Struct aws_sdk_sagemaker::types::AutoMlJobChannel

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

A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2).

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.

channel_type: Option<AutoMlChannelType>

The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

The type of channel defaults to training for the time-series forecasting problem type.

content_type: Option<String>

The content type of the data from the input source. The following are the allowed content types for different problems:

• For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

• For image classification: image/png, image/jpeg, or image/*. The default value is image/*.

• For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

• For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

compression_type: Option<CompressionType>

The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

data_source: Option<AutoMlDataSource>

The data source for an AutoML channel (Required).

Implementations

impl AutoMlJobChannel

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

The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

The type of channel defaults to training for the time-series forecasting problem type.

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

The content type of the data from the input source. The following are the allowed content types for different problems:

• For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

• For image classification: image/png, image/jpeg, or image/*. The default value is image/*.

• For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

• For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

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

The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

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

The data source for an AutoML channel (Required).

impl AutoMlJobChannel

pub fn builder() -> AutoMlJobChannelBuilder

Creates a new builder-style object to manufacture AutoMlJobChannel.

Trait Implementations

impl Clone for AutoMlJobChannel

fn clone(&self) -> AutoMlJobChannel

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlJobChannel

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

Formats the value using the given formatter.

impl PartialEq<AutoMlJobChannel> for AutoMlJobChannel

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for AutoMlJobChannel