Multi-file
ai-mlOfficial
Official Provider SkillView repo
Three-role optimization loop for a PyTorch training workload: the Main Loop Agent owns the end-to-end run, authoritative profiling, and hotspot selection; a disposable Optimizer subagent reworks one hotspot per round in an isolated context; a Judge subagent renders an evidence-based verdict driving iterate/pass. All cross-role state lives in files.
Files21 files
SKILL.md139 lines
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Skill details
Versionv1.0.0
AuthorNVIDIA
Categoryai-ml
Skill IDNVIDIA/recsys-examples/corelib/talos/skills/torch_optimize
Files21 files
Related skills
Torch Perf AnalysisAnalyze an existing nsys trace of a PyTorch workload (training or inference, NVTX-instrumented) and produce an end-to-end performance diagnosis. Uses the bundled `veloq` CLI for evidence extraction.Nemoclaw Maintainer Cross Issue SweepFind open issues that a NemoClaw PR may also fix or conflict with. Use when related-issue analysis is requested.Nemoclaw Maintainer Fix E2e FailuresContinuously maintain automatic NemoClaw main E2E results through coordinated repairs. Use for ongoing maintenance, not one-time dispatch or diagnosis.Nemoclaw Maintainer MorningPrepare the NemoClaw morning maintainer plan: triage the backlog, select a target version, and identify release candidates and stragglers.Nemoclaw Maintainer Runtime ProviderImplement or review a native managed NemoClaw runtime provider and its activation or qualification. Excludes the portable experimental profile.Nemoclaw Maintainer TriagePropose and apply authorized Issue Type, Project fields, and labels for NemoClaw issues or PRs, individually or in a batch.