---

name: rayorch-acceleration description: "Accelerate DataFlow operators with RayOrch while preserving compile semantics, storage behavior, CPU fallback, deterministic ordering, and actor cleanup." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

RayOrch acceleration

Use this sub-skill when you want to wrap an existing DataFlow operator in RayOrch and keep the surrounding pipeline behavior unchanged.

Covers

• RayAcceleratedOperator
• .op_cls_init(...) and wrapped operator constructor args
• replicas, num_gpus_per_replica, and optional env
• normal, batched, and stream-batched pipelines
• CPU fallback, fractional GPU allocation, deterministic ordering, and actor cleanup

Keep unchanged

• compile-time key validation
• storage selection and cache layout
• operator authoring and backend choice

Route elsewhere

• operator authoring, storage classes, or input_* / output_* validation -> pipeline-foundations
• serving backend choice or local model selection -> serving-cli or the workflow owner

Read first

• references/api-reference.md
• references/workflows.md
• references/troubleshooting.md
• scripts/smoke_rayorch_cpu.py

Rules of thumb

1. Wrap only row-independent, deterministic operators.
2. Use num_gpus_per_replica=0.0 for CPU-only smoke and fallback.
3. Make fractional GPU allocation explicit when sharing devices.
4. Call shutdown() when you manage Ray stages manually; compiled pipelines auto-shutdown after each stage.
5. Keep outer storage as the normal DataFlow storage; the Ray layer only accelerates execution.

If a request needs operator schema changes or key validation help, hand it off to pipeline-foundations before touching RayOrch.