]

pub struct DeployedModel {
    pub id: String,
    pub model: String,
    pub display_name: String,
    pub create_time: Option<Timestamp>,
    pub service_account: String,
    pub disable_container_logging: bool,
    pub enable_access_logging: bool,
    pub prediction_resources: Option<PredictionResources>,
}

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

Fields

id: String

Output only. The ID of the DeployedModel.

model: String

Required. The 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.

display_name: String

The display name of the DeployedModel. If not provided upon creation, the Model’s display_name is used.

create_time: Option<Timestamp>

Output only. Timestamp when the DeployedModel was created.

service_account: 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.

disable_container_logging: bool

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.

enable_access_logging: bool

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.

prediction_resources: Option<PredictionResources>

The prediction (for example, the machine) resources that the DeployedModel uses. The user is billed for the resources (at least their minimal amount) even if the DeployedModel receives no traffic. Not all Models support all resources types. See [Model.supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types].

Trait Implementations

impl Clone for DeployedModel[src]

impl Debug for DeployedModel[src]

impl Default for DeployedModel[src]

impl Message for DeployedModel[src]

impl PartialEq<DeployedModel> for DeployedModel[src]

impl StructuralPartialEq for DeployedModel[src]

Auto Trait Implementations

impl RefUnwindSafe for DeployedModel

impl Send for DeployedModel

impl Sync for DeployedModel

impl Unpin for DeployedModel

impl UnwindSafe for DeployedModel

Blanket Implementations

impl<T> Any for T where
    T: 'static + ?Sized
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impl<T> Borrow<T> for T where
    T: ?Sized
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impl<T> BorrowMut<T> for T where
    T: ?Sized
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impl<T> From<T> for T[src]

impl<T> Instrument for T[src]

impl<T> Instrument for T[src]

impl<T, U> Into<U> for T where
    U: From<T>, 
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impl<T> IntoRequest<T> for T[src]

impl<T> ToOwned for T where
    T: Clone
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type Owned = T

The resulting type after obtaining ownership.

impl<T, U> TryFrom<U> for T where
    U: Into<T>, 
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type Error = Infallible

The type returned in the event of a conversion error.

impl<T, U> TryInto<U> for T where
    U: TryFrom<T>, 
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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.

impl<V, T> VZip<V> for T where
    V: MultiLane<T>, 
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impl<T> WithSubscriber for T[src]