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aws_sagemaker_hyperparametertuningjob

account_id

Type: STRING

autotune

Type: STRUCT
Provider name: Autotune
Description: A flag to indicate if autotune is enabled for the hyperparameter tuning job.

  • mode
    Type: STRING
    Provider name: Mode
    Description: Set Mode to Enabled if you want to use Autotune.

best_training_job

Type: STRUCT
Provider name: BestTrainingJob
Description: A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

  • creation_time
    Type: TIMESTAMP
    Provider name: CreationTime
    Description: The date and time that the training job was created.
  • failure_reason
    Type: STRING
    Provider name: FailureReason
    Description: The reason that the training job failed.
  • final_hyper_parameter_tuning_job_objective_metric
    Type: STRUCT
    Provider name: FinalHyperParameterTuningJobObjectiveMetric
    Description: The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
    • metric_name
      Type: STRING
      Provider name: MetricName
      Description: The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
    • type
      Type: STRING
      Provider name: Type
      Description: Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
    • value
      Type: FLOAT
      Provider name: Value
      Description: The value of the objective metric.
  • objective_status
    Type: STRING
    Provider name: ObjectiveStatus
    Description: The status of the objective metric for the training job:
    • Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
    • Pending: The training job is in progress and evaluation of its final objective metric is pending.
    • Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
  • training_end_time
    Type: TIMESTAMP
    Provider name: TrainingEndTime
    Description: Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
  • training_job_arn
    Type: STRING
    Provider name: TrainingJobArn
    Description: The Amazon Resource Name (ARN) of the training job.
  • training_job_definition_name
    Type: STRING
    Provider name: TrainingJobDefinitionName
    Description: The training job definition name.
  • training_job_name
    Type: STRING
    Provider name: TrainingJobName
    Description: The name of the training job.
  • training_job_status
    Type: STRING
    Provider name: TrainingJobStatus
    Description: The status of the training job.
  • training_start_time
    Type: TIMESTAMP
    Provider name: TrainingStartTime
    Description: The date and time that the training job started.
  • tuned_hyper_parameters
    Type: MAP_STRING_STRING
    Provider name: TunedHyperParameters
    Description: A list of the hyperparameters for which you specified ranges to search.
  • tuning_job_name
    Type: STRING
    Provider name: TuningJobName
    Description: The HyperParameter tuning job that launched the training job.

consumed_resources

Type: STRUCT
Provider name: ConsumedResources

  • runtime_in_seconds
    Type: INT32
    Provider name: RuntimeInSeconds
    Description: The wall clock runtime in seconds used by your hyperparameter tuning job.

creation_time

Type: TIMESTAMP
Provider name: CreationTime
Description: The date and time that the tuning job started.

failure_reason

Type: STRING
Provider name: FailureReason
Description: If the tuning job failed, the reason it failed.

hyper_parameter_tuning_end_time

Type: TIMESTAMP
Provider name: HyperParameterTuningEndTime
Description: The date and time that the tuning job ended.

hyper_parameter_tuning_job_arn

Type: STRING
Provider name: HyperParameterTuningJobArn
Description: The Amazon Resource Name (ARN) of the tuning job.

hyper_parameter_tuning_job_config

Type: STRUCT
Provider name: HyperParameterTuningJobConfig
Description: The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

  • hyper_parameter_tuning_job_objective
    Type: STRUCT
    Provider name: HyperParameterTuningJobObjective
    Description: The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
    • metric_name
      Type: STRING
      Provider name: MetricName
      Description: The name of the metric to use for the objective metric.
    • type
      Type: STRING
      Provider name: Type
      Description: Whether to minimize or maximize the objective metric.
  • parameter_ranges
    Type: STRUCT
    Provider name: ParameterRanges
    Description: The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
    • auto_parameters
      Type: UNORDERED_LIST_STRUCT
      Provider name: AutoParameters
      Description: A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the hyperparameter to optimize using Autotune.
      • value_hint
        Type: STRING
        Provider name: ValueHint
        Description: An example value of the hyperparameter to optimize using Autotune.
    • categorical_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: CategoricalParameterRanges
      Description: The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the categorical hyperparameter to tune.
      • values
        Type: UNORDERED_LIST_STRING
        Provider name: Values
        Description: A list of the categories for the hyperparameter.
    • continuous_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: ContinuousParameterRanges
      Description: The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
      • max_value
        Type: STRING
        Provider name: MaxValue
        Description: The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
      • min_value
        Type: STRING
        Provider name: MinValue
        Description: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the continuous hyperparameter to tune.
      • scaling_type
        Type: STRING
        Provider name: ScalingType
        Description: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
        Auto
        SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
        Linear
        Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
        Logarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.
        ReverseLogarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

    • integer_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: IntegerParameterRanges
      Description: The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
      • max_value
        Type: STRING
        Provider name: MaxValue
        Description: The maximum value of the hyperparameter to search.
      • min_value
        Type: STRING
        Provider name: MinValue
        Description: The minimum value of the hyperparameter to search.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the hyperparameter to search.
      • scaling_type
        Type: STRING
        Provider name: ScalingType
        Description: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
        Auto
        SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
        Linear
        Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
        Logarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.

