cuOpt in the NemoClaw sandbox

Infrastructure for solving with cuOpt inside NemoClaw: probe/smoke gates, remote env vars, and handoff to vendored formulation/API skills.

When to use

• Constructive planning from uploaded constraint data (schedule, assign, route, roster — any wording). See references/intent-and-triggers.md.
• CSV upload + plan → optimization-from-data-orchestrator + references/activation.md.
• ImportError / cudaErrorInsufficientDriver.

Mandatory order

Complete before any assignment output, feasibility verdict, or custom solver code:

Inspecting uploaded data for columns and constraints is fine; emit a completed plan only after smoke succeeds.

Quick reference

Imports (LP/MILP/QP):

from cuopt.linear_programming.problem import Problem, INTEGER, MINIMIZE
from cuopt.linear_programming.solver_settings import SolverSettings

Interfaces: LP/MILP/QP → gRPC :5001 + CUOPT_REMOTE_*; routing → REST :5000. See references/interfaces.md, references/routing-rest-only.md.

Reference index

Orchestration skills (local)

After gates: optimization-from-data-orchestrator → optimization-intent-router → tabular-optimization-ingestion → cuopt-model-mapper (and optimization-mode-router when replay/audit signals appear).

Vendored upstream skills

Installed under /sandbox/.openclaw/skills/ by install-skill: numerical-optimization-formulation, cuopt-numerical-optimization-api-python, routing-formulation, cuopt-routing-api-python, cuopt-server-api-python, cuopt-user-rules, etc.