Struct CreateTrainedModelInput 

#[non_exhaustive]
pub struct CreateTrainedModelInput {

    pub membership_identifier: Option<String>,
    pub name: Option<String>,
    pub configured_model_algorithm_association_arn: Option<String>,
    pub hyperparameters: Option<HashMap<String, String>>,
    pub environment: Option<HashMap<String, String>>,
    pub resource_config: Option<ResourceConfig>,
    pub stopping_condition: Option<StoppingCondition>,
    pub incremental_training_data_channels: Option<Vec<IncrementalTrainingDataChannel>>,
    pub data_channels: Option<Vec<ModelTrainingDataChannel>>,
    pub training_input_mode: Option<TrainingInputMode>,
    pub description: Option<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 member that is creating the trained model.

name: Option<String>

The name of the trained model.

configured_model_algorithm_association_arn: Option<String>

The associated configured model algorithm used to train this model.

hyperparameters: Option<HashMap<String, String>>

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.

environment: Option<HashMap<String, String>>

The environment variables to set in the Docker container.

resource_config: Option<ResourceConfig>

Information about the EC2 resources that are used to train this model.

stopping_condition: Option<StoppingCondition>

The criteria that is used to stop model training.

incremental_training_data_channels: Option<Vec<IncrementalTrainingDataChannel>>

Specifies the incremental training data channels for the trained model.

Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.

Limit: Maximum of 20 channels total (including both incrementalTrainingDataChannels and dataChannels).

data_channels: Option<Vec<ModelTrainingDataChannel>>

Defines the data channels that are used as input for the trained model request.

Limit: Maximum of 20 channels total (including both dataChannels and incrementalTrainingDataChannels).

training_input_mode: Option<TrainingInputMode>

The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:

• File - The training data is downloaded to the training instance and made available as files.

• FastFile - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.

• Pipe - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.

description: Option<String>

The description of 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 the 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 CreateTrainedModelInput

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

The membership ID of the member that is creating the trained model.

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

The name of the trained model.

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

The associated configured model algorithm used to train this model.

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

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.

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

The environment variables to set in the Docker container.

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

Information about the EC2 resources that are used to train this model.

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

The criteria that is used to stop model training.

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

Specifies the incremental training data channels for the trained model.

Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.

Limit: Maximum of 20 channels total (including both incrementalTrainingDataChannels and dataChannels).

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 data_channels(&self) -> &[ModelTrainingDataChannel]

Defines the data channels that are used as input for the trained model request.

Limit: Maximum of 20 channels total (including both dataChannels and incrementalTrainingDataChannels).

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

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

The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:

• File - The training data is downloaded to the training instance and made available as files.

• FastFile - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.

• Pipe - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.

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

The description of 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 the 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 CreateTrainedModelInput

pub fn builder() -> CreateTrainedModelInputBuilder

Creates a new builder-style object to manufacture CreateTrainedModelInput.

Trait Implementations

impl Clone for CreateTrainedModelInput

fn clone(&self) -> CreateTrainedModelInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateTrainedModelInput

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

Formats the value using the given formatter.

impl PartialEq for CreateTrainedModelInput

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