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README.md

slack

Argoproj - Get stuff done with Kubernetes

Argo Image

Quickstart

kubectl create namespace argo
kubectl apply -n argo -f https://raw.githubusercontent.com/argoproj/argo/stable/manifests/install.yaml

What is Argoproj?

Argoproj is a collection of tools for getting work done with Kubernetes.

  • Argo Workflows - Container-native Workflow Engine
  • Argo CD - Declarative GitOps Continuous Delivery
  • Argo Events - Event-based Dependency Manager
  • Argo Rollouts - Deployment CR with support for Canary and Blue Green deployment strategies

Also argoproj-labs is a separate GitHub org that we setup for community contributions related to the Argoproj ecosystem. Repos in argoproj-labs are administered by the owners of each project. Please reach out to us on the Argo slack channel if you have a project that you would like to add to the org to make it easier to others in the Argo community to find, use, and contribute back.

What is Argo Workflows?

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).

  • Define workflows where each step in the workflow is a container.
  • Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a graph (DAG).
  • Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes.
  • Run CI/CD pipelines natively on Kubernetes without configuring complex software development products.

Why Argo Workflows?

  • Designed from the ground up for containers without the overhead and limitations of legacy VM and server-based environments.
  • Cloud agnostic and can run on any Kubernetes cluster.
  • Easily orchestrate highly parallel jobs on Kubernetes.
  • Argo Workflows puts a cloud-scale supercomputer at your fingertips!

Documentation

Features

  • DAG or Steps based declaration of workflows
  • Artifact support (S3, Artifactory, HTTP, Git, raw)
  • Step level input & outputs (artifacts/parameters)
  • Loops
  • Parameterization
  • Conditionals
  • Timeouts (step & workflow level)
  • Retry (step & workflow level)
  • Resubmit (memoized)
  • Suspend & Resume
  • Cancellation
  • K8s resource orchestration
  • Exit Hooks (notifications, cleanup)
  • Garbage collection of completed workflow
  • Scheduling (affinity/tolerations/node selectors)
  • Volumes (ephemeral/existing)
  • Parallelism limits
  • Daemoned steps
  • DinD (docker-in-docker)
  • Script steps

Who uses Argo?

As the Argo Community grows, we'd like to keep track of our users. Please send a PR with your organization name.

Currently officially using Argo:

  1. Adevinta
  2. Admiralty
  3. Adobe
  4. Alibaba Cloud
  5. BioBox Analytics
  6. BlackRock
  7. Canva
  8. CCRi
  9. Codec
  10. Commodus Tech
  11. CoreFiling
  12. Cratejoy
  13. CyberAgent
  14. Cyrus Biotechnology
  15. Datadog
  16. DataStax
  17. Equinor
  18. Fairwinds
  19. Gardener
  20. Gladly
  21. GitHub
  22. Google
  23. HOVER
  24. IBM
  25. InsideBoard
  26. Interline Technologies
  27. Intuit
  28. Karius
  29. KintoHub
  30. Localytics
  31. Maersk
  32. Max Kelsen
  33. Mirantis
  34. NVIDIA
  35. OVH
  36. Peak AI
  37. Preferred Networks
  38. Quantibio
  39. Red Hat
  40. SAP Fieldglass
  41. SAP Hybris
  42. Sidecar Technologies
  43. Styra
  44. Threekit
  45. Tiger Analytics
  46. Wavefront

Community Blogs and Presentations

Project Resources

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