  • random_seed
    Type: INT32
    Provider name: RandomSeed
    Description: A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
  • resource_limits
    Type: STRUCT
    Provider name: ResourceLimits
    Description: The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
    • max_number_of_training_jobs
      Type: INT32
      Provider name: MaxNumberOfTrainingJobs
      Description: The maximum number of training jobs that a hyperparameter tuning job can launch.
    • max_parallel_training_jobs
      Type: INT32
      Provider name: MaxParallelTrainingJobs
      Description: The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
    • max_runtime_in_seconds
      Type: INT32
      Provider name: MaxRuntimeInSeconds
      Description: The maximum time in seconds that a hyperparameter tuning job can run.
  • strategy
    Type: STRING
    Provider name: Strategy
    Description: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
  • strategy_config
    Type: STRUCT
    Provider name: StrategyConfig
    Description: The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.
    • hyperband_strategy_config
      Type: STRUCT
      Provider name: HyperbandStrategyConfig
      Description: The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
      • max_resource
        Type: INT32
        Provider name: MaxResource
        Description: The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTraining attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters:
        • epochs
        • numepochs
        • n-epochs
        • n_epochs
        • num_epochs
        If HyperbandStrategyConfig is unable to infer a value for MaxResource, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
      • min_resource
        Type: INT32
        Provider name: MinResource
        Description: The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband.
  • training_job_early_stopping_type
    Type: STRING
    Provider name: TrainingJobEarlyStoppingType
    Description: Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):
    OFF
    Training jobs launched by the hyperparameter tuning job do not use early stopping.
    AUTO
    SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

  • tuning_job_completion_criteria
    Type: STRUCT
    Provider name: TuningJobCompletionCriteria
    Description: The tuning job’s completion criteria.
    • best_objective_not_improving
      Type: STRUCT
      Provider name: BestObjectiveNotImproving
      Description: A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
      • max_number_of_training_jobs_not_improving
        Type: INT32
        Provider name: MaxNumberOfTrainingJobsNotImproving
        Description: The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
    • convergence_detected
      Type: STRUCT
      Provider name: ConvergenceDetected
      Description: A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
      • complete_on_convergence
        Type: STRING
        Provider name: CompleteOnConvergence
        Description: A flag to stop a tuning job once AMT has detected that the job has converged.
    • target_objective_metric_value
      Type: FLOAT
      Provider name: TargetObjectiveMetricValue
      Description: The value of the objective metric.

hyper_parameter_tuning_job_name

Type: STRING
Provider name: HyperParameterTuningJobName
Description: The name of the hyperparameter tuning job.

hyper_parameter_tuning_job_status

Type: STRING
Provider name: HyperParameterTuningJobStatus
Description: The status of the tuning job.

last_modified_time

Type: TIMESTAMP
Provider name: LastModifiedTime
Description: The date and time that the status of the tuning job was modified.

objective_status_counters

Type: STRUCT
Provider name: ObjectiveStatusCounters
Description: The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

  • failed
    Type: INT32
    Provider name: Failed
    Description: The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
  • pending
    Type: INT32
    Provider name: Pending
    Description: The number of training jobs that are in progress and pending evaluation of their final objective metric.
  • succeeded
    Type: INT32
    Provider name: Succeeded
    Description: The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

overall_best_training_job

Type: STRUCT
Provider name: OverallBestTrainingJob
Description: If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

