PyWren是一个基于AWS Lambda的Python计算框架,模拟了Python futures包的map/reduce功能,非常适用于机器学习参数调优(parameter tuning)等科学计算任务。

Behind the scenes, PyWren serializes the function with the data, using Python’s Pickle serialization function and a bit of technology borrowed from the PySpark project. PyWren places serialized data and function into S3, then evokes Lambda, along with a slimmed-down version of Anaconda, a packaged version of Python and supporting tools offered by Continuum IO. The results are delivered back to S3, then unpickled, and returned to the user.


Hello world

import pywren
import numpy as np

def addone(x):
    return x + 1

    wrenexec = pywren.default_executor()
    xlist = np.arange(10)
    futures =, xlist)

    print [f.result() for f in futures]


loopcnt = 10

def big_flops(std_dev):
    running_sum = 0
    for i in loopcnt:
        A = np.random.normal(0, std_dev, (4096, 4096))
        B = np.random.normal(0, std_dev, (4096, 4096))
        c =, B)
        running_sum += np.sum(c)
    return running_sum

wrenexec = pywren.default_executor()
std_devs = np.linspace(1, 10, 1600)
futures =, std_devs)



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