# Configuring SageMaker Debugger to save tensors
<a name="debugger-configure-hook"></a>

*Tensors* are data collections of updated parameters from the backward and forward pass of each training iteration. SageMaker Debugger collects the output tensors to analyze the state of a training job. SageMaker Debugger's [https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.CollectionConfig](https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.CollectionConfig) and [https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.DebuggerHookConfig](https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.DebuggerHookConfig) API operations provide methods for grouping tensors into *collections* and saving them to a target S3 bucket. The following topics show how to use the `CollectionConfig` and `DebuggerHookConfig` API operations, followed by examples of how to use Debugger hook to save, access, and visualize output tensors.

While constructing a SageMaker AI estimator, activate SageMaker Debugger by specifying the `debugger_hook_config` parameter. The following topics include examples of how to set up the `debugger_hook_config` using the `CollectionConfig` and `DebuggerHookConfig` API operations to pull tensors out of your training jobs and save them.

**Note**  
After properly configured and activated, SageMaker Debugger saves the output tensors in a default S3 bucket, unless otherwise specified. The format of the default S3 bucket URI is `s3://amzn-s3-demo-bucket-sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/`.

**Topics**
+ [Configure tensor collections using the `CollectionConfig` API](debugger-configure-tensor-collections.md)
+ [Configure the `DebuggerHookConfig` API to save tensors](debugger-configure-tensor-hook.md)
+ [Example notebooks and code samples to configure Debugger hook](debugger-save-tensors.md)