software.amazon.awssdk.services.sagemaker.model

Class ResourceConfig

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.ResourceConfig

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<ResourceConfig.Builder,ResourceConfig>

@Generated(value="software.amazon.awssdk:codegen")
public final class ResourceConfig
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<ResourceConfig.Builder,ResourceConfig>

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

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	ResourceConfig.Builder

Method Summary

Modifier and Type	Method and Description
static ResourceConfig.Builder	builder()
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
boolean	hasInstanceGroups() For responses, this returns true if the service returned a value for the InstanceGroups property.
Integer	instanceCount() The number of ML compute instances to use.
List<InstanceGroup>	instanceGroups() The configuration of a heterogeneous cluster in JSON format.
TrainingInstanceType	instanceType() The ML compute instance type.
String	instanceTypeAsString() The ML compute instance type.
Integer	keepAlivePeriodInSeconds() The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
List<SdkField<?>>	sdkFields()
static Class<? extends ResourceConfig.Builder>	serializableBuilderClass()
ResourceConfig.Builder	toBuilder()
String	toString() Returns a string representation of this object.
String	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.
Integer	volumeSizeInGB() The size of the ML storage volume that you want to provision.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

instanceType

public final TrainingInstanceType 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.

If the service returns an enum value that is not available in the current SDK version, instanceType will return TrainingInstanceType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from instanceTypeAsString().

Returns:

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.

See Also:

TrainingInstanceType

instanceTypeAsString

public final String instanceTypeAsString()

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.

If the service returns an enum value that is not available in the current SDK version, instanceType will return TrainingInstanceType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from instanceTypeAsString().

Returns:

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.

See Also:

TrainingInstanceType

instanceCount

public final Integer instanceCount()

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

Returns:

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

volumeSizeInGB

public final Integer 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.

Returns:

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.

volumeKmsKeyId

public final String 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"

Returns:

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"

hasInstanceGroups

public final boolean hasInstanceGroups()

For responses, this returns true if the service returned a value for the InstanceGroups property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

instanceGroups

public final List<InstanceGroup> instanceGroups()

The configuration of a heterogeneous cluster in JSON format.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasInstanceGroups() method.

Returns:

The configuration of a heterogeneous cluster in JSON format.

keepAlivePeriodInSeconds

public final Integer keepAlivePeriodInSeconds()

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

Returns:

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

toBuilder

public ResourceConfig.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<ResourceConfig.Builder,ResourceConfig>

builder

public static ResourceConfig.Builder builder()

serializableBuilderClass

public static Class<? extends ResourceConfig.Builder> serializableBuilderClass()

hashCode

public final int hashCode()

Overrides:

hashCode in class Object

equals

public final boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public final boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public final String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

getValueForField

public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)

sdkFields

public final List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo