@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class ResourceConfig extends Object implements Serializable, Cloneable, StructuredPojo
Describes the resources, including machine learning (ML) compute instances and ML storage volumes, to use for model training.
| Constructor and Description |
|---|
ResourceConfig () |
| Modifier and Type | Method and Description |
|---|---|
ResourceConfig |
clone () |
boolean |
equals (Object obj) |
Integer |
getInstanceCount ()
The number of ML compute instances to use.
|
List<InstanceGroup> |
getInstanceGroups ()
The configuration of a heterogeneous cluster in JSON format.
|
String |
getInstanceType ()
The ML compute instance type.
|
Integer |
getKeepAlivePeriodInSeconds ()
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
|
String |
getVolumeKmsKeyId ()
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 |
getVolumeSizeInGB ()
The size of the ML storage volume that you want to provision.
|
int |
hashCode () |
void |
marshall (ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setInstanceCount (Integer instanceCount)
The number of ML compute instances to use.
|
void |
setInstanceGroups (Collection<InstanceGroup> instanceGroups)
The configuration of a heterogeneous cluster in JSON format.
|
void |
setInstanceType (String instanceType)
The ML compute instance type.
|
void |
setKeepAlivePeriodInSeconds (Integer keepAlivePeriodInSeconds)
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
|
void |
setVolumeKmsKeyId (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.
|
void |
setVolumeSizeInGB (Integer volumeSizeInGB)
The size of the ML storage volume that you want to provision.
|
String |
toString ()
Returns a string representation of this object.
|
ResourceConfig |
withInstanceCount (Integer instanceCount)
The number of ML compute instances to use.
|
ResourceConfig |
withInstanceGroups (Collection<InstanceGroup> instanceGroups)
The configuration of a heterogeneous cluster in JSON format.
|
ResourceConfig |
withInstanceGroups (InstanceGroup... instanceGroups)
The configuration of a heterogeneous cluster in JSON format.
|
ResourceConfig |
withInstanceType (String instanceType)
The ML compute instance type.
|
ResourceConfig |
withInstanceType (TrainingInstanceType instanceType)
The ML compute instance type.
|
ResourceConfig |
withKeepAlivePeriodInSeconds (Integer keepAlivePeriodInSeconds)
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
|
ResourceConfig |
withVolumeKmsKeyId (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.
|
ResourceConfig |
withVolumeSizeInGB (Integer volumeSizeInGB)
The size of the ML storage volume that you want to provision.
|
public ResourceConfig()
public void setInstanceType(String 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.
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.
TrainingInstanceType public String getInstanceType()
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.
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.
TrainingInstanceType public ResourceConfig withInstanceType(String 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.
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.
TrainingInstanceType public ResourceConfig withInstanceType(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.
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.
TrainingInstanceType public void setInstanceCount(Integer instanceCount)
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
instanceCount - The number of ML compute instances to use. For distributed training, provide a value greater than 1.public Integer getInstanceCount()
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
public ResourceConfig withInstanceCount(Integer instanceCount)
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
instanceCount - The number of ML compute instances to use. For distributed training, provide a value greater than 1.public void setVolumeSizeInGB(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.
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.
public Integer getVolumeSizeInGB()
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.
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.
public ResourceConfig withVolumeSizeInGB(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.
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.
public void setVolumeKmsKeyId(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"
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"
public String getVolumeKmsKeyId()
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"
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"
public ResourceConfig withVolumeKmsKeyId(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"
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"
public void setKeepAlivePeriodInSeconds(Integer keepAlivePeriodInSeconds)
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
keepAlivePeriodInSeconds - The duration of time in seconds to retain configured resources in a warm pool for subsequent training
jobs.public Integer getKeepAlivePeriodInSeconds()
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
public ResourceConfig withKeepAlivePeriodInSeconds(Integer keepAlivePeriodInSeconds)
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
keepAlivePeriodInSeconds - The duration of time in seconds to retain configured resources in a warm pool for subsequent training
jobs.public List<InstanceGroup> getInstanceGroups()
The configuration of a heterogeneous cluster in JSON format.
public void setInstanceGroups(Collection<InstanceGroup> instanceGroups)
The configuration of a heterogeneous cluster in JSON format.
instanceGroups - The configuration of a heterogeneous cluster in JSON format.public ResourceConfig withInstanceGroups(InstanceGroup... instanceGroups)
The configuration of a heterogeneous cluster in JSON format.
NOTE: This method appends the values to the existing list (if any). Use
setInstanceGroups(java.util.Collection) or withInstanceGroups(java.util.Collection) if you want
to override the existing values.
instanceGroups - The configuration of a heterogeneous cluster in JSON format.public ResourceConfig withInstanceGroups(Collection<InstanceGroup> instanceGroups)
The configuration of a heterogeneous cluster in JSON format.
instanceGroups - The configuration of a heterogeneous cluster in JSON format.public String toString()
toString in class Object Object.toString() public ResourceConfig clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo ProtocolMarshaller.marshall in interface StructuredPojo protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.