# Actions on rules using Amazon CloudWatch and Amazon Lambda
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Amazon CloudWatch collects Amazon SageMaker AI model training job logs and Amazon SageMaker Debugger rule processing job logs. Configure Debugger with Amazon CloudWatch Events and Amazon Lambda to take action based on Debugger rule evaluation status. 

## Example notebooks
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You can run the following example notebooks, which are prepared for experimenting with stopping a training job using actions on Debugger's built-in rules using Amazon CloudWatch and Amazon Lambda.
+ [Amazon SageMaker Debugger - Reacting to CloudWatch Events from Rules](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-debugger/tensorflow_action_on_rule/tf-mnist-stop-training-job.html)

  This example notebook runs a training job that has a vanishing gradient issue. The Debugger [VanishingGradient](debugger-built-in-rules.md#vanishing-gradient) built-in rule is used while constructing the SageMaker AI TensorFlow estimator. When the Debugger rule detects the issue, the training job is terminated.
+ [Detect Stalled Training and Invoke Actions Using SageMaker Debugger Rule](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-debugger/tensorflow_action_on_rule/detect_stalled_training_job_and_actions.html)

  This example notebook runs a training script with a code line that forces it to sleep for 10 minutes. The Debugger [StalledTrainingRule](debugger-built-in-rules.md#stalled-training) built-in rule invokes issues and stops the training job.

**Topics**
+ [Example notebooks](#debugger-test-stop-training)
+ [Access CloudWatch logs for Debugger rules and training jobs](debugger-cloudwatch-metric.md)
+ [Set up Debugger for automated training job termination using CloudWatch and Lambda](debugger-stop-training.md)
+ [Disable the CloudWatch Events rule to stop using the automated training job termination](debugger-disable-cw.md)