Hyperparameters and HPO
Important
After you create a solution, you can’t change its configuration. By default, all new solutions use automatic training. With automatic training, you incur training costs while
your solution is active. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training
costs, see Amazon Personalize pricing
You specify hyperparameters before training to optimize the trained model for your particular use case. This contrasts with model parameters whose values are determined during the training process.
Hyperparameters are specified using the
algorithmHyperParameters key that is part of the SolutionConfig
object that is passed to the CreateSolution operation.
A condensed version of the CreateSolution request is
below. The example includes the solutionConfig object. You
use solutionConfig to override the default parameters of a
recipe.
{ "name": "string", "recipeArn": "string", "eventType": "string", "solutionConfig": { "optimizationObjective": { "itemAttribute": "string", "objectiveSensitivity": "string" }, "eventValueThreshold": "string", "featureTransformationParameters": { "string" : "string" }, "algorithmHyperParameters": { "string" : "string" }, "hpoConfig": { "algorithmHyperParameterRanges": { ... }, "hpoResourceConfig": { "maxNumberOfTrainingJobs": "string", "maxParallelTrainingJobs": "string" } }, }, }
Different recipes use different hyperparameters. For the available hyperparameters, see the individual recipes in Choosing a recipe.
Enabling hyperparameter optimization
Hyperparameter optimization (HPO), or tuning, is the task of
choosing optimal hyperparameters for a specific learning objective.
The optimal hyperparameters are determined by running many training
jobs using different values from the specified ranges of
possibilities. By default, Amazon Personalize does not perform HPO. To
use HPO, set performHPO to true, and
include the hpoConfig object.
Hyperparameters can be categorical, continuous, or integer-valued.
The hpoConfig object has keys that correspond to each
of these types, where you specify the hyperparameters and their
ranges. You must provide each type in your request, but if a recipe doesn't have
a parameter of a type, you can leave it empty. For example, User-Personalization does
not have a tunable hyperparameter of continuous type. So for the continousHyperParameterRange, you would pass an empty array.
The following code shows how to create a solution with HPO enabled using the SDK for Python (Boto3).
The solution in the example uses the
User-Personalization recipe
recipe and
has HPO set to true. The code provides a value for hidden_dimension and the categoricalHyperParameterRanges and integerHyperParameterRanges.
The continousHyperParameterRange is empty and the hpoResourceConfig sets the maxNumberOfTrainingJobs and maxParallelTrainingJobs.
create_solution_response = personalize.create_solution( name = solutionName, datasetGroupArn = 'arn:aws:personalize:region:accountId:dataset-group/datasetGroupName', recipeArn = 'arn:aws:personalize:::recipe/aws-user-personalization', performHPO = True, solutionConfig = { "algorithmHyperParameters": { "hidden_dimension": "55" }, "hpoConfig": { "algorithmHyperParameterRanges": { "categoricalHyperParameterRanges": [ { "name": "recency_mask", "values": [ "true", "false"] } ], "integerHyperParameterRanges": [ { "name": "bptt", "minValue": 2, "maxValue": 22 } ], "continuousHyperParameterRanges": [ ] }, "hpoResourceConfig": { "maxNumberOfTrainingJobs": "4", "maxParallelTrainingJobs": "2" } } } )
For more information about HPO, see Automatic model tuning
Viewing hyperparameters
You can view the hyperparameters of the solution by calling the DescribeSolution operation. The following
sample shows a DescribeSolution output. After creating a solution version (training a model),
you can also view hyperparameters with the DescribeSolutionVersion operation.
{ "solution": { "name": "hpo_coonfig_solution", "solutionArn": "arn:aws:personalize:region:accountId:solution/solutionName", "performHPO": true, "performAutoML": false, "recipeArn": "arn:aws:personalize:::recipe/aws-user-personalization", "datasetGroupArn": "arn:aws:personalize:region:accountId:dataset-group/datasetGroupName", "eventType": "click", "solutionConfig": { "hpoConfig": { "hpoResourceConfig": { "maxNumberOfTrainingJobs": "4", "maxParallelTrainingJobs": "2" }, "algorithmHyperParameterRanges": { "integerHyperParameterRanges": [ { "name": "training.bptt", "minValue": 2, "maxValue": 22 } ], "continuousHyperParameterRanges": [], "categoricalHyperParameterRanges": [ { "name": "data.recency_mask", "values": [ "true", "false" ] } ] } }, "algorithmHyperParameters": { "hidden_dimension": "55" } }, "status": "ACTIVE", "creationDateTime": "2022-07-08T12:12:48.565000-07:00", "lastUpdatedDateTime": "2022-07-08T12:12:48.565000-07:00" } }