Struct GetTrainedModelOutput 

#[non_exhaustive]
pub struct GetTrainedModelOutput {

    pub membership_identifier: String,
    pub collaboration_identifier: String,
    pub trained_model_arn: String,
    pub version_identifier: Option<String>,
    pub incremental_training_data_channels: Option<Vec<IncrementalTrainingDataChannelOutput>>,
    pub name: String,
    pub description: Option<String>,
    pub status: TrainedModelStatus,
    pub status_details: Option<StatusDetails>,
    pub configured_model_algorithm_association_arn: String,
    pub resource_config: Option<ResourceConfig>,
    pub training_input_mode: Option<TrainingInputMode>,
    pub stopping_condition: Option<StoppingCondition>,
    pub metrics_status: Option<MetricsStatus>,
    pub metrics_status_details: Option<String>,
    pub logs_status: Option<LogsStatus>,
    pub logs_status_details: Option<String>,
    pub training_container_image_digest: Option<String>,
    pub create_time: DateTime,
    pub update_time: DateTime,
    pub hyperparameters: Option<HashMap<String, String>>,
    pub environment: Option<HashMap<String, String>>,
    pub kms_key_arn: Option<String>,
    pub tags: Option<HashMap<String, String>>,
    pub data_channels: Vec<ModelTrainingDataChannel>,
    /* 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.

membership_identifier: String

The membership ID of the member that created the trained model.

collaboration_identifier: String

The collaboration ID of the collaboration that contains the trained model.

trained_model_arn: String

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

version_identifier: Option<String>

The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.

incremental_training_data_channels: Option<Vec<IncrementalTrainingDataChannelOutput>>

Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.

name: String

The name of the trained model.

description: Option<String>

The description of the trained model.

status: TrainedModelStatus

The status of the trained model.

status_details: Option<StatusDetails>

Details about the status of a resource.

configured_model_algorithm_association_arn: String

The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create the trained model.

resource_config: Option<ResourceConfig>

The EC2 resource configuration that was used to create the trained model.

training_input_mode: Option<TrainingInputMode>

The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.

stopping_condition: Option<StoppingCondition>

The stopping condition that was used to terminate model training.

metrics_status: Option<MetricsStatus>

The status of the model metrics.

metrics_status_details: Option<String>

Details about the metrics status for the trained model.

logs_status: Option<LogsStatus>

The logs status for the trained model.

logs_status_details: Option<String>

Details about the logs status for the trained model.

training_container_image_digest: Option<String>

Information about the training image container.

create_time: DateTime

The time at which the trained model was created.

update_time: DateTime

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

hyperparameters: Option<HashMap<String, String>>

The hyperparameters that were used to create the trained model.

environment: Option<HashMap<String, String>>

The EC2 environment that was used to create the trained model.

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 trained ML model and associated data.

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.

data_channels: Vec<ModelTrainingDataChannel>

The data channels that were used for the trained model.

Implementations

impl GetTrainedModelOutput

pub fn membership_identifier(&self) -> &str

The membership ID of the member that created the trained model.

pub fn collaboration_identifier(&self) -> &str

The collaboration ID of the collaboration that contains the trained model.

pub fn trained_model_arn(&self) -> &str

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

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

The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.

pub fn incremental_training_data_channels( &self, ) -> &[IncrementalTrainingDataChannelOutput]

Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .incremental_training_data_channels.is_none().

pub fn name(&self) -> &str

The name of the trained model.

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

The description of the trained model.

pub fn status(&self) -> &TrainedModelStatus

The status of the trained model.

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

Details about the status of a resource.

pub fn configured_model_algorithm_association_arn(&self) -> &str

The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create the trained model.

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

The EC2 resource configuration that was used to create the trained model.

pub fn training_input_mode(&self) -> Option<&TrainingInputMode>

The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.

pub fn stopping_condition(&self) -> Option<&StoppingCondition>

The stopping condition that was used to terminate model training.

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

The status of the model metrics.

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

Details about the metrics status for the trained model.

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

The logs status for the trained model.

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

Details about the logs status for the trained model.

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

Information about the training image container.

pub fn create_time(&self) -> &DateTime

The time at which the trained model was created.

pub fn update_time(&self) -> &DateTime

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

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

The hyperparameters that were used to create the trained model.

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

The EC2 environment that was used to create the trained model.

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 trained ML model and associated data.

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.

pub fn data_channels(&self) -> &[ModelTrainingDataChannel]

The data channels that were used for the trained model.

impl GetTrainedModelOutput

pub fn builder() -> GetTrainedModelOutputBuilder

Creates a new builder-style object to manufacture GetTrainedModelOutput.

Trait Implementations

impl Clone for GetTrainedModelOutput

fn clone(&self) -> GetTrainedModelOutput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GetTrainedModelOutput

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

Formats the value using the given formatter.

impl PartialEq for GetTrainedModelOutput

fn eq(&self, other: &GetTrainedModelOutput) -> 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 GetTrainedModelOutput

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

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

impl StructuralPartialEq for GetTrainedModelOutput