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.