Amazon SageMaker
Developer Guide

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Use an Algorithm to Run 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 and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose. For more information, see Automatic Model Tuning.

You can create use an algorithm resource to create a hyperparameter tuning job by using the Amazon SageMaker console, the low-level Amazon SageMaker API, or the Amazon SageMaker Python SDK.

Use an Algorithm to Run a Hyperparameter Tuning Job (Console)

To use an algorithm to run a hyperparameter tuning job (console)

  1. Open the Amazon SageMaker console at https://console.aws.amazon.com/sagemaker/.

  2. Choose Algorithms.

  3. Choose an algorithm that you created from the list on the My algorithms tab or choose an algorithm that you subscribed to on the AWS Marketplace subscriptions tab.

  4. Choose Create hyperparameter tuning job.

    The algorithm you chose will automatically be selected.

  5. On the Create hyperparameter tuning job page, provide the following information:

    1. For Warm start, choose Enable warm start to use the information from previous hyperparameter tuning jobs as a starting point for this hyperparameter tuning job. For more information, see Run a Warm Start Hyperparameter Tuning Job.

      1. Choose Identical data and algorithm if your input data is the same as the input data for the parent jobs of this hyperparameter tuning job, or choose Transfer learning to use additional or different input data for this hyperparameter tuning job.

      2. For Parent hyperparameter tuning job(s), choose up to 5 hyperparameter tuning jobs to use as parents to this hyperparameter tuning job.

    2. For Hyperparameter tuning job name, type a name for the tuning job.

    3. For IAM role, choose an IAM role that has the required permissions to run hyperparameter tuning jobs in Amazon SageMaker, or choose Create a new role to allow Amazon SageMaker to create a role that has the AmazonSageMakerFullAccess managed policy attached. For information, see Amazon SageMaker Roles .

    4. For VPC, choose a Amazon VPC that you want to allow the training jobs that the tuning job launches to access. For more information, see Give Amazon SageMaker Training Jobs Access to Resources in Your Amazon VPC.

    5. Choose Next.

    6. For Objective metric, choose the metric that the hyperparameter tuning job uses to determine the best combination of hyperparameters, and choose whether to minimize or maximize this metric. For more information, see View the Best Training Job.

    7. For Hyperparameter configuration, choose ranges for the tunable hyperparameters that you want the tuning job to search, and set static values for hyperparameters that you want to remain constant in all training jobs that the hyperparameter tuning job launches. For more information, see Define Hyperparameter Ranges.

    8. Choose Next.

    9. For Input data configuration, specify the following values for each channel of input data to use for the hyperparameter tuning job. You can see what channels the algorithm you're using for hyperparameter tuning supports, and the content type, supported compression type, and supported input modes for each channel, under Channel specification section of the Algorithm summary page for the algorithm.

      1. For Channel name, type the name of the input channel.

      2. For Content type, type the content type of the data that the algorithm expects for the channel.

      3. For Compression type, choose the data compression type to use, if any.

      4. For Record wrapper, choose RecordIO if the algorithm expects data in the RecordIO format.

      5. For S3 data type, S3 data distribution type, and S3 location, specify the appropriate values. For information about what these values mean, see S3DataSource.

      6. For Input mode, choose File to download the data from to the provisioned ML storage volume, and mount the directory to a Docker volume. Choose PipeTo stream data directly from Amazon S3 to the container.

      7. To add another input channel, choose Add channel. If you are finished adding input channels, choose Done.

    10. For Output location, specify the following values:

      1. For S3 output path, choose the S3 location where the training jobs that this hyperparameter tuning job launches store output, such as model artifacts.

        Note

        You use the model artifacts stored at this location to create a model or model package from this hyperparameter tuning job.

      2. For Encryption key, if you want Amazon SageMaker to use a AWS KMS key to encrypt output data at rest in the S3 location.

    11. For Resource configuration, provide the following information:

      1. For Instance type, choose the instance type to use for each training job that the hyperparameter tuning job launches.

      2. For Instance count, type the number of ML instances to use for each training job that the hyperparameter tuning job launches.

      3. For Additional volume per instance (GB), type the size of the ML storage volume that you want to provision each training job that the hyperparameter tuning job launches. ML storage volumes store model artifacts and incremental states.

      4. For Encryption key, if you want Amazon SageMaker to use an AWS Key Management Service key to encrypt data in the ML storage volume attached to the training instances, specify the key.

    12. For Resource limits, provide the following information:

      1. For Maximum training jobs, specify the maximum number of training jobs that you want the hyperparameter tuning job to launch. A hyperparameter tuning job can launch a maximum of 500 training jobs.

      2. For Maximum parallel training jobs, specify the maximum number of concurrent training jobs that the hyperparameter tuning job can launch. A hyperparameter tuning job can launch a maximum of 10 concurrent training jobs.

      3. For Stopping condition, specify the maximum amount of time in seconds, minutes, hours, or days, that you want each training job that the hyperparameter tuning job launches to run.

    13. For Tags, specify one or more tags to manage the hyperparameter tuning job. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.

    14. Choose Create jobs to run the hyperparameter tuning job.

Use an Algorithm to Run a Hyperparameter Tuning Job (API)

To use an algorithm to run a hyperparameter tuning job by using the Amazon SageMaker API, specify either the name or the Amazon Resource Name (ARN) of the algorithm as the AlgorithmName field of the AlgorithmSpecification object that you pass to CreateHyperParameterTuningJob. For information about hyperparameter tuning in Amazon SageMaker, see Automatic Model Tuning.

Use an Algorithm to Run a Hyperparameter Tuning Job (Amazon SageMaker Python SDK)

Use an algorithm that you created or subscribed to on AWS Marketplace to create a hyperparameter tuning job, create an AlgorithmEstimator object and specify either the Amazon Resource Name (ARN) or the name of the algorithm as the value of the algorithm_arn argument. Then initialize a HyperparameterTuner object with the AlgorithmEstimator you created as the value of the estimator argument. Finally, call the fit method of the AlgorithmEstimator. For example:

from sagemaker import AlgorithmEstimator from sagemaker.tuner import HyperparameterTuner data_path = os.path.join(DATA_DIR, 'marketplace', 'training') algo = AlgorithmEstimator( algorithm_arn='arn:aws:sagemaker:us-east-2:764419575721:algorithm/scikit-decision-trees-1542410022', role='SageMakerRole', train_instance_count=1, train_instance_type='ml.c4.xlarge', sagemaker_session=sagemaker_session, base_job_name='test-marketplace') train_input = algo.sagemaker_session.upload_data( path=data_path, key_prefix='integ-test-data/marketplace/train') algo.set_hyperparameters(max_leaf_nodes=10) tuner = HyperparameterTuner(estimator=algo, base_tuning_job_name='some-name', objective_metric_name='validation:accuracy', hyperparameter_ranges=hyperparameter_ranges, max_jobs=2, max_parallel_jobs=2) tuner.fit({'training': train_input}, include_cls_metadata=False) tuner.wait()