HyperParameterTuningResourceConfig

The configuration of resources, including compute instances and storage volumes for use in training jobs launched by hyperparameter tuning jobs. HyperParameterTuningResourceConfig is similar to ResourceConfig, but has the additional InstanceConfigs and AllocationStrategy fields to allow for flexible instance management. Specify one or more instance types, count, and the allocation strategy for instance selection.

Note

HyperParameterTuningResourceConfig supports the capabilities of ResourceConfig with the exception of KeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools by default, which reuse clusters between training jobs.

AllocationStrategy

The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

Type: String

Valid Values: Prioritized

Required: No

InstanceConfigs

A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

Note

If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount.

Type: Array of HyperParameterTuningInstanceConfig objects

Array Members: Minimum number of 1 item. Maximum number of 6 items.

Required: No

InstanceCount

The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.

Type: Integer

Valid Range: Minimum value of 0.

Required: No

InstanceType

The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

Type: String

Required: No

VolumeKmsKeyId

A key used by AWS Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

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"

Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about AWS Key Management Service, see KMS encryption for more information.

Type: String

Length Constraints: Minimum length of 0. Maximum length of 2048.

Pattern: [a-zA-Z0-9:/_-]*

Required: No

VolumeSizeInGB

The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.

Note

SageMaker supports only the General Purpose SSD (gp2) storage volume type.

Type: Integer

Valid Range: Minimum value of 0.

Required: No

HyperParameterTuningResourceConfig

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See Also