python有一个简单的基于过程的并行映射吗？

Question

我正在为python寻找一个简单的基于进程的并行映射，即一个函数

parmap(function,[data])

这将在不同的进程（好吧，在不同的内核上，但是AFAIK，在python的不同内核上运行东西的唯一方法是启动多个解释器）的每个元素上运行函数，并返回结果列表。

有这样的东西吗？我想要一些简单的东西，所以一个简单的模块就好了。当然，如果不存在这样的东西，我会选择一个大图书馆

Answer 1

对于那些寻找与R的mclapply（）等价的Python的人，这里是我的实现。这是对以下两个示例的改进：

• @Rafael提到的“Parallelize Pandas map() or apply()” 瓦莱罗。
• How to apply map to functions with multiple arguments。

它可以应用于具有单个或多个参数的映射函数。

import numpy as np, pandas as pd
from scipy import sparse
import functools, multiprocessing
from multiprocessing import Pool

num_cores = multiprocessing.cpu_count()

def parallelize_dataframe(df, func, U=None, V=None):

    #blockSize = 5000
    num_partitions = 5 # int( np.ceil(df.shape[0]*(1.0/blockSize)) )
    blocks = np.array_split(df, num_partitions)

    pool = Pool(num_cores)
    if V is not None and U is not None:
        # apply func with multiple arguments to dataframe (i.e. involves multiple columns)
        df = pd.concat(pool.map(functools.partial(func, U=U, V=V), blocks))
    else:
        # apply func with one argument to dataframe (i.e. involves single column)
        df = pd.concat(pool.map(func, blocks))

    pool.close()
    pool.join()

    return df

def square(x):
    return x**2

def test_func(data):
    print("Process working on: ", data.shape)
    data["squareV"] = data["testV"].apply(square)
    return data

def vecProd(row, U, V):
    return np.sum( np.multiply(U[int(row["obsI"]),:], V[int(row["obsJ"]),:]) )

def mProd_func(data, U, V):
    data["predV"] = data.apply( lambda row: vecProd(row, U, V), axis=1 )
    return data

def generate_simulated_data():

    N, D, nnz, K = [302, 184, 5000, 5]
    I = np.random.choice(N, size=nnz, replace=True)
    J = np.random.choice(D, size=nnz, replace=True)
    vals = np.random.sample(nnz)

    sparseY = sparse.csc_matrix((vals, (I, J)), shape=[N, D])

    # Generate parameters U and V which could be used to reconstruct the matrix Y
    U = np.random.sample(N*K).reshape([N,K])
    V = np.random.sample(D*K).reshape([D,K])

    return sparseY, U, V

def main():
    Y, U, V = generate_simulated_data()

    # find row, column indices and obvseved values for sparse matrix Y
    (testI, testJ, testV) = sparse.find(Y)

    colNames = ["obsI", "obsJ", "testV", "predV", "squareV"]
    dtypes = {"obsI":int, "obsJ":int, "testV":float, "predV":float, "squareV": float}

    obsValDF = pd.DataFrame(np.zeros((len(testV), len(colNames))), columns=colNames)
    obsValDF["obsI"] = testI
    obsValDF["obsJ"] = testJ
    obsValDF["testV"] = testV
    obsValDF = obsValDF.astype(dtype=dtypes)

    print("Y.shape: {!s}, #obsVals: {}, obsValDF.shape: {!s}".format(Y.shape, len(testV), obsValDF.shape))

    # calculate the square of testVals    
    obsValDF = parallelize_dataframe(obsValDF, test_func)

    # reconstruct prediction of testVals using parameters U and V
    obsValDF = parallelize_dataframe(obsValDF, mProd_func, U, V)

    print("obsValDF.shape after reconstruction: {!s}".format(obsValDF.shape))
    print("First 5 elements of obsValDF:\n", obsValDF.iloc[:5,:])

if __name__ == '__main__':
    main()

Answer 2

这可以通过Ray来实现，该系统允许您轻松地并行化和分发Python代码。

要并行化示例，需要使用@ray.remote装饰器定义映射函数，然后使用.remote调用它。这将确保远程函数的每个实例都将在不同的进程中执行。

import time
import ray

ray.init()

# Define the function you want to apply map on, as remote function. 
@ray.remote
def f(x):
    # Do some work...
    time.sleep(1)
    return x*x

# Define a helper parmap(f, list) function.
# This function executes a copy of f() on each element in "list".
# Each copy of f() runs in a different process.
# Note f.remote(x) returns a future of its result (i.e., 
# an identifier of the result) rather than the result itself.  
def parmap(f, list):
    return [f.remote(x) for x in list]

# Call parmap() on a list consisting of first 5 integers.
result_ids = parmap(f, range(1, 6))

# Get the results
results = ray.get(result_ids)
print(results)

这将打印：

[1, 4, 9, 16, 25]

它将以大约len(list)/p（四舍五入到最近的整数）结束，其中p是您机器上的核心数。假设一台机器有两个内核，我们的示例将在5/2四舍五入（即大约3秒）内执行。

与multiprocessing模块相比，使用Ray有许多优点。尤其是，同一代码将在一台机器上以及在一组机器上运行。有关Ray的更多优点，请参见this related post。

Answer 3

我觉得你需要的是map method in multiprocessing.Pool()：

map(func, iterable[, chunksize])

A parallel equivalent of the map() built-in function (it supports only
one iterable argument though). It blocks till the result is ready.

This method chops the iterable into a number of chunks which it submits to the 
process pool as separate tasks. The (approximate) size of these chunks can be 
specified by setting chunksize to a positive integ

例如，如果要映射此函数：

def f(x):
    return x**2

要设置范围（10），可以使用内置的map（）函数：

map(f, range(10))

或者使用多处理.Pool（）对象的方法map（）：

import multiprocessing
pool = multiprocessing.Pool()
print pool.map(f, range(10))