  • creation_time
    Type: TIMESTAMP
    Provider name: CreationTime
    Description: The date and time that the training job was created.
  • failure_reason
    Type: STRING
    Provider name: FailureReason
    Description: The reason that the training job failed.
  • final_hyper_parameter_tuning_job_objective_metric
    Type: STRUCT
    Provider name: FinalHyperParameterTuningJobObjectiveMetric
    Description: The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
    • metric_name
      Type: STRING
      Provider name: MetricName
      Description: The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
    • type
      Type: STRING
      Provider name: Type
      Description: Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
    • value
      Type: FLOAT
      Provider name: Value
      Description: The value of the objective metric.
  • objective_status
    Type: STRING
    Provider name: ObjectiveStatus
    Description: The status of the objective metric for the training job:
    • Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
    • Pending: The training job is in progress and evaluation of its final objective metric is pending.
    • Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
  • training_end_time
    Type: TIMESTAMP
    Provider name: TrainingEndTime
    Description: Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
  • training_job_arn
    Type: STRING
    Provider name: TrainingJobArn
    Description: The Amazon Resource Name (ARN) of the training job.
  • training_job_definition_name
    Type: STRING
    Provider name: TrainingJobDefinitionName
    Description: The training job definition name.
  • training_job_name
    Type: STRING
    Provider name: TrainingJobName
    Description: The name of the training job.
  • training_job_status
    Type: STRING
    Provider name: TrainingJobStatus
    Description: The status of the training job.
  • training_start_time
    Type: TIMESTAMP
    Provider name: TrainingStartTime
    Description: The date and time that the training job started.
  • tuned_hyper_parameters
    Type: MAP_STRING_STRING
    Provider name: TunedHyperParameters
    Description: A list of the hyperparameters for which you specified ranges to search.
  • tuning_job_name
    Type: STRING
    Provider name: TuningJobName
    Description: The HyperParameter tuning job that launched the training job.

tags

Type: UNORDERED_LIST_STRING

training_job_definition

Type: STRUCT
Provider name: TrainingJobDefinition
Description: The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

  • algorithm_specification
    Type: STRUCT
    Provider name: AlgorithmSpecification
    Description: The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
    • algorithm_name
      Type: STRING
      Provider name: AlgorithmName
      Description: The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.
    • metric_definitions
      Type: UNORDERED_LIST_STRUCT
      Provider name: MetricDefinitions
      Description: An array of MetricDefinition objects that specify the metrics that the algorithm emits.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the metric.
      • regex
        Type: STRING
        Provider name: Regex
        Description: A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
    • training_image
      Type: STRING
      Provider name: TrainingImage
      Description: The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    • training_input_mode
      Type: STRING
      Provider name: TrainingInputMode
  • checkpoint_config
    Type: STRUCT
    Provider name: CheckpointConfig
    • local_path
      Type: STRING
      Provider name: LocalPath
      Description: (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
    • s3_uri
      Type: STRING
      Provider name: S3Uri
      Description: Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
  • definition_name
    Type: STRING
    Provider name: DefinitionName
    Description: The job definition name.
  • enable_inter_container_traffic_encryption
    Type: BOOLEAN
    Provider name: EnableInterContainerTrafficEncryption
    Description: 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.
  • enable_managed_spot_training
    Type: BOOLEAN
    Provider name: EnableManagedSpotTraining
    Description: A Boolean indicating whether managed spot training is enabled (True) or not (False).
  • enable_network_isolation
    Type: BOOLEAN
    Provider name: EnableNetworkIsolation
    Description: 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.
  • environment
    Type: MAP_STRING_STRING
    Provider name: Environment
    Description: 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.
  • hyper_parameter_ranges
    Type: STRUCT
    Provider name: HyperParameterRanges
    • auto_parameters
      Type: UNORDERED_LIST_STRUCT
      Provider name: AutoParameters
      Description: A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the hyperparameter to optimize using Autotune.
      • value_hint
        Type: STRING
        Provider name: ValueHint
        Description: An example value of the hyperparameter to optimize using Autotune.
    • categorical_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: CategoricalParameterRanges
      Description: The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the categorical hyperparameter to tune.
      • values
        Type: UNORDERED_LIST_STRING
        Provider name: Values
        Description: A list of the categories for the hyperparameter.
    • continuous_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: ContinuousParameterRanges
      Description: The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
      • max_value
        Type: STRING
        Provider name: MaxValue
        Description: The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
      • min_value
        Type: STRING
        Provider name: MinValue
        Description: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the continuous hyperparameter to tune.
      • scaling_type
        Type: STRING
        Provider name: ScalingType
        Description: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
        Auto
        SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
        Linear
        Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
        Logarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.
        ReverseLogarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

    • integer_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: IntegerParameterRanges
      Description: The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
      • max_value
        Type: STRING
        Provider name: MaxValue
        Description: The maximum value of the hyperparameter to search.
      • min_value
        Type: STRING
        Provider name: MinValue
        Description: The minimum value of the hyperparameter to search.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the hyperparameter to search.
      • scaling_type
        Type: STRING
        Provider name: ScalingType
        Description: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
        Auto
        SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
        Linear
        Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
        Logarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.

