Use Debugger built-in rules with the default parameter settings

To specify Debugger built-in rules in an estimator, you need to configure a list object. The following example code shows the basic structure of listing the Debugger built-in rules:

from sagemaker.debugger import Rule, rule_configs

rules=[
    Rule.sagemaker(rule_configs.built_in_rule_name_1()),
    Rule.sagemaker(rule_configs.built_in_rule_name_2()),
    ...
    Rule.sagemaker(rule_configs.built_in_rule_name_n()),
    ... # You can also append more profiler rules in the ProfilerRule.sagemaker(rule_configs.*()) format.
]

For more information about default parameter values and descriptions of the built-in rule, see List of Debugger built-in rules.

To find the SageMaker Debugger API reference, see sagemaker.debugger.rule_configs and sagemaker.debugger.Rule.

For example, to inspect the overall training performance and progress of your model, construct a SageMaker AI estimator with the following built-in rule configuration.

from sagemaker.debugger import Rule, rule_configs

rules=[
    Rule.sagemaker(rule_configs.loss_not_decreasing()),
    Rule.sagemaker(rule_configs.overfit()),
    Rule.sagemaker(rule_configs.overtraining()),
    Rule.sagemaker(rule_configs.stalled_training_rule())
]

When you start the training job, Debugger collects system resource utilization data every 500 milliseconds and the loss and accuracy values every 500 steps by default. Debugger analyzes the resource utilization to identify if your model is having bottleneck problems. The loss_not_decreasing, overfit, overtraining, and stalled_training_rule monitors if your model is optimizing the loss function without those training issues. If the rules detect training anomalies, the rule evaluation status changes to IssueFound. You can set up automated actions, such as notifying training issues and stopping training jobs using Amazon CloudWatch Events and Amazon Lambda. For more information, see Action on Amazon SageMaker Debugger rules.