The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html">HyperParameterTuningJobConfig</a> object that specifies the configuration of the tuning job.

The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a> object that specifies the definition of the training jobs that this tuning job launches.

A list of the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a> objects launched for this tuning job.

The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobStatusCounters.html">TrainingJobStatusCounters</a> object that specifies the number of training jobs, categorized by status, that this tuning job launched.

The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ObjectiveStatusCounters.html">ObjectiveStatusCounters</a> object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobSummary.html">TrainingJobSummary</a> object that describes the training job that completed with the best current <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html">HyperParameterTuningJobObjective</a>.

If the hyperparameter tuning job is an warm start tuning job with a <code>WarmStartType</code> of <code>IDENTICAL_DATA_AND_ALGORITHM</code>, this is the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobSummary.html">TrainingJobSummary</a> 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.

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.

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.

The total resources consumed by your hyperparameter tuning job.