ListHyperParameterTuningJobs

class SageMaker.Paginator.ListHyperParameterTuningJobs

paginator = client.get_paginator('list_hyper_parameter_tuning_jobs')

paginate(**kwargs)

Creates an iterator that will paginate through responses from SageMaker.Client.list_hyper_parameter_tuning_jobs().

See also: AWS API Documentation

Request Syntax

response_iterator = paginator.paginate(
    SortBy='Name'|'Status'|'CreationTime',
    SortOrder='Ascending'|'Descending',
    NameContains='string',
    CreationTimeAfter=datetime(2015, 1, 1),
    CreationTimeBefore=datetime(2015, 1, 1),
    LastModifiedTimeAfter=datetime(2015, 1, 1),
    LastModifiedTimeBefore=datetime(2015, 1, 1),
    StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
    PaginationConfig={
        'MaxItems': 123,
        'PageSize': 123,
        'StartingToken': 'string'
    }
)

Parameters

• SortBy (string) -- The field to sort results by. The default is Name .
• SortOrder (string) -- The sort order for results. The default is Ascending .
• NameContains (string) -- A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
• CreationTimeAfter (datetime) -- A filter that returns only tuning jobs that were created after the specified time.
• CreationTimeBefore (datetime) -- A filter that returns only tuning jobs that were created before the specified time.
• LastModifiedTimeAfter (datetime) -- A filter that returns only tuning jobs that were modified after the specified time.
• LastModifiedTimeBefore (datetime) -- A filter that returns only tuning jobs that were modified before the specified time.
• StatusEquals (string) -- A filter that returns only tuning jobs with the specified status.
• PaginationConfig (dict) --

  A dictionary that provides parameters to control pagination.

  • MaxItems (integer) --

    The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.

  • PageSize (integer) --

    The size of each page.

  • StartingToken (string) --

    A token to specify where to start paginating. This is the NextToken from a previous response.

Return type

dict

Returns

Response Syntax

{
    'HyperParameterTuningJobSummaries': [
        {
            'HyperParameterTuningJobName': 'string',
            'HyperParameterTuningJobArn': 'string',
            'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
            'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
            'CreationTime': datetime(2015, 1, 1),
            'HyperParameterTuningEndTime': datetime(2015, 1, 1),
            'LastModifiedTime': datetime(2015, 1, 1),
            'TrainingJobStatusCounters': {
                'Completed': 123,
                'InProgress': 123,
                'RetryableError': 123,
                'NonRetryableError': 123,
                'Stopped': 123
            },
            'ObjectiveStatusCounters': {
                'Succeeded': 123,
                'Pending': 123,
                'Failed': 123
            },
            'ResourceLimits': {
                'MaxNumberOfTrainingJobs': 123,
                'MaxParallelTrainingJobs': 123,
                'MaxRuntimeInSeconds': 123
            }
        },
    ],

}

Response Structure

• (dict) --

  • HyperParameterTuningJobSummaries (list) --

    A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.

    • (dict) --

      Provides summary information about a hyperparameter tuning job.

      • HyperParameterTuningJobName (string) --

        The name of the tuning job.

      • HyperParameterTuningJobArn (string) --

        The Amazon Resource Name (ARN) of the tuning job.

      • HyperParameterTuningJobStatus (string) --

        The status of the tuning job.

      • Strategy (string) --

        Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration.

      • CreationTime (datetime) --

        The date and time that the tuning job was created.

      • HyperParameterTuningEndTime (datetime) --

        The date and time that the tuning job ended.

      • LastModifiedTime (datetime) --

        The date and time that the tuning job was modified.

      • TrainingJobStatusCounters (dict) --

        The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.

        • Completed (integer) --

          The number of completed training jobs launched by the hyperparameter tuning job.

        • InProgress (integer) --

          The number of in-progress training jobs launched by a hyperparameter tuning job.

        • RetryableError (integer) --

          The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.

        • NonRetryableError (integer) --

          The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.

        • Stopped (integer) --

          The number of training jobs launched by a hyperparameter tuning job that were manually stopped.

      • ObjectiveStatusCounters (dict) --

        The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.

        • Succeeded (integer) --

          The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

        • Pending (integer) --

          The number of training jobs that are in progress and pending evaluation of their final objective metric.

        • Failed (integer) --

          The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

      • ResourceLimits (dict) --

        The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.

        • MaxNumberOfTrainingJobs (integer) --

          The maximum number of training jobs that a hyperparameter tuning job can launch.

        • MaxParallelTrainingJobs (integer) --

          The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

        • MaxRuntimeInSeconds (integer) --

          The maximum time in seconds that a hyperparameter tuning job can run.