---

name: core-runtime description: "Use Ray Core for Python tasks, actors, object refs, resources, runtime environments, and local runtime troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Ray Core Runtime

Use this sub-skill when the task is about distributed Python with Ray Core primitives: ray.init, ray.remote, tasks, actors, ObjectRef, ray.put, ray.get, ray.wait, resource annotations, runtime environments, local startup/shutdown, or performance issues in task/actor/object workflows.

Quick Start

import ray

ray.init()  # Starts a local runtime or connects according to Ray's address rules.

@ray.remote
def square(value):
    return value * value

refs = [square.remote(i) for i in range(4)]
print(ray.get(refs))  # [0, 1, 4, 9]

ray.shutdown()

Read Next

• Read references/api-reference.md for the verified Core API signatures, startup options, task/actor options, and object API behavior.
• Read references/workflows.md for recipes covering tasks, actors, object-store usage, ray.wait polling, resources, runtime environments, retries, and anti-pattern rewrites.
• Read references/troubleshooting.md when code blocks on ray.get, pins object-store memory, fails serialization, deadlocks on resources, exits workers, or leaks local Ray processes.
• Run python scripts/core_smoke.py --help for a safe helper. Add --run-local only when starting a tiny local Ray runtime is acceptable.

Boundaries

• Use this sub-skill for Python Ray Core. C++ and Java APIs are out of scope.
• For Ray CLI, jobs, cluster startup, dashboard, and state commands, route to ../cluster-ops/SKILL.md.
• For Ray Data datasets and IO pipelines, route to ../data-pipelines/SKILL.md.
• For Ray Train or Tune orchestration, route to ../train-tune/SKILL.md.
• For Ray Serve deployments, route to ../serve-deployments/SKILL.md.
• For RLlib algorithms and environments, route to ../rllib-workloads/SKILL.md.