| name | ray-llm |
| description | LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill. |
Ray LLM
Library-reference skill for LLM workloads on open-source Ray — 13 rules across 5 categories covering ray.serve.llm (OpenAI-compatible, vLLM-backed serving) and ray.data.llm (batch inference). This surface churned faster than any other part of Ray — a standalone repo was absorbed and archived, entry points were renamed, and config shapes restructured — so the examples a model learned from mostly no longer run. Each rule names the wrong default it corrects; there is no rule for things a capable model already gets right.
Scope is the LLM-specific layer. Generic Serve/Data/cluster decisions (deployment lifecycle, autoscaling semantics, KubeRay) are the sibling ray skill — the two compose.
Pinned to ray 2.57.0 (ray[llm] extra, which pins its matching vLLM). API claims were verified against the unpacked 2.57.0 wheel and the installed package source, and every config example in the rules was constructed under CPU-only pydantic validation (including the traps, which fail exactly as described); engine/GPU runtime behavior is source-verified only — no model was actually served.
When to Apply
- Standing up or reviewing an OpenAI-compatible LLM serving deployment on Ray
- Writing batch LLM inference over datasets — summarization, embedding, scoring at scale
- Sizing or placing multi-GPU models — tensor/pipeline parallelism, accelerator selection
- Scaling LLM deployments — replica autoscaling, ingress sizing, request routing
- Serving families of LoRA fine-tunes of a shared base model
- Migrating code that uses the archived ray-llm repo, hand-rolled vLLM engines, or pre-2.5x
ray.data.llm names
Rule Categories
| # | Category | Prefix | Covers |
|---|
| 1 | Serving Setup | serve- | LLMConfig + build_openai_app over hand-rolled engines and the archived repo; model_id vs model_source; the ray[llm]↔vLLM version pin; relocated LLMServer/OpenAiIngress imports |
| 2 | Batch Inference | batch- | build_processor (old name removed), stage configs over boolean flags, CPU-default accelerator_type and autoscaling concurrency, HTTP/Serve processor alternatives |
| 3 | Placement & Accelerators | place- | The engine's own TP×PP placement group (and when to override its strategy), validated accelerator_type names |
| 4 | Autoscaling & Routing | scale- | deployment_config.autoscaling_config with engine-sized replicas, ingress replica sizing, prefix-cache-affinity routing |
| 5 | LoRA & API Surface | api- | Dynamic LoRA multiplexing over per-adapter deployments; the full OpenAI endpoint surface; GPU-free config validation |
Quick Reference
1. Serving Setup
2. Batch Inference
3. Placement & Accelerators
4. Autoscaling & Routing
5. LoRA & API Surface
How to Use
Read a reference file when its decision comes up. Each rule names the wrong default it corrects, then shows the canonical way (with an incorrect/correct contrast only where the wrong way is a real trap).
Related Skills
ray — the sibling rule pack for classic-ML Ray (Train, Tune, Data, Serve, Core, KubeRay production topology); generic Serve and cluster decisions live there
Reference Files