  • hyper_parameter_tuning_resource_config
    Type: STRUCT
    Provider name: HyperParameterTuningResourceConfig
    Description: 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).
    • allocation_strategy
      Type: STRING
      Provider name: AllocationStrategy
      Description: The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
    • instance_configs
      Type: UNORDERED_LIST_STRUCT
      Provider name: InstanceConfigs
      Description: 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. 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.
      • instance_count
        Type: INT32
        Provider name: InstanceCount
        Description: The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
      • instance_type
        Type: STRING
        Provider name: InstanceType
        Description: The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
      • volume_size_in_gb
        Type: INT32
        Provider name: VolumeSizeInGB
        Description: The volume size in GB of the data to be processed for hyperparameter optimization (optional).
    • instance_count
      Type: INT32
      Provider name: InstanceCount
      Description: The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.
    • instance_type
      Type: STRING
      Provider name: InstanceType
      Description: The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
    • volume_kms_key_id
      Type: STRING
      Provider name: VolumeKmsKeyId
      Description: A key used by Amazon Web Services 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 Amazon Web Services Key Management Service, see KMS encryption for more information.
    • volume_size_in_gb
      Type: INT32
      Provider name: VolumeSizeInGB
      Description: 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. SageMaker supports only the General Purpose SSD (gp2) storage volume type.
  • input_data_config
    Type: UNORDERED_LIST_STRUCT
    Provider name: InputDataConfig
    Description: An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    • channel_name
      Type: STRING
      Provider name: ChannelName
      Description: The name of the channel.
    • compression_type
      Type: STRING
      Provider name: CompressionType
      Description: If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    • content_type
      Type: STRING
      Provider name: ContentType
      Description: The MIME type of the data.
    • data_source
      Type: STRUCT
      Provider name: DataSource
      Description: The location of the channel data.
      • file_system_data_source
        Type: STRUCT
        Provider name: FileSystemDataSource
        Description: The file system that is associated with a channel.
        • directory_path
          Type: STRING
          Provider name: DirectoryPath
          Description: The full path to the directory to associate with the channel.
        • file_system_access_mode
          Type: STRING
          Provider name: FileSystemAccessMode
          Description: The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
        • file_system_id
          Type: STRING
          Provider name: FileSystemId
          Description: The file system id.
        • file_system_type
          Type: STRING
          Provider name: FileSystemType
          Description: The file system type.
      • s3_data_source
        Type: STRUCT
        Provider name: S3DataSource
        Description: The S3 location of the data source that is associated with a channel.
        • attribute_names
          Type: UNORDERED_LIST_STRING
          Provider name: AttributeNames
          Description: A list of one or more attribute names to use that are found in a specified augmented manifest file.
        • hub_access_config
          Type: STRUCT
          Provider name: HubAccessConfig
          Description: The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model.
          • hub_content_arn
            Type: STRING
            Provider name: HubContentArn
            Description: The ARN of your private model hub content. This should be a ModelReference resource type that points to a SageMaker JumpStart public hub model.
        • instance_group_names
          Type: UNORDERED_LIST_STRING
          Provider name: InstanceGroupNames
          Description: A list of names of instance groups that get data from the S3 data source.
        • model_access_config
          Type: STRUCT
          Provider name: ModelAccessConfig
          • accept_eula
            Type: BOOLEAN
            Provider name: AcceptEula
            Description: Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
        • s3_data_distribution_type
          Type: STRING
          Provider name: S3DataDistributionType
          Description: If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated. If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don’t choose more ML compute instances for training than available S3 objects. If you do, some nodes won’t get any data and you will pay for nodes that aren’t getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.
        • s3_data_type
          Type: STRING
          Provider name: S3DataType
          Description: If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel’s input mode is Pipe.
        • s3_uri
          Type: STRING
          Provider name: S3Uri
          Description: Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
          • A key name prefix might look like this: s3://bucketname/exampleprefix/
          • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {“prefix”: “s3://customer_bucket/some/prefix/”}, “relative/path/to/custdata-1”, “relative/path/custdata-2”, … “relative/path/custdata-N” ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 … s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
          Your input bucket must be located in same Amazon Web Services region as your training job.
    • input_mode
      Type: STRING
      Provider name: InputMode
      Description: (Optional) The input mode to use for the data channel in a training job. If you don’t set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job’s general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode. To use a model for incremental training, choose File input model.
    • record_wrapper_type
      Type: STRING
      Provider name: RecordWrapperType
      Description: Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don’t need to set this attribute. For more information, see Create a Dataset Using RecordIO. In File mode, leave this field unset or set it to None.
    • shuffle_config
      Type: STRUCT
      Provider name: ShuffleConfig
      Description: A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value. For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
      • seed
        Type: INT64
        Provider name: Seed
        Description: Determines the shuffling order in ShuffleConfig value.
  • output_data_config
    Type: STRUCT
    Provider name: OutputDataConfig
    Description: Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
    • compression_type
      Type: STRING
      Provider name: CompressionType
      Description: The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
    • kms_key_id
      Type: STRING
      Provider name: KmsKeyId
      Description: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be 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”
      • // KMS Key Alias “alias/ExampleAlias”
      • // Amazon Resource Name (ARN) of a KMS Key Alias “arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias”
      If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don’t provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role’s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
    • s3_output_path
      Type: STRING
      Provider name: S3OutputPath
      Description: Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
  • resource_config
    Type: STRUCT
    Provider name: ResourceConfig
    Description: 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.
    • instance_count
      Type: INT32
      Provider name: InstanceCount
      Description: The number of ML compute instances to use. For distributed training, provide a value greater than 1.
    • instance_groups
      Type: UNORDERED_LIST_STRUCT
      Provider name: InstanceGroups
      Description: The configuration of a heterogeneous cluster in JSON format.
      • instance_count
        Type: INT32
        Provider name: InstanceCount
        Description: Specifies the number of instances of the instance group.
      • instance_group_name
        Type: STRING
        Provider name: InstanceGroupName
        Description: Specifies the name of the instance group.
      • instance_type
        Type: STRING
        Provider name: InstanceType
        Description: Specifies the instance type of the instance group.
    • instance_type
      Type: STRING
      Provider name: InstanceType
      Description: 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.
    • keep_alive_period_in_seconds
      Type: INT32
      Provider name: KeepAlivePeriodInSeconds
      Description: The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
    • training_plan_arn
      Type: STRING
      Provider name: TrainingPlanArn
      Description: The Amazon Resource Name (ARN); of the training plan to use for this resource configuration.
    • volume_kms_key_id
      Type: STRING
      Provider name: VolumeKmsKeyId
      Description: 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”
    • volume_size_in_gb
      Type: INT32
      Provider name: VolumeSizeInGB
      Description: 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.
  • retry_strategy
    Type: STRUCT
    Provider name: RetryStrategy
    Description: The number of times to retry the job when the job fails due to an InternalServerError.
    • maximum_retry_attempts
      Type: INT32
      Provider name: MaximumRetryAttempts
      Description: The number of times to retry the job. When the job is retried, it’s SecondaryStatus is changed to STARTING.
  • role_arn
    Type: STRING
    Provider name: RoleArn
    Description: The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
  • static_hyper_parameters
    Type: MAP_STRING_STRING
    Provider name: StaticHyperParameters
    Description: Specifies the values of hyperparameters that do not change for the tuning job.
  • stopping_condition
    Type: STRUCT
    Provider name: StoppingCondition
    Description: 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.
    • max_pending_time_in_seconds
      Type: INT32
      Provider name: MaxPendingTimeInSeconds
      Description: The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped. When working with training jobs that use capacity from training plans, not all Pending job states count against the MaxPendingTimeInSeconds limit. The following scenarios do not increment the MaxPendingTimeInSeconds counter:
      • The plan is in a Scheduled state: Jobs queued (in Pending status) before a plan’s start date (waiting for scheduled start time)
      • Between capacity reservations: Jobs temporarily back to Pending status between two capacity reservation periods
      MaxPendingTimeInSeconds only increments when jobs are actively waiting for capacity in an Active plan.
    • max_runtime_in_seconds
      Type: INT32
      Provider name: MaxRuntimeInSeconds
      Description: The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
    • max_wait_time_in_seconds
      Type: INT32
      Provider name: MaxWaitTimeInSeconds
      Description: The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
  • tuning_objective
    Type: STRUCT
    Provider name: TuningObjective
    • metric_name
      Type: STRING
      Provider name: MetricName
      Description: The name of the metric to use for the objective metric.
    • type
      Type: STRING
      Provider name: Type
      Description: Whether to minimize or maximize the objective metric.
  • vpc_config
    Type: STRUCT
    Provider name: VpcConfig
    Description: 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.
    • security_group_ids
      Type: UNORDERED_LIST_STRING
      Provider name: SecurityGroupIds
      Description: The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
    • subnets
      Type: UNORDERED_LIST_STRING
      Provider name: Subnets
      Description: The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

