I have an array (called data_inputs) containing the names of hundreds of astronomy images files. These images are then manipulated. My code works and takes a few seconds to process each image. However, it can only do one image at a time because I'm running the array through a for loop:
for name in data_inputs: sci=fits.open(name+'.fits') #image is manipulated There is no reason why I have to modify an image before any other, so is it possible to utilise all 4 cores on my machine with each core running through the for loop on a different image?
I've read about the multiprocessing module but I'm unsure how to implement it in my case. I'm keen to get multiprocessing to work because eventually I'll have to run this on 10,000+ images.
4 Answers
You can simply use multiprocessing.Pool:
from multiprocessing import Pool def process_image(name): sci=fits.open('{}.fits'.format(name)) <process> if __name__ == '__main__': pool = Pool() # Create a multiprocessing Pool pool.map(process_image, data_inputs) # process data_inputs iterable with pool You can use multiprocessing.Pool:
from multiprocessing import Pool class Engine(object): def __init__(self, parameters): self.parameters = parameters def __call__(self, filename): sci = fits.open(filename + '.fits') manipulated = manipulate_image(sci, self.parameters) return manipulated try: pool = Pool(8) # on 8 processors engine = Engine(my_parameters) data_outputs = pool.map(engine, data_inputs) finally: # To make sure processes are closed in the end, even if errors happen pool.close() pool.join() Alternatively
with Pool() as pool: pool.map(fits.open, [name + '.fits' for name in datainput]) I would suggest to use imap_unordered with chunksize if you are only using a for loop to iterate over an iterable. It will return results from each loop as soon as they are calculated. map waits for all results to be computed and hence is blocking.
