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.ResourceConfig

Description

Documentation

data ResourceConfig Source #

Describes the resources, including machine learning (ML) compute instances and ML storage volumes, to use for model training.

See: newResourceConfig smart constructor.

Constructors

ResourceConfig'

Fields

instanceCount :: Maybe Natural
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
instanceGroups :: Maybe [InstanceGroup]
The configuration of a heterogeneous cluster in JSON format.
instanceType :: Maybe TrainingInstanceType
The ML compute instance type.
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
keepAlivePeriodInSeconds :: Maybe Natural
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
volumeKmsKeyId :: Maybe Text
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
The VolumeKmsKeyId can be in any of the following formats:
- // KMS Key ID
```
"1234abcd-12ab-34cd-56ef-1234567890ab"
```
- // Amazon Resource Name (ARN) of a KMS Key
```
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
```
volumeSizeInGB :: Natural
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

Instances

Instances details

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

newResourceConfig Source #

Arguments

:: Natural	`$sel:volumeSizeInGB:ResourceConfig'`
-> ResourceConfig

Create a value of ResourceConfig 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:

ResourceConfig, resourceConfig_instanceCount - The number of ML compute instances to use. For distributed training, provide a value greater than 1.

$sel:instanceGroups:ResourceConfig', resourceConfig_instanceGroups - The configuration of a heterogeneous cluster in JSON format.

ResourceConfig, resourceConfig_instanceType - The ML compute instance type.

SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)

To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

$sel:keepAlivePeriodInSeconds:ResourceConfig', resourceConfig_keepAlivePeriodInSeconds - The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

$sel:volumeKmsKeyId:ResourceConfig', resourceConfig_volumeKmsKeyId - The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

For a list of instance types that support local instance storage, see Instance Store Volumes.

For more information about local instance storage encryption, see SSD Instance Store Volumes.

The VolumeKmsKeyId can be in any of the following formats:

// KMS Key ID
```
"1234abcd-12ab-34cd-56ef-1234567890ab"
```

// Amazon Resource Name (ARN) of a KMS Key

"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

$sel:volumeSizeInGB:ResourceConfig', resourceConfig_volumeSizeInGB - The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.

When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.

To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

resourceConfig_instanceCount :: Lens' ResourceConfig (Maybe Natural) Source #

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

resourceConfig_instanceGroups :: Lens' ResourceConfig (Maybe [InstanceGroup]) Source #

The configuration of a heterogeneous cluster in JSON format.

resourceConfig_instanceType :: Lens' ResourceConfig (Maybe TrainingInstanceType) Source #

The ML compute instance type.

SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)

To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

resourceConfig_keepAlivePeriodInSeconds :: Lens' ResourceConfig (Maybe Natural) Source #

The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

resourceConfig_volumeKmsKeyId :: Lens' ResourceConfig (Maybe Text) Source #

The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.