ray

import ray import time import os # Initialize Ray (connects to an existing cluster or starts a local one) # For a cluster, you might use ray.init(address="auto") or specify an address. # For local testing, ray.init() is sufficient. ray.init(address=os.environ.get('RAY_ADDRESS', None), ignore_reinit_error=True) @ray.remote def fibonacci(n): if n <= 1: return n return fibonacci.remote(n - 1) + fibonacci.remote(n - 2) @ray.remote def slow_square(x): time.sleep(1) # Simulate a slow computation return x * x if __name__ == '__main__': print("--- Ray Tasks Example ---") # Run tasks in parallel futures = [slow_square.remote(i) for i in range(5)] results = ray.get(futures) print(f"Results from slow_square: {results}") # Example of recursive Ray tasks (note: fibonacci is a common but inefficient example for Ray's overhead) # For larger N, this can quickly create too many tasks. # result_fib = ray.get(fibonacci.remote(10)) # print(f"Fibonacci(10) using Ray: {result_fib}") print("Ray initialized successfully, dashboard at:", ray.get_dashboard_url()) ray.shutdown() print("Ray shutdown.")