Define Hyperparameter Ranges - Amazon SageMaker
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Define Hyperparameter Ranges

This guide shows how to use SageMaker APIs to define hyperparameter ranges. It also provides a list of hyperparameter scaling types that you can use.

Choosing hyperparameters and ranges significantly affects the performance of your tuning job. Hyperparameter tuning finds the best hyperparameter values for your model by searching over a range of values that you specify for each tunable hyperparameter. You can also specify up to 100 static hyperparameters that do not change over the course of the tuning job. You can use up to 100 hyperparameters in total (static + tunable). For guidance on choosing hyperparameters and ranges, see Best Practices for Hyperparameter Tuning.

Note

SageMaker Automatic Model Tuning (AMT) may add additional hyperparameters(s) that contribute to the limit of 100 total hyperparameters. Currently, to pass your objective metric to the tuning job for use during training, SageMaker adds _tuning_objective_metric automatically.

Use static hyperparameters for the following cases: For example, you can use AMT to tune your model using param1 (a tunable parameter) and param2 (a static parameter). If you do, then use a search space for param1 that lies between two values, and pass param2 as a static hyperparameter, as follows.

param1: ["range_min","range_max"] param2: "static_value"

Static hyperparameters have the following structure:

"StaticHyperParameters": { "objective" : "reg:squarederror", "dropout_rate": "0.3" }

You can use the Amazon SageMaker API to specify key value pairs in the StaticHyperParameters field of the HyperParameterTrainingJobDefinition parameter that you pass to the CreateHyperParameterTuningJob operation.

You can use the SageMaker API to define hyperparameter ranges. Specify the names of hyperparameters and ranges of values in the ParameterRanges field of the HyperParameterTuningJobConfig parameter that you pass to the CreateHyperParameterTuningJob operation.

The ParameterRanges field has three subfields: categorical, integer, and continuous. You can define up to 30 total (categorical + integer + continuous) tunable hyperparameters to search over.

Note

Each categorical hyperparameter can have at most 30 different values.

Hyperparameter ranges have the following structure:

"ParameterRanges": { "CategoricalParameterRanges": [ { "Name": "tree_method", "Values": ["auto", "exact", "approx", "hist"] } ], "ContinuousParameterRanges": [ { "Name": "eta", "MaxValue" : "0.5", "MinValue": "0", "ScalingType": "Auto" } ], "IntegerParameterRanges": [ { "Name": "max_depth", "MaxValue": "10", "MinValue": "1", "ScalingType": "Auto" } ] }

If you create a tuning job with a Grid strategy, you can only specify categorical values. You don't need to provide the MaxNumberofTrainingJobs. This value is inferred from the total number of configurations that can be produced from your categorical parameters. If specified, the value of MaxNumberOfTrainingJobs should be equal to the total number of distinct categorical combinations possible.

Hyperparameter scaling types

For integer and continuous hyperparameter ranges, you can choose the scale that you want hyperparameter tuning to use. For example, to search the range of values, you can specify a value for the ScalingType field of the hyperparameter range. You can choose from the following hyperparameter scaling types:

Auto

SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Typically, you choose this if the range of all values from the lowest to the highest is relatively small (within one order of magnitude). Uniformly searching values from the range provides a reasonable exploration of the entire range.

Logarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have values greater than 0.

Choose logarithmic scaling when you're searching a range that spans several orders of magnitude.

For example, if you're tuning a Tune a linear learner model model, and you specify a range of values between .0001 and 1.0 for the learning_rate hyperparameter, consider the following: Searching uniformly on a logarithmic scale gives you a better sample of the entire range than searching on a linear scale would. This is because searching on a linear scale would, on average, devote 90 percent of your training budget to only the values between .1 and 1.0. As a result, that leaves only 10 percent of your training budget for the values between .0001 and .1.

ReverseLogarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling is supported only for continuous hyperparameter ranges. It is not supported for integer hyperparameter ranges.

Choose reverse logarithmic scaling when you are searching a range that is highly sensitive to small changes that are very close to 1.

Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

For an example notebook that uses hyperparameter scaling, see these Amazon SageMaker hyperparameter examples on GitHub.