LMDeploy

ML inferencelmdeploy

InternLM toolkit that compresses and serves LLMs behind an OpenAI-compatible API server.

Provider
default
Category
Compute
Properties · 4 set by this component
propertytypevaluesrequiredkind defaultthis component
acceleratorenumnone | gpu | asicnonone▸ gpu
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
apiKeyRequired · component propertyboolean—nofalseinherited
backend · component propertyenumturbomind | pytorchnoturbomindinherited
embeddingSyncMinutes · advancedinteger—no—inherited
inputsenumlive | historical | bothnolive▸ live
instancesinteger—no—inherited
managedboolean—nofalse▸ false
port · component propertyinteger—no23333inherited
snapshotToObjectStore · advancedboolean—nofalseinherited
tensorParallelSize · component propertyinteger—no1inherited
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: "lmdeploy", component: "lmdeploy" }
Other ML inference products · 37
Source
# yaml-language-server: $schema=../../schema/component.schema.json
# LMDeploy: InternLM's toolkit for compressing and serving LLMs, with an OpenAI-compatible api_server.
# Per "OpenAI Compatible Server", `lmdeploy serve api_server` listens on port 23333, runs the TurboMind engine unless
# the PyTorch engine is chosen, and uses tensor parallel size 1; no API key is set unless one is passed.
schemaVersion: "1"
type: "component"
id: "lmdeploy"
kind: "ml-inference"
provider: "default"
docs: "https://lmdeploy.readthedocs.io/en/latest/llm/api_server.html"
defaults:
  training: "batch"
  inputs: "live"
  accelerator: "gpu"
  managed: false
properties:
  port:
    type: "integer"
    default: 23333
  backend:
    type: "enum"
    values: ["turbomind", "pytorch"]
    default: "turbomind"
  tensorParallelSize:
    type: "integer"
    default: 1
  apiKeyRequired:
    type: "boolean"
    default: false

The same page as markdown, for an agent: /catalogue/components/lmdeploy.md