# 传统分布式编程（复杂）
import multiprocessing
import queue
import threading

class DistributedInference:
    def __init__(self, num_workers):
        self.num_workers = num_workers
        self.workers = []
        self.task_queue = queue.Queue()
        self.result_queue = queue.Queue()
        
        # 需要手动管理进程、队列、通信等
        for i in range(num_workers):
            worker = multiprocessing.Process(
                target=self.worker_function,
                args=(i,)
            )
            worker.start()
            self.workers.append(worker)

# Ray分布式编程（简单）
@ray.remote
def inference_task(input_data):
    return model.generate(input_data)

# 使用Ray
futures = [inference_task.remote(data) for data in batch]
results = ray.get(futures)