class Aws::SageMaker::Types::HyperParameterTuningJobConfig
Configures a hyperparameter tuning job.
@note When making an API call, you may pass HyperParameterTuningJobConfig
data as a hash: { strategy: "Bayesian", # required, accepts Bayesian, Random hyper_parameter_tuning_job_objective: { type: "Maximize", # required, accepts Maximize, Minimize metric_name: "MetricName", # required }, resource_limits: { # required max_number_of_training_jobs: 1, # required max_parallel_training_jobs: 1, # required }, parameter_ranges: { integer_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], continuous_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], categorical_parameter_ranges: [ { name: "ParameterKey", # required values: ["ParameterValue"], # required }, ], }, training_job_early_stopping_type: "Off", # accepts Off, Auto tuning_job_completion_criteria: { target_objective_metric_value: 1.0, # required }, }
@!attribute [rw] strategy
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to `Bayesian`. To randomly search, set it to `Random`. For information about search strategies, see [How Hyperparameter Tuning Works][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html @return [String]
@!attribute [rw] hyper_parameter_tuning_job_objective
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job. @return [Types::HyperParameterTuningJobObjective]
@!attribute [rw] resource_limits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job. @return [Types::ResourceLimits]
@!attribute [rw] parameter_ranges
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches. @return [Types::ParameterRanges]
@!attribute [rw] training_job_early_stopping_type
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is `OFF`): OFF : Training jobs launched by the hyperparameter tuning job do not use early stopping. AUTO : Amazon 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 [Stop Training Jobs Early][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html @return [String]
@!attribute [rw] tuning_job_completion_criteria
The tuning job's completion criteria. @return [Types::TuningJobCompletionCriteria]
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/HyperParameterTuningJobConfig AWS API Documentation
Constants
- SENSITIVE