Struct ModelTrainingDataChannel 

#[non_exhaustive]
pub struct ModelTrainingDataChannel {
    pub ml_input_channel_arn: String,
    pub channel_name: String,
    pub s3_data_distribution_type: S3DataDistributionType,
}

Information about the model training data channel. A training data channel is a named data source that the training algorithms can consume.

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.

ml_input_channel_arn: String

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

channel_name: String

The name of the training data channel.

s3_data_distribution_type: 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.

Implementations

impl ModelTrainingDataChannel

pub fn ml_input_channel_arn(&self) -> &str

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

pub fn channel_name(&self) -> &str

The name of the training data channel.

pub fn s3_data_distribution_type(&self) -> &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.

impl ModelTrainingDataChannel

pub fn builder() -> ModelTrainingDataChannelBuilder

Creates a new builder-style object to manufacture ModelTrainingDataChannel.

Trait Implementations

impl Clone for ModelTrainingDataChannel

fn clone(&self) -> ModelTrainingDataChannel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ModelTrainingDataChannel

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

Formats the value using the given formatter.

impl PartialEq for ModelTrainingDataChannel

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