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Amazon SageMaker debugger RulesConfig

Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol, Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramachandran, Ishaaq Chandy, Ishan Bhatt, Zhihan Li, Kohen Chia, Neelesh Dodda, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan, Jeffrey Geevarghes, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi
2020
Last updated January 25, 2024
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Amazon SageMaker Debugger is designed to be a debugger for machine learning models. It lets you go beyond just looking at scalars like losses and accuracies during training and gives you full visibility into all tensors 'flowing through the graph' during training or inference.

Amazon SageMaker Debugger RulesConfig provides a mapping of builtin rules with default configurations. These configurations will affect both DebugHookConfig and DebugRuleConfigurations in the Amazon SageMaker Python SDK.

This library, intended to be used with Amazon SageMaker Python SDK, helps you specify builtin rules without worrying about any details or tweak the configuration of builtin rules. These builtin rules are available in Amazon SageMaker.

Amazon SageMaker Debugger RulesConfig package can be used with Amazon SageMaker Debugger or as stand-alone rule config retriever. In addition to retrieving builtin rules, configuration for common collections can be retrieved as well.

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