This repo contains demos from ArgoCon and Open Source Summit.
- Mastering Argo Workflows at Scale: A Practical Guide to Scalability Excellence
- Orchestrating Python Functions Natively in Argo Using Hera
- Panel: Argo for ML: Achieving Scalability and User Experience
- Demystifying Argo Workflows: An Architectural Deep Dive
- Scaling Cloud-Native CI/CD with Jenkins and the Argo Projects
- Argo CD - Plugins as Services
- Scaling to Thousands of Data and CI/CD Pipelines using Argo and Virtual Clusters
- Migrating CI/CD from Jenkins to Argo
- Streamlining big data workflows with memoization and work avoidance
- How to Train an LLM with Argo Workflows with Hera
- Comparing Airflow and Argo Workflows at Scale
- Configuring Volumes for Parallel Workflow Reads and Writes
- Managing Artifacts at Scale for CI and Data Processing
- Leveraging Argo WorkflowTemplates Within a Data Platform
Pipekit’s team of Argo experts provide professional support for companies using Argo Workflows. Pipekit's Argo contributors help platform teams optimize pipeline performance, squash upstream bugs, and make faster technical decisions. Learn more at pipekit.io/services.
For more information about Argo Workflows, please see the following resources:
- The Argo Workflows Documentation
- The Argo Workflows GitHub Repository
- The Argo Workflows Slack Channel
Pipekit is the control plane for Argo Workflows. Platform teams use Pipekit to manage data & CI pipelines at scale, while giving developers self-serve access to Argo. Pipekit's unified logging view, Workflow Metrics dashboards, enterprise-grade RBAC and multi-cluster management capabilities lower maintenance costs for platform teams while delivering a superior devex for Argo users. Sign up for a 30-day free trial at pipekit.io/signup.