Dask图书馆的延迟装饰师-结果适得其反

尝试学习如何使用dask库并点击链接 https://www.machinelearningplus.com/python/dask-tutorial/

使用dask延迟装饰器的代码

import time
import dask
from dask import delayed

@delayed
def square(x):
    return x*x

@delayed
def double(x):
    return x*2

@delayed
def add(x, y):
    return x + y

@delayed
def sum(output):
    sum = 0
    for i in output:
        sum += i

    return sum

t1 = time.time()

# For loop that calls the above functions for each data
output = []
for i in range(99999):
    a = square(i)
    b = double(i)
    c = add(a, b)
    output.append(c)

total = dask.delayed(sum)(output)
print(total)

print("Elapsed time: ", time.time() - t1)

运行时间: ~8.46秒

没有任何dask/decorator的普通代码

import time

def square(x):
    return x*x

def double(x):
    return x*2

def add(x, y):
    return x + y

def sum(output):
    sum = 0
    for i in output:
        sum += i

    return sum

t1 = time.time()

# For loop that calls the above functions for each data
output = []
for i in range(99999):
    a = square(i)
    b = double(i)
    c = add(a, b)
    output.append(c)

total = sum(output)
print(total)

print("Elapsed time: ", time.time() - t1)

运行时间: 约0.043秒

两种代码变体都在上执行,

Windows计算机

4芯

8个逻辑核心

Python 3.11.0

dask版本2023.6.0

代码不应该与 @延迟的 与其他以串行顺序执行函数的变体相比,来自dask的decorator执行得更好?通过任务图识别要并行或串行执行的任务是否会产生开销,从而适得其反?想知道迭代次数是否太少,无法实现dask库的好处,尝试增加值,但它仍然是一样的。

有人能澄清一下吗?