Configure tensor collections using the CollectionConfig API

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.

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 in the Amazon SageMaker Python SDK. 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"
        }
    )
]