createHyperParameterTuningJob method
- required HyperParameterTuningJobConfig hyperParameterTuningJobConfig,
- required String hyperParameterTuningJobName,
- List<
Tag> ? tags, - HyperParameterTrainingJobDefinition? trainingJobDefinition,
- List<
HyperParameterTrainingJobDefinition> ? trainingJobDefinitions, - HyperParameterTuningJobWarmStartConfig? warmStartConfig,
Starts 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.
May throw ResourceInUse. May throw ResourceLimitExceeded.
Parameter hyperParameterTuningJobConfig
:
The HyperParameterTuningJobConfig object that describes the tuning
job, including the search strategy, the objective metric used to evaluate
training jobs, ranges of parameters to search, and resource limits for the
tuning job. For more information, see How
Hyperparameter Tuning Works.
Parameter hyperParameterTuningJobName
:
The name of the tuning job. This name is the prefix for the names of all
training jobs that this tuning job launches. The name must be unique
within the same AWS account and AWS Region. The name must have 1 to 32
characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % -
(hyphen). The name is not case sensitive.
Parameter tags
:
An array of key-value pairs. You can use tags to categorize your AWS
resources in different ways, for example, by purpose, owner, or
environment. For more information, see Tagging
AWS Resources.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
Parameter trainingJobDefinition
:
The HyperParameterTrainingJobDefinition object that describes the
training jobs that this tuning job launches, including static
hyperparameters, input data configuration, output data configuration,
resource configuration, and stopping condition.
Parameter trainingJobDefinitions
:
A list of the HyperParameterTrainingJobDefinition objects launched
for this tuning job.
Parameter warmStartConfig
:
Specifies the configuration for starting the hyperparameter 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.
All training jobs launched by the new hyperparameter tuning job are
evaluated by using the objective metric. If you specify
IDENTICAL_DATA_AND_ALGORITHM
as the
WarmStartType
value for the warm start configuration, the
training job that performs the best in the new tuning job is compared to
the best training jobs from the parent tuning jobs. From these, the
training job that performs the best as measured by the objective metric is
returned as the overall best training job.
Implementation
Future<CreateHyperParameterTuningJobResponse> createHyperParameterTuningJob({
required HyperParameterTuningJobConfig hyperParameterTuningJobConfig,
required String hyperParameterTuningJobName,
List<Tag>? tags,
HyperParameterTrainingJobDefinition? trainingJobDefinition,
List<HyperParameterTrainingJobDefinition>? trainingJobDefinitions,
HyperParameterTuningJobWarmStartConfig? warmStartConfig,
}) async {
ArgumentError.checkNotNull(
hyperParameterTuningJobConfig, 'hyperParameterTuningJobConfig');
ArgumentError.checkNotNull(
hyperParameterTuningJobName, 'hyperParameterTuningJobName');
_s.validateStringLength(
'hyperParameterTuningJobName',
hyperParameterTuningJobName,
1,
32,
isRequired: true,
);
final headers = <String, String>{
'Content-Type': 'application/x-amz-json-1.1',
'X-Amz-Target': 'SageMaker.CreateHyperParameterTuningJob'
};
final jsonResponse = await _protocol.send(
method: 'POST',
requestUri: '/',
exceptionFnMap: _exceptionFns,
// TODO queryParams
headers: headers,
payload: {
'HyperParameterTuningJobConfig': hyperParameterTuningJobConfig,
'HyperParameterTuningJobName': hyperParameterTuningJobName,
if (tags != null) 'Tags': tags,
if (trainingJobDefinition != null)
'TrainingJobDefinition': trainingJobDefinition,
if (trainingJobDefinitions != null)
'TrainingJobDefinitions': trainingJobDefinitions,
if (warmStartConfig != null) 'WarmStartConfig': warmStartConfig,
},
);
return CreateHyperParameterTuningJobResponse.fromJson(jsonResponse.body);
}