NVIDIA/cuopt-examples

cuopt-model-mapper

Map interpreted optimization problems into cuOpt-native models for the fast path with minimal clarifying questions.

cuopt-sandbox

Run cuOpt in the NemoClaw sandbox — probe/smoke gates, prefer cancelable Python gRPC jobs, use legacy remote execution only when that API is unavailable, then vendored cuOpt skills.

optimization-mode-router

Choose fast direct-to-cuOpt solve versus replayable or auditable model artifact mode.

tabular-optimization-ingestion

Infer optimization structure from uploaded tables and identify minimal clarifications before cuOpt modeling.

cuopt-debugging

Troubleshoot cuOpt LP/MILP problems including errors, wrong results, infeasible solutions, performance issues, and status codes. Use when the user says something isn't working, gets unexpected results, or needs help diagnosing issues.

optimization-from-data-orchestrator

Coordinate uploaded data plus a natural-language question into interpretation, clarification, cuOpt solve, and a user-facing answer.

optimization-intent-router

Classify whether a data-backed request is LP, MILP, QP, routing, or non-optimization analytics.

generic-max-supply

Multi-period supply chain planning model: data files, BOM structure, variable/constraint reference for the max-supply base model.