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Configure the DebuggerHookConfig API to save tensors - Amazon SageMaker AI

Configure the DebuggerHookConfig API to save tensors

Note

After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Debugger, effective 7/30/26. 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.

Use the DebuggerHookConfig API to create a debugger_hook_config object using the collection_configs object you created in the previous step.

from sagemaker.core.debugger import DebuggerHookConfig

debugger_hook_config=DebuggerHookConfig(
    collection_configs=collection_configs
)

Debugger saves the model training output tensors into the default S3 bucket. The format of the default S3 bucket URI is s3://amzn-s3-demo-bucket-sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.

If you want to specify an exact S3 bucket URI, use the following code example:

from sagemaker.core.debugger import DebuggerHookConfig

debugger_hook_config=DebuggerHookConfig(
    s3_output_path="specify-uri",
    collection_configs=collection_configs
)

For more information, see DebuggerHookConfig in the Amazon SageMaker Python SDK.