When using multiprocessing in Python, I usually see examples where the join() function is called in a separate loop to where each process was actually created.
For example, this:
processes = []
for i in range(10):
p = Process(target=my_func)
processes.append(p)
p.start()
for p in processes:
p.join()
is more common than this:
processes = []
for i in range(10):
p = Process(target=my_func)
processes.append(p)
p.start()
p.join()
But from my understanding of join(), it just tells the script not to exit until that process has finished. Therefore, it shouldn't matter when join() is called. So why is it usually called in a separate loop?
join() is blocking operation.
In first example you start 10 processes and then you are waiting for all procces to finish. All processes are running at same time.
In second example you start one process at time and you are waiting for finish before you start another process. There is only one running process at same time
First example:
def wait()
time.sleep(1)
# You start 10 processes
for i in range(10):
p = Process(target=wait)
processes.append(p)
p.start()
# One second after all processes can be finished you check them all and finish
for p in processes:
p.join()
Execution time of whole script can be near one second.
Second example:
for i in range(10):
p = Process(target=wait) # Here you start one process
processes.append(p)
p.start()
p.join() # Here you will have to wait one second before process finished.
Execution time of whole script can be near 10 seconds!.
Related
When trying to run multiprocessing on a for loop all the process are being executed but the program is not terminated and keep running.
How can i stop the program after all elements in the list got processed?
def multiprocess_for_loop(*args, list_to_iter_over, function):
# the element in iterable is the first argument in the function
output = Queue()
processes = [Process(target=function, args=(elem,) + args) for elem in list_to_iter_over]
logging.info(f"start multiprocess for-loop on list: {list_to_iter_over}")
for p in processes:
p.start()
results = []
for p in processes:
results.append(output.get())
for p in processes:
p.join()
logging.info("finished multiprocessing for-loop")
return results
Every process write on Queue?
If a process don't write in it, your code keep stuck in that for.
I am trying to learn multiprocessing, and created an example, however it's behaving unexpectedly.
the parent process run, then create a child process, but resources doesnt go back to parent until child is done.
code:
from multiprocessing import Process
import time
def f():
newTime = time.time() + 7
while(time.time() < newTime):
print("inside child process")
time.sleep(int(5))
if __name__ == '__main__':
bln = True
while(True):
newTime = time.time() + 4
while(time.time() < newTime):
print("printing fillers")
if(bln):
p = Process(target=f)
p.start()
p.join()
bln = False
result
"inside child process"
(wait for 5 sec)
"inside child process"
"printing fillers"
"printing fillers"
[...]
If I remove 'p.join()' then it will work. But from my understanding, p.join() is to tell the program to wait for this thread/process to finish before ending the program.
Can someone tell me why this is happening?
But from my understanding, p.join() is to tell the program to wait for
this thread/process to finish before ending the program.
Nope, It blocks the main thread right then and there until the thread / process finishes. By doing that right after you start the process, you don't let the loop continue until each process completes.
It would be better to collect all the Process objects you create into a list, so they can be accessed after the loop creating them. Then in a new loop, wait for them to finish only after they are all created and started.
#for example
processes = []
for i in whatever:
p = Process(target=foo)
p.start()
processes.append(p)
for p in processes:
p.join()
If you want to be able to do things in the meantime (while waiting for join), it is most common to use yet another thread or process. You can also choose to only wait a short time on join by giving it a timeout value, and if the process doesn't complete in that amount of time, an exception will be thrown which you can catch with a try block, and decide to go do something else before trying to join again.
p.join() isn't for ending the program, it's for waiting for a subprocess to finish. If you need to end the program, use something like sys.exit(0) or raise SystemExit('your reason here')
I am running a multiprocessing pool in python, where I have ~2000 tasks, being mapped to 24 workers with the pool.
each task creates a file based on some data analysis and webservices.
I want to run a new task, when all the tasks in the pool were finished. how can I tell when all the processes in the pool have finished?
You want to use the join method, which halts the main process thread from moving forward until all sub-processes ends:
Block the calling thread until the process whose join() method is called terminates or until the optional timeout occurs.
from multiprocessing import Process
def f(name):
print 'hello', name
if __name__ == '__main__':
processes = []
for i in range(10):
p = Process(target=f, args=('bob',))
processes.append(p)
for p in processes:
p.start()
p.join()
# only get here once all processes have finished.
print('finished!')
EDIT:
To use join with pools
pool = Pool(processes=4) # start 4 worker processes
result = pool.apply_async(f, (10,)) # do some work
pool.close()
pool.join() # block at this line until all processes are done
print("completed")
You can use the wait() method of the ApplyResult object (which is what pool.apply_async returns).
import multiprocessing
def create_file(i):
open(f'{i}.txt', 'a').close()
if __name__ == '__main__':
# The default for n_processes is the detected number of CPUs
with multiprocessing.Pool() as pool:
# Launch the first round of tasks, building a list of ApplyResult objects
results = [pool.apply_async(create_file, (i,)) for i in range(50)]
# Wait for every task to finish
[result.wait() for result in results]
# {start your next task... the pool is still available}
# {when you reach here, the pool is closed}
This method works even if you're planning on using your pool again and don't want to close it--as an example, you might want to keep it around for the next iteration of your algorithm. Use a with statement or call pool.close() manually when you're done using it, or bad things will happen.
