Struct DeployedModel

#[non_exhaustive]
pub struct DeployedModel {

    pub id: String,
    pub model: String,
    pub model_version_id: String,
    pub display_name: String,
    pub create_time: Option<Timestamp>,
    pub explanation_spec: Option<ExplanationSpec>,
    pub disable_explanations: bool,
    pub service_account: String,
    pub disable_container_logging: bool,
    pub enable_access_logging: bool,
    pub private_endpoints: Option<PrivateEndpoints>,
    pub faster_deployment_config: Option<FasterDeploymentConfig>,
    pub status: Option<Status>,
    pub system_labels: HashMap<String, String>,
    pub speculative_decoding_spec: Option<SpeculativeDecodingSpec>,
    pub prediction_resources: Option<PredictionResources>,
    /* private fields */
}

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

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

id: 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: 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.

model_version_id: String

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

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.

explanation_spec: Option<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.

disable_explanations: bool

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

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 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.

enable_access_logging: 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.

private_endpoints: Option<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.

faster_deployment_config: Option<FasterDeploymentConfig>

Configuration for faster model deployment.

status: Option<Status>

Output only. Runtime status of the deployed model.

system_labels: HashMap<String, String>

System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.

speculative_decoding_spec: Option<SpeculativeDecodingSpec>

Optional. Spec for configuring speculative decoding.

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. Required except for Large Model Deploy use cases.

Implementations

impl DeployedModel

pub fn new() -> Self

pub fn set_id<T: Into<String>>(self, v: T) -> Self

Sets the value of id.

pub fn set_model<T: Into<String>>(self, v: T) -> Self

Sets the value of model.

pub fn set_model_version_id<T: Into<String>>(self, v: T) -> Self

Sets the value of model_version_id.

pub fn set_display_name<T: Into<String>>(self, v: T) -> Self

Sets the value of display_name.

pub fn set_create_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of create_time.

pub fn set_explanation_spec<T: Into<Option<ExplanationSpec>>>( self, v: T, ) -> Self

Sets the value of explanation_spec.

pub fn set_disable_explanations<T: Into<bool>>(self, v: T) -> Self

Sets the value of disable_explanations.

pub fn set_service_account<T: Into<String>>(self, v: T) -> Self

Sets the value of service_account.

pub fn set_disable_container_logging<T: Into<bool>>(self, v: T) -> Self

Sets the value of disable_container_logging.

pub fn set_enable_access_logging<T: Into<bool>>(self, v: T) -> Self

Sets the value of enable_access_logging.

pub fn set_private_endpoints<T: Into<Option<PrivateEndpoints>>>( self, v: T, ) -> Self

Sets the value of private_endpoints.

pub fn set_faster_deployment_config<T: Into<Option<FasterDeploymentConfig>>>( self, v: T, ) -> Self

Sets the value of faster_deployment_config.

pub fn set_status<T: Into<Option<Status>>>(self, v: T) -> Self

Sets the value of status.

pub fn set_system_labels<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of system_labels.

pub fn set_speculative_decoding_spec<T: Into<Option<SpeculativeDecodingSpec>>>( self, v: T, ) -> Self

Sets the value of speculative_decoding_spec.

pub fn set_prediction_resources<T: Into<Option<PredictionResources>>>( self, v: T, ) -> Self

Sets the value of prediction_resources.

Note that all the setters affecting prediction_resources are mutually exclusive.

pub fn dedicated_resources(&self) -> Option<&Box<DedicatedResources>>

The value of prediction_resources if it holds a DedicatedResources, None if the field is not set or holds a different branch.

pub fn set_dedicated_resources<T: Into<Box<DedicatedResources>>>( self, v: T, ) -> Self

Sets the value of prediction_resources to hold a DedicatedResources.

Note that all the setters affecting prediction_resources are mutually exclusive.

pub fn automatic_resources(&self) -> Option<&Box<AutomaticResources>>

The value of prediction_resources if it holds a AutomaticResources, None if the field is not set or holds a different branch.

pub fn set_automatic_resources<T: Into<Box<AutomaticResources>>>( self, v: T, ) -> Self

Sets the value of prediction_resources to hold a AutomaticResources.

Note that all the setters affecting prediction_resources are mutually exclusive.

pub fn shared_resources(&self) -> Option<&String>

The value of prediction_resources if it holds a SharedResources, None if the field is not set or holds a different branch.

pub fn set_shared_resources<T: Into<String>>(self, v: T) -> Self

Sets the value of prediction_resources to hold a SharedResources.

Note that all the setters affecting prediction_resources are mutually exclusive.

Trait Implementations

impl Clone for DeployedModel

fn clone(&self) -> DeployedModel

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DeployedModel

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

Formats the value using the given formatter.

impl Default for DeployedModel

fn default() -> DeployedModel

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for DeployedModel

where DeployedModel: Default,

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Message for DeployedModel

fn typename() -> &'static str

The typename of this message.

impl PartialEq for DeployedModel

fn eq(&self, other: &DeployedModel) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for DeployedModel

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for DeployedModel