Struct ModelTrainingDataChannelBuilder 

#[non_exhaustive]
pub struct ModelTrainingDataChannelBuilder { /* private fields */ }

A builder for ModelTrainingDataChannel.

Implementations

impl ModelTrainingDataChannelBuilder

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

The Amazon Resource Name (ARN) of the ML input channel for this model training data channel.

This field is required.

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

The Amazon Resource Name (ARN) of the ML input channel for this model training data channel.

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

The Amazon Resource Name (ARN) of the ML input channel for this model training data channel.

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

The name of the training data channel.

This field is required.

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

The name of the training data channel.

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

The name of the training data channel.

pub fn s3_data_distribution_type(self, input: S3DataDistributionType) -> Self

Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job:

• FullyReplicated - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset.

• ShardedByS3Key - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data.

pub fn set_s3_data_distribution_type( self, input: Option<S3DataDistributionType>, ) -> Self

Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job:

• FullyReplicated - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset.

• ShardedByS3Key - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data.

pub fn get_s3_data_distribution_type(&self) -> &Option<S3DataDistributionType>

Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job:

• FullyReplicated - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset.

• ShardedByS3Key - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data.

pub fn build(self) -> Result<ModelTrainingDataChannel, BuildError>

Consumes the builder and constructs a ModelTrainingDataChannel. This method will fail if any of the following fields are not set:

• ml_input_channel_arn
• channel_name

Trait Implementations

impl Clone for ModelTrainingDataChannelBuilder

fn clone(&self) -> ModelTrainingDataChannelBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ModelTrainingDataChannelBuilder

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

Formats the value using the given formatter.

impl Default for ModelTrainingDataChannelBuilder

fn default() -> ModelTrainingDataChannelBuilder

Returns the “default value” for a type.

impl PartialEq for ModelTrainingDataChannelBuilder

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