Argo Workflows

Define complex multi-step workflows as Directed Acyclic Graphs (DAGs) using YAML manifests. Each step runs in its own container with explicit dependency declarations.

• Parallel execution: Run independent steps simultaneously to maximize GPU utilization
• Conditional branching: Implement if/else logic based on step outputs or exit codes
• Recursive DAGs: Nest workflows within workflows for modular, reusable pipeline components

Example: A training pipeline that fans out hyperparameter sweeps across 16 GPUs, then aggregates results in a final evaluation step.

Automatically pass files and data between workflow steps without manual volume mounting. Argo integrates with S3-compatible object stores, GCS, and Azure Blob Storage.

• Input/Output artifacts: Declare files that steps produce or consume
• Artifact repositories: Configure MinIO or local S3 endpoints for air-gapped clusters
• Compression and archiving: Automatic tarball creation for directory artifacts

Critical for disconnected environments: configure an on-premises artifact repository to store intermediate model checkpoints and training datasets without external network access.

Encapsulate logic into reusable WorkflowTemplates and ClusterWorkflowTemplates that can be referenced across teams and namespaces.

• Steps template: Define sequential or parallel execution blocks
• DAG template: Specify complex dependency graphs between tasks
• Script template: Run inline scripts with custom images
• Resource template: Directly create and manage Kubernetes resources

This composability enables platform teams to build a library of approved ML pipeline components that data scientists can assemble without writing infrastructure code.

Implement robust error handling and cleanup logic with exit handlers that execute regardless of workflow success or failure.

• On-exit hooks: Run cleanup containers to release GPU allocations or delete temporary volumes
• Retry strategies: Configure exponential backoff with max retry limits per step
• Timeout controls: Set active deadline seconds to prevent runaway training jobs

Example: A workflow that automatically archives training logs to a local persistent volume and cordons failed GPU nodes when a distributed training job crashes.

Run workflows on a time-based schedule using CronWorkflow resources, a first-class custom resource that extends Argo's capabilities.

• Standard cron syntax: Define schedules with minute-hour-day-month-weekday precision
• Concurrency policy: Allow, forbid, or replace overlapping workflow runs
• Starting deadline: Set maximum tolerated delay for missed triggers
• Time zone support: Schedule based on local time zones for regional data processing

Ideal for periodic model retraining pipelines, nightly data aggregation jobs, or scheduled inference batch processing in air-gapped environments.

Monitor and debug pipelines with built-in visualization and persistent logging capabilities.

• Argo UI: Interactive DAG visualization showing real-time step status and logs
• Workflow archive: Persist completed workflow records with TTL-based cleanup
• Prometheus metrics: Export custom metrics for integration with existing monitoring stacks
• Pod annotations: Propagate metadata to underlying pods for log aggregation

Configure the workflow controller to archive completed runs to an on-premises PostgreSQL database, enabling audit trails for compliance without external SaaS dependencies.