A workflow engine built for Kubernetes.

Argo lets you define multi-step workflows as containers. These run inside your cluster. Each step is managed by Kubernetes. No extra services. No external runners.

You get clear execution, native scaling, and simple YAML-based configuration for data jobs, machine learning, and CI/CD.

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What Is Argo?

Argo is a Kubernetes-native workflow engine. It runs jobs as containers and manages them using Kubernetes resources.

The core tool is Argo Workflows. It is implemented as a Kubernetes Custom Resource Definition (CRD). Each workflow is a YAML file. Each step is a container. You can run tasks in order or in parallel using a Directed Acyclic Graph (DAG).

This lets you break work into small parts. You control how tasks connect. Argo handles execution in the cluster.

You can:

• Run compute-heavy jobs
• Build CI/CD pipelines
• Automate complex multi-step systems

Argo works with any Kubernetes cluster. You do not need wrappers or external engines. You get full control from inside Kubernetes.

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How Argo Works

Argo runs as a controller in your Kubernetes cluster. It listens for workflows. Each workflow is a live resource, just like a pod or deployment.

You define the workflow in YAML. It includes:

• A spec with logic and parameters
• A list of templates
• An entrypoint to start the workflow

Each step runs in its own container. Tasks can run in order or in parallel. Argo uses your cluster to create pods, manage volumes, and track status.

This structure gives you full control. You can debug single steps, control resources per container, and repeat runs with the same logic.

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Why Argo for Parallel Jobs

Argo is built for container-native jobs. It does not require outside schedulers or agents. It speaks Kubernetes.

Here’s how that helps:

• Built-in parallelism: DAGs let you define what runs at the same time. Argo runs those steps in parallel.
• Simple resource control: Each step is a pod. Set memory and CPU in YAML. Mount volumes as needed.
• Low overhead: There is no need to manage extra infrastructure. Argo runs inside your cluster.
• Reproducible runs: Every workflow lives in code. You can version and reuse it.
• Full visibility: Use the CLI or UI to see progress, logs, and failures.

Argo fits:

• Data pipelines with many steps
• CI/CD pipelines with separate stages
• ML workflows with multiple model runs
• Batch jobs with dependency rules

If your workloads already live in Kubernetes, Argo helps you control them from the inside.

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Core Concepts

To use Argo well, you should understand these parts.

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Workflows

A workflow is a top-level resource. It defines how a job runs. Argo watches it and manages its state.

The workflow spec includes:

• The entrypoint step
• A list of templates
• Optional inputs, labels, or volumes

Each workflow is written in YAML and can be saved in version control.

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Templates

Templates define tasks. Each one is a reusable block.

Common types:

• Container: Runs a container with a command
• Script: Runs inline code inside a container
• Resource: Creates or deletes Kubernetes objects
• Steps: Runs tasks in sequence
• DAG: Runs tasks using dependencies

You can pass inputs, get outputs, and reuse templates across workflows.

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DAGs

DAGs show which tasks depend on each other. Argo reads the graph and runs tasks when they are ready.

Use DAGs to:

• Train multiple models in parallel
• Run pre-processing before training
• Run alerts only after final steps finish

Each DAG node runs in its own container. You get full isolation and clear logs.

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Parameters and Artifacts

Use parameters for simple values like strings or paths. Use artifacts for files.

Artifacts are stored in object storage like S3 or GCS. Argo handles upload and download.

You can share data between steps without mounting shared volumes. This makes jobs portable and easy to test.

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FAQs

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What is Argo in Kubernetes?

It is a workflow engine that runs containers as jobs inside Kubernetes.

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Is Argo open source?

Yes. Argo is part of the Cloud Native Computing Foundation and is free to use.

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How is Argo implemented?

As a Kubernetes CRD. You submit workflows as YAML. Argo handles the rest.

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How is it different from other tools?

Argo runs fully in Kubernetes. Other tools may run outside the cluster and need extra services.

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Can it run jobs in parallel?

Yes. Argo uses DAGs to handle parallel execution.

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What kind of work is Argo best for?

Use it for:

• Data pipelines
• CI/CD
• ML training
• Batch jobs
• Event-driven jobs

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Can steps share data?

Yes. Use parameters for small values and artifacts for larger files.

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What do I need to run it?

A Kubernetes cluster and kubectl. Install the controller with a few YAML files.

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Is it ready for production?

Yes. Argo is used in production by many companies. It supports RBAC, retries, and full logging.

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Is it tied to one cloud provider?

No. Argo works on any Kubernetes cluster.

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Summary

Argo is a simple but powerful workflow engine for Kubernetes. It lets you break big jobs into small, trackable steps. Each one runs in a container and follows clear rules.

You get full control using YAML. Each workflow is repeatable, versioned, and observable. You can use Argo for training models, building CI/CD pipelines, or processing large datasets.

Argo runs inside Kubernetes. You don’t need to manage outside tools. You use the same APIs and resources already in place.

If your team needs a way to run multi-step work inside Kubernetes, Argo gives you a clear path with real control. It helps you ship faster, debug smarter, and scale safely.

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