Agent skill · pproenca
ray
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.
What it needs
About 4k tokens when loaded.
What this skill does
Ray Library-reference skill for production, open-source Ray — 26 rules across 6 categories covering the path from training to serving. Ray's API surface churned hard through the 2.x line (Train V2 became the default, Serve removed parameters and handle classes outright, Ray Data reversed a deprecation), so a model fluent in the older corpus produces code that warns, errors, or silently means something else. Each rule names the wrong default it corrects; there is no rule for things a capable model already gets right. Scope is classic-ML Ray on self-hosted/KubeRay clusters. LLM serving and batch inference (ray.serve.llm, ray.data.llm) are the sibling ray-llm skill. Pinned to ray 2.57.0 (Python ≥ 3.10). API claims were verified against the unpacked 2.57.0 wheel; classic-ML examples were exercised on a live local Ray 2.57.0 runtime. When to Apply Writing or reviewing distributed training code — TorchTrainer, ScalingConfig, checkpointing, fault tolerance Building data pipelines with Ray Data — reads, mapbatches, GPU inference pools, training ingest Running hyperparameter sweeps with Ray Tune, especially combined with Ray Train Writing or reviewing Ray Serve deployments — scaling, handles, composition, production config Using Ray Core primitives directly — tasks, actors, object store, retries Standing up or reviewing production Ray infrastructure — KubeRay CRDs, job submission, fault tolerance, observability Rule Categories # Category Prefix Covers --- ---------- -------- -------- 1 Ray Train train- Train V2 as the default (deprecated config fields), ray.train.report over ray.air session, config imports and elastic scaling, the preparemodel/preparedataloader wrappers 2 Ray Serve serve- maxongoingrequests (old name removed), current autoscaling fields, DeploymentResponse handles, serve.run/build/deploy lifecycle, replica placement options 3 Ray Data data- compute= strategies (the concurrency reversal), overridenumblocks, streaming execution replacing pipelines, torch inges …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills pproenca/ray