Skip to content

ContinuousParameterRange

Source code in docs_src/capo_sagemaker/types/continuous_parameter_range.py 
13
14
15
16
17
18
19
20
21
22
23
class ContinuousParameterRange(TypedDict, closed=True):
    name: NotRequired["capo_sagemaker.types.parameter_key.ParameterKey"]
    """<p>The name of the continuous hyperparameter to tune.</p>"""
    min_value: NotRequired["capo_sagemaker.types.parameter_value.ParameterValue"]
    """<p>The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and <code>MaxValue</code>for tuning.</p>"""
    max_value: NotRequired["capo_sagemaker.types.parameter_value.ParameterValue"]
    """<p>The maximum value for the hyperparameter. The tuning job uses floating-point values between <code>MinValue</code> value and this value for tuning.</p>"""
    scaling_type: NotRequired[
        "capo_sagemaker.types.hyper_parameter_scaling_type.HyperParameterScalingType"
    ]
    """<p>The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.</p> </dd> <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling works only for ranges that have only values greater than 0.</p> </dd> <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse logarithmic scaling works only for ranges that are entirely within the range 0&lt;=x&lt;1.0.</p> </dd> </dl>"""

max_value instance-attribute 

max_value: NotRequired[ParameterValue]

The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

min_value instance-attribute 

min_value: NotRequired[ParameterValue]

The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

name instance-attribute 

name: NotRequired[ParameterKey]

The name of the continuous hyperparameter to tune.

scaling_type instance-attribute 

scaling_type: NotRequired[HyperParameterScalingType]

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

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.

Logarithmic

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

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

ReverseLogarithmic

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

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