---

name: deployment-and-backends description: "Routes Mars supervisor, worker, Ray, GPU, Kubernetes, and YARN backend requests to verified CLI and prerequisite guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Deployment and Backends

Use this sub-skill when the user asks how to start Mars services, inspect CLI flags, choose a backend, use GPU placement, or deploy on Ray, Kubernetes, or YARN. The CPU/local package baseline is verified; optional backend execution is prerequisite-bound and must not be claimed as verified unless a backend smoke has passed in the user's environment.

Trigger phrases

• "Start mars-supervisor and mars-worker."
• "What flags does mars-worker accept?"
• "Use Mars on Ray."
• "Run Mars tensor/DataFrame on GPU."
• "Deploy Mars on Kubernetes or YARN."
• "Set CUDA devices, ports, supervisors, or worker resources."

What belongs here

• mars-supervisor and mars-worker CLI help.
• Local cluster startup concepts.
• Ray sessions: new_session(backend='ray'), new_ray_session, and related prerequisites.
• GPU/CUDA tensor and DataFrame placement: gpu=True, to_gpu, to_cpu, and worker --cuda-devices.
• Kubernetes and YARN cluster APIs and service prerequisites.

What stays elsewhere

• Local tensor/DataFrame operations -> tensor-dataframe-core.
• Remote callable DAGs -> remote-and-scripts.
• Mars Learn estimator and optional ML integration APIs -> learn-and-integrations.

Read these bundled files

• references/cli-reference.md for verified mars-supervisor and mars-worker options.
• references/backends.md for Ray, GPU, Kubernetes, and YARN prerequisites.
• references/troubleshooting.md for port, optional dependency, device, and cluster-tooling failures.
• scripts/check_mars_cli.py for a safe CLI help smoke.

Minimal route

1. For parser or entry-point questions, run the bundled CLI helper or use mars-supervisor --help and mars-worker --help.
2. For local service startup, select supervisor and worker endpoints and ports before starting long-running processes.
3. For Ray/GPU/Kubernetes/YARN, verify optional packages and host services before running a real backend workflow.
4. When the task needs compute semantics after backend startup, route back to the owning tensor/DataFrame, remote, or learn sub-skill.

Common decisions

• -h is help; use -H / --host for service binding.
• --cuda-devices "" disables GPU devices for a worker, while auto discovers visible CUDA devices.
• Real Kubernetes/YARN examples need external cluster credentials and are not safe baseline verification cases.
• The repo's Docker image helper is reference-only because it invokes Docker and can push images; do not run it as a bundled runtime script.

Quality bar

A future agent should be able to list the safe CLI help path, choose the right backend route, explain missing prerequisites, and avoid accidentally starting or mutating external cluster resources.