Smaws_Client_SageMaker.CreateHyperParameterTuningJobStarts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..
val error_to_string :
[ Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded ] ->
stringval request :
'http_type Smaws_Lib.Context.t ->
Types.create_hyper_parameter_tuning_job_request ->
(Types.create_hyper_parameter_tuning_job_response,
[> Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded ])
Stdlib.resultval request_with_metadata :
'http_type Smaws_Lib.Context.t ->
Types.create_hyper_parameter_tuning_job_request ->
(Types.create_hyper_parameter_tuning_job_response Smaws_Lib.Response.t,
[> Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded ]
* Smaws_Lib.Response.metadata)
Stdlib.result