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 trial and error.

aws.sagemaker_optimization_job

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
creation_time	core	timestamp	The time when you created the optimization job.
deployment_instance_type	core	string	The type of instance that hosts the optimized model that you create with the optimization job.
failure_reason	core	string	If the optimization job status is FAILED, the reason for the failure.
last_modified_time	core	timestamp	The time when the optimization job was last updated.
model_source	core	json	The location of the source model to optimize with an optimization job.
optimization_configs	core	json	Settings for each of the optimization techniques that the job applies.
optimization_end_time	core	timestamp	The time when the optimization job finished processing.
optimization_environment	core	hstore	The environment variables to set in the model container.
optimization_job_arn	core	string	The Amazon Resource Name (ARN) of the optimization job.
optimization_job_name	core	string	The name that you assigned to the optimization job.
optimization_job_status	core	string	The current status of the optimization job.
optimization_output	core	json	Output values produced by an optimization job.
optimization_start_time	core	timestamp	The time when the optimization job started.
output_config	core	json	Details for where to store the optimized model that you create with the optimization job.
role_arn	core	string	The ARN of the IAM role that you assigned to the optimization job.
stopping_condition	core	json	Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs. To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost. The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel. The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
tags	core	hstore
vpc_config	core	json	A VPC in Amazon VPC that your optimized model has access to.