Package software.amazon.awssdk.services.sagemaker.model

Class HyperParameterTrainingJobDefinition

java.lang.Object
software.amazon.awssdk.services.sagemaker.model.HyperParameterTrainingJobDefinition
All Implemented Interfaces:
Serializable, SdkPojo, ToCopyableBuilder<HyperParameterTrainingJobDefinition.Builder,HyperParameterTrainingJobDefinition>
@Generated("software.amazon.awssdk:codegen") public final class HyperParameterTrainingJobDefinition extends Object implements SdkPojo, Serializable, ToCopyableBuilder<HyperParameterTrainingJobDefinition.Builder,HyperParameterTrainingJobDefinition>

Defines the training jobs launched by a hyperparameter tuning job.

See Also:

  • Method Details

    • definitionName

      public final String definitionName()

      The job definition name.

      Returns:
      The job definition name.

    • tuningObjective

      public final HyperParameterTuningJobObjective tuningObjective()
      Returns the value of the TuningObjective property for this object.
      Returns:
      The value of the TuningObjective property for this object.

    • hyperParameterRanges

      public final ParameterRanges hyperParameterRanges()
      Returns the value of the HyperParameterRanges property for this object.
      Returns:
      The value of the HyperParameterRanges property for this object.

    • hasStaticHyperParameters

      public final boolean hasStaticHyperParameters()
      For responses, this returns true if the service returned a value for the StaticHyperParameters 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.

    • staticHyperParameters

      public final Map<String,String> staticHyperParameters()

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

      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 hasStaticHyperParameters() method.

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

    • algorithmSpecification

      public final HyperParameterAlgorithmSpecification algorithmSpecification()

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

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

    • roleArn

      public final String roleArn()

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

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

    • hasInputDataConfig

      public final boolean hasInputDataConfig()
      For responses, this returns true if the service returned a value for the InputDataConfig 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.

    • inputDataConfig

      public final List<Channel> inputDataConfig()

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

      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 hasInputDataConfig() method.

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

    • vpcConfig

      public final VpcConfig vpcConfig()

      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.

      Returns:
      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.

    • outputDataConfig

      public final OutputDataConfig outputDataConfig()

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

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

    • resourceConfig

      public final ResourceConfig resourceConfig()

      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.

      If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

      Returns:
      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.

      If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

    • hyperParameterTuningResourceConfig

      public final HyperParameterTuningResourceConfig hyperParameterTuningResourceConfig()

      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).

      Returns:
      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).

    • stoppingCondition

      public final StoppingCondition stoppingCondition()

      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.

      Returns:
      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.

    • enableNetworkIsolation

      public final Boolean enableNetworkIsolation()

      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.

      Returns:
      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.

    • enableInterContainerTrafficEncryption

      public final Boolean enableInterContainerTrafficEncryption()

      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.

      Returns:
      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.

    • enableManagedSpotTraining

      public final Boolean enableManagedSpotTraining()

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

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

    • checkpointConfig

      public final CheckpointConfig checkpointConfig()
      Returns the value of the CheckpointConfig property for this object.
      Returns:
      The value of the CheckpointConfig property for this object.

    • retryStrategy

      public final RetryStrategy retryStrategy()

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

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

    • hasEnvironment

      public final boolean hasEnvironment()
      For responses, this returns true if the service returned a value for the Environment 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.

    • environment

      public final Map<String,String> environment()

      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.

      The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.

      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 hasEnvironment() method.

      Returns:
      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.

      The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.

    • toBuilder

      public HyperParameterTrainingJobDefinition.Builder toBuilder()
      Description copied from interface: ToCopyableBuilder
      Take this object and create a builder that contains all of the current property values of this object.
      Specified by:
      toBuilder in interface ToCopyableBuilder<HyperParameterTrainingJobDefinition.Builder,HyperParameterTrainingJobDefinition>
      Returns:
      a builder for type T

    • builder

      public static HyperParameterTrainingJobDefinition.Builder builder()

    • serializableBuilderClass

      public static Class<? extends HyperParameterTrainingJobDefinition.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)
      Description copied from interface: SdkPojo
      Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in an SdkPojo class, and is generated based on a service model.

      If an SdkPojo class does not have any inherited fields, equalsBySdkFields and equals are essentially the same.

      Specified by:
      equalsBySdkFields in interface SdkPojo
      Parameters:
      obj - the object to be compared with
      Returns:
      true if the other object equals to this object by sdk fields, false otherwise.

    • 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
      Returns:
      List of SdkField in this POJO. May be empty list but should never be null.