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Class: Aws::SageMaker::Types::HyperParameterTuningJobConfig
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::HyperParameterTuningJobConfig
- Defined in:
- (unknown)
Overview
When passing HyperParameterTuningJobConfig as input to an Aws::Client method, you can use a vanilla 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
},
}
Configures a hyperparameter tuning job.
Returned by:
Instance Attribute Summary collapse
-
#hyper_parameter_tuning_job_objective ⇒ Types::HyperParameterTuningJobObjective
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
-
#parameter_ranges ⇒ Types::ParameterRanges
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
-
#resource_limits ⇒ Types::ResourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
-
#strategy ⇒ String
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches.
-
#training_job_early_stopping_type ⇒ String
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.
-
#tuning_job_completion_criteria ⇒ Types::TuningJobCompletionCriteria
The tuning job\'s completion criteria.
Instance Attribute Details
#hyper_parameter_tuning_job_objective ⇒ Types::HyperParameterTuningJobObjective
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
#parameter_ranges ⇒ Types::ParameterRanges
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
#resource_limits ⇒ Types::ResourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
#strategy ⇒ String
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.
#training_job_early_stopping_type ⇒ String
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.
#tuning_job_completion_criteria ⇒ Types::TuningJobCompletionCriteria
The tuning job\'s completion criteria.