多进程Python/numpy代码，用于更快地处理数据

Question

我正在阅读数百个HDF文件中，并分别处理每个HDF的数据。但是，这需要大量时间，因为它一次在一个HDF文件上工作。我只是偶然发现了http://docs.python.org/library/multiprocessing.html，现在想知道我如何使用Multiprocessing加速事物。

到目前为止，我想到了：

import numpy as np
from multiprocessing import Pool
def myhdf(date):
    ii      = dates.index(date)
    year    = date[0:4]
    month   = date[4:6]
    day     = date[6:8]
    rootdir = 'data/mydata/'
    filename = 'no2track'+year+month+day
    records = read_my_hdf(rootdir,filename)
    if records.size:
        results[ii] = np.mean(records)
dates = ['20080105','20080106','20080107','20080108','20080109']
results = np.zeros(len(dates))
pool = Pool(len(dates))
pool.map(myhdf,dates)

但是，这显然是不正确的。您能跟随我的思想链吗？我需要更改？

Answer 1

尝试友好的 multiprocessing包装器：

from joblib import Parallel, delayed
def myhdf(date):
    # do work
    return np.mean(records)
results = Parallel(n_jobs=-1)(delayed(myhdf)(d) for d in dates)

Answer 2

池类类映射功能就像标准的Python库map功能，您可以保证您以将它们放入的顺序取回结果。知道这是唯一的其他技巧是您需要以一致的方式返回结果，然后过滤它们。

import numpy as np
from multiprocessing import Pool
def myhdf(date):
    year    = date[0:4]
    month   = date[4:6]
    day     = date[6:8]
    rootdir = 'data/mydata/'
    filename = 'no2track'+year+month+day
    records = read_my_hdf(rootdir,filename)
    if records.size:
        return np.mean(records)
dates = ['20080105','20080106','20080107','20080108','20080109']
pool = Pool(len(dates))
results = pool.map(myhdf,dates)
results = [ result for result in results if result ]
results = np.array(results)

如果您确实想要结果，则可以使用imap_unordered