I have a python code that uses the subprocess package to run in shell:
subprocess.call(mycode.py, shell=inshell)
When I execute the top command I see that I am only using ~30% or less of CPU. I realize some commands may be using disk and not cpu therefore I was timing the speed. The speed running this on a linux system seems slower than a mac 2 core system.
How do I parallelize this with threading or multiprocessing package so that I can use multiple CPU cores on said linux system?
To parallelize the work done in mycode.py, you need to organize the code so that it fits into this basic pattern:
# Import the kind of pool you want to use (processes or threads).
from multiprocessing import Pool
from multiprocessing.dummy import Pool as ThreadPool
# Collect work items as an iterable of single values (eg tuples,
# dicts, or objects). If you can't hold all items in memory,
# define a function that yields work items instead.
work_items = [
(1, 'A', True),
(2, 'X', False),
...
]
# Define a callable to do the work. It should take one work item.
def worker(tup):
# Do the work.
...
# Return any results.
...
# Create a ThreadPool (or a process Pool) of desired size.
# What size? Experiment. Slowly increase until it stops helping.
pool = ThreadPool(4)
# Do work and collect results.
# Or use pool.imap() or pool.imap_unordered().
work_results = pool.map(worker, work_items)
# Wrap up.
pool.close()
pool.join()
---------------------
# Or, in Python 3.3+ you can do it like this, skipping the wrap-up code.
with ThreadPool(4) as pool:
work_results = pool.map(worker, work_items)
A little change to FMc's answer,
work_items = [(1, 'A', True), (2, 'X', False), (3, 'B', False)]
def worker(tup):
for i in range(5000000):
print(work_items)
return
pool = Pool(processes = 8)
start = time.time()
work_results = pool.map(worker, work_items)
end = time.time()
print(end-start)
pool.close()
pool.join()
The code above takes 53.60 seconds. The trick below however, takes 27.34 seconds.
from multiprocessing import Pool
import functools
import time
work_items = [(1, 'A', True), (2, 'X', False), (3, 'B', False)]
def worker(tup):
for i in range(5000000):
print(work_items)
return
def parallel_attribute(worker):
def easy_parallelize(worker, work_items):
pool = Pool(processes = 8)
work_results = pool.map(worker, work_items)
pool.close()
pool.join()
from functools import partial
return partial(easy_parallelize, worker)
start = time.time()
worker.parallel = parallel_attribute(worker(work_items))
end = time.time()
print(end - start)
Two comments: 1) I didn't see much of a difference with using multiprocessing dummy 2) Using Python's partial function (scope with nesting) works like a wonderful wrapper that reduces the computation time by 1/2. Reference: https://www.binpress.com/tutorial/simple-python-parallelism/121
Also, Thank you FMc!
Well, you can create first a thread, then pass to it the function you want to parallelize. Inside the function you have the subprocess.
import threading
import subprocess
def worker():
"""thread worker function"""
print 'Worker'
subprocess.call(mycode.py, shell=inshell)
return
threads = []
for i in range(5):
t = threading.Thread(target=worker)
threads.append(t)
t.start()
