[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-nvrx-attr":3,"mdc--6zczij-key":34,"related-repo-nvidia-nvrx-attr":444,"related-org-nvidia-nvrx-attr":497},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"nvrx-attr","analyze attribution quality for Megatron-LM","Orchestration layer over nvidia_resiliency_ext attribution modules. Provides log-analysis, fr-analysis, and a Megatron-LM-oriented fault-injection feedback loop for benchmarking attribution quality on SLURM workloads.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Logs","logs","tag",{"name":17,"slug":18,"type":15},"Benchmarking","benchmarking",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"AI Infrastructure","ai-infrastructure",308,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnvidia-resiliency-ext","2026-07-14T05:36:19.02075",null,56,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"NVIDIA Resiliency Extension is a python package for framework developers and users to implement fault-tolerant features. It improves the effective training time by minimizing the downtime due to failures and interruptions.","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnvidia-resiliency-ext\u002Ftree\u002FHEAD\u002Fsrc\u002Fnvidia_resiliency_ext\u002Fskills\u002Fnvrx-attr","---\nname: nvrx-attr\ndescription: >\n  Orchestration layer over nvidia_resiliency_ext attribution modules. Provides\n  log-analysis, fr-analysis, and a Megatron-LM-oriented fault-injection feedback\n  loop for benchmarking attribution quality on SLURM workloads.\ncompatibility: Requires Python 3.10+, nvidia-resiliency-ext installed, logsage, langchain-openai, and LLM_API_KEY (env var, LLM_API_KEY_FILE, or ~\u002F.llm_api_key). The fault-injection loop has only been validated with Megatron-LM workloads.\nmetadata:\n  author: nvidia\n---\n\n# Attribution Skills\n\nHigh-level orchestration layer over the `nvidia_resiliency_ext.attribution` modules.\nEach subdirectory is a self-contained skill with its own `SKILL.md` and helper scripts.\n\n## Skills\n\n| Directory | Purpose | Entry point |\n|-----------|---------|------------|\n| [`log-analysis\u002F`](.\u002Flog-analysis\u002FSKILL.md) | Analyze SLURM job logs for failure root-cause and restart decisions | `NVRxLogAnalyzer` (`nvrx_logsage.py`) |\n| [`fr-analysis\u002F`](.\u002Ffr-analysis\u002FSKILL.md) | Analyze NCCL flight-recorder dumps for collective-hang root-cause | `CollectiveAnalyzer` (`fr_attribution.py`) |\n| [`fault-injection-loop\u002F`](.\u002Ffault-injection-loop\u002FSKILL.md) | Run a batched SLURM fault-injection feedback loop and score attribution accuracy | `prepare_node_alloc.sh` \u002F `watch_and_analyze.sh` |\n\n## How skills relate to the library\n\n```\nsrc\u002Fnvidia_resiliency_ext\u002F\n├── attribution\u002F\n│   ├── log_analyzer\u002Fnvrx_logsage.py      ← log-analysis implementation\n│   ├── trace_analyzer\u002Ffr_attribution.py  ← fr-analysis implementation\n│   ├── analyzer\u002Fengine.py                ← combined orchestration entry point\n│   └── combined_log_fr\u002F                  ← optional log + FR fusion\n└── skills\u002F\n    └── nvrx-attr\u002F                        ← this skill bundle\n        ├── log-analysis\u002F\n        ├── fr-analysis\u002F\n        └── fault-injection-loop\u002F\n```\n\nThe `Analyzer` (`analyzer\u002Fengine.py`) is the recommended entry point when you need\nrequest coalescing, result caching, or the combined `LOG_AND_TRACE` pipeline.\nUse the individual skills when you want to run one analysis type directly without the\nfull coalescing stack.\n\n## Common prerequisites\n\n- `LLM_API_KEY` environment variable, `LLM_API_KEY_FILE`, or `~\u002F.llm_api_key`\n- `langchain-openai` installed\n- `logsage` package installed (required by `log_analysis`)\n- Package installed: `pip install nvidia-resiliency-ext` or `pip install -e .` from repo root\n- The fault-injection loop has only been validated with Megatron-LM training scripts\n\n## Fault-Loop Local Setup\n\nBefore using `fault-injection-loop\u002F`, create the local config file from the tracked\ntemplate and fill in your site-specific values:\n\n```bash\ncp scripts\u002Fuser.env.example scripts\u002Fuser.env\n```\n\nThe feedback-loop scripts require `src\u002Fnvidia_resiliency_ext\u002Fskills\u002Fnvrx-attr\u002Fscripts\u002Fuser.env`\nto exist at runtime. Keep `user.env` local and untracked.\n",{"data":35,"body":38},{"name":4,"description":6,"compatibility":36,"metadata":37},"Requires Python 3.10+, nvidia-resiliency-ext installed, logsage, langchain-openai, and LLM_API_KEY (env var, LLM_API_KEY_FILE, or ~\u002F.llm_api_key). 