Ray Serve

ML inferenceray-serve

Model serving library on a Ray cluster with replica scaling and composition.

Provider
default
Category
Compute
Properties · 4 set by this component
propertytypevaluesrequiredkind defaultthis component
acceleratorenumnone | gpu | asicnononeinherited
acceleratorModel · advancedenumnvidia-h100 | nvidia-h200 | nvidia-b200 | nvidia-l4 | nvidia-a100 | amd-mi300x | aws-trainium | aws-inferentia | google-tpu | intel-gaudi | groq-lpu | cerebras-wseno—inherited
autoscaling · component propertyboolean—nofalseinherited
embeddingSyncMinutes · advancedinteger—no—inherited
inputsenumlive | historical | bothnolive▸ live
instancesinteger—no—▸ 1
managedboolean—nofalse▸ false
maxOngoingRequests · component propertyinteger—no5inherited
modelComposition · component propertyboolean—nofalseinherited
snapshotToObjectStore · advancedboolean—nofalseinherited
trainingenumbatch | onlinenobatch▸ batch
Rules that apply · 1

Every rule whose selector covers this component's kind or its category. Whether a rule fires depends on the design: its properties, its edges and its groups.

stateful-node-without-zone-redundancy

A stateful compute node pinned to exactly one availability zone disappears with that zone, and its state goes with it. Nodes inside a cell are exempt, because there the cell is deliberately single-zone and redundancy comes from the other cells.

  • — all of these hold:
  • — the node's `stateless` is false
  • — the node sits inside exactly 1 availability-zone group
  • — this does not hold:
  • — the node sits inside at least one cell group
Use it in a design
nodes:
  - { id: "ray-serve", component: "ray-serve" }
Other ML inference products · 37
Source
# yaml-language-server: $schema=../../schema/component.schema.json
# Ray Serve: model serving library on a Ray cluster, one deployment per served model.
# Defaults from "Configure Ray Serve deployments": num_replicas 1 with autoscaling opt-in via
# num_replicas="auto", and max_ongoing_requests 5 since Ray 2.32.0.
schemaVersion: "1"
type: "component"
id: "ray-serve"
kind: "ml-inference"
provider: "default"
docs: "https://docs.ray.io/en/latest/serve/configure-serve-deployment.html"
defaults:
  training: "batch"
  inputs: "live"
  instances: 1
  managed: false
properties:
  autoscaling:
    type: "boolean"
    default: false
  maxOngoingRequests:
    type: "integer"
    default: 5
  modelComposition:
    type: "boolean"
    default: false

The same page as markdown, for an agent: /catalogue/components/ray-serve.md