Copyright	(c) 2013-2021 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay <brendan.g.hay+amazonka@gmail.com>
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	None

Amazonka.SageMaker.Types.TrainingJobDefinition

Description

Synopsis

Documentation

data TrainingJobDefinition Source #

Defines the input needed to run a training job using the algorithm.

See: newTrainingJobDefinition smart constructor.

Constructors

TrainingJobDefinition'

Fields

hyperParameters :: Maybe (HashMap Text Text)
The hyperparameters used for the training job.
trainingInputMode :: TrainingInputMode
inputDataConfig :: NonEmpty Channel
An array of Channel objects, each of which specifies an input source.
outputDataConfig :: OutputDataConfig
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
resourceConfig :: ResourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
stoppingCondition :: StoppingCondition
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

Instances

Instances details

Eq TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods (==) :: TrainingJobDefinition -> TrainingJobDefinition -> Bool # (/=) :: TrainingJobDefinition -> TrainingJobDefinition -> Bool #
Read TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods readsPrec :: Int -> ReadS TrainingJobDefinition # readList :: ReadS [TrainingJobDefinition] # readPrec :: ReadPrec TrainingJobDefinition # readListPrec :: ReadPrec [TrainingJobDefinition] #
Show TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods showsPrec :: Int -> TrainingJobDefinition -> ShowS # show :: TrainingJobDefinition -> String # showList :: [TrainingJobDefinition] -> ShowS #
Generic TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Associated Types type Rep TrainingJobDefinition :: Type -> Type # Methods from :: TrainingJobDefinition -> Rep TrainingJobDefinition x # to :: Rep TrainingJobDefinition x -> TrainingJobDefinition #
NFData TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods rnf :: TrainingJobDefinition -> () #
Hashable TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods hashWithSalt :: Int -> TrainingJobDefinition -> Int # hash :: TrainingJobDefinition -> Int #
ToJSON TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods toJSON :: TrainingJobDefinition -> Value # toEncoding :: TrainingJobDefinition -> Encoding # toJSONList :: [TrainingJobDefinition] -> Value # toEncodingList :: [TrainingJobDefinition] -> Encoding #
FromJSON TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition Methods parseJSON :: Value -> Parser TrainingJobDefinition # parseJSONList :: Value -> Parser [TrainingJobDefinition] #
type Rep TrainingJobDefinition Source #
Instance details Defined in Amazonka.SageMaker.Types.TrainingJobDefinition type Rep TrainingJobDefinition = D1 ('MetaData "TrainingJobDefinition" "Amazonka.SageMaker.Types.TrainingJobDefinition" "libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker" 'False) (C1 ('MetaCons "TrainingJobDefinition'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "hyperParameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :: (S1 ('MetaSel ('Just "trainingInputMode") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 TrainingInputMode) :: S1 ('MetaSel ('Just "inputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (NonEmpty Channel)))) :: (S1 ('MetaSel ('Just "outputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 OutputDataConfig) :: (S1 ('MetaSel ('Just "resourceConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 ResourceConfig) :*: S1 ('MetaSel ('Just "stoppingCondition") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 StoppingCondition)))))

newTrainingJobDefinition Source #

Arguments

:: TrainingInputMode	`$sel:trainingInputMode:TrainingJobDefinition'`
-> NonEmpty Channel	`$sel:inputDataConfig:TrainingJobDefinition'`
-> OutputDataConfig	`$sel:outputDataConfig:TrainingJobDefinition'`
-> ResourceConfig	`$sel:resourceConfig:TrainingJobDefinition'`
-> StoppingCondition	`$sel:stoppingCondition:TrainingJobDefinition'`
-> TrainingJobDefinition

Create a value of TrainingJobDefinition 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:hyperParameters:TrainingJobDefinition', trainingJobDefinition_hyperParameters - The hyperparameters used for the training job.

$sel:trainingInputMode:TrainingJobDefinition', trainingJobDefinition_trainingInputMode - Undocumented member.

$sel:inputDataConfig:TrainingJobDefinition', trainingJobDefinition_inputDataConfig - An array of Channel objects, each of which specifies an input source.

$sel:outputDataConfig:TrainingJobDefinition', trainingJobDefinition_outputDataConfig - the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

$sel:resourceConfig:TrainingJobDefinition', trainingJobDefinition_resourceConfig - The resources, including the ML compute instances and ML storage volumes, to use for model training.

$sel:stoppingCondition:TrainingJobDefinition', trainingJobDefinition_stoppingCondition - Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

trainingJobDefinition_hyperParameters :: Lens' TrainingJobDefinition (Maybe (HashMap Text Text)) Source #

The hyperparameters used for the training job.

trainingJobDefinition_trainingInputMode :: Lens' TrainingJobDefinition TrainingInputMode Source #

Undocumented member.

trainingJobDefinition_inputDataConfig :: Lens' TrainingJobDefinition (NonEmpty Channel) Source #

An array of Channel objects, each of which specifies an input source.

trainingJobDefinition_outputDataConfig :: Lens' TrainingJobDefinition OutputDataConfig Source #

the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

trainingJobDefinition_resourceConfig :: Lens' TrainingJobDefinition ResourceConfig Source #

The resources, including the ML compute instances and ML storage volumes, to use for model training.

trainingJobDefinition_stoppingCondition :: Lens' TrainingJobDefinition StoppingCondition Source #

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.