Struct CreateTrainedModelFluentBuilder 

pub struct CreateTrainedModelFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateTrainedModel.

Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.

Implementations

impl CreateTrainedModelFluentBuilder

pub fn as_input(&self) -> &CreateTrainedModelInputBuilder

Access the CreateTrainedModel as a reference.

pub async fn send( self, ) -> Result<CreateTrainedModelOutput, SdkError<CreateTrainedModelError, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self, ) -> CustomizableOperation<CreateTrainedModelOutput, CreateTrainedModelError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

pub fn membership_identifier(self, input: impl Into<String>) -> Self

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

pub fn set_membership_identifier(self, input: Option<String>) -> Self

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

pub fn get_membership_identifier(&self) -> &Option<String>

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

pub fn name(self, input: impl Into<String>) -> Self

The name of the trained model.

pub fn set_name(self, input: Option<String>) -> Self

The name of the trained model.

pub fn get_name(&self) -> &Option<String>

The name of the trained model.

pub fn configured_model_algorithm_association_arn( self, input: impl Into<String>, ) -> Self

The associated configured model algorithm used to train this model.

pub fn set_configured_model_algorithm_association_arn( self, input: Option<String>, ) -> Self

The associated configured model algorithm used to train this model.

pub fn get_configured_model_algorithm_association_arn(&self) -> &Option<String>

The associated configured model algorithm used to train this model.

pub fn hyperparameters(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to hyperparameters.

To override the contents of this collection use set_hyperparameters.

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

pub fn set_hyperparameters(self, input: Option<HashMap<String, String>>) -> Self

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

pub fn get_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, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to environment.

To override the contents of this collection use set_environment.

The environment variables to set in the Docker container.

pub fn set_environment(self, input: Option<HashMap<String, String>>) -> Self

The environment variables to set in the Docker container.

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

The environment variables to set in the Docker container.

pub fn resource_config(self, input: ResourceConfig) -> Self

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

pub fn set_resource_config(self, input: Option<ResourceConfig>) -> Self

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

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

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

pub fn stopping_condition(self, input: StoppingCondition) -> Self

The criteria that is used to stop model training.

pub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self

The criteria that is used to stop model training.

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

The criteria that is used to stop model training.

pub fn incremental_training_data_channels( self, input: IncrementalTrainingDataChannel, ) -> Self

Appends an item to incrementalTrainingDataChannels.

To override the contents of this collection use set_incremental_training_data_channels.

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).

pub fn set_incremental_training_data_channels( self, input: Option<Vec<IncrementalTrainingDataChannel>>, ) -> Self

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).

pub fn get_incremental_training_data_channels( &self, ) -> &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).

pub fn data_channels(self, input: ModelTrainingDataChannel) -> Self

Appends an item to dataChannels.

To override the contents of this collection use set_data_channels.

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).

pub fn set_data_channels( self, input: Option<Vec<ModelTrainingDataChannel>>, ) -> Self

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).

pub fn get_data_channels(&self) -> &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).

pub fn training_input_mode(self, input: TrainingInputMode) -> Self

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 set_training_input_mode(self, input: Option<TrainingInputMode>) -> Self

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 get_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, input: impl Into<String>) -> Self

The description of the trained model.

pub fn set_description(self, input: Option<String>) -> Self

The description of the trained model.

pub fn get_description(&self) -> &Option<String>

The description of the trained model.

pub fn kms_key_arn(self, input: impl Into<String>) -> Self

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 set_kms_key_arn(self, input: Option<String>) -> Self

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 get_kms_key_arn(&self) -> &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.

pub fn tags(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to tags.

To override the contents of this collection use set_tags.

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.

pub fn set_tags(self, input: Option<HashMap<String, String>>) -> Self

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.

pub fn get_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.

Trait Implementations

impl Clone for CreateTrainedModelFluentBuilder

fn clone(&self) -> CreateTrainedModelFluentBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateTrainedModelFluentBuilder

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

Formats the value using the given formatter.