# Debugging training jobs using Amazon SageMaker Debugger
<a name="debugger-debug-training-jobs"></a>

To prepare your training script and run training jobs with SageMaker Debugger to debug model training progress, you follow the typical two-step process: modify your training script using the `sagemaker-debugger` Python SDK, and construct a SageMaker AI estimator using the SageMaker Python SDK. Go through the following topics to learn how to use SageMaker Debugger's debugging functionality.

**Topics**
+ [Adapting your training script to register a hook](debugger-modify-script.md)
+ [Launch training jobs with Debugger using the SageMaker Python SDK](debugger-configuration-for-debugging.md)
+ [SageMaker Debugger interactive report for XGBoost](debugger-report-xgboost.md)
+ [Action on Amazon SageMaker Debugger rules](debugger-action-on-rules.md)
+ [Visualize Amazon SageMaker Debugger output tensors in TensorBoard](debugger-enable-tensorboard-summaries.md)