class Aws::SageMaker::Types::HyperParameterTuningJobWarmStartConfig

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter 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, and the training job that performs the best 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.

<note markdown=“1”> All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

</note>

@note When making an API call, you may pass HyperParameterTuningJobWarmStartConfig

data as a hash:

    {
      parent_hyper_parameter_tuning_jobs: [ # required
        {
          hyper_parameter_tuning_job_name: "HyperParameterTuningJobName",
        },
      ],
      warm_start_type: "IdenticalDataAndAlgorithm", # required, accepts IdenticalDataAndAlgorithm, TransferLearning
    }

@!attribute [rw] parent_hyper_parameter_tuning_jobs

An array of hyperparameter tuning jobs that are used as the starting
point for the new hyperparameter tuning job. For more information
about warm starting a hyperparameter tuning job, see [Using a
Previous Hyperparameter Tuning Job as a Starting Point][1].

Hyperparameter tuning jobs created before October 1, 2018 cannot be
used as parent jobs for warm start tuning jobs.

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html
@return [Array<Types::ParentHyperParameterTuningJob>]

@!attribute [rw] warm_start_type

Specifies one of the following:

IDENTICAL\_DATA\_AND\_ALGORITHM

: The new hyperparameter tuning job uses the same input data and
  training image as the parent tuning jobs. You can change the
  hyperparameter ranges to search and the maximum number of training
  jobs that the hyperparameter tuning job launches. You cannot use a
  new version of the training algorithm, unless the changes in the
  new version do not affect the algorithm itself. For example,
  changes that improve logging or adding support for a different
  data format are allowed. You can also change hyperparameters from
  tunable to static, and from static to tunable, but the total
  number of static plus tunable hyperparameters must remain the same
  as it is in all parent jobs. The objective metric for the new
  tuning job must be the same as for all parent jobs.

TRANSFER\_LEARNING

: The new hyperparameter tuning job can include input data,
  hyperparameter ranges, maximum number of concurrent training jobs,
  and maximum number of training jobs that are different than those
  of its parent hyperparameter tuning jobs. The training image can
  also be a different version from the version used in the parent
  hyperparameter tuning job. You can also change hyperparameters
  from tunable to static, and from static to tunable, but the total
  number of static plus tunable hyperparameters must remain the same
  as it is in all parent jobs. The objective metric for the new
  tuning job must be the same as for all parent jobs.
@return [String]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/HyperParameterTuningJobWarmStartConfig AWS API Documentation

Constants

SENSITIVE