🌍 Deploy Anywhere

Here's the ultimate superpower: Same code runs everywhere. Just change the configuration.

Good news: Your runnable pipeline is already production-ready. You've been building with production in mind this whole time.

Your code never changes

from runnable import Pipeline, PythonTask

def train_model():
    # Your model training logic
    print("Training model...")
    return "model_v1.pkl"

def main():
    pipeline = Pipeline(steps=[
        PythonTask(function=train_model, name="training")
    ])

    pipeline.execute()
    return pipeline

if __name__ == "__main__":
    main()

Change where it runs

💻 Local development

pipeline.execute()  # Runs on your laptop

🐳 Container execution

pipeline.execute(configuration_file="local-container.yaml")  # Runs in Docker

☁️ Cloud platforms

pipeline.execute(configuration_file="argo.yaml")  # Runs on Argo Workflows

Configuration files define the environment

The same pipeline code runs in any environment - just change the configuration:

DevelopmentTestingContainerizedProduction

Fast local development (default configuration):

local.yaml

pipeline-executor:
  type: local

run-log-store:
  type: file-system

catalog:
  type: file-system

secrets:
  type: env-secrets

# Uses default configuration
uv run my_pipeline.py

Pipeline validation without execution:

mocked.yaml

pipeline-executor:
  type: mocked

run-log-store:
  type: file-system

catalog:
  type: file-system

secrets:
  type: env-secrets

export RUNNABLE_CONFIGURATION_FILE=mocked.yaml
uv run my_pipeline.py

Isolated execution in containers:

local-container.yaml

pipeline-executor:
  type: local-container
  config:
    # Build from project root to include runnable + dependencies
    docker_image: "my-pipeline:latest"  # or use existing image with runnable installed

run-log-store:
  type: file-system

catalog:
  type: file-system

secrets:
  type: env-secrets

Container Image Requirements

The Docker image must have Runnable installed. Either:

• Build from your project root: docker build -t my-pipeline:latest . (includes your code + runnable)
• Use a base image with runnable: FROM python:3.11 then RUN pip install runnable
• Never use bare python:3.11 - it doesn't include runnable

export RUNNABLE_CONFIGURATION_FILE=local-container.yaml
uv run my_pipeline.py

Production orchestration on Kubernetes:

argo.yaml

pipeline-executor:
  type: argo
  config:
    pvc_for_runnable: runnable
    defaults:
      image: "my-pipeline:v1.0"
      resources:
        limits:
          cpu: "2"
          memory: 4Gi
        requests:
          cpu: "1"
          memory: 2Gi
    argoWorkflow:
      metadata:
        generateName: "pipeline-"
        namespace: production
      spec:
        serviceAccountName: "pipeline-executor"

run-log-store:
  type: chunked-fs
  config:
    log_folder: /mnt/run_log_store

catalog:
  type: s3
  config:
    bucket: production-data

secrets:
  type: env-secrets

export RUNNABLE_CONFIGURATION_FILE=argo.yaml
uv run my_pipeline.py

Same code, different environments - just change the RUNNABLE_CONFIGURATION_FILE.

The power of environment-agnostic code

Development:

# Quick local testing
uv run my_pipeline.py

Production:

# Same code, production environment
export RUNNABLE_CONFIGURATION_FILE=production-argo.yaml
uv run my_pipeline.py

Real-world example

From development to production:

from runnable import Pipeline, PythonTask
from examples.common.functions import hello

def main():
    # Same code, different environments
    task = PythonTask(function=hello, name="say_hello")
    pipeline = Pipeline(steps=[task])

    # This execute() call works for both development and production
    # Environment determined by RUNNABLE_CONFIGURATION_FILE
    pipeline.execute()
    return pipeline

if __name__ == "__main__":
    main()

Development (uses default local config):

uv run my_pipeline.py

Production (same code, argo config):

export RUNNABLE_CONFIGURATION_FILE=argo.yaml
uv run my_pipeline.py

See complete runnable code

examples/01-tasks/python_tasks.py

"""
You can execute this pipeline by:

    python examples/01-tasks/python_tasks.py

The stdout of "Hello World!" would be captured as execution
log and stored in the catalog.
"""

from examples.common.functions import hello
from runnable import Pipeline, PythonTask


def main():
    # Create a tasks which calls the function "hello"
    # If this step executes successfully,
    # the pipeline will terminate with success
    hello_task = PythonTask(  # [concept:task]
        name="hello",
        function=hello,
    )

    # The pipeline has only one step.
    pipeline = Pipeline(steps=[hello_task])  # [concept:pipeline]

    pipeline.execute()  # [concept:execution]
    return pipeline


if __name__ == "__main__":
    main()

Try it now:

uv run examples/01-tasks/python_tasks.py

Dev environment: Runs in 2 seconds on your laptop Prod environment: Runs on Kubernetes with monitoring, logging, and auto-scaling

Zero code changes.

Configuration Reference

Ready to customize your deployment? Check the configuration documentation:

• Pipeline Executors - Choose where pipelines run (local, argo, etc.)
• Job Executors - Configure task execution (local, containers, kubernetes)
• Storage - Set up data persistence (local, S3, MinIO)
• Logging - Configure execution logs
• Secrets - Manage sensitive configuration

You now know the core concepts! Start with the examples above, then dive into the configuration reference for advanced setups.