Orchestrate Twin Deployments with Kubeflow and AWS IoT TwinMaker SDK

                      
                     
from typing import Dict, Any
import os
import json
import boto3
from kubeflow import Client

# Configuration
AWS_REGION = os.getenv('AWS_REGION', 'us-west-2')
AWS_IOT_ENDPOINT = os.getenv('AWS_IOT_ENDPOINT')
KUBEFLOW_NAMESPACE = os.getenv('KUBEFLOW_NAMESPACE', 'kubeflow')

# Initialize AWS IoT client
client = boto3.client('iot', region_name=AWS_REGION)

# Initialize Kubeflow client
kf_client = Client(namespace=KUBEFLOW_NAMESPACE)

def create_twin_deployment(deployment_data: Dict[str, Any]) -> None:
    try:
        # Create the twin deployment in AWS IoT TwinMaker
        response = client.create_scene(
            workspaceId=deployment_data['workspaceId'],
            sceneId=deployment_data['sceneId'],
            scene=deployment_data['scene']
        )
        print(f"Deployment created: {response['sceneId']}")
    except Exception as e:
        print(f"Error creating deployment: {str(e)}")

def orchestrate_deployment() -> None:
    try:
        # Sample deployment data
        deployment_data = {
            'workspaceId': 'my_workspace',
            'sceneId': 'my_scene',
            'scene': {'name': 'MyScene', 'description': 'A test scene'}
        }
        create_twin_deployment(deployment_data)
    except Exception as e:
        print(f"Error in orchestration: {str(e)}")

if __name__ == '__main__':
    orchestrate_deployment()