Cuopt Sandbox

cuOpt in the NemoClaw sandbox

Infrastructure for solving with cuOpt inside NemoClaw: probe/smoke gates,

capability-based gRPC execution, 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:

| Step | Action | Reference |

|---|---|---|

| 0 | Probe capability → gRPC smoke | references/grpc-connectivity-and-smoke.md |

| 1 | Formulate | vendored *-formulation skills |

| 2 | Solve (one job, terminal status) | references/long-running-jobs.md |

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
# When available (preferred):
from cuopt.grpc.linear_programming import Client, GrpcError, JobStatus

Interfaces: LP/MILP/QP → prefer async Python gRPC client on :5001,

otherwise use the legacy remote fallback; routing → REST :5000. See

references/async-grpc-python.md, references/remote-execution-fallback.md,

references/interfaces.md, and references/routing-rest-only.md.

Reference index

| Topic | File |

|---|---|

| Activation / skill order | references/activation.md |

| Intent / paraphrases | references/intent-and-triggers.md |

| Gates / common mistakes | references/gates-and-first-actions.md |

| Async Python gRPC jobs | references/async-grpc-python.md |

| Legacy remote fallback | references/remote-execution-fallback.md |

| Connectivity + smoke | references/grpc-connectivity-and-smoke.md |

| Python imports | references/python-imports.md |

| gRPC vs REST | references/interfaces.md |

| Routing REST | references/routing-rest-only.md |

| Paths + probe | references/environment-and-networking.md |

| Long-running jobs | references/long-running-jobs.md |

| Troubleshooting | references/troubleshooting.md |

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.

For LP/MILP/QP, use upstream skills to build the model. Execute with this

skill's async Client lifecycle when importable; only then fall back to the

legacy remote Problem.solve() path, which cannot cancel submitted work.