用 Ray 并行采样估算 π 并追踪进度-未完纪

把一个大计算拆成互不依赖的小任务，是理解分布式执行的好起点。Ray 官方的两篇示例都选择了蒙特卡洛估算 π：一篇用任务完成采样、用 Actor 汇总进度；另一篇把相同的计算扩展到大量批次。下面按这个顺序合并译解两篇正文，并说明示例搬到实际环境时需要补上的边界。

来源与版本：Ray documentation contributors / The Ray Team。依据 2026-10-05 读取的 Monte Carlo Estimation of π 与 Using Ray for Highly Parallelizable Tasks；当日页头为 Ray 2.59.0，latest 会继续变化。本文为授权中文翻译与技术整理，新增说明和代码差异均另行标出。未运行 Ray、未压测、未租用算力。

未完纪原创技术示意图。圆内外的点只用于解释面积比，不是实测采样结果。

先理解面积比，再考虑并行

在横、纵坐标都落在 −1 到 1 的正方形中均匀随机取点。正方形边长为 2，面积为 4；以原点为圆心、半径为 1 的圆面积为 π。如果某点满足 x² + y² ≤ 1，它就落在圆内。因此，圆内点数占全部点数的比例可以估计 π/4：

π 的估计值 = 4 × 圆内点数 ÷ 总采样数。

每个点的判断都不需要知道其他点的结果，可以把样本分给多个进程或节点。任务返回圆内计数，驱动程序把这些计数相加即可。这个设计减少了任务之间的数据依赖，也不用把所有坐标传回主进程。

原文说增加样本会使结果更接近 π。这里需要补充统计含义：更多独立、均匀样本通常降低估计的不确定性，典型误差规模随样本数的平方根倒数缩小；某一次新计算的数值仍可能比前一次更偏离 π。任务数、CPU 数和小数点后“看起来正确”的位数之间不存在直接保证。

任务返回结果，Actor 保存进度

Ray 的远程任务由普通 Python 函数加上 @ray.remote 定义。调用 sampling_task.remote(...) 会立即返回一个 ObjectRef，真实计算随后由可用 worker 执行。ObjectRef 可以理解为指向将来结果的引用；ray.get(ref) 会等待并取回结果。

Actor 则是保存状态的远程对象。它适合记录“每项采样任务已经处理了多少点”。下面保留官方示例的关键实现，中文注释仅解释原来的行为：

import math
import random
import time
import ray

@ray.remote
class ProgressActor:
    def __init__(self, total_num_samples: int):
        self.total_num_samples = total_num_samples
        self.num_samples_completed_per_task = {}

    def report_progress(self, task_id: int, num_samples_completed: int) -> None:
        self.num_samples_completed_per_task[task_id] = num_samples_completed

    def get_progress(self) -> float:
        return sum(self.num_samples_completed_per_task.values()) / self.total_num_samples

@ray.remote
def sampling_task(num_samples: int, task_id: int,
                  progress_actor: ray.actor.ActorHandle) -> int:
    num_inside = 0
    for i in range(num_samples):
        x, y = random.uniform(-1, 1), random.uniform(-1, 1)
        if math.hypot(x, y) <= 1:
            num_inside += 1
        if (i + 1) % 1_000_000 == 0:
            progress_actor.report_progress.remote(task_id, i + 1)
    progress_actor.report_progress.remote(task_id, num_samples)
    return num_inside

math.hypot(x, y) 求点到原点的距离。任务每处理一百万个样本，异步报告一次累计数量，结束时再报告最终数量。Actor 以任务编号为字典键，覆盖该任务旧的累计计数，而不是把每次报告相加；否则同一任务的中途报告和结束报告会重复计数。

这种进度属于观测状态。多个任务的报告到达时间不同，显示值会滞后于真实执行。若任务重试，计数还可能回退；原示例没有区分重试的 attempt，也没有为 Actor 增加持久恢复逻辑。最终数值应来自成功任务的返回值。

从本地小规模开始提交任务

原文安装提示是 pip install -U ray，其中 -U 会升级到当时可取得的版本。需要复现时应使用独立虚拟环境并记录实际版本，不能把本文读取时的文档版本当作已经安装、验证过的运行环境。

原进度示例默认提交 10 个任务，每个任务采样 10,000,000 次，总计一亿次。下面仅把提交规模改成 4 个任务、每项 100,000 次，并限制本地 Ray 的 CPU 资源声明。它是编辑调整的学习配置，未实测；由于每项不足一百万样本，Actor 通常只收到结束报告，进度会按任务完成阶梯式跳变。

ray.init(num_cpus=2, include_dashboard=False)

NUM_SAMPLING_TASKS = 4
NUM_SAMPLES_PER_TASK = 100_000
TOTAL_NUM_SAMPLES = NUM_SAMPLING_TASKS * NUM_SAMPLES_PER_TASK

progress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)
results = [
    sampling_task.remote(NUM_SAMPLES_PER_TASK, task_id, progress_actor)
    for task_id in range(NUM_SAMPLING_TASKS)
]

