Copyright	(c) 2013-2023 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	Safe-Inferred
Language	Haskell2010

Amazonka.SageMaker.Types.AutoMLChannel

Description

Documentation

data AutoMLChannel Source #

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.

See: newAutoMLChannel smart constructor.

Constructors

AutoMLChannel'

Fields

channelType :: Maybe 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 .
compressionType :: Maybe CompressionType
You can use Gzip or None. The default value is None.
contentType :: Maybe Text
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.
dataSource :: AutoMLDataSource
The data source for an AutoML channel.
targetAttributeName :: Text
The name of the target variable in supervised learning, usually represented by 'y'.

Instances

Instances details

FromJSON AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods parseJSON :: Value -> Parser AutoMLChannel # parseJSONList :: Value -> Parser [AutoMLChannel] #
ToJSON AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods toJSON :: AutoMLChannel -> Value # toEncoding :: AutoMLChannel -> Encoding # toJSONList :: [AutoMLChannel] -> Value # toEncodingList :: [AutoMLChannel] -> Encoding #
Generic AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Associated Types type Rep AutoMLChannel :: Type -> Type # Methods from :: AutoMLChannel -> Rep AutoMLChannel x # to :: Rep AutoMLChannel x -> AutoMLChannel #
Read AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods readsPrec :: Int -> ReadS AutoMLChannel # readList :: ReadS [AutoMLChannel] # readPrec :: ReadPrec AutoMLChannel # readListPrec :: ReadPrec [AutoMLChannel] #
Show AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods showsPrec :: Int -> AutoMLChannel -> ShowS # show :: AutoMLChannel -> String # showList :: [AutoMLChannel] -> ShowS #
NFData AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods rnf :: AutoMLChannel -> () #
Eq AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods (==) :: AutoMLChannel -> AutoMLChannel -> Bool # (/=) :: AutoMLChannel -> AutoMLChannel -> Bool #
Hashable AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel Methods hashWithSalt :: Int -> AutoMLChannel -> Int # hash :: AutoMLChannel -> Int #
type Rep AutoMLChannel Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLChannel type Rep AutoMLChannel = D1 ('MetaData "AutoMLChannel" "Amazonka.SageMaker.Types.AutoMLChannel" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" 'False) (C1 ('MetaCons "AutoMLChannel'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "channelType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe AutoMLChannelType)) :: S1 ('MetaSel ('Just "compressionType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe CompressionType))) :: (S1 ('MetaSel ('Just "contentType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "dataSource") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 AutoMLDataSource) :: S1 ('MetaSel ('Just "targetAttributeName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))

newAutoMLChannel Source #

Arguments

:: AutoMLDataSource	`$sel:dataSource:AutoMLChannel'`
-> Text	`$sel:targetAttributeName:AutoMLChannel'`
-> AutoMLChannel

Create a value of AutoMLChannel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:channelType:AutoMLChannel', autoMLChannel_channelType - 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 .

$sel:compressionType:AutoMLChannel', autoMLChannel_compressionType - You can use Gzip or None. The default value is None.

$sel:contentType:AutoMLChannel', autoMLChannel_contentType - 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.

$sel:dataSource:AutoMLChannel', autoMLChannel_dataSource - The data source for an AutoML channel.

$sel:targetAttributeName:AutoMLChannel', autoMLChannel_targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.

autoMLChannel_channelType :: Lens' AutoMLChannel (Maybe AutoMLChannelType) Source #

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 .

autoMLChannel_compressionType :: Lens' AutoMLChannel (Maybe CompressionType) Source #

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

autoMLChannel_contentType :: Lens' AutoMLChannel (Maybe Text) Source #

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.

autoMLChannel_dataSource :: Lens' AutoMLChannel AutoMLDataSource Source #

The data source for an AutoML channel.

autoMLChannel_targetAttributeName :: Lens' AutoMLChannel Text Source #

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