Struct GoogleCloudAiplatformV1DeployedModel

pub struct GoogleCloudAiplatformV1DeployedModel {

    pub shared_resources: Option<String>,
    pub create_time: Option<DateTime<Utc>>,
    pub explanation_spec: Option<GoogleCloudAiplatformV1ExplanationSpec>,
    pub disable_explanations: Option<bool>,
    pub enable_access_logging: Option<bool>,
    pub model_version_id: Option<String>,
    pub disable_container_logging: Option<bool>,
    pub model: Option<String>,
    pub id: Option<String>,
    pub display_name: Option<String>,
    pub automatic_resources: Option<GoogleCloudAiplatformV1AutomaticResources>,
    pub service_account: Option<String>,
    pub dedicated_resources: Option<GoogleCloudAiplatformV1DedicatedResources>,
    pub private_endpoints: Option<GoogleCloudAiplatformV1PrivateEndpoints>,
}

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

This type is not used in any activity, and only used as part of another schema.

Fields

shared_resources: Option<String>

The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

create_time: Option<DateTime<Utc>>

Output only. Timestamp when the DeployedModel was created.

explanation_spec: Option<GoogleCloudAiplatformV1ExplanationSpec>

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.

disable_explanations: Option<bool>

If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.

enable_access_logging: Option<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.

model_version_id: Option<String>

Output only. The version ID of the model that is deployed.

disable_container_logging: Option<bool>

For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud 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.

model: Option<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. 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.

id: Option<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]/.

display_name: Option<String>

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

automatic_resources: Option<GoogleCloudAiplatformV1AutomaticResources>

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

service_account: Option<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.

dedicated_resources: Option<GoogleCloudAiplatformV1DedicatedResources>

A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

private_endpoints: Option<GoogleCloudAiplatformV1PrivateEndpoints>

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.

Trait Implementations

impl Clone for GoogleCloudAiplatformV1DeployedModel

fn clone(&self) -> GoogleCloudAiplatformV1DeployedModel

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudAiplatformV1DeployedModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GoogleCloudAiplatformV1DeployedModel

fn default() -> GoogleCloudAiplatformV1DeployedModel

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudAiplatformV1DeployedModel

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudAiplatformV1DeployedModel

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for GoogleCloudAiplatformV1DeployedModel