Amazonka.SageMaker.Types.AutoMLChannel

Documentation

data AutoMLChannel

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

FromJSON AutoMLChannel

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

parseJSON :: Value -> Parser AutoMLChannel

parseJSONList :: Value -> Parser [AutoMLChannel]

ToJSON AutoMLChannel

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

toJSON :: AutoMLChannel -> Value

toEncoding :: AutoMLChannel -> Encoding

toJSONList :: [AutoMLChannel] -> Value

toEncodingList :: [AutoMLChannel] -> Encoding

Generic AutoMLChannel

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

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

readsPrec :: Int -> ReadS AutoMLChannel

readList :: ReadS [AutoMLChannel]

readPrec :: ReadPrec AutoMLChannel

readListPrec :: ReadPrec [AutoMLChannel]

Show AutoMLChannel

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

showsPrec :: Int -> AutoMLChannel -> ShowS

show :: AutoMLChannel -> String

showList :: [AutoMLChannel] -> ShowS

NFData AutoMLChannel

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

rnf :: AutoMLChannel -> ()

Eq AutoMLChannel

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

(==) :: AutoMLChannel -> AutoMLChannel -> Bool

(/=) :: AutoMLChannel -> AutoMLChannel -> Bool

Hashable AutoMLChannel

Defined in Amazonka.SageMaker.Types.AutoMLChannel

Methods

hashWithSalt :: Int -> AutoMLChannel -> Int

hash :: AutoMLChannel -> Int

type Rep AutoMLChannel

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

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)

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)

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

autoMLChannel_contentType :: Lens' AutoMLChannel (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.

autoMLChannel_dataSource :: Lens' AutoMLChannel AutoMLDataSource

The data source for an AutoML channel.

autoMLChannel_targetAttributeName :: Lens' AutoMLChannel Text

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