Use Debugger built-in rules with custom parameter values - Amazon SageMaker AI

Use Debugger built-in rules with custom parameter values

If you want to adjust the built-in rule parameter values and customize tensor collection regex, configure the base_config and rule_parameters parameters for the ProfilerRule.sagemaker and Rule.sagemaker classmethods. In case of the Rule.sagemaker class methods, you can also customize tensor collections through the collections_to_save parameter. The instruction of how to use the CollectionConfig class is provided at Configure tensor collections using the CollectionConfig API.

Use the following configuration template for built-in rules to customize parameter values. By changing the rule parameters as you want, you can adjust the sensitivity of the rules to be triggered.

  • The base_config argument is where you call the built-in rule methods.

  • The rule_parameters argument is to adjust the default key values of the built-in rules listed in List of Debugger built-in rules.

  • The collections_to_save argument takes in a tensor configuration through the CollectionConfig API, which requires name and parameters arguments.

For more information about the Debugger rule class, methods, and parameters, see SageMaker AI Debugger Rule class in the Amazon SageMaker Python SDK.

from sagemaker.debugger import Rule, ProfilerRule, rule_configs, CollectionConfig

rules=[
    Rule.sagemaker(
        base_config=rule_configs.built_in_rule_name(),
        rule_parameters={
                "key": "value"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="tensor_collection_name", 
                parameters={
                    "key": "value"
                } 
            )
        ]
    )
]

The parameter descriptions and value customization examples are provided for each rule at List of Debugger built-in rules.