我想在共享内存中使用numpy数组，以便与多处理模块一起使用。困难在于使用它就像一个numpy数组，而不仅仅是一个ctypes数组。
from multiprocessing import Process, Array
import scipy
def f(a):
a[0] = -a[0]
if __name__ == '__main__':
# Create the array
N = int(10)
unshared_arr = scipy.rand(N)
arr = Array('d', unshared_arr)
print "Originally, the first two elements of arr = %s"%(arr[:2])
# Create, start, and finish the child processes
p = Process(target=f, args=(arr,))
p.start()
p.join()
# Printing out the changed values
print "Now, the first two elements of arr = %s"%arr[:2]
这会产生如下输出：
Originally, the first two elements of arr = [0.3518653236697369, 0.517794725524976]
Now, the first two elements of arr = [-0.3518653236697369, 0.517794725524976]
可以以ctypes方式访问数组，例如arr[i]是有道理的。但是，它不是一个numpy数组，我无法执行-1*arr或arr.sum()等操作。我想解决方案是将ctypes数组转换为numpy数组。然而（除了无法完成这项工作），我不相信它会再被分享。
似乎会有一个标准解决方案来解决常见问题。
答案 0 :(得分：70)
添加到@ unutbu（不再可用）和@Henry Gomersall的答案。您可以使用shared_arr.get_lock()在需要时同步访问权限：
shared_arr = mp.Array(ctypes.c_double, N)
# ...
def f(i): # could be anything numpy accepts as an index such another numpy array
with shared_arr.get_lock(): # synchronize access
arr = np.frombuffer(shared_arr.get_obj()) # no data copying
arr[i] = -arr[i]
import ctypes
import logging
import multiprocessing as mp
from contextlib import closing
import numpy as np
info = mp.get_logger().info
def main():
logger = mp.log_to_stderr()
logger.setLevel(logging.INFO)
# create shared array
N, M = 100, 11
shared_arr = mp.Array(ctypes.c_double, N)
arr = tonumpyarray(shared_arr)
# fill with random values
arr[:] = np.random.uniform(size=N)
arr_orig = arr.copy()
# write to arr from different processes
with closing(mp.Pool(initializer=init, initargs=(shared_arr,))) as p:
# many processes access the same slice
stop_f = N // 10
p.map_async(f, [slice(stop_f)]*M)
# many processes access different slices of the same array
assert M % 2 # odd
step = N // 10
p.map_async(g, [slice(i, i + step) for i in range(stop_f, N, step)])
p.join()
assert np.allclose(((-1)**M)*tonumpyarray(shared_arr), arr_orig)
def init(shared_arr_):
global shared_arr
shared_arr = shared_arr_ # must be inherited, not passed as an argument
def tonumpyarray(mp_arr):
return np.frombuffer(mp_arr.get_obj())
def f(i):
"""synchronized."""
with shared_arr.get_lock(): # synchronize access
g(i)
def g(i):
"""no synchronization."""
info("start %s" % (i,))
arr = tonumpyarray(shared_arr)
arr[i] = -1 * arr[i]
info("end %s" % (i,))
if __name__ == '__main__':
mp.freeze_support()
main()
如果您不需要同步访问或创建自己的锁，则不需要mp.Array()。在这种情况下，您可以使用mp.sharedctypes.RawArray。
答案 1 :(得分：17)
Array对象具有与之关联的get_obj()方法，该方法返回呈现缓冲区接口的ctypes数组。我认为以下内容应该有效......
from multiprocessing import Process, Array
import scipy
import numpy
def f(a):
a[0] = -a[0]
if __name__ == '__main__':
# Create the array
N = int(10)
unshared_arr = scipy.rand(N)
a = Array('d', unshared_arr)
print "Originally, the first two elements of arr = %s"%(a[:2])
# Create, start, and finish the child process
p = Process(target=f, args=(a,))
p.start()
p.join()
# Print out the changed values
print "Now, the first two elements of arr = %s"%a[:2]
b = numpy.frombuffer(a.get_obj())
b[0] = 10.0
print a[0]
运行时，会打印出a的第一个元素，现在为10.0，显示a和b只是同一个内存中的两个视图。
为了确保它仍然是多处理器安全的，我相信您必须使用acquire对象release上存在的Array和a方法，它内置锁定以确保所有安全访问（虽然我不是多处理器模块的专家）。
答案 2 :(得分：12)
虽然已经给出的答案很好，但只要满足两个条件，就可以更容易地解决这个问题：
在这种情况下，您不需要明确地使变量共享，因为子进程将使用fork创建。分叉子项自动共享父项的内存空间。在Python多处理的上下文中，这意味着它共享所有模块级变量;请注意，对于您明确传递给子进程或您在Traceback (most recent call last):
File "overlap.py", line 210, in <module>
print bdv_json_to_geodf(contours_bdv)
File "overlap.py", line 148, in json_to_geodf
return geopandas.GeoDataFrame.from_features(collection['features'])
File "/Library/Python/2.7/site-packages/geopandas/geodataframe.py", line 179, in from_features
d = {'geometry': shape(f['geometry'])}
File "/Library/Frameworks/GEOS.framework/Versions/3/Python/2.7/site-packages/shapely/geometry/geo.py", line 40, in shape
return MultiPolygon(ob["coordinates"], context_type='geojson')
File "/Library/Frameworks/GEOS.framework/Versions/3/Python/2.7/site-packages/shapely/geometry/multipolygon.py", line 64, in __init__
self._geom, self._ndim = geos_multipolygon_from_py(polygons)
File "/Library/Frameworks/GEOS.framework/Versions/3/Python/2.7/site-packages/shapely/geometry/multipolygon.py", line 138, in geos_multipolygon_from_py
N = len(ob[0][0][0])
TypeError: object of type 'float' has no len()
左右调用的函数的参数，此不包含。
一个简单的例子：
multiprocessing.Pool答案 3 :(得分：9)
我编写了一个小python模块，它使用POSIX共享内存在python解释器之间共享numpy数组。也许你会发现它很方便。
https://pypi.python.org/pypi/SharedArray
以下是它的工作原理：
import numpy as np
import SharedArray as sa
# Create an array in shared memory
a = sa.create("test1", 10)
# Attach it as a different array. This can be done from another
# python interpreter as long as it runs on the same computer.
b = sa.attach("test1")
# See how they are actually sharing the same memory block
a[0] = 42
print(b[0])
# Destroying a does not affect b.
del a
print(b[0])
# See how "test1" is still present in shared memory even though we
# destroyed the array a.
sa.list()
# Now destroy the array "test1" from memory.
sa.delete("test1")
# The array b is not affected, but once you destroy it then the
# data are lost.
print(b[0])
答案 4 :(得分：8)
您可以使用sharedmem模块：https://bitbucket.org/cleemesser/numpy-sharedmem
这是你的原始代码，这次使用的行为类似于NumPy数组的共享内存（注意调用NumPy sum()函数的附加最后一个语句）：
from multiprocessing import Process
import sharedmem
import scipy
def f(a):
a[0] = -a[0]
if __name__ == '__main__':
# Create the array
N = int(10)
unshared_arr = scipy.rand(N)
arr = sharedmem.empty(N)
arr[:] = unshared_arr.copy()
print "Originally, the first two elements of arr = %s"%(arr[:2])
# Create, start, and finish the child process
p = Process(target=f, args=(arr,))
p.start()
p.join()
# Print out the changed values
print "Now, the first two elements of arr = %s"%arr[:2]
# Perform some NumPy operation
print arr.sum()