training_job_definitions

Type: UNORDERED_LIST_STRUCT
Provider name: TrainingJobDefinitions
Description: A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

  • algorithm_specification
    Type: STRUCT
    Provider name: AlgorithmSpecification
    Description: The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
    • algorithm_name
      Type: STRING
      Provider name: AlgorithmName
      Description: The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.
    • metric_definitions
      Type: UNORDERED_LIST_STRUCT
      Provider name: MetricDefinitions
      Description: An array of MetricDefinition objects that specify the metrics that the algorithm emits.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the metric.
      • regex
        Type: STRING
        Provider name: Regex
        Description: A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
    • training_image
      Type: STRING
      Provider name: TrainingImage
      Description: The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    • training_input_mode
      Type: STRING
      Provider name: TrainingInputMode
  • checkpoint_config
    Type: STRUCT
    Provider name: CheckpointConfig
    • local_path
      Type: STRING
      Provider name: LocalPath
      Description: (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
    • s3_uri
      Type: STRING
      Provider name: S3Uri
      Description: Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
  • definition_name
    Type: STRING
    Provider name: DefinitionName
    Description: The job definition name.
  • enable_inter_container_traffic_encryption
    Type: BOOLEAN
    Provider name: EnableInterContainerTrafficEncryption
    Description: 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.
  • enable_managed_spot_training
    Type: BOOLEAN
    Provider name: EnableManagedSpotTraining
    Description: A Boolean indicating whether managed spot training is enabled (True) or not (False).
  • enable_network_isolation
    Type: BOOLEAN
    Provider name: EnableNetworkIsolation
    Description: 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.
  • environment
    Type: MAP_STRING_STRING
    Provider name: Environment
    Description: 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.
  • hyper_parameter_ranges
    Type: STRUCT
    Provider name: HyperParameterRanges
    • auto_parameters
      Type: UNORDERED_LIST_STRUCT
      Provider name: AutoParameters
      Description: A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the hyperparameter to optimize using Autotune.
      • value_hint
        Type: STRING
        Provider name: ValueHint
        Description: An example value of the hyperparameter to optimize using Autotune.
    • categorical_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: CategoricalParameterRanges
      Description: The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the categorical hyperparameter to tune.
      • values
        Type: UNORDERED_LIST_STRING
        Provider name: Values
        Description: A list of the categories for the hyperparameter.
    • continuous_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: ContinuousParameterRanges
      Description: The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
      • max_value
        Type: STRING
        Provider name: MaxValue
        Description: The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
      • min_value
        Type: STRING
        Provider name: MinValue
        Description: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the continuous hyperparameter to tune.
      • scaling_type
        Type: STRING
        Provider name: ScalingType
        Description: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
        Auto
        SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
        Linear
        Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
        Logarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.
        ReverseLogarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

    • integer_parameter_ranges
      Type: UNORDERED_LIST_STRUCT
      Provider name: IntegerParameterRanges
      Description: The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
      • max_value
        Type: STRING
        Provider name: MaxValue
        Description: The maximum value of the hyperparameter to search.
      • min_value
        Type: STRING
        Provider name: MinValue
        Description: The minimum value of the hyperparameter to search.
      • name
        Type: STRING
        Provider name: Name
        Description: The name of the hyperparameter to search.
      • scaling_type
        Type: STRING
        Provider name: ScalingType
        Description: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
        Auto
        SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
        Linear
        Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
        Logarithmic
        Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.

