# Turn off Debugger
<a name="debugger-turn-off"></a>

If you want to completely turn off Debugger, do one of the following:
+ Before starting a training job, do the following:

  To stop both monitoring and profiling, include the `disable_profiler` parameter to your estimator and set it to `True`.
**Warning**  
If you disable it, you won't be able to view the comprehensive Studio Debugger insights dashboard and the autogenerated profiling report.

  To stop debugging, set the `debugger_hook_config` parameter to `False`.
**Warning**  
If you disable it, you won't be able to collect output tensors and cannot debug your model parameters.

  ```
  estimator=Estimator(
      ...
      disable_profiler=True
      debugger_hook_config=False
  )
  ```

  For more information about the Debugger-specific parameters, see [SageMaker AI Estimator](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Estimator) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable).
+ While a training job is running, do the following:

  To disable both monitoring and profiling while your training job is running, use the following estimator classmethod:

  ```
  estimator.disable_profiling()
  ```

  To disable framework profiling only and keep system monitoring, use the `update_profiler` method:

  ```
  estimator.update_profiler(disable_framework_metrics=true)
  ```

  For more information about the estimator extension methods, see the [estimator.disable\$1profiling](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Estimator.disable_profiling) and [estimator.update\$1profiler](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Estimator.update_profiler) classmethods in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) documentation.