/** * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * SPDX-License-Identifier: Apache-2.0. */ #pragma once #include #include #include #include #include #include namespace Aws { namespace Utils { namespace Json { class JsonValue; class JsonView; } // namespace Json } // namespace Utils namespace SageMaker { namespace Model { /** *

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

See Also:

* AWS * API Reference

*/ class ResourceConfig { public: AWS_SAGEMAKER_API ResourceConfig(); AWS_SAGEMAKER_API ResourceConfig(Aws::Utils::Json::JsonView jsonValue); AWS_SAGEMAKER_API ResourceConfig& operator=(Aws::Utils::Json::JsonView jsonValue); AWS_SAGEMAKER_API Aws::Utils::Json::JsonValue Jsonize() const; /** *

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.

*/ inline const TrainingInstanceType& GetInstanceType() const{ return m_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.

*/ inline bool InstanceTypeHasBeenSet() const { return m_instanceTypeHasBeenSet; } /** *

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.

*/ inline void SetInstanceType(const TrainingInstanceType& value) { m_instanceTypeHasBeenSet = true; m_instanceType = value; } /** *

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.

*/ inline void SetInstanceType(TrainingInstanceType&& value) { m_instanceTypeHasBeenSet = true; m_instanceType = std::move(value); } /** *

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.

*/ inline ResourceConfig& WithInstanceType(const TrainingInstanceType& value) { SetInstanceType(value); return *this;} /** *

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.

*/ inline ResourceConfig& WithInstanceType(TrainingInstanceType&& value) { SetInstanceType(std::move(value)); return *this;} /** *

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

*/ inline int GetInstanceCount() const{ return m_instanceCount; } /** *

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

*/ inline bool InstanceCountHasBeenSet() const { return m_instanceCountHasBeenSet; } /** *

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

*/ inline void SetInstanceCount(int value) { m_instanceCountHasBeenSet = true; m_instanceCount = value; } /** *

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

*/ inline ResourceConfig& WithInstanceCount(int value) { SetInstanceCount(value); return *this;} /** *

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.

*/ inline int GetVolumeSizeInGB() const{ return m_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.

*/ inline bool VolumeSizeInGBHasBeenSet() const { return m_volumeSizeInGBHasBeenSet; } /** *

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.

*/ inline void SetVolumeSizeInGB(int value) { m_volumeSizeInGBHasBeenSet = true; m_volumeSizeInGB = value; } /** *

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.

*/ inline ResourceConfig& WithVolumeSizeInGB(int value) { SetVolumeSizeInGB(value); return *this;} /** *

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" *

*/ inline const Aws::String& GetVolumeKmsKeyId() const{ return m_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" *

*/ inline bool VolumeKmsKeyIdHasBeenSet() const { return m_volumeKmsKeyIdHasBeenSet; } /** *

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" *

*/ inline void SetVolumeKmsKeyId(const Aws::String& value) { m_volumeKmsKeyIdHasBeenSet = true; m_volumeKmsKeyId = value; } /** *

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" *

*/ inline void SetVolumeKmsKeyId(Aws::String&& value) { m_volumeKmsKeyIdHasBeenSet = true; m_volumeKmsKeyId = std::move(value); } /** *

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" *

*/ inline void SetVolumeKmsKeyId(const char* value) { m_volumeKmsKeyIdHasBeenSet = true; m_volumeKmsKeyId.assign(value); } /** *

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" *

*/ inline ResourceConfig& WithVolumeKmsKeyId(const Aws::String& value) { SetVolumeKmsKeyId(value); return *this;} /** *

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" *

*/ inline ResourceConfig& WithVolumeKmsKeyId(Aws::String&& value) { SetVolumeKmsKeyId(std::move(value)); return *this;} /** *

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" *

*/ inline ResourceConfig& WithVolumeKmsKeyId(const char* value) { SetVolumeKmsKeyId(value); return *this;} /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline const Aws::Vector& GetInstanceGroups() const{ return m_instanceGroups; } /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline bool InstanceGroupsHasBeenSet() const { return m_instanceGroupsHasBeenSet; } /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline void SetInstanceGroups(const Aws::Vector& value) { m_instanceGroupsHasBeenSet = true; m_instanceGroups = value; } /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline void SetInstanceGroups(Aws::Vector&& value) { m_instanceGroupsHasBeenSet = true; m_instanceGroups = std::move(value); } /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline ResourceConfig& WithInstanceGroups(const Aws::Vector& value) { SetInstanceGroups(value); return *this;} /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline ResourceConfig& WithInstanceGroups(Aws::Vector&& value) { SetInstanceGroups(std::move(value)); return *this;} /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline ResourceConfig& AddInstanceGroups(const InstanceGroup& value) { m_instanceGroupsHasBeenSet = true; m_instanceGroups.push_back(value); return *this; } /** *

The configuration of a heterogeneous cluster in JSON format.

*/ inline ResourceConfig& AddInstanceGroups(InstanceGroup&& value) { m_instanceGroupsHasBeenSet = true; m_instanceGroups.push_back(std::move(value)); return *this; } /** *

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

*/ inline int GetKeepAlivePeriodInSeconds() const{ return m_keepAlivePeriodInSeconds; } /** *

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

*/ inline bool KeepAlivePeriodInSecondsHasBeenSet() const { return m_keepAlivePeriodInSecondsHasBeenSet; } /** *

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

*/ inline void SetKeepAlivePeriodInSeconds(int value) { m_keepAlivePeriodInSecondsHasBeenSet = true; m_keepAlivePeriodInSeconds = value; } /** *

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

*/ inline ResourceConfig& WithKeepAlivePeriodInSeconds(int value) { SetKeepAlivePeriodInSeconds(value); return *this;} private: TrainingInstanceType m_instanceType; bool m_instanceTypeHasBeenSet = false; int m_instanceCount; bool m_instanceCountHasBeenSet = false; int m_volumeSizeInGB; bool m_volumeSizeInGBHasBeenSet = false; Aws::String m_volumeKmsKeyId; bool m_volumeKmsKeyIdHasBeenSet = false; Aws::Vector m_instanceGroups; bool m_instanceGroupsHasBeenSet = false; int m_keepAlivePeriodInSeconds; bool m_keepAlivePeriodInSecondsHasBeenSet = false; }; } // namespace Model } // namespace SageMaker } // namespace Aws