  • hyper_parameter_tuning_resource_config
    Type: STRUCT
    Provider name: HyperParameterTuningResourceConfig
    Description: 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).
    • allocation_strategy
      Type: STRING
      Provider name: AllocationStrategy
      Description: The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
    • instance_configs
      Type: UNORDERED_LIST_STRUCT
      Provider name: InstanceConfigs
      Description: 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. 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.
      • instance_count
        Type: INT32
        Provider name: InstanceCount
        Description: The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
      • instance_type
        Type: STRING
        Provider name: InstanceType
        Description: The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
      • volume_size_in_gb
        Type: INT32
        Provider name: VolumeSizeInGB
        Description: The volume size in GB of the data to be processed for hyperparameter optimization (optional).
    • instance_count
      Type: INT32
      Provider name: InstanceCount
      Description: The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.
    • instance_type
      Type: STRING
      Provider name: InstanceType
      Description: The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
    • volume_kms_key_id
      Type: STRING
      Provider name: VolumeKmsKeyId
      Description: A key used by Amazon Web Services 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 Amazon Web Services Key Management Service, see KMS encryption for more information.
    • volume_size_in_gb
      Type: INT32
      Provider name: VolumeSizeInGB
      Description: 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. SageMaker supports only the General Purpose SSD (gp2) storage volume type.
  • input_data_config
    Type: UNORDERED_LIST_STRUCT
    Provider name: InputDataConfig
    Description: An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    • channel_name
      Type: STRING
      Provider name: ChannelName
      Description: The name of the channel.
    • compression_type
      Type: STRING
      Provider name: CompressionType
      Description: If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    • content_type
      Type: STRING
      Provider name: ContentType
      Description: The MIME type of the data.
    • data_source
      Type: STRUCT
      Provider name: DataSource
      Description: The location of the channel data.
      • file_system_data_source
        Type: STRUCT
        Provider name: FileSystemDataSource
        Description: The file system that is associated with a channel.
        • directory_path
          Type: STRING
          Provider name: DirectoryPath
          Description: The full path to the directory to associate with the channel.
        • file_system_access_mode
          Type: STRING
          Provider name: FileSystemAccessMode
          Description: The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
        • file_system_id
          Type: STRING
          Provider name: FileSystemId
          Description: The file system id.
        • file_system_type
          Type: STRING
          Provider name: FileSystemType
          Description: The file system type.
      • s3_data_source
        Type: STRUCT
        Provider name: S3DataSource
        Description: The S3 location of the data source that is associated with a channel.
        • attribute_names
          Type: UNORDERED_LIST_STRING
          Provider name: AttributeNames
          Description: A list of one or more attribute names to use that are found in a specified augmented manifest file.
        • hub_access_config
          Type: STRUCT
          Provider name: HubAccessConfig
          Description: The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model.
          • hub_content_arn
            Type: STRING
            Provider name: HubContentArn
            Description: The ARN of your private model hub content. This should be a ModelReference resource type that points to a SageMaker JumpStart public hub model.
        • instance_group_names
          Type: UNORDERED_LIST_STRING
          Provider name: InstanceGroupNames
          Description: A list of names of instance groups that get data from the S3 data source.
        • model_access_config
          Type: STRUCT
          Provider name: ModelAccessConfig
          • accept_eula
            Type: BOOLEAN
            Provider name: AcceptEula
            Description: Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
        • s3_data_distribution_type
          Type: STRING
          Provider name: S3DataDistributionType
          Description: If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated. If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don’t choose more ML compute instances for training than available S3 objects. If you do, some nodes won’t get any data and you will pay for nodes that aren’t getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.
        • s3_data_type
          Type: STRING
          Provider name: S3DataType
          Description: If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel’s input mode is Pipe.
        • s3_uri
          Type: STRING
          Provider name: S3Uri
          Description: Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
          • A key name prefix might look like this: s3://bucketname/exampleprefix/
          • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {“prefix”: “s3://customer_bucket/some/prefix/”}, “relative/path/to/custdata-1”, “relative/path/custdata-2”, … “relative/path/custdata-N” ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 … s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
          Your input bucket must be located in same Amazon Web Services region as your training job.
    • input_mode
      Type: STRING
      Provider name: InputMode
      Description: (Optional) The input mode to use for the data channel in a training job. If you don’t set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job’s general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode. To use a model for incremental training, choose File input model.
