Struct StartTrainedModelInferenceJobInput 

#[non_exhaustive]
pub struct StartTrainedModelInferenceJobInput {

    pub membership_identifier: Option<String>,
    pub name: Option<String>,
    pub trained_model_arn: Option<String>,
    pub trained_model_version_identifier: Option<String>,
    pub configured_model_algorithm_association_arn: Option<String>,
    pub resource_config: Option<InferenceResourceConfig>,
    pub output_configuration: Option<InferenceOutputConfiguration>,
    pub data_source: Option<ModelInferenceDataSource>,
    pub description: Option<String>,
    pub container_execution_parameters: Option<InferenceContainerExecutionParameters>,
    pub environment: Option<HashMap<String, String>>,
    pub kms_key_arn: Option<String>,
    pub tags: Option<HashMap<String, String>>,
}

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.

membership_identifier: Option<String>

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

name: Option<String>

The name of the trained model inference job.

trained_model_arn: Option<String>

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

trained_model_version_identifier: Option<String>

The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data.

configured_model_algorithm_association_arn: Option<String>

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

resource_config: Option<InferenceResourceConfig>

Defines the resource configuration for the trained model inference job.

output_configuration: Option<InferenceOutputConfiguration>

Defines the output configuration information for the trained model inference job.

data_source: Option<ModelInferenceDataSource>

Defines the data source that is used for the trained model inference job.

description: Option<String>

The description of the trained model inference job.

container_execution_parameters: Option<InferenceContainerExecutionParameters>

The execution parameters for the container.

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.

tags: Option<HashMap<String, String>>

The optional metadata that you apply 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 StartTrainedModelInferenceJobInput

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

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

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

The name of the trained model inference job.

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

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

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

The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data.

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

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

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

Defines the resource configuration for the trained model inference job.

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

Defines the output configuration information for the trained model inference job.

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

Defines the data source that is used for the trained model inference job.

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

The description of the trained model inference job.

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

The execution parameters for the container.

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

The optional metadata that you apply 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 StartTrainedModelInferenceJobInput

pub fn builder() -> StartTrainedModelInferenceJobInputBuilder

Creates a new builder-style object to manufacture StartTrainedModelInferenceJobInput.

Trait Implementations

impl Clone for StartTrainedModelInferenceJobInput

fn clone(&self) -> StartTrainedModelInferenceJobInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StartTrainedModelInferenceJobInput

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

Formats the value using the given formatter.

impl PartialEq for StartTrainedModelInferenceJobInput

fn eq(&self, other: &StartTrainedModelInferenceJobInput) -> 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 StructuralPartialEq for StartTrainedModelInferenceJobInput