File size: 2,147 Bytes
a89d9fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0):
"""
A parallel version of the map function with a progress bar.
Args:
array (array-like): An array to iterate over.
function (function): A python function to apply to the elements of array
n_jobs (int, default=16): The number of cores to use
use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of
keyword arguments to function
front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job.
Useful for catching bugs
Returns:
[function(array[0]), function(array[1]), ...]
"""
# We run the first few iterations serially to catch bugs
if front_num > 0:
front = [function(**a) if use_kwargs else function(a)
for a in array[:front_num]]
else:
front = []
# If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging.
if n_jobs == 1:
return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])]
# Assemble the workers
with ProcessPoolExecutor(max_workers=n_jobs) as pool:
# Pass the elements of array into function
if use_kwargs:
futures = [pool.submit(function, **a) for a in array[front_num:]]
else:
futures = [pool.submit(function, a) for a in array[front_num:]]
kwargs = {
'total': len(futures),
'unit': 'it',
'unit_scale': True,
'leave': True
}
# Print out the progress as tasks complete
for f in tqdm(as_completed(futures), **kwargs):
pass
out = []
# Get the results from the futures.
for i, future in tqdm(enumerate(futures)):
try:
out.append(future.result())
except Exception as e:
out.append(e)
return front + out
|