ProgressActor.remote(...) 创建并启动 Actor，返回句柄；这个句柄作为参数传给每个任务。任务数并不等于同时运行的数量：Ray 会根据可用资源调度。小任务拆得过细时，调度、序列化和通信开销可能比采样计算还显著；拆得过大则不利于负载均衡和观察中途进度。

等待结束时，也要检查任务是否失败

官方原循环每秒查询一次 Actor，直到 progress == 1，然后才调用 ray.get(results)。如果某项任务永久失败、没有报告最终数量，这个循环可能一直等待，任务异常也不会及时通过结果读取暴露出来。若 Actor 丢失或查询无法及时完成，纯进度等待同样缺少边界。

以下是编辑提供的替换循环：用 ray.wait 收集已就绪引用，立即读取结果以传播异常，同时给整体等待和 Actor 查询设置期限。60 秒只是示范预算，不代表此规模应在 60 秒内完成。它保留原算法，改变了驱动端的终止判据和失败处理；未执行测试。

pending = list(results)
total_num_inside = 0
deadline = time.monotonic() + 60

try:
    while pending:
        if time.monotonic() >= deadline:
            raise TimeoutError("Sampling exceeded the demonstration deadline")
        ready, pending = ray.wait(pending, num_returns=1, timeout=1.0)
        for ref in ready:
            total_num_inside += ray.get(ref)
        progress = ray.get(progress_actor.get_progress.remote(), timeout=5.0)
        print(f"Reported progress: {progress:.0%}")

    pi_estimate = 4 * total_num_inside / TOTAL_NUM_SAMPLES
    print(f"Estimated pi: {pi_estimate}")
except Exception:
    for ref in results:
        ray.cancel(ref, force=False)
    raise
finally:
    ray.shutdown()

这里的取消请求不是事务回滚，也不保证所有远端工作瞬间停止。ray.shutdown() 用于释放本次本地会话；不要把它当作管理任意共享生产集群的方案。即使任务结果全部返回，最后一次 Actor 报告仍可能正在传递，因此报告值短暂低于 100% 也不影响按返回计数计算结果。

原文示例最后输出 3.1412202，仅表示官方展示的一次结果。本文没有生成新的估计值，也没有把原文输出冒充本次测试日志。

另一种写法：把 π/4 作为每批结果

第二篇官方教程从一个“相同算法处理很多独立输入”的场景引入并行：十万个任务若各需一分钟，单处理器需要约 70 天；更多处理器可以分摊工作。原文的这些估算用来说明规模关系，实际完成时间还受调度、数据传输、资源争用和任务分布影响。

它的采样区域改为 [0, 1) × [0, 1)，统计单位圆第一象限的四分之一圆。面积比仍然是 π/4。每个任务返回 Fraction(in_count, sample_count)，用分数保存本批比例：

from fractions import Fraction

@ray.remote
def pi4_sample(sample_count):
    in_count = 0
    for _ in range(sample_count):
        x = random.random()
        y = random.random()
        if x * x + y * y <= 1:
            in_count += 1
    return Fraction(in_count, sample_count)

原文先对一百万个样本计时并取回结果，然后把工作扩大到一千亿个样本，每批一百万个，总计十万项任务。所有远程调用先放进列表，最后一次 ray.get(results) 等待结果，从而避免在提交循环内逐项阻塞。每批样本数量相等时，sum(output) * 4 / len(output) 就是整体估计；若批次大小不同，需要按样本数加权，直接平均各批比例会改变估计权重。

这组十万任务、一千亿采样的参数不适合作为个人电脑默认值。驱动端会保存大量引用，调度系统也会承受集中提交的压力。扩展时应限制在途任务数、分批提交和收集，并根据集群资源确定每批粒度。该教程使用 ray.init(address="auto")，前提是已经建立 Ray 集群，并在能连接该集群的环境中运行；它不是“自动为你创建任意规模集群”。

原文给出过一次一百万采样约 1.49 秒的输出，后文又以单核每批约 0.4 秒估算约 11 小时，并展示 168 个核心工作的 Dashboard 与约 80% 效率。不同示例上下文不能直接拼成可复现的性能承诺。它展示的估计值 3.14159518188 和约 8.05 × 10⁻⁷ 的相对偏差，也只属于原作者的那次计算。本文使用原创流程图，没有制作或修改性能截图。

静态代码审查与适用范围

本次已逐段审查两个示例的导入、远程调用、随机采样、进度汇总和结果聚合，没有在这些代码段中发现硬编码秘密、命令注入或文件删除操作。发现的实际问题是：较大的默认计算规模；依靠进度值无限等待；缺少重试状态区分和 Actor 恢复；高并行版集中提交十万任务；以及性能数字容易被脱离上下文引用。前文已分别解释，并明确标注改动。

Ray 集群可以执行提交的 Python 代码，因此只能连接自己有权使用的可信环境，不能把示例端口和控制接口随意暴露给不可信网络。本次没有启动任何集群，也没有验证操作系统、依赖或集群安全配置。静态未发现某类问题不等于代码或部署环境不存在漏洞。

Ray 示例代码 Apache License 2.0

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