Struct GetTrainedModelInferenceJobOutput 

#[non_exhaustive]
pub struct GetTrainedModelInferenceJobOutput {

    pub create_time: DateTime,
    pub update_time: DateTime,
    pub trained_model_inference_job_arn: String,
    pub configured_model_algorithm_association_arn: Option<String>,
    pub name: String,
    pub status: TrainedModelInferenceJobStatus,
    pub trained_model_arn: String,
    pub trained_model_version_identifier: Option<String>,
    pub resource_config: Option<InferenceResourceConfig>,
    pub output_configuration: Option<InferenceOutputConfiguration>,
    pub membership_identifier: String,
    pub data_source: Option<ModelInferenceDataSource>,
    pub container_execution_parameters: Option<InferenceContainerExecutionParameters>,
    pub status_details: Option<StatusDetails>,
    pub description: Option<String>,
    pub inference_container_image_digest: Option<String>,
    pub environment: Option<HashMap<String, String>>,
    pub kms_key_arn: Option<String>,
    pub metrics_status: Option<MetricsStatus>,
    pub metrics_status_details: Option<String>,
    pub logs_status: Option<LogsStatus>,
    pub logs_status_details: Option<String>,
    pub tags: Option<HashMap<String, String>>,
    /* private fields */
}

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.

create_time: DateTime

The time at which the trained model inference job was created.

update_time: DateTime

The most recent time at which the trained model inference job was updated.

trained_model_inference_job_arn: String

The Amazon Resource Name (ARN) of the trained model inference job.

configured_model_algorithm_association_arn: Option<String>

The Amazon Resource Name (ARN) of the configured model algorithm association that was used for the trained model inference job.

name: String

The name of the trained model inference job.

status: TrainedModelInferenceJobStatus

The status of the trained model inference job.

trained_model_arn: String

The Amazon Resource Name (ARN) for the trained model that was used for the trained model inference job.

trained_model_version_identifier: Option<String>

The version identifier of the trained model used for this inference job. This identifies the specific version of the trained model that was used to generate the inference results.

resource_config: Option<InferenceResourceConfig>

The resource configuration information for the trained model inference job.

output_configuration: Option<InferenceOutputConfiguration>

The output configuration information for the trained model inference job.

membership_identifier: String

The membership ID of the membership that contains the trained model inference job.

data_source: Option<ModelInferenceDataSource>

The data source that was used for the trained model inference job.

container_execution_parameters: Option<InferenceContainerExecutionParameters>

The execution parameters for the model inference job container.

status_details: Option<StatusDetails>

Details about the status of a resource.

description: Option<String>

The description of the trained model inference job.

inference_container_image_digest: Option<String>

Information about the training container image.

environment: Option<HashMap<String, String>>

The environment variables to set in the Docker container.

kms_key_arn: Option<String>

The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.

metrics_status: Option<MetricsStatus>

The metrics status for the trained model inference job.

metrics_status_details: Option<String>

Details about the metrics status for the trained model inference job.

logs_status: Option<LogsStatus>

The logs status for the trained model inference job.

logs_status_details: Option<String>

Details about the logs status for the trained model inference job.

tags: Option<HashMap<String, String>>

The optional metadata that you applied to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

• Maximum number of tags per resource - 50.

• For each resource, each tag key must be unique, and each tag key can have only one value.

• Maximum key length - 128 Unicode characters in UTF-8.

• Maximum value length - 256 Unicode characters in UTF-8.

• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

• Tag keys and values are case sensitive.

• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

Implementations

impl GetTrainedModelInferenceJobOutput

pub fn create_time(&self) -> &DateTime

The time at which the trained model inference job was created.

pub fn update_time(&self) -> &DateTime

The most recent time at which the trained model inference job was updated.

pub fn trained_model_inference_job_arn(&self) -> &str

The Amazon Resource Name (ARN) of the trained model inference job.

pub fn configured_model_algorithm_association_arn(&self) -> Option<&str>

The Amazon Resource Name (ARN) of the configured model algorithm association that was used for the trained model inference job.

pub fn name(&self) -> &str

The name of the trained model inference job.

pub fn status(&self) -> &TrainedModelInferenceJobStatus

The status of the trained model inference job.

pub fn trained_model_arn(&self) -> &str

The Amazon Resource Name (ARN) for the trained model that was used for the trained model inference job.

pub fn trained_model_version_identifier(&self) -> Option<&str>

The version identifier of the trained model used for this inference job. This identifies the specific version of the trained model that was used to generate the inference results.

pub fn resource_config(&self) -> Option<&InferenceResourceConfig>

The resource configuration information for the trained model inference job.

pub fn output_configuration(&self) -> Option<&InferenceOutputConfiguration>

The output configuration information for the trained model inference job.

pub fn membership_identifier(&self) -> &str

The membership ID of the membership that contains the trained model inference job.

pub fn data_source(&self) -> Option<&ModelInferenceDataSource>

The data source that was used for the trained model inference job.

pub fn container_execution_parameters( &self, ) -> Option<&InferenceContainerExecutionParameters>

The execution parameters for the model inference job container.

pub fn status_details(&self) -> Option<&StatusDetails>

Details about the status of a resource.

pub fn description(&self) -> Option<&str>

The description of the trained model inference job.

pub fn inference_container_image_digest(&self) -> Option<&str>

Information about the training container image.

pub fn environment(&self) -> Option<&HashMap<String, String>>

The environment variables to set in the Docker container.

pub fn kms_key_arn(&self) -> Option<&str>

The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.

pub fn metrics_status(&self) -> Option<&MetricsStatus>

The metrics status for the trained model inference job.

pub fn metrics_status_details(&self) -> Option<&str>

Details about the metrics status for the trained model inference job.

pub fn logs_status(&self) -> Option<&LogsStatus>

The logs status for the trained model inference job.

pub fn logs_status_details(&self) -> Option<&str>

Details about the logs status for the trained model inference job.

pub fn tags(&self) -> Option<&HashMap<String, String>>

The optional metadata that you applied to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

• Maximum number of tags per resource - 50.

• For each resource, each tag key must be unique, and each tag key can have only one value.

• Maximum key length - 128 Unicode characters in UTF-8.

• Maximum value length - 256 Unicode characters in UTF-8.

• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

• Tag keys and values are case sensitive.

• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

impl GetTrainedModelInferenceJobOutput

pub fn builder() -> GetTrainedModelInferenceJobOutputBuilder

Creates a new builder-style object to manufacture GetTrainedModelInferenceJobOutput.

Trait Implementations

impl Clone for GetTrainedModelInferenceJobOutput

fn clone(&self) -> GetTrainedModelInferenceJobOutput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GetTrainedModelInferenceJobOutput

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

Formats the value using the given formatter.

impl PartialEq for GetTrainedModelInferenceJobOutput

fn eq(&self, other: &GetTrainedModelInferenceJobOutput) -> 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 RequestId for GetTrainedModelInferenceJobOutput

fn request_id(&self) -> Option<&str>

Returns the request ID, or None if the service could not be reached.

impl StructuralPartialEq for GetTrainedModelInferenceJobOutput