Parameters

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class sagemaker.parameter.ParameterRange(min_value, max_value, scaling_type='Auto')

Bases: object

Base class for representing parameter ranges.

This is used to define what hyperparameters to tune for an Amazon SageMaker hyperparameter tuning job and to verify hyperparameters for Marketplace Algorithms.

Initialize a parameter range.

Parameters

• min_value (float or int or PipelineVariable) – The minimum value for the range.

• max_value (float or int or PipelineVariable) – The maximum value for the range.

• scaling_type (str or PipelineVariable) – The scale used for searching the range during tuning (default: ‘Auto’). Valid values: ‘Auto’, ‘Linear’, ‘Logarithmic’ and ‘ReverseLogarithmic’.

is_valid(value)

Determine if a value is valid within this ParameterRange.

Parameters

value (float or int) – The value to be verified.

Returns

True if valid, False otherwise.

Return type

bool

classmethod cast_to_type(value)

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as_tuning_range(name)

Represent the parameter range as a dictionary.

It is suitable for a request to create an Amazon SageMaker hyperparameter tuning job.

Parameters

name (str) – The name of the hyperparameter.

Returns

A dictionary that contains the name and values of the hyperparameter.

Return type

dict[str, str]

class sagemaker.parameter.ContinuousParameter(min_value, max_value, scaling_type='Auto')

Bases: ParameterRange

A class for representing hyperparameters that have a continuous range of possible values.

Parameters

• min_value (float) – The minimum value for the range.

• max_value (float) – The maximum value for the range.

• scaling_type (Union[str, PipelineVariable]) –

Initialize a parameter range.

Parameters

• min_value (float or int or PipelineVariable) – The minimum value for the range.

• max_value (float or int or PipelineVariable) – The maximum value for the range.

• scaling_type (str or PipelineVariable) – The scale used for searching the range during tuning (default: ‘Auto’). Valid values: ‘Auto’, ‘Linear’, ‘Logarithmic’ and ‘ReverseLogarithmic’.

classmethod cast_to_type(value)

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class sagemaker.parameter.CategoricalParameter(values)

Bases: ParameterRange

A class for representing hyperparameters that have a discrete list of possible values.

Initialize a CategoricalParameter.

Parameters

values (list or object) – The possible values for the hyperparameter. This input will be converted into a list of strings.

as_tuning_range(name)

Represent the parameter range as a dictionary.

It is suitable for a request to create an Amazon SageMaker hyperparameter tuning job.

Parameters

name (str) – The name of the hyperparameter.

Returns

A dictionary that contains the name and values of the hyperparameter.

Return type

dict[str, list[str]]

as_json_range(name)

Represent the parameter range as a dictionary.

Dictionary is suitable for a request to create an Amazon SageMaker hyperparameter tuning job using one of the deep learning frameworks.

The deep learning framework images require that hyperparameters be serialized as JSON.

Parameters

name (str) – The name of the hyperparameter.

Returns

A dictionary that contains the name and values of the hyperparameter, where the values are serialized as JSON.

Return type

dict[str, list[str]]

is_valid(value)

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classmethod cast_to_type(value)

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class sagemaker.parameter.IntegerParameter(min_value, max_value, scaling_type='Auto')

Bases: ParameterRange

A class for representing hyperparameters that have an integer range of possible values.

Parameters

• min_value (int) – The minimum value for the range.

• max_value (int) – The maximum value for the range.

• scaling_type (Union[str, PipelineVariable]) –

Initialize a parameter range.

Parameters

• min_value (float or int or PipelineVariable) – The minimum value for the range.

• max_value (float or int or PipelineVariable) – The maximum value for the range.

• scaling_type (str or PipelineVariable) – The scale used for searching the range during tuning (default: ‘Auto’). Valid values: ‘Auto’, ‘Linear’, ‘Logarithmic’ and ‘ReverseLogarithmic’.

classmethod cast_to_type(value)

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