Ray: Scale AI Workloads with 44k+ GitHub Stars
From the repo · “Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.”
Ray accelerates machine learning workloads by providing a distributed compute engine for scalable AI applications
- •You need to scale your ML model training across multiple machines
- •You want to accelerate your AI workflows with a high-performance compute engine
- •You're building a real-time AI application that requires low-latency processing
ray.init(); @ray.remote; def train_model(data): # your model training code here; train_model.remote([1, 2, 3])
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Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/ray && curl -fsSL https://workflowstacks.com/api/skills/ray/claude-skill -o ~/.claude/skills/ray/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Ray: Scale AI Workloads with 44k+ GitHub Stars is a very large Python project (~968k lines across 4640 code files, plus 1888 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, Apache-2.0 license, has a test suite.
- 1README.rstStart here — what it does and how to install it
- 2AGENTS.mdThe instructions the AI actually follows
- 3pyproject.tomlDependencies and the commands it exposes
- 4src/ray/pubsub/README.mdInside src/ — the main logic begins here
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/ray && curl -fsSL https://workflowstacks.com/api/skills/ray/claude-skill -o ~/.claude/skills/ray/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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