NVIDIA 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.