如何使用多重处理来加速以下功能?

时间:2019-05-15 20:54:30

标签: python python-3.x numpy python-multiprocessing

我有以下for循环:

$ git remote rename heroku heroku-staging

for j in range(len(a_nested_list_of_ints)): arr_1_, arr_2_, arr_3_ = foo(a_nested_list_of_ints[j]) arr_1[j,:] = arr_1_.data.numpy() arr_2[j,:] = arr_2_.data.numpy() arr_3[j,:] = arr_3_.data.numpy() 是int的嵌套列表。但是,这需要很多时间才能完成。如何通过多处理对其进行优化?到目前为止,我尝试使用a_nested_list_of_ints

multiprocessing

但是,我得到了:

p = Pool(5)
for j in range(len(a_nested_list_of_ints)):
    arr_1_, arr_2_, arr_3_ = p.map(foo,a_nested_list_of_ints[j])
    arr_1[j,:] = arr_1_.data.numpy()
    arr_2[j,:] = arr_2_.data.numpy()
    arr_3[j,:] = arr_3_.data.numpy()

此处:

ValueError: not enough values to unpack (expected 3, got 2)

关于如何使上述操作更快的任何想法?我什至还尝试过使用starmap,但是它不能正常工作。

2 个答案:

答案 0 :(得分:4)

这是一个有效的pool演示:

In [11]: def foo(i): 
    ...:     return np.arange(i), np.arange(10-i) 
    ...:                                                                        
In [12]: with multiprocessing.Pool(processes=2) as pool: 
    ...:     x = pool.map(foo, range(10)) 
    ...:                                                                        
In [13]: x                                                                      
Out[13]: 
[(array([], dtype=int64), array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])),
 (array([0]), array([0, 1, 2, 3, 4, 5, 6, 7, 8])),
 (array([0, 1]), array([0, 1, 2, 3, 4, 5, 6, 7])),
 (array([0, 1, 2]), array([0, 1, 2, 3, 4, 5, 6])),
 (array([0, 1, 2, 3]), array([0, 1, 2, 3, 4, 5])),
 (array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4])),
 (array([0, 1, 2, 3, 4, 5]), array([0, 1, 2, 3])),
 (array([0, 1, 2, 3, 4, 5, 6]), array([0, 1, 2])),
 (array([0, 1, 2, 3, 4, 5, 6, 7]), array([0, 1])),
 (array([0, 1, 2, 3, 4, 5, 6, 7, 8]), array([0]))]

pool.map正在执行迭代,而不是外部for外部循环。

并进一步接近您的示例:

In [14]: def foo(alist): 
    ...:     return np.arange(*alist), np.zeros(alist,int) 
    ...:      
    ...:                                                                        
In [15]: alists=[(0,3),(1,4),(1,6,2)]                                           
In [16]: with multiprocessing.Pool(processes=2) as pool: 
    ...:     x = pool.map(foo, alists) 
    ...:                                                                        
In [17]: x                                                                      
Out[17]: 
[(array([0, 1, 2]), array([], shape=(0, 3), dtype=int64)),
 (array([1, 2, 3]), array([[0, 0, 0, 0]])),
 (array([1, 3, 5]), array([[[0, 0],
          [0, 0],
          [0, 0],
          [0, 0],
          [0, 0],
          [0, 0]]]))]

请注意,pool.map返回一个列表,其中所有情况均来自alists。解开x的包装没有意义。

 x,y = pool.map(...)   # too many values to pack error

我可以使用x惯用法解开zip*的包装:

In [21]: list(zip(*x))                                                          
Out[21]: 
[(array([0, 1, 2]), array([1, 2, 3]), array([1, 3, 5])),
 (array([], shape=(0, 3), dtype=int64), array([[0, 0, 0, 0]]), array([[[0, 0],
          [0, 0],
          [0, 0],
          [0, 0],
          [0, 0],
          [0, 0]]]))]

这是2个元组的列表;实际上是转置的列表版本。可以将其打开:

In [23]: y,z = zip(*x)                                                          
In [24]: y                                                                      
Out[24]: (array([0, 1, 2]), array([1, 2, 3]), array([1, 3, 5]))
In [25]: z                                                                      
Out[25]: 
(array([], shape=(0, 3), dtype=int64), array([[0, 0, 0, 0]]), array([[[0, 0],
         [0, 0],
         [0, 0],
         [0, 0],
         [0, 0],
         [0, 0]]]))

答案 1 :(得分:0)

这是我经常使用的多处理实现。它将把您的列表(在这种情况下为a_nested_list_of_ints)划分为多个核心。然后,它在每个拆分列表上运行foo函数,每个核一个列表。

def split_list(all_params, instances):
    return list(np.array_split(all_params, instances))

# split the list up into equal chucks for each core
n_proc = multiprocessing.cpu_count()
split_items = split_list(to_calc, n_proc)

# create the multiprocessing pool
pool = Pool(processes=n_proc)
all_calcs = []
for i in range(n_proc):
    # the arguments to the foo definition have to be a tuple - (split[i],)
    async_calc = pool.apply_async(foo, (split_items[i],))
    all_calcs.append(async_calc)

pool.close()
pool.join()

# get results
all_results = []
for result in all_calcs:
    all_results += result.get()

print(all_results)