SageMaker HyperParameter Tuning Job

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SageMaker HyperParameter Tuning Job is an AWS resource that automatically searches for the best set of hyperparameters for machine learning models. It runs multiple training jobs with different parameter combinations, evaluates their performance, and selects the most optimal configuration. This helps improve model accuracy and efficiency without requiring manual experimentation.

aws.sagemaker_hyper_parameter_tuning_job

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
autotune	core	json	A flag to indicate if autotune is enabled for the hyperparameter tuning job.
best_training_job	core	json	A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
consumed_resources	core	json	The total resources consumed by your hyperparameter tuning job.
creation_time	core	timestamp	The date and time that the tuning job started.
failure_reason	core	string	If the tuning job failed, the reason it failed.
hyper_parameter_tuning_end_time	core	timestamp	The date and time that the tuning job ended.
hyper_parameter_tuning_job_arn	core	string	The Amazon Resource Name (ARN) of the tuning job.
hyper_parameter_tuning_job_config	core	json	The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
hyper_parameter_tuning_job_name	core	string	The name of the hyperparameter tuning job.
hyper_parameter_tuning_job_status	core	string	The status of the tuning job.
last_modified_time	core	timestamp	The date and time that the status of the tuning job was modified.
objective_status_counters	core	json	The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
overall_best_training_job	core	json	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.
tags	core	hstore
training_job_definition	core	json	The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
training_job_definitions	core	json	A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
training_job_status_counters	core	json	The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
tuning_job_completion_details	core	json	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.
warm_start_config	core	json	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.