Zeema
New design
Catalogue / ml-platform

Argilla

ML platformargilla

Open-source server for curating AI datasets with human feedback, now part of Hugging Face.

Provider
default
Kind
ML platform
Category
Compute
Official docs
https://docs.argilla.io/latest/getting_started/how-to-deploy-argilla-with-docker/
Properties · 3 set by this component
propertytypevaluesrequiredkind defaultthis component
computeInstancesinteger—no—inherited
database · component propertyenumsqlite | postgresqlnosqliteinherited
deployment · component propertyenumdocker | hf-spaces | kubernetesnodockerinherited
experimentTrackingboolean—notrue▸ false
gpuEnabledboolean—no—inherited
managedboolean—nofalse▸ false
modelRegistryboolean—notrue▸ false
port · component propertyinteger—no6900inherited
publicAccessboolean—no—inherited
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 redundancyerror
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: "argilla", component: "argilla" }
Other ML platform products · 61
Platform for AIalibaba-paialibabacloudml-platformAWS Clean Rooms MLaws-clean-rooms-mlawsml-platformAmazon SageMaker AIaws-sagemakerawsml-platformAmazon SageMaker Canvasaws-sagemaker-canvasawsml-platformAmazon SageMaker Feature Storeaws-sagemaker-feature-storeawsml-platformManaged MLflow on Amazon SageMaker AIaws-sagemaker-mlflowawsml-platformAmazon SageMaker Unified Studioaws-sagemaker-unified-studioawsml-platformMicrosoft Foundryazure-ai-foundryazureml-platformAzure Machine Learningazure-machine-learningazureml-platformAbacus.AIabacus-aiml-platformAlteryxalteryxml-platformC3 AI Platformc3-aiml-platformClearMLclearmlml-platformCometcomet-mlml-platformCVATcvatml-platformDataikudataikuml-platformDataRobotdatarobotml-platformDatasaurdatasaurml-platformDetermined AIdeterminedml-platformDomino Data Labdomino-data-labml-platformDVCdvcml-platformEncordencordml-platformFeastfeastml-platformFlyteflyteml-platformH2O.aih2oml-platformKilikiliml-platformKNIMEknimeml-platformKubeflowkubeflowml-platformLabel Studiolabel-studioml-platformLabelboxlabelboxml-platformMetaflowmetaflowml-platformGeneric ML platformml-platformml-platformMLflowmlflowml-platformneptune.aineptune-aiml-platformNVIDIA DGX Cloudnvidia-dgx-cloudml-platformPachydermpachydermml-platformPolyaxonpolyaxonml-platformProdigyprodigyml-platformRapidMiner AI Hubrapidminerml-platformRoboflowroboflowml-platform
and 21 more → ML platform page
Source
# yaml-language-server: $schema=../../schema/component.schema.json
# Argilla: open-source collaboration server for building and curating AI datasets with human feedback. Hugging Face
# acquired Argilla in June 2024. Per "Deploy with Docker", the server listens on port 6900 and depends on Elasticsearch,
# a PostgreSQL or SQLite database and Redis; it can also run as a Hugging Face Space.
schemaVersion: "1"
type: "component"
id: "argilla"
kind: "ml-platform"
provider: "default"
docs: "https://docs.argilla.io/latest/getting_started/how-to-deploy-argilla-with-docker/"
defaults:
  managed: false
  experimentTracking: false
  modelRegistry: false
properties:
  port:
    type: "integer"
    default: 6900
  database:
    type: "enum"
    values: ["sqlite", "postgresql"]
    default: "sqlite"
  deployment:
    type: "enum"
    values: ["docker", "hf-spaces", "kubernetes"]
    default: "docker"

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