Orchestrate Distributed AI Workloads with Ray and Kubernetes Python Client

                      
                     
from typing import List, Dict
import os
import ray
from kubernetes import client, config

# Configuration
KUBE_CONFIG_PATH = os.getenv('KUBE_CONFIG_PATH', '/path/to/kube/config')
RAY_ADDRESS = os.getenv('RAY_ADDRESS', 'auto')

# Initialize Kubernetes client
config.load_kube_config(KUBE_CONFIG_PATH)

# Initialize Ray
ray.init(address=RAY_ADDRESS)

# Define a simple Ray remote function
@ray.remote
def compute_square(x: int) -> int:
    return x * x

# Function to submit tasks to Ray
def submit_tasks(numbers: List[int]) -> Dict[int, int]:
    try:
        futures = [compute_square.remote(num) for num in numbers]
        results = ray.get(futures)
        return dict(zip(numbers, results))
    except Exception as e:
        print(f'Error occurred: {str(e)}')
        return {}

if __name__ == '__main__':
    numbers_to_process = [1, 2, 3, 4, 5]
    results = submit_tasks(numbers_to_process)
    print(f'Results: {results}')