HyperParameterTrainingJobDefinition

The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

Type: HyperParameterAlgorithmSpecification object

Required: Yes

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

Type: OutputDataConfig object

Required: Yes

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Pattern: ^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$

Required: Yes

Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

Type: StoppingCondition object

Required: Yes

Contains information about the output location for managed spot training checkpoint data.

Type: CheckpointConfig object

Required: No

The job definition name.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 64.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,63}

Required: No

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

Type: Boolean

Required: No

A Boolean indicating whether managed spot training is enabled (True) or not (False).

Type: Boolean

Required: No

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Type: Boolean

Required: No

An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.

Type: String to string map

Map Entries: Maximum number of 48 items.

Key Length Constraints: Maximum length of 512.

Key Pattern: [a-zA-Z_][a-zA-Z0-9_]*

Value Length Constraints: Maximum length of 512.

Value Pattern: [\S\s]*

Required: No

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

Type: ParameterRanges object

Required: No

The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).

Type: HyperParameterTuningResourceConfig object

Required: No

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

Type: Array of Channel objects

Array Members: Minimum number of 1 item. Maximum number of 20 items.

Required: No

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

Type: ResourceConfig object

Required: No

The number of times to retry the job when the job fails due to an InternalServerError.

Type: RetryStrategy object

Required: No

Specifies the values of hyperparameters that do not change for the tuning job.

Type: String to string map

Map Entries: Minimum number of 0 items. Maximum number of 100 items.

Key Length Constraints: Maximum length of 256.

Key Pattern: .*

Value Length Constraints: Maximum length of 2500.

Value Pattern: .*

Required: No

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables.

Type: HyperParameterTuningJobObjective object

Required: No

The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Type: VpcConfig object

Required: No