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 |
Synopsis
- data HyperParameterTrainingJobDefinition = HyperParameterTrainingJobDefinition' {
- checkpointConfig :: Maybe CheckpointConfig
- definitionName :: Maybe Text
- enableInterContainerTrafficEncryption :: Maybe Bool
- enableManagedSpotTraining :: Maybe Bool
- enableNetworkIsolation :: Maybe Bool
- hyperParameterRanges :: Maybe ParameterRanges
- hyperParameterTuningResourceConfig :: Maybe HyperParameterTuningResourceConfig
- inputDataConfig :: Maybe (NonEmpty Channel)
- resourceConfig :: Maybe ResourceConfig
- retryStrategy :: Maybe RetryStrategy
- staticHyperParameters :: Maybe (HashMap Text Text)
- tuningObjective :: Maybe HyperParameterTuningJobObjective
- vpcConfig :: Maybe VpcConfig
- algorithmSpecification :: HyperParameterAlgorithmSpecification
- roleArn :: Text
- outputDataConfig :: OutputDataConfig
- stoppingCondition :: StoppingCondition
- newHyperParameterTrainingJobDefinition :: HyperParameterAlgorithmSpecification -> Text -> OutputDataConfig -> StoppingCondition -> HyperParameterTrainingJobDefinition
- hyperParameterTrainingJobDefinition_checkpointConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe CheckpointConfig)
- hyperParameterTrainingJobDefinition_definitionName :: Lens' HyperParameterTrainingJobDefinition (Maybe Text)
- hyperParameterTrainingJobDefinition_enableInterContainerTrafficEncryption :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool)
- hyperParameterTrainingJobDefinition_enableManagedSpotTraining :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool)
- hyperParameterTrainingJobDefinition_enableNetworkIsolation :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool)
- hyperParameterTrainingJobDefinition_hyperParameterRanges :: Lens' HyperParameterTrainingJobDefinition (Maybe ParameterRanges)
- hyperParameterTrainingJobDefinition_hyperParameterTuningResourceConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe HyperParameterTuningResourceConfig)
- hyperParameterTrainingJobDefinition_inputDataConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe (NonEmpty Channel))
- hyperParameterTrainingJobDefinition_resourceConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe ResourceConfig)
- hyperParameterTrainingJobDefinition_retryStrategy :: Lens' HyperParameterTrainingJobDefinition (Maybe RetryStrategy)
- hyperParameterTrainingJobDefinition_staticHyperParameters :: Lens' HyperParameterTrainingJobDefinition (Maybe (HashMap Text Text))
- hyperParameterTrainingJobDefinition_tuningObjective :: Lens' HyperParameterTrainingJobDefinition (Maybe HyperParameterTuningJobObjective)
- hyperParameterTrainingJobDefinition_vpcConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe VpcConfig)
- hyperParameterTrainingJobDefinition_algorithmSpecification :: Lens' HyperParameterTrainingJobDefinition HyperParameterAlgorithmSpecification
- hyperParameterTrainingJobDefinition_roleArn :: Lens' HyperParameterTrainingJobDefinition Text
- hyperParameterTrainingJobDefinition_outputDataConfig :: Lens' HyperParameterTrainingJobDefinition OutputDataConfig
- hyperParameterTrainingJobDefinition_stoppingCondition :: Lens' HyperParameterTrainingJobDefinition StoppingCondition
Documentation
data HyperParameterTrainingJobDefinition Source #
Defines the training jobs launched by a hyperparameter tuning job.
See: newHyperParameterTrainingJobDefinition
smart constructor.
HyperParameterTrainingJobDefinition' | |
|
Instances
newHyperParameterTrainingJobDefinition Source #
:: HyperParameterAlgorithmSpecification |
|
-> Text | |
-> OutputDataConfig | |
-> StoppingCondition | |
-> HyperParameterTrainingJobDefinition |
Create a value of HyperParameterTrainingJobDefinition
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:checkpointConfig:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_checkpointConfig
- Undocumented member.
$sel:definitionName:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_definitionName
- The job definition name.
$sel:enableInterContainerTrafficEncryption:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_enableInterContainerTrafficEncryption
- To encrypt all communications between ML compute instances in
distributed training, choose True
. Encryption provides greater
security for distributed training, but training might take longer. How
long it takes depends on the amount of communication between compute
instances, especially if you use a deep learning algorithm in
distributed training.
$sel:enableManagedSpotTraining:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_enableManagedSpotTraining
- A Boolean indicating whether managed spot training is enabled (True
)
or not (False
).
$sel:enableNetworkIsolation:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_enableNetworkIsolation
- Isolates the training container. No inbound or outbound network calls
can be made, except for calls between peers within a training cluster
for distributed training. If network isolation is used for training jobs
that are configured to use a VPC, SageMaker downloads and uploads
customer data and model artifacts through the specified VPC, but the
training container does not have network access.
$sel:hyperParameterRanges:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_hyperParameterRanges
- Undocumented member.
$sel:hyperParameterTuningResourceConfig:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_hyperParameterTuningResourceConfig
- The configuration for the hyperparameter tuning resources, including the
compute instances and storage volumes, used for training jobs launched
by the tuning job. By default, storage volumes hold model artifacts and
incremental states. Choose File
for TrainingInputMode
in the
AlgorithmSpecification
parameter to additionally store training data
in the storage volume (optional).