I want to create many processes,
each process runs 5 seconds later than a previous process,
namely, the time interval between each process starts is 5 seconds,
so that:
run process 1
wait 5 seconds
run process 2
wait 5 seconds
run process 3
wait 5 seconds
.....
like:
for i in range(10):
p = multiprocessing.Process(target=func)
p.start()
sleep(5)
#after all child process exit
do_something()
but I want to call do_something() after all the process exit
I don't know how to do the synchronization here
with a python pool libary, I can have
pool = multiprocessing.Pool(processes=4)
for i in xrange(500):
pool.apply_async(func, i)
pool.close()
pool.join()
do_something()
but in this way, 4 processes will run simultaneously,
I can't decide the time interval between processes,
is it possible to create a process pool and then fetch each process, something like
pool = multiprocessing.Pool(processes=4)
for i in xrange(500):
process = pool.fetch_one()
process(func, i)
time.sleep(5)
pool.close()
pool.join()
do_something()
are there such a library or source code snippets which satisfy my needs?
thanks
Just to put suggestions together, you could do something like:
plist = []
for i in range(10):
p = multiprocessing.Process(target=func)
p.start()
plist.append(p)
sleep(5)
for p in plist:
p.join()
do_something()
You could give a timeout argument to join() in order to handle stuck processes; in that case you'd have to keep iterating through the list, removing terminated processes until the list is empty.
I am reading various tutorials on the multiprocessing module in Python, and am having trouble understanding why/when to call process.join(). For example, I stumbled across this example:
nums = range(100000)
nprocs = 4
def worker(nums, out_q):
""" The worker function, invoked in a process. 'nums' is a
list of numbers to factor. The results are placed in
a dictionary that's pushed to a queue.
"""
outdict = {}
for n in nums:
outdict[n] = factorize_naive(n)
out_q.put(outdict)
# Each process will get 'chunksize' nums and a queue to put his out
# dict into
out_q = Queue()
chunksize = int(math.ceil(len(nums) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(
target=worker,
args=(nums[chunksize * i:chunksize * (i + 1)],
out_q))
procs.append(p)
p.start()
# Collect all results into a single result dict. We know how many dicts
# with results to expect.
resultdict = {}
for i in range(nprocs):
resultdict.update(out_q.get())
# Wait for all worker processes to finish
for p in procs:
p.join()
print resultdict
From what I understand, process.join() will block the calling process until the process whose join method was called has completed execution. I also believe that the child processes which have been started in the above code example complete execution upon completing the target function, that is, after they have pushed their results to the out_q. Lastly, I believe that out_q.get() blocks the calling process until there are results to be pulled. Thus, if you consider the code:
resultdict = {}
for i in range(nprocs):
resultdict.update(out_q.get())
# Wait for all worker processes to finish
for p in procs:
p.join()
the main process is blocked by the out_q.get() calls until every single worker process has finished pushing its results to the queue. Thus, by the time the main process exits the for loop, each child process should have completed execution, correct?
If that is the case, is there any reason for calling the p.join() methods at this point? Haven't all worker processes already finished, so how does that cause the main process to "wait for all worker processes to finish?" I ask mainly because I have seen this in multiple different examples, and I am curious if I have failed to understand something.
Try to run this:
import math
import time
from multiprocessing import Queue
import multiprocessing
def factorize_naive(n):
factors = []
for div in range(2, int(n**.5)+1):
while not n % div:
factors.append(div)
n //= div
if n != 1:
factors.append(n)
return factors
nums = range(100000)
nprocs = 4
def worker(nums, out_q):
""" The worker function, invoked in a process. 'nums' is a
list of numbers to factor. The results are placed in
a dictionary that's pushed to a queue.
"""
outdict = {}
for n in nums:
outdict[n] = factorize_naive(n)
out_q.put(outdict)
# Each process will get 'chunksize' nums and a queue to put his out
# dict into
out_q = Queue()
chunksize = int(math.ceil(len(nums) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(
target=worker,
args=(nums[chunksize * i:chunksize * (i + 1)],
out_q))
procs.append(p)
p.start()
# Collect all results into a single result dict. We know how many dicts
# with results to expect.
resultdict = {}
for i in range(nprocs):
resultdict.update(out_q.get())
time.sleep(5)
# Wait for all worker processes to finish
for p in procs:
p.join()
print resultdict
time.sleep(15)
And open the task-manager. You should be able to see that the 4 subprocesses go in zombie state for some seconds before being terminated by the OS(due to the join calls):
With more complex situations the child processes could stay in zombie state forever(like the situation you was asking about in an other question), and if you create enough child-processes you could fill the process table causing troubles to the OS(which may kill your main process to avoid failures).
At the point just before you call join, all workers have put their results into their queues, but they did not necessarily return, and their processes may not yet have terminated. They may or may not have done so, depending on timing.
Calling join makes sure that all processes are given the time to properly terminate.
I am not exactly sure of the implementation details, but join also seems to be necessary to reflect that a process has indeed terminated (after calling terminate on it for example). In the example here, if you don't call join after terminating a process, process.is_alive() returns True, even though the process was terminated with a process.terminate() call.