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FR fusion\n└── skills\u002F\n    └── nvrx-attr\u002F                        ← this skill bundle\n        ├── log-analysis\u002F\n        ├── fr-analysis\u002F\n        └── fault-injection-loop\u002F\n",[243],{"type":42,"tag":57,"props":244,"children":246},{"__ignoreMap":245},"",[247],{"type":48,"value":241},{"type":42,"tag":51,"props":249,"children":250},{},[251,253,259,260,266,268,274],{"type":48,"value":252},"The ",{"type":42,"tag":57,"props":254,"children":256},{"className":255},[],[257],{"type":48,"value":258},"Analyzer",{"type":48,"value":145},{"type":42,"tag":57,"props":261,"children":263},{"className":262},[],[264],{"type":48,"value":265},"analyzer\u002Fengine.py",{"type":48,"value":267},") is the recommended entry point when you need\nrequest coalescing, result caching, or the combined ",{"type":42,"tag":57,"props":269,"children":271},{"className":270},[],[272],{"type":48,"value":273},"LOG_AND_TRACE",{"type":48,"value":275}," pipeline.\nUse the individual skills when you want 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Draws from a prioritized pool of (fault_type, rank, iter, nodes) experiments and submits them 2 at a time via sbatch — waiting for each pair to finish before submitting the next — to bound filesystem load. GPU-related faults are front-loaded in the pool. After all jobs complete, runs \u002Flog-analysis and \u002Ffr-analysis on every experiment, scores attribution vs. ground truth, aggregates gaps, and iterates on attribution modules to close them.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[452,453,454,457],{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":455,"slug":456,"type":15},"Simulation","simulation",{"name":458,"slug":459,"type":15},"Testing","testing","2026-07-14T05:35:12.409884",{"slug":462,"name":462,"fn":463,"description":464,"org":465,"tags":466,"stars":23,"repoUrl":24,"updatedAt":477},"fr-analysis","analyze NCCL flight-recorder dumps","Analyze PyTorch NCCL flight-recorder (FR) dumps to identify collective operation hangs and isolate the responsible ranks using CollectiveAnalyzer. Use when a distributed training job hangs due to an NCCL collective timeout and FR dump files are available. 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Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[541,544],{"name":542,"slug":543,"type":15},"CI\u002FCD","ci-cd",{"name":527,"slug":528,"type":15},"2026-07-14T05:25:59.97109",{"slug":547,"name":547,"fn":548,"description":549,"org":550,"tags":551,"stars":532,"repoUrl":533,"updatedAt":557},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[552,553,554],{"name":542,"slug":543,"type":15},{"name":527,"slug":528,"type":15},{"name":555,"slug":556,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":559,"name":559,"fn":560,"description":561,"org":562,"tags":563,"stars":532,"repoUrl":533,"updatedAt":569},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[564,565,566],{"name":471,"slug":472,"type":15},{"name":555,"slug":556,"type":15},{"name":567,"slug":568,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":571,"name":571,"fn":572,"description":573,"org":574,"tags":575,"stars":532,"repoUrl":533,"updatedAt":582},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[576,579],{"name":577,"slug":578,"type":15},"Best Practices","best-practices",{"name":580,"slug":581,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":584,"name":584,"fn":585,"description":586,"org":587,"tags":588,"stars":532,"repoUrl":533,"updatedAt":596},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[589,592,595],{"name":590,"slug":591,"type":15},"Machine Learning","machine-learning",{"name":593,"slug":594,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":598,"name":598,"fn":599,"description":600,"org":601,"tags":602,"stars":532,"repoUrl":533,"updatedAt":607},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[603,606],{"name":604,"slug":605,"type":15},"QA","qa",{"name":458,"slug":459,"type":15},"2026-07-14T05:25:53.673039",{"slug":609,"name":609,"fn":610,"description":611,"org":612,"tags":613,"stars":532,"repoUrl":533,"updatedAt":618},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[614,615],{"name":527,"slug":528,"type":15},{"name":616,"slug":617,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":620,"name":620,"fn":621,"description":622,"org":623,"tags":624,"stars":532,"repoUrl":533,"updatedAt":632},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[625,628,629],{"name":626,"slug":627,"type":15},"Code Review","code-review",{"name":555,"slug":556,"type":15},{"name":630,"slug":631,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":634,"name":634,"fn":635,"description":636,"org":637,"tags":638,"stars":532,"repoUrl":533,"updatedAt":641},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[639,640],{"name":604,"slug":605,"type":15},{"name":458,"slug":459,"type":15},"2026-07-14T05:25:54.928983",{"slug":643,"name":643,"fn":644,"description":645,"org":646,"tags":647,"stars":532,"repoUrl":533,"updatedAt":652},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[648,651],{"name":649,"slug":650,"type":15},"Automation","automation",{"name":542,"slug":543,"type":15},"2026-07-30T05:29:03.275638",496]