    • record_wrapper_type
      Type: STRING
      Provider name: RecordWrapperType
      Description: Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don’t need to set this attribute. For more information, see Create a Dataset Using RecordIO. In File mode, leave this field unset or set it to None.
    • shuffle_config
      Type: STRUCT
      Provider name: ShuffleConfig
      Description: A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value. For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
      • seed
        Type: INT64
        Provider name: Seed
        Description: Determines the shuffling order in ShuffleConfig value.
  • output_data_config
    Type: STRUCT
    Provider name: OutputDataConfig
    Description: Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
    • compression_type
      Type: STRING
      Provider name: CompressionType
      Description: The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
    • kms_key_id
      Type: STRING
      Provider name: KmsKeyId
      Description: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be 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”
      • // KMS Key Alias “alias/ExampleAlias”
      • // Amazon Resource Name (ARN) of a KMS Key Alias “arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias”
      If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don’t provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role’s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
    • s3_output_path
      Type: STRING
      Provider name: S3OutputPath
      Description: Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
  • resource_config
    Type: STRUCT
    Provider name: ResourceConfig
    Description: 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.
    • instance_count
      Type: INT32
      Provider name: InstanceCount
      Description: The number of ML compute instances to use. For distributed training, provide a value greater than 1.
    • instance_groups
      Type: UNORDERED_LIST_STRUCT
      Provider name: InstanceGroups
      Description: The configuration of a heterogeneous cluster in JSON format.
      • instance_count
        Type: INT32
        Provider name: InstanceCount
        Description: Specifies the number of instances of the instance group.
      • instance_group_name
        Type: STRING
        Provider name: InstanceGroupName
        Description: Specifies the name of the instance group.
      • instance_type
        Type: STRING
        Provider name: InstanceType
        Description: Specifies the instance type of the instance group.
    • instance_type
      Type: STRING
      Provider name: InstanceType
      Description: 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.
    • keep_alive_period_in_seconds
      Type: INT32
      Provider name: KeepAlivePeriodInSeconds
      Description: The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
    • training_plan_arn
      Type: STRING
      Provider name: TrainingPlanArn
      Description: The Amazon Resource Name (ARN); of the training plan to use for this resource configuration.
    • volume_kms_key_id
      Type: STRING
      Provider name: VolumeKmsKeyId
      Description: 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”
    • volume_size_in_gb
      Type: INT32
      Provider name: VolumeSizeInGB
      Description: 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.
  • retry_strategy
    Type: STRUCT
    Provider name: RetryStrategy
    Description: The number of times to retry the job when the job fails due to an InternalServerError.
    • maximum_retry_attempts
      Type: INT32
      Provider name: MaximumRetryAttempts
      Description: The number of times to retry the job. When the job is retried, it’s SecondaryStatus is changed to STARTING.
  • role_arn
    Type: STRING
    Provider name: RoleArn
    Description: The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
  • static_hyper_parameters
    Type: MAP_STRING_STRING
    Provider name: StaticHyperParameters
    Description: Specifies the values of hyperparameters that do not change for the tuning job.
  • stopping_condition
    Type: STRUCT
    Provider name: StoppingCondition
    Description: 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.
    • max_pending_time_in_seconds
      Type: INT32
      Provider name: MaxPendingTimeInSeconds
      Description: The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped. When working with training jobs that use capacity from training plans, not all Pending job states count against the MaxPendingTimeInSeconds limit. The following scenarios do not increment the MaxPendingTimeInSeconds counter:
      • The plan is in a Scheduled state: Jobs queued (in Pending status) before a plan’s start date (waiting for scheduled start time)
      • Between capacity reservations: Jobs temporarily back to Pending status between two capacity reservation periods
      MaxPendingTimeInSeconds only increments when jobs are actively waiting for capacity in an Active plan.
    • max_runtime_in_seconds
      Type: INT32
      Provider name: MaxRuntimeInSeconds
      Description: The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
    • max_wait_time_in_seconds
      Type: INT32
      Provider name: MaxWaitTimeInSeconds
      Description: The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
  • tuning_objective
    Type: STRUCT
    Provider name: TuningObjective
    • metric_name
      Type: STRING
      Provider name: MetricName
      Description: The name of the metric to use for the objective metric.
    • type
      Type: STRING
      Provider name: Type
      Description: Whether to minimize or maximize the objective metric.
  • vpc_config
    Type: STRUCT
    Provider name: VpcConfig
    Description: 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.
    • security_group_ids
      Type: UNORDERED_LIST_STRING
      Provider name: SecurityGroupIds
      Description: The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
    • subnets
      Type: UNORDERED_LIST_STRING
      Provider name: Subnets
      Description: The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

