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README.md

slack CI

Argo Image

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 directed acyclic 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.

Argo is a Cloud Native Computing Foundation (CNCF) hosted project.

Argo Workflows in 5 minutes

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!

Quickstart

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

Who uses Argo Workflows?

Official Argo Workflows user list

Documentation

Features

  • UI to visualize and manage Workflows
  • Artifact support (S3, Artifactory, Alibaba Cloud OSS, HTTP, Git, GCS, raw)
  • Workflow templating to store commonly used Workflows in the cluster
  • Archiving Workflows after executing for later access
  • Scheduled workflows
  • Server interface with REST API
  • DAG or Steps based declaration of workflows
  • 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
  • Event emission
  • Prometheus metrics
  • Multiple executors
  • Multiple pod and workflow garbage collection strategies
  • Automatically calculated resource usage per step
  • Pod Disruption Budget support

Community Meetings

We host monthly community meetings where we and the community showcase demos and discuss the current and future state of the project. Feel free to join us! For Community Meeting information, minutes and recordings please see here.

Community Blogs and Presentations

Project Resources

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