HyperParameterTuningJobConfig ¶

class HyperParameterTuningJobConfig(TypedDict, closed=True):
    strategy: NotRequired[
        "capo_sagemaker.types.hyper_parameter_tuning_job_strategy_type.HyperParameterTuningJobStrategyType"
    ]
    """<p>Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How Hyperparameter Tuning Works</a>.</p>"""
    strategy_config: NotRequired[
        "capo_sagemaker.types.hyper_parameter_tuning_job_strategy_config.HyperParameterTuningJobStrategyConfig"
    ]
    """<p>The configuration for the <code>Hyperband</code> optimization strategy. This parameter should be provided only if <code>Hyperband</code> is selected as the strategy for <code>HyperParameterTuningJobConfig</code>.</p>"""
    hyper_parameter_tuning_job_objective: NotRequired[
        "capo_sagemaker.types.hyper_parameter_tuning_job_objective.HyperParameterTuningJobObjective"
    ]
    """<p>The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html">HyperParameterTuningJobObjective</a> specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.</p>"""
    resource_limits: NotRequired["capo_sagemaker.types.resource_limits.ResourceLimits"]
    """<p>The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html">ResourceLimits</a> object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.</p>"""
    parameter_ranges: NotRequired[
        "capo_sagemaker.types.parameter_ranges.ParameterRanges"
    ]
    """<p>The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ParameterRanges.html">ParameterRanges</a> 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. </p>"""
    training_job_early_stopping_type: NotRequired[
        "capo_sagemaker.types.training_job_early_stopping_type.TrainingJobEarlyStoppingType"
    ]
    """<p>Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the <code>Hyperband</code> strategy has its own advanced internal early stopping mechanism, <code>TrainingJobEarlyStoppingType</code> must be <code>OFF</code> to use <code>Hyperband</code>. This parameter can take on one of the following values (the default value is <code>OFF</code>):</p> <dl> <dt>OFF</dt> <dd> <p>Training jobs launched by the hyperparameter tuning job do not use early stopping.</p> </dd> <dt>AUTO</dt> <dd> <p>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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html">Stop Training Jobs Early</a>.</p> </dd> </dl>"""
    tuning_job_completion_criteria: NotRequired[
        "capo_sagemaker.types.tuning_job_completion_criteria.TuningJobCompletionCriteria"
    ]
    """<p>The tuning job's completion criteria.</p>"""
    random_seed: NotRequired["capo_sagemaker.types.random_seed.RandomSeed"]
    """<p>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.</p>"""