---

name: ray description: "Use Ray for distributed Python applications, clusters, Data pipelines, Train/Tune experiments, Serve deployments, and RLlib workloads." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Ray

Use this repo skill when a task is about Ray, ray, Ray Core, Ray clusters, Ray Data, Ray Train, Ray Tune, Ray Serve, or RLlib. Ray is a distributed Python runtime plus AI libraries for scalable data processing, training, tuning, serving, and reinforcement learning.

Start Here

1. Confirm the user’s workflow and install surface. Prefer narrow extras such as ray[default], ray[data], ray[train], ray[tune], ray[serve], or ray[rllib]; avoid ray[all] unless the user explicitly needs many unrelated Ray libraries.

2. Run the root environment checker when package state is unclear:

   python scripts/check_ray_environment.py --help
   python scripts/check_ray_environment.py --require core --require data --json
   

3. Route to the focused sub-skill before writing detailed code, commands, configs, or troubleshooting steps.

4. For cluster-mutating commands such as ray start, ray stop, ray up, ray down, ray job submit, or serve deploy, confirm the target cluster/address and side effects first.

Route Map

User task	Read
Use ray.remote, tasks, actors, ObjectRef, ray.get, ray.wait, resource annotations, object store behavior, or Core anti-patterns	sub-skills/core-runtime/SKILL.md
Operate clusters with ray CLI, Jobs, dashboard, runtime environments, ray status, ray list, logs, state APIs, or OOM observability	sub-skills/cluster-ops/SKILL.md
Build ray.data pipelines, load local/cloud data, transform rows/batches, write datasets, tune blocks/concurrency, or debug Data schemas/OOM	sub-skills/data-pipelines/SKILL.md
Use Ray Train or Tune for distributed training, checkpoints, storage, Tuner, TuneConfig, schedulers, resources, or ResultGrid	sub-skills/train-tune/SKILL.md
Build, run, deploy, lint, update, or troubleshoot Ray Serve apps, @serve.deployment, Serve YAML, serve run, or serve deploy	sub-skills/serve-deployments/SKILL.md
Configure RLlib AlgorithmConfig/PPOConfig, Gymnasium envs, EnvRunners, Learners, RLlib checkpoints, or RLlib Tune sweeps	sub-skills/rllib-workloads/SKILL.md

Cross-Workflow Rules

• Use core-runtime for Python runtime semantics even when a higher-level library starts Ray under the hood.
• Use cluster-ops for Jobs, dashboard, state, logs, and runtime environment packaging around any Ray application.
• Use data-pipelines for dataset construction before handing training/tuning to train-tune or model serving to serve-deployments.
• Use train-tune for generic Tune search spaces, schedulers, resources, result analysis, and storage; use rllib-workloads only for RLlib-specific config/env/algorithm work.
• Use serve-deployments for Serve application configs and lifecycle, but route generic cluster startup, dashboard, and node visibility back to cluster-ops.

Installation And Import Checks

Ray’s Python package is distributed as ray and imports as ray. The source baseline for this skill supports Python >=3.10.

pip install ray
python - <<'PY'
import ray
print(ray.__version__)
PY

Choose extras by workflow:

pip install "ray[default]"  # cluster dashboard, jobs, state APIs
pip install "ray[data]"     # Ray Data readers/transforms
pip install "ray[train]"    # Ray Train
pip install "ray[tune]"     # Ray Tune
pip install "ray[serve]"    # Ray Serve
pip install "ray[rllib]" torch  # RLlib with common PyTorch backend

Shared References

• Read references/troubleshooting.md for cross-cutting install/import, optional extras, Python version, cluster command safety, and workflow routing failures.
• Read references/repo-provenance.md when checking whether this skill is stale relative to a Ray checkout or package version.
• references/repo-routing-metadata.json is structured import metadata consumed by repo-skills-router during managed import.

Bundled Root Script

• scripts/check_ray_environment.py checks package metadata, selected imports, optional CLI commands, and common workflow extras without starting a Ray cluster.

Out Of Default Scope

This skill is Python-focused. Java, C++, LLM extra-extra workflows, heavy benchmarks, release engineering, CI/build infrastructure, cloud credential provisioning, Kubernetes operator deployment authoring, and GPU/service-specific notebooks are recorded as gaps unless the user explicitly asks for those areas and provides the needed environment or platform constraints.