Description
Argo Workflows is an open-source, container-native workflow engine specifically designed for orchestrating parallel jobs on Kubernetes. As a Kubernetes Custom Resource Definition (CRD), it allows users to define workflows where each step is a container, modeling multi-step workflows as a sequence of tasks or capturing dependencies using a directed acyclic graph (DAG). This makes it particularly useful for compute-intensive jobs such as machine learning and data processing, allowing them to run in a fraction of the time.
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Argo Workflows is a graduated project of the Cloud Native Computing Foundation (CNCF) and is recognized as the most popular workflow execution engine for Kubernetes. It is lightweight, scalable, and user-friendly, with support for Python users through the Hera Python SDK. The platform is cloud-agnostic, capable of running on any Kubernetes cluster, and eliminates the overhead and limitations of legacy VM and server-based environments.
The engine supports a wide range of features, including a user interface to visualize and manage workflows, artifact support, workflow templating, and archiving. It also offers scheduled workflows using cron, a server interface with REST API, and various workflow declaration methods. Additional capabilities include step-level input and outputs, loops, parameterization, conditionals, timeouts, retries, and resource orchestration.
Argo Workflows is utilized by over 200 organizations and integrates with several projects such as Argo Events, Hera, Katib, and Kubeflow Pipelines. It provides client libraries in Java, Golang, Python, and Typescript, and supports multiple executors, pod disruption budgets, and webhook triggering. The platform is ideal for industries involved in machine learning, data processing, and CI/CD, offering a robust solution for managing complex workflows on Kubernetes.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen Source
- DeploymentKubernetes
- PlatformsCloud
Argo Workflows's Core Features
Open-source workflow engine
Kubernetes CRD implementation
Supports DAG and step-based workflows
Artifact support with multiple storage options
Workflow templating and archiving
Scheduled workflows using cron
REST API server interface
Supports multiple programming languages
How to use Argo Workflows?
Define: Create workflows using containers
Model: Use DAGs or steps for task dependencies
Run: Execute workflows on Kubernetes clusters
Monitor: Use UI to visualize and manage workflows
Argo Workflows's Use Cases
- Machine Learning Pipelines
- Data Processing
- CI/CD
- Infrastructure Automation
- Batch Processing
