Amazonka.SageMaker.Types.TrainingJobDefinition

Documentation

data TrainingJobDefinition

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. 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, SageMaker ends the training job. Use this API to cap model training costs.

  To stop a job, 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

FromJSON TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

parseJSON :: Value -> Parser TrainingJobDefinition

parseJSONList :: Value -> Parser [TrainingJobDefinition]

ToJSON TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

toJSON :: TrainingJobDefinition -> Value

toEncoding :: TrainingJobDefinition -> Encoding

toJSONList :: [TrainingJobDefinition] -> Value

toEncodingList :: [TrainingJobDefinition] -> Encoding

Generic TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Associated Types

type Rep TrainingJobDefinition :: Type -> Type

Methods

from :: TrainingJobDefinition -> Rep TrainingJobDefinition x

to :: Rep TrainingJobDefinition x -> TrainingJobDefinition

Read TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

readsPrec :: Int -> ReadS TrainingJobDefinition

readList :: ReadS [TrainingJobDefinition]

readPrec :: ReadPrec TrainingJobDefinition

readListPrec :: ReadPrec [TrainingJobDefinition]

Show TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

showsPrec :: Int -> TrainingJobDefinition -> ShowS

show :: TrainingJobDefinition -> String

showList :: [TrainingJobDefinition] -> ShowS

NFData TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

rnf :: TrainingJobDefinition -> ()

Eq TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

(==) :: TrainingJobDefinition -> TrainingJobDefinition -> Bool

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

Hashable TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

Methods

hashWithSalt :: Int -> TrainingJobDefinition -> Int

hash :: TrainingJobDefinition -> Int

type Rep TrainingJobDefinition

Defined in Amazonka.SageMaker.Types.TrainingJobDefinition

type Rep TrainingJobDefinition = D1 ('MetaData "TrainingJobDefinition" "Amazonka.SageMaker.Types.TrainingJobDefinition" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" '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

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. 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, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, 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))

The hyperparameters used for the training job.

trainingJobDefinition_trainingInputMode :: Lens' TrainingJobDefinition TrainingInputMode

Undocumented member.

trainingJobDefinition_inputDataConfig :: Lens' TrainingJobDefinition (NonEmpty Channel)

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

trainingJobDefinition_outputDataConfig :: Lens' TrainingJobDefinition OutputDataConfig

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

trainingJobDefinition_resourceConfig :: Lens' TrainingJobDefinition ResourceConfig

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

trainingJobDefinition_stoppingCondition :: Lens' 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, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, 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.