Orchestrate Multi-Cloud AI Workloads with SkyPilot and Docker SDK

                      
                     
import os
import docker
from sky import SkyPilot
from typing import Dict, Any

# Configuration
sky = SkyPilot(api_key=os.getenv('SKY_API_KEY'))
docker_client = docker.from_env()

# Function to deploy AI workload on multiple clouds
async def deploy_workload(cloud: str, params: Dict[str, Any]) -> None:
    try:
        # Create Docker container
        container = docker_client.containers.run(
            'my-ai-model',
            detach=True,
            environment={'MODEL_PARAMS': params['model_params']},
            ports={80: 80}
        )
        # Deploy to SkyPilot
        sky.deploy(container, cloud)
        print(f'Deployed to {cloud} successfully.')
    except Exception as error:
        print(f'Error deploying to {cloud}: {str(error)}')

# Main execution
if __name__ == '__main__':
    clouds = ['aws', 'gcp', 'azure']
    model_parameters = {'model_params': 'param1,param2,param3'}
    for cloud in clouds:
        deploy_workload(cloud, model_parameters)