Struct aws_sdk_sagemaker::model::AutoMlChannel

#[non_exhaustive]
pub struct AutoMlChannel {
    pub data_source: Option<AutoMlDataSource>,
    pub compression_type: Option<CompressionType>,
    pub target_attribute_name: Option<String>,
    pub content_type: Option<String>,
    pub channel_type: Option<AutoMlChannelType>,
}

A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see .

A validation dataset must contain the same headers as the training dataset.

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.

data_source: Option<AutoMlDataSource>

The data source for an AutoML channel.

compression_type: Option<CompressionType>

You can use Gzip or None. The default value is None.

target_attribute_name: Option<String>

The name of the target variable in supervised learning, usually represented by 'y'.

content_type: Option<String>

The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

channel_type: Option<AutoMlChannelType>

The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets .

Implementations

impl AutoMlChannel

pub fn data_source(&self) -> Option<&AutoMlDataSource>

The data source for an AutoML channel.

pub fn compression_type(&self) -> Option<&CompressionType>

You can use Gzip or None. The default value is None.

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

The name of the target variable in supervised learning, usually represented by 'y'.

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

The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

pub fn channel_type(&self) -> Option<&AutoMlChannelType>

The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets .

impl AutoMlChannel

pub fn builder() -> Builder

Creates a new builder-style object to manufacture AutoMlChannel.

Trait Implementations

impl Clone for AutoMlChannel

fn clone(&self) -> AutoMlChannel

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlChannel

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

Formats the value using the given formatter.

impl PartialEq<AutoMlChannel> for AutoMlChannel

fn eq(&self, other: &AutoMlChannel) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &AutoMlChannel) -> bool

This method tests for !=.

impl StructuralPartialEq for AutoMlChannel