() as executor: # 提交任务到线程池 futures = [executor.submit(nested_loop, i, j) for i in outer_range for j in inner_range] # 获取并行计算的结果 results = [future.result() for future in concurrent.futures.as_completed(futures)] # 打印结果 print(results) # 调用并行化嵌套循环的...
upper() print(f"Task {name} completed") return result if __name__ == '__main__': with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(task, i) for i in range(5)] for future in concurrent.futures.as_completed(futures): result = future.result() print...
使用concurrent.futures的优势是可以充分利用多核处理器的性能,提高程序的执行效率。它可以自动管理线程或进程的创建和销毁,简化了并发编程的复杂性。同时,它还提供了一些方便的方法来处理任务的返回结果,如as_completed和wait方法。 在云计算领域,使用concurrent.futures可以在处理大量数据或执行耗时任务时提高计算效率。例...
一、Python标准模块--concurrent.futures(并发未来) concurent.future模块需要了解的 1.concurent.future模块是用来创建并行的任务,提供了更高级别的接口, 为了异步执行调用 2.concurent.future这个模块用起来非常方便,它的接口也封装的非常简单 3.concurent.future模块既可以实现进程池,也可以实现线程池 4.模块导入进程...
One way to look at this is to think of a future as having either a value or an exceptional value, determined by the computation. Futures are an old concept already implemented in multi-lisp. Note though that our notion of a future is not "safe" in the sense ...
a Future<T> class that represents the promise of a computed value at some point in the future, a TaskManager class that represents a scheduler used to execute tasks and computational futures, and a handful of supporting types for effectively constructing and managing tasks...
import java.util.concurrent.Callable; import java.util.concurrent.Executors; import com.google.common.util.concurrent.FutureCallback; import com.google.common.util.concurrent.Futures; import com.google.common.util.concurrent.ListenableFuture; import com.google.common.util.concurrent.ListeningExecutorService;...
This includes a Task class that represents an individual work item that can be scheduled for execution, a Future<T> class that represents the promise of a computed value at some point in the future, a TaskManager class that represents a scheduler used to execute tasks and computational futures...
max_connections) as executor: futures = [executor.submit(download_and_save_image, urls[i], path,str(i)) for i in range(0,len(img_urls)) for future in as_completed(futures): try: future.result() except Exception as e: print(f"An error occurred: {e}") print("All tasks completed"...
The expected behavior is that the last response (finish=True) is pushed to the queue in forward_thread. However, for the following test script. import os import sys from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor from lmdeploy.serve.openai.api_client import APIClient ...