--- id: smdebug-rulesconfig version: "1.0.1" license: Apache License Version 2.0 license_treatment: permissive maintenance: abandoned --- # smdebug-rulesconfig — SMDebug RulesConfig License: permissive · Maintenance: abandoned · Downloads: 17.5M/mo ## What it is and what it does smdebug-rulesconfig is a configuration helper library for Amazon SageMaker Debugger that bundles predefined monitoring rules you can apply to SageMaker training jobs. It lets you reference built-in rules by name and customize their settings without having to understand the underlying rule mechanics or JSON schema. The package was designed as a companion to the Amazon SageMaker Python SDK and can also be used as a standalone rule configuration retriever. The library has been abandoned since late 2020 and receives no active maintenance. Its last commit was in December 2022, but no new releases have been published since December 2020. If you are starting a new project with SageMaker Debugger, you should verify whether this functionality has been integrated into the main SageMaker SDK or replaced by a newer approach. Use it for: - Configure built-in SageMaker Debugger rules for a training job without writing rule JSON manually. - Retrieve preset rule configurations for common monitoring scenarios (e.g., loss tracking, gradient anomalies). - Integrate rule definitions into a SageMaker training pipeline using the Python SDK. ## Worth the install? AI-flagged interpretation of the facts above — verify before relying. Provides preconfigured rule definitions and configuration helpers for Amazon SageMaker Debugger, allowing you to apply built-in monitoring rules to training jobs without manual setup. No, unless you are maintaining legacy code that already depends on this package. The project is abandoned (last commit 2022-12-09, no releases since 2020-12-18), and its functionality may have been absorbed into the SageMaker Python SDK. For new projects, use the SageMaker SDK directly or verify current best practices for SageMaker Debugger rule configuration. ## Install pip install smdebug-rulesconfig uv add smdebug-rulesconfig poetry add smdebug-rulesconfig ## Installing smdebug-rulesconfig Before you install: Low install friction with no runtime dependencies. However, the package is abandoned—last commit was 2022-12-09 and no releases since 2020-12-18. Use only if you are locked into an older SageMaker Debugger workflow; active projects should check whether the SageMaker Python SDK has superseded this functionality. License in practice: Licensed under Apache License 2.0 (permissive). You may use, modify, and distribute this package freely in commercial and private projects, provided you retain license notices. Quickstart: pip install smdebug-rulesconfig from smdebug_rulesconfig import rules rule = rules.LossNotDecreasing() config = rule.to_json() Requires Python >=2.7, but intended for use with the Amazon SageMaker Python SDK; standalone use may be limited without that context. Verify before relying: - Whether this package is still required for current SageMaker Debugger workflows or if functionality has been merged into the main SageMaker SDK. - Exact scope of 'common collections' configuration retrieval mentioned in the description. ## Package facts - License: Apache License Version 2.0 (permissive) - Python support: supports_current - Install friction: low - Maintenance: abandoned - Downloads: 17.5M/month (top 5,000 on PyPI) - Known vulnerabilities: none known ## Tags sagemaker debugger rules, builtin monitoring rules config, sagemaker training job rules, debugger rule configuration, sagemaker model debugging, sagemaker, model-debugging, abandoned [View on SkillFed](https://skillfed.io/packages/smdebug-rulesconfig) · [View on PyPI](https://pypi.org/project/smdebug-rulesconfig/)