[英]Fastest way to run a single function in python in parallel for multiple parameters
Suppose I have a single function processing
.假设我有一个单一的功能processing
。 I want to run the same function multiple times for multiple parameters parallelly instead of sequentially one after the other.我想为多个参数并行运行相同的函数多次,而不是一个接一个地依次运行。
def processing(image_location):
image = rasterio.open(image_location)
...
...
return(result)
#calling function serially one after the other with different parameters and saving the results to a variable.
results1 = processing(r'/home/test/image_1.tif')
results2 = processing(r'/home/test/image_2.tif')
results3 = processing(r'/home/test/image_3.tif')
For example, If I run delineation(r'/home/test/image_1.tif')
then delineation(r'/home/test/image_2.tif')
and then delineation(r'/home/test/image_3.tif')
, as shown in the above code, it will run sequentially one after the other and if it takes 5 minutes for one function to run then running these three will take 5x3=15 minutes.例如,如果我运行delineation(r'/home/test/image_1.tif')
然后delineation(r'/home/test/image_1.tif')
delineation(r'/home/test/image_2.tif')
然后delineation(r'/home/test/image_2.tif')
delineation(r'/home/test/image_3.tif')
,如上面的代码所示,它会一个接一个地依次运行,如果一个函数运行需要5分钟,那么运行这三个函数需要5x3=15分钟。 Hence, I am wondering if I can run these three parallelly/embarrassingly parallel so that it takes only 5 minutes to execute the function for all the three different parameters.因此,我想知道我是否可以并行/尴尬地并行运行这三个,以便对所有三个不同参数执行该函数只需要 5 分钟。
Help me with the fastest way to do this job.帮助我以最快的方式完成这项工作。 The script should be able to utilize all the resources/CPU/ram available by default to do this task.该脚本应该能够利用默认情况下可用的所有资源/CPU/ram 来执行此任务。
You can use multiprocessing
to execute functions in parallel and save results to results
variable:您可以使用multiprocessing
并行执行函数并将结果保存到results
变量:
from multiprocessing.pool import ThreadPool
pool = ThreadPool()
images = [r'/home/test/image_1.tif', r'/home/test/image_2.tif', r'/home/test/image_3.tif']
results = pool.map(delineation, images)
You might want to take a look at IPython Parallel .您可能想看看IPython Parallel 。 It allows you to easily run functions on a load-balanced (local) cluster.它允许您轻松地在负载平衡(本地)集群上运行函数。
For this little example, make sure you have IPython Parallel , NumPy and Pillow installed.对于这个小例子,确保你已经安装了IPython Parallel 、 NumPy和Pillow 。 To run the the example, you need first to launch the cluster.要运行该示例,您首先需要启动集群。 To launch a local cluster with four parallel engines, type into a terminal (one engine for one processor core seems a reasonable choice):要启动具有四个并行引擎的本地集群,请在终端中键入(一个处理器内核一个引擎似乎是一个合理的选择):
ipcluster 4
Then you can run the following script, which searches for jpg-images in a given directory and counts the number of pixels in each image:然后您可以运行以下脚本,该脚本在给定目录中搜索 jpg-images 并计算每个图像中的像素数:
import ipyparallel as ipp
rc = ipp.Client()
with rc[:].sync_imports(): # import on all engines
import numpy
from pathlib import Path
from PIL import Image
lview = rc.load_balanced_view() # default load-balanced view
lview.block = True # block until map() is finished
@lview.parallel()
def count_pixels(fn: Path):
"""Silly function to count the number of pixels in an image file"""
im = Image.open(fn)
xx = numpy.asarray(im)
num_pixels = xx.shape[0] * xx.shape[1]
return fn.stem, num_pixels
pic_dir = Path('Pictures')
fn_lst = pic_dir.glob('*.jpg') # list all jpg-files in pic_dir
results = count_pixels.map(fn_lst) # execute in parallel
for n_, cnt in results:
print(f"'{n_}' has {cnt} pixels.")
Another way of writing with the multiprocessing
library (see @Alderven for a different function).使用multiprocessing
库编写的另一种方式(请参阅@Alderven 了解不同的功能)。
import multiprocessing as mp
def calculate(input_args):
result = input_args * 2
return result
N = mp.cpu_count()
parallel_input = np.arange(0, 100)
print('Amount of CPUs ', N)
print('Amount of iterations ', len(parallel_input))
with mp.Pool(processes=N) as p:
results = p.map(calculate, list(parallel_input))
The results
variable will contain a list with your processed data. results
变量将包含一个包含您处理过的数据的列表。 Which you are then able to write.然后你就可以写了。
I think one of the easiest methods is using joblib
:我认为最简单的方法之一是使用joblib
:
import joblib
allJobs = []
allJobs.append(joblib.delayed(processing)(r'/home/test/image_1.tif'))
allJobs.append(joblib.delayed(processing)(r'/home/test/image_2.tif'))
allJobs.append(joblib.delayed(processing)(r'/home/test/image_3.tif'))
results = joblib.Parallel(n_jobs=joblib.cpu_count(), verbose=10)(allJobs)
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