FeelShy/argo-workflows

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.

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

Use Cases

Machine Learning pipelines
Data and batch processing
ETL
Infrastructure automation
CI/CD

Why Argo Workflows?

Argo Workflows is the most popular workflow execution engine for Kubernetes.
It can run 1000s of workflows a day, each with 1000s of concurrent tasks.
Our users say it is lighter-weight, faster, more powerful, and easier to use
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.

Read what people said in our latest survey

Try Argo Workflows

Access the demo environment (login using Github)

Screenshot

Documentation

View the docs

Ecosystem

Just some of the projects that use or rely on Argo Workflows:

Client Libraries

Check out our Java, Golang and Python clients.

Quickstart

The following commands install Argo Workflows as well as some commmonly used components:

kubectl create ns argo
kubectl apply -n argo -f https://raw.githubusercontent.com/argoproj/argo-workflows/master/manifests/quick-start-postgres.yaml

These manifests are intended to help you get started quickly. They contain hard-coded passwords that are publicly available and are not suitable in production.

Who uses Argo Workflows?

Official Argo Workflows user list

Documentation

Features

UI to visualize and manage Workflows
Artifact support (S3, Artifactory, Alibaba Cloud OSS, Azure Blob Storage, HTTP, Git, GCS, raw)
Workflow templating to store commonly used Workflows in the cluster
Archiving Workflows after executing for later access
Scheduled workflows using cron
Server interface with REST API (HTTP and GRPC)
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
Java/Golang/Python SDKs
Pod Disruption Budget support
Single-sign on (OAuth2/OIDC)
Webhook triggering
CLI
Out-of-the box and custom Prometheus metrics
Windows container support
Embedded widgets
Multiplex log viewer

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.

Participation in the Argo Workflows project is governed by the CNCF Code of Conduct