training_job_status_counters

Type: STRUCT
Provider name: TrainingJobStatusCounters
Description: The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

  • completed
    Type: INT32
    Provider name: Completed
    Description: The number of completed training jobs launched by the hyperparameter tuning job.
  • in_progress
    Type: INT32
    Provider name: InProgress
    Description: The number of in-progress training jobs launched by a hyperparameter tuning job.
  • non_retryable_error
    Type: INT32
    Provider name: NonRetryableError
    Description: The number of training jobs that failed and can’t be retried. A failed training job can’t be retried if it failed because a client error occurred.
  • retryable_error
    Type: INT32
    Provider name: RetryableError
    Description: The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
  • stopped
    Type: INT32
    Provider name: Stopped
    Description: The number of training jobs launched by a hyperparameter tuning job that were manually stopped.

tuning_job_completion_details

Type: STRUCT
Provider name: TuningJobCompletionDetails
Description: Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

  • convergence_detected_time
    Type: TIMESTAMP
    Provider name: ConvergenceDetectedTime
    Description: The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
  • number_of_training_jobs_objective_not_improving
    Type: INT32
    Provider name: NumberOfTrainingJobsObjectiveNotImproving
    Description: The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.

warm_start_config

Type: STRUCT
Provider name: WarmStartConfig
Description: The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

  • parent_hyper_parameter_tuning_jobs
    Type: UNORDERED_LIST_STRUCT
    Provider name: ParentHyperParameterTuningJobs
    Description: An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point. Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
    • hyper_parameter_tuning_job_name
      Type: STRING
      Provider name: HyperParameterTuningJobName
      Description: The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
  • warm_start_type
    Type: STRING
    Provider name: WarmStartType
    Description: Specifies one of the following:
    IDENTICAL_DATA_AND_ALGORITHM
    The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
    TRANSFER_LEARNING
    The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.