Struct google_api_proto::google::cloud::aiplatform::v1::DeployedModel

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 prediction_resources: Option<PredictionResources>,
}

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

Fields

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][google.cloud.aiplatform.v1.EndpointService.DeployModel], this value overrides the value of [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec]. All fields of [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] are optional in the request. If a field of [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] is not populated, the value of the same field of [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] is inherited. If the corresponding [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] is not populated, all fields of the [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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][google.cloud.aiplatform.v1.Model.explanation_spec] or [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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][google.cloud.aiplatform.v1.Endpoint.network] is configured.

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]. Required except for Large Model Deploy use cases.

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() -> Self

Returns the “default value” for a type.

impl Message for DeployedModel

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for DeployedModel

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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for DeployedModel