Deploying Kubiya Workflows

This guide explains how to deploy Kubiya workflows in various environments.

Local Deployment

For local development and testing, you can run Kubiya workflows directly in your Python application.

Python

from kubiya_sdk import Workflow
from kubiya_sdk.tools import tool

@tool()
def hello_world(name: str) -> str:
    return f"Hello, {name}!"

workflow = Workflow(
    id="hello-workflow",
    description="A simple hello world workflow",
    tools=[hello_world]
)

result = workflow.execute({"name": "User"})
print(result)  # Output: "Hello, User!"

Server Deployment

For production environments, you can deploy Kubiya workflows as a service using the Kubiya server component:

1. Install Server Dependencies

Bash

pip install "kubiya-sdk[server]"

2. Create a Server Configuration

Python

# server.py
from kubiya_sdk.server import KubiyaServer
from kubiya_sdk import Workflow
from kubiya_sdk.tools import tool

@tool()
def hello_world(name: str) -> str:
    return f"Hello, {name}!"

# Create a workflow
workflow = Workflow(
    id="hello-workflow",
    description="A simple hello world workflow",
    tools=[hello_world]
)

# Create and start the server
server = KubiyaServer()
server.register_workflow(workflow)

if __name__ == "__main__":
    server.start()

3. Start the Server

Bash

python server.py

The server will start on http://localhost:8000 by default.

Docker Deployment

You can also deploy Kubiya workflows using Docker:

1. Create a Dockerfile

Docker

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "server.py"]

2. Create a requirements.txt file

Text Only

kubiya-sdk[server]

3. Build and Run the Docker Container

Bash

# Build the Docker image
docker build -t kubiya-workflows .

# Run the Docker container
docker run -p 8000:8000 kubiya-workflows

Environment Configuration

You can configure your Kubiya deployment using environment variables:

• KUBIYA_LOG_LEVEL: Set the logging level (e.g., INFO, DEBUG)
• KUBIYA_PORT: The port for the server to listen on
• KUBIYA_HOST: The host address to bind to

Example:

Bash

KUBIYA_LOG_LEVEL=DEBUG KUBIYA_PORT=9000 python server.py

Next Steps

• Secure your deployment with API keys and authentication
• Set up monitoring and logging for your deployments
• Consider implementing rate limiting and caching for production workloads