# Configure tensor collections using the `CollectionConfig` API
<a name="debugger-configure-tensor-collections"></a>

**Note**  
After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Debugger, effective 6/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Debugger, but we do not plan to introduce new features. For more information, see [Debugger availability change](debugger-availability-change.md). 

Use the `CollectionConfig` API operation to configure tensor collections. Debugger provides pre-built tensor collections that cover a variety of regular expressions (regex) of parameters if using Debugger-supported deep learning frameworks and machine learning algorithms. As shown in the following example code, add the built-in tensor collections you want to debug.

```
from sagemaker.core.debugger import CollectionConfig

collection_configs=[
    CollectionConfig(name="weights"),
    CollectionConfig(name="gradients")
]
```

The preceding collections set up the Debugger hook to save the tensors every 500 steps based on the default `"save_interval"` value.

For a full list of available Debugger built-in collections, see [Debugger Built-in Collections](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/api.md#collection).

If you want to customize the built-in collections, such as changing the save intervals and tensor regex, use the following `CollectionConfig` template to adjust parameters.

```
from sagemaker.core.debugger import CollectionConfig

collection_configs=[
    CollectionConfig(
        name="{{tensor_collection}}",
        parameters={
            "{{key_1}}": "{{value_1}}",
            "{{key_2}}": "{{value_2}}",
            ...
            "{{key_n}}": "{{value_n}}"
        }
    )
]
```

For more information about available parameter keys, see [CollectionConfig](https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.CollectionConfig) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). For example, the following code example shows how you can adjust the save intervals of the "losses" tensor collection at different phases of training: save loss every 100 steps in training phase and validation loss every 10 steps in validation phase. 

```
from sagemaker.core.debugger import CollectionConfig

collection_configs=[
    CollectionConfig(
        name="{{losses}}",
        parameters={
            "{{train.save_interval}}": "{{100}}",
            "{{eval.save_interval}}": "{{10}}"
        }
    )
]
```

**Tip**  
This tensor collection configuration object can be used for both [DebuggerHookConfig](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-configure-hook.html#debugger-configure-tensor-hook) and [Rule](https://docs.aws.amazon.com/sagemaker/latest/dg/use-debugger-built-in-rules.html#debugger-built-in-rules-configuration-param-change) API operations.