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p_tqdm

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p_tqdm makes parallel processing with progress bars easy.

p_tqdm is a wrapper around pathos.multiprocessing and tqdm. Unlike Python's default multiprocessing library, pathos provides a more flexible parallel map which can apply almost any type of function --- including lambda functions, nested functions, and class methods --- and can easily handle functions with multiple arguments. tqdm is applied on top of pathos's parallel map and displays a progress bar including an estimated time to completion.

Installation

pip install p_tqdm

Example

Let's say you want to add two lists element by element. Without any parallelism, this can be done easily with a Python map.

l1 = ['1', '2', '3']
l2 = ['a', 'b', 'c']

def add(a, b):
    return a + b
    
added = map(add, l1, l2)
# added == ['1a', '2b', '3c']

But if the lists are much larger or the computation is more intense, parallelism becomes a necessity. However, the syntax is often cumbersome. p_tqdm makes it easy and adds a progress bar too.

from p_tqdm import p_map

added = p_map(add, l1, l2)
# added == ['1a', '2b', '3c']
  0%|                                    | 0/3 [00:00<?, ?it/s]
 33%|████████████                        | 1/3 [00:01<00:02, 1.00s/it]
 66%|████████████████████████            | 2/3 [00:02<00:01, 1.00s/it]
100%|████████████████████████████████████| 3/3 [00:03<00:00, 1.00s/it]

p_tqdm functions

Parallel maps

  • p_map - parallel ordered map
  • p_imap - iterator for parallel ordered map
  • p_umap - parallel unordered map
  • p_uimap - iterator for parallel unordered map

Sequential maps

  • t_map - sequential ordered map
  • t_imap - iterator for sequential ordered map

p_map

Performs an ordered map in parallel.

from p_tqdm import p_map

def add(a, b):
    return a + b

added = p_map(add, ['1', '2', '3'], ['a', 'b', 'c'])
# added = ['1a', '2b', '3c']

p_imap

Returns an iterator for an ordered map in parallel.

from p_tqdm import p_imap

def add(a, b):
    return a + b

iterator = p_imap(add, ['1', '2', '3'], ['a', 'b', 'c'])

for result in iterator:
    print(result) # prints '1a', '2b', '3c'

p_umap

Performs an unordered map in parallel.

from p_tqdm import p_umap

def add(a, b):
    return a + b

added = p_umap(add, ['1', '2', '3'], ['a', 'b', 'c'])
# added is an array with '1a', '2b', '3c' in any order

p_uimap

Returns an iterator for an unordered map in parallel.

from p_tqdm import p_uimap

def add(a, b):
    return a + b

iterator = p_uimap(add, ['1', '2', '3'], ['a', 'b', 'c'])

for result in iterator:
    print(result) # prints '1a', '2b', '3c' in any order

t_map

Performs an ordered map sequentially.

from p_tqdm import t_map

def add(a, b):
    return a + b

added = t_map(add, ['1', '2', '3'], ['a', 'b', 'c'])
# added == ['1a', '2b', '3c']

t_imap

Returns an iterator for an ordered map to be performed sequentially.

from p_tqdm import p_imap

def add(a, b):
    return a + b

iterator = t_imap(add, ['1', '2', '3'], ['a', 'b', 'c'])

for result in iterator:
    print(result) # prints '1a', '2b', '3c'

Shared properties

Arguments

All p_tqdm functions accept any number of iterables as input, as long as the number of iterables matches the number of arguments of the function.

To repeat a non-iterable argument along with the iterables, use Python's partial from the functools library. See the example below.

from functools import partial

l1 = ['1', '2', '3']
l2 = ['a', 'b', 'c']

def add(a, b, c=''):
    return a + b + c

added = p_map(partial(add, c='!'), l1, l2)
# added == ['1a!', '2b!', '3c!']

CPUs

All the parallel p_tqdm functions can be passed the keyword num_cpus to indicate how many CPUs to use. The default is all CPUs. num_cpus can either be an integer to indicate the exact number of CPUs to use or a float to indicate the proportion of CPUs to use.

Note that the parallel Pool objects used by p_tqdm are automatically closed when the map finishes processing.


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