Struct aws_sdk_sagemaker::types::builders::AutoMlJobChannelBuilder

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

A builder for AutoMlJobChannel.

Implementations

impl AutoMlJobChannelBuilder

pub fn channel_type(self, input: AutoMlChannelType) -> Self

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 set_channel_type(self, input: Option<AutoMlChannelType>) -> Self

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

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.

• For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

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

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.

• For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

pub fn get_content_type(&self) -> &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.

• For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

pub fn compression_type(self, input: CompressionType) -> Self

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 set_compression_type(self, input: Option<CompressionType>) -> Self

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 get_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, input: AutoMlDataSource) -> Self

The data source for an AutoML channel (Required).

pub fn set_data_source(self, input: Option<AutoMlDataSource>) -> Self

The data source for an AutoML channel (Required).

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

The data source for an AutoML channel (Required).

pub fn build(self) -> AutoMlJobChannel

Consumes the builder and constructs a AutoMlJobChannel.

Trait Implementations

impl Clone for AutoMlJobChannelBuilder

fn clone(&self) -> AutoMlJobChannelBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlJobChannelBuilder

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

Formats the value using the given formatter.

impl Default for AutoMlJobChannelBuilder

fn default() -> AutoMlJobChannelBuilder

Returns the “default value” for a type.

impl PartialEq for AutoMlJobChannelBuilder

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