# Ray

> Scales AI and Python applications across clusters with distributed computing primitives for ML workloads.

- Skill: `neuralblitz/ray` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/ray`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/ray/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Model Training & Fine-tuning
- Tags: Distributed Computing, Ml Workloads, Ray, Ray Serve, Ray Tune, Rllib
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/neuralblitz/ray

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# Ray

Ray is a unified framework for scaling AI and Python applications. It provides distributed computing primitives for ML workloads, enabling seamless scaling from single machines to clusters.

## Key Concepts

- Ray Tasks and Actors
- Ray Serve for deployment
- Ray Datasets
- Ray Tune for hyperparameter search
- Ray RLlib for reinforcement learning

## Common Use Cases

- Distributed training
- Hyperparameter tuning
- Batch inference
- Reinforcement learning
- Scalable Python applications

## Best Practices

- Design for actor isolation
- Use object stores efficiently
- Configure proper resource allocation
- Monitor Ray dashboard
- Use Ray Serve for production

## Resources

- Docs: docs.ray.io
- Related Skills: distributed-systems, pytorch, tensorflow