$sel:inputDataConfig:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_inputDataConfig
- An array of Channel objects that specify the input for the training jobs
that the tuning job launches.
$sel:resourceConfig:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_resourceConfig
- The resources, including the compute instances and storage volumes, to
use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training
algorithms might also use storage volumes for scratch space. If you want
SageMaker to use the storage volume to store the training data, choose
File
as the TrainingInputMode
in the algorithm specification. For
distributed training algorithms, specify an instance count greater than
1.
If you want to use hyperparameter optimization with instance type
flexibility, use HyperParameterTuningResourceConfig
instead.
$sel:retryStrategy:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_retryStrategy
- The number of times to retry the job when the job fails due to an
InternalServerError
.
$sel:staticHyperParameters:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_staticHyperParameters
- Specifies the values of hyperparameters that do not change for the
tuning job.
$sel:tuningObjective:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_tuningObjective
- Undocumented member.
$sel:vpcConfig:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_vpcConfig
- The VpcConfig object that specifies the VPC that you want the training
jobs that this hyperparameter tuning job launches to connect to. Control
access to and from your training container by configuring the VPC. For
more information, see
Protect Training Jobs by Using an Amazon Virtual Private Cloud.
$sel:algorithmSpecification:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_algorithmSpecification
- The HyperParameterAlgorithmSpecification object that specifies the
resource algorithm to use for the training jobs that the tuning job
launches.
$sel:roleArn:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_roleArn
- The Amazon Resource Name (ARN) of the IAM role associated with the
training jobs that the tuning job launches.
$sel:outputDataConfig:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_outputDataConfig
- Specifies the path to the Amazon S3 bucket where you store model
artifacts from the training jobs that the tuning job launches.
$sel:stoppingCondition:HyperParameterTrainingJobDefinition'
, hyperParameterTrainingJobDefinition_stoppingCondition
- Specifies a limit to how long a model hyperparameter 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.
hyperParameterTrainingJobDefinition_checkpointConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe CheckpointConfig) Source #
Undocumented member.
hyperParameterTrainingJobDefinition_definitionName :: Lens' HyperParameterTrainingJobDefinition (Maybe Text) Source #
The job definition name.
hyperParameterTrainingJobDefinition_enableInterContainerTrafficEncryption :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool) Source #
To encrypt all communications between ML compute instances in
distributed training, choose True
. Encryption provides greater
security for distributed training, but training might take longer. How
long it takes depends on the amount of communication between compute
instances, especially if you use a deep learning algorithm in
distributed training.
hyperParameterTrainingJobDefinition_enableManagedSpotTraining :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool) Source #
A Boolean indicating whether managed spot training is enabled (True
)
or not (False
).
hyperParameterTrainingJobDefinition_enableNetworkIsolation :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool) Source #
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
hyperParameterTrainingJobDefinition_hyperParameterRanges :: Lens' HyperParameterTrainingJobDefinition (Maybe ParameterRanges) Source #
Undocumented member.
hyperParameterTrainingJobDefinition_hyperParameterTuningResourceConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe HyperParameterTuningResourceConfig) Source #
The configuration for the hyperparameter tuning resources, including the
compute instances and storage volumes, used for training jobs launched
by the tuning job. By default, storage volumes hold model artifacts and
incremental states. Choose File
for TrainingInputMode
in the
AlgorithmSpecification
parameter to additionally store training data
in the storage volume (optional).
hyperParameterTrainingJobDefinition_inputDataConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe (NonEmpty Channel)) Source #
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
hyperParameterTrainingJobDefinition_resourceConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe ResourceConfig) Source #
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training
algorithms might also use storage volumes for scratch space. If you want
SageMaker to use the storage volume to store the training data, choose
File
as the TrainingInputMode
in the algorithm specification. For
distributed training algorithms, specify an instance count greater than
1.
If you want to use hyperparameter optimization with instance type
flexibility, use HyperParameterTuningResourceConfig
instead.
hyperParameterTrainingJobDefinition_retryStrategy :: Lens' HyperParameterTrainingJobDefinition (Maybe RetryStrategy) Source #
The number of times to retry the job when the job fails due to an
InternalServerError
.
hyperParameterTrainingJobDefinition_staticHyperParameters :: Lens' HyperParameterTrainingJobDefinition (Maybe (HashMap Text Text)) Source #
Specifies the values of hyperparameters that do not change for the tuning job.
hyperParameterTrainingJobDefinition_tuningObjective :: Lens' HyperParameterTrainingJobDefinition (Maybe HyperParameterTuningJobObjective) Source #
Undocumented member.
hyperParameterTrainingJobDefinition_vpcConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe VpcConfig) Source #
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
hyperParameterTrainingJobDefinition_algorithmSpecification :: Lens' HyperParameterTrainingJobDefinition HyperParameterAlgorithmSpecification Source #
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
hyperParameterTrainingJobDefinition_roleArn :: Lens' HyperParameterTrainingJobDefinition Text Source #
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
hyperParameterTrainingJobDefinition_outputDataConfig :: Lens' HyperParameterTrainingJobDefinition OutputDataConfig Source #
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
hyperParameterTrainingJobDefinition_stoppingCondition :: Lens' HyperParameterTrainingJobDefinition StoppingCondition Source #
Specifies a limit to how long a model hyperparameter 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.