Argoproj - Get stuff done with Kubernetes
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!
- DAG or Steps based declaration of workflows
- Artifact support (S3, Artifactory, HTTP, Git, raw)
- Step level input & outputs (artifacts/parameters)
- Timeouts (step & workflow level)
- Retry (step & workflow level)
- Resubmit (memoized)
- Suspend & Resume
- 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:
- Alibaba Cloud
- BioBox Analytics
- Commodus Tech
- Cyrus Biotechnology
- Interline Technologies
- Max Kelsen
- Peak AI
- Preferred Networks
- Red Hat
- SAP Fieldglass
- SAP Hybris
- Sidecar Technologies
- Tiger Analytics
Community Blogs and Presentations
- Argo Ansible role: Provisioning Argo Workflows on OpenShift
- Argo Workflows vs Apache Airflow
- CI/CD with Argo on Kubernetes
- Running Argo Workflows Across Multiple Kubernetes Clusters
- Open Source Model Management Roundup: Polyaxon, Argo, and Seldon
- Producing 200 OpenStreetMap extracts in 35 minutes using a scalable data workflow
- Argo integration review
- TGI Kubernetes with Joe Beda: Argo workflow system
- Community meeting minutes and recordings