python - 使用 python joblib 访问和更改全局数组

Question

我试图在 python 中使用 joblib 来加速一些数据处理，但我在尝试找出如何将输出分配为所需格式时遇到了问题。我试图生成一个可能过于简单的代码来显示我遇到的问题:

from joblib import Parallel, delayed
import numpy as np

def main():
    print "Nested loop array assignment:"
    regular()
    print "Parallel nested loop assignment using a single process:"
    par2(1)
    print "Parallel nested loop assignment using multiple process:"
    par2(2)

def regular():
    # Define variables
    a = [0,1,2,3,4]
    b = [0,1,2,3,4]
    # Set array variable to global and define size and shape
    global ab
    ab = np.zeros((2,np.size(a),np.size(b)))

    # Iterate to populate array
    for i in range(0,np.size(a)):
        for j in range(0,np.size(b)):
            func(i,j,a,b)

    # Show array output
    print ab

def par2(process):
    # Define variables
    a2 = [0,1,2,3,4]
    b2 = [0,1,2,3,4]
    # Set array variable to global and define size and shape
    global ab2
    ab2 = np.zeros((2,np.size(a2),np.size(b2)))

    # Parallel process in order to populate array
    Parallel(n_jobs=process)(delayed(func2)(i,j,a2,b2) for i in xrange(0,np.size(a2)) for j in xrange(0,np.size(b2)))

    # Show array output
    print ab2

def func(i,j,a,b):
    # Populate array
    ab[0,i,j] = a[i]+b[j]
    ab[1,i,j] = a[i]*b[j]

def func2(i,j,a2,b2):
    # Populate array
    ab2[0,i,j] = a2[i]+b2[j]
    ab2[1,i,j] = a2[i]*b2[j]

# Run script
main()

它的输出看起来像这样:

Nested loop array assignment:
[[[  0.   1.   2.   3.   4.]
  [  1.   2.   3.   4.   5.]
  [  2.   3.   4.   5.   6.]
  [  3.   4.   5.   6.   7.]
  [  4.   5.   6.   7.   8.]]

 [[  0.   0.   0.   0.   0.]
  [  0.   1.   2.   3.   4.]
  [  0.   2.   4.   6.   8.]
  [  0.   3.   6.   9.  12.]
  [  0.   4.   8.  12.  16.]]]
Parallel nested loop assignment using a single process:
[[[  0.   1.   2.   3.   4.]
  [  1.   2.   3.   4.   5.]
  [  2.   3.   4.   5.   6.]
  [  3.   4.   5.   6.   7.]
  [  4.   5.   6.   7.   8.]]

 [[  0.   0.   0.   0.   0.]
  [  0.   1.   2.   3.   4.]
  [  0.   2.   4.   6.   8.]
  [  0.   3.   6.   9.  12.]
  [  0.   4.   8.  12.  16.]]]
Parallel nested loop assignment using multiple process:
[[[ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]]

 [[ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]
  [ 0.  0.  0.  0.  0.]]]

从 Google 和 StackOverflow 搜索功能看来，当使用 joblib 时，全局数组不会在每个子进程之间共享。我不确定这是 joblib 的限制还是有办法解决这个问题？

实际上我的脚本被其他代码包围，这些代码依赖于这个全局数组的最终输出是 (4,x,x) 格式，其中x 是可变的(但通常在 100 到几千之间)。这是我目前考虑并行处理的原因，因为对于 x = 2400，整个过程最多可能需要 2 个小时。

joblib 的使用不是必需的(但我喜欢命名法和简单性)所以请随意提出简单的替代方法，最好牢记最终数组的要求。我正在使用 python 2.7.3 和 joblib 0.7.1。

Answer 1

我能够使用 numpy 的内存映射解决这个简单示例的问题。使用 memmap 并遵循 joblib documentation webpage 上的示例后，我仍然遇到问题但我通过 pip 升级到最新的 joblib 版本(0.9.3)并且一切运行顺利。这是工作代码:

from joblib import Parallel, delayed
import numpy as np
import os
import tempfile
import shutil

def main():

    print "Nested loop array assignment:"
    regular()

    print "Parallel nested loop assignment using numpy's memmap:"
    par3(4)

def regular():
    # Define variables
    a = [0,1,2,3,4]
    b = [0,1,2,3,4]

    # Set array variable to global and define size and shape
    global ab
    ab = np.zeros((2,np.size(a),np.size(b)))

    # Iterate to populate array
    for i in range(0,np.size(a)):
        for j in range(0,np.size(b)):
            func(i,j,a,b)

    # Show array output
    print ab

def par3(process):

    # Creat a temporary directory and define the array path
    path = tempfile.mkdtemp()
    ab3path = os.path.join(path,'ab3.mmap')

    # Define variables
    a3 = [0,1,2,3,4]
    b3 = [0,1,2,3,4]

    # Create the array using numpy's memmap
    ab3 = np.memmap(ab3path, dtype=float, shape=(2,np.size(a3),np.size(b3)), mode='w+')

    # Parallel process in order to populate array
    Parallel(n_jobs=process)(delayed(func3)(i,a3,b3,ab3) for i in xrange(0,np.size(a3)))

    # Show array output
    print ab3

    # Delete the temporary directory and contents
    try:
        shutil.rmtree(path)
    except:
        print "Couldn't delete folder: "+str(path)

def func(i,j,a,b):
    # Populate array
    ab[0,i,j] = a[i]+b[j]
    ab[1,i,j] = a[i]*b[j]

def func3(i,a3,b3,ab3):
    # Populate array
    for j in range(0,np.size(b3)):
        ab3[0,i,j] = a3[i]+b3[j]
        ab3[1,i,j] = a3[i]*b3[j]

# Run script
main()

给出以下结果:

Nested loop array assignment:
[[[  0.   1.   2.   3.   4.]
  [  1.   2.   3.   4.   5.]
  [  2.   3.   4.   5.   6.]
  [  3.   4.   5.   6.   7.]
  [  4.   5.   6.   7.   8.]]

 [[  0.   0.   0.   0.   0.]
  [  0.   1.   2.   3.   4.]
  [  0.   2.   4.   6.   8.]
  [  0.   3.   6.   9.  12.]
  [  0.   4.   8.  12.  16.]]]
Parallel nested loop assignment using numpy's memmap:
[[[  0.   1.   2.   3.   4.]
  [  1.   2.   3.   4.   5.]
  [  2.   3.   4.   5.   6.]
  [  3.   4.   5.   6.   7.]
  [  4.   5.   6.   7.   8.]]

 [[  0.   0.   0.   0.   0.]
  [  0.   1.   2.   3.   4.]
  [  0.   2.   4.   6.   8.]
  [  0.   3.   6.   9.  12.]
  [  0.   4.   8.  12.  16.]]]

我的一些想法供 future 的读者注意:

• 在小型阵列上，准备并行环境所花费的时间(通常称为开销)意味着这个运行速度比简单的 for 循环。
• 比较更大的数组，例如。将 a 和 a3 设置为np.arange(0,10000)，b 和 b3 到 np.arange(0,1000)给了“常规”方法的时间为 12.4s，joblib 的时间为 7.7s方法。
• 开销意味着让每个核心执行的速度更快内部 j 循环(参见 func3)。这是有道理的，因为我只是启动 10,000 个进程而不是启动 10,000,000 个
  每个过程都需要设置。