I am trying to use multiprocessing for the below code. The code seems to run a bit faster than the for loop inside the function.
How can I confirm I using the library and not the just the for loop?
from multiprocessing import Pool
from multiprocessing import cpu_count
import requests
import pandas as pd
data= pd.read_csv('~/Downloads/50kNAE000.txt.1' ,sep="\t", header=None)
data = data[0].str.strip("0 ")
lst = []
def request(x):
for i,v in x.items():
print(i)
file = requests.get(v)
lst.append(file.text)
#time.sleep(1)
if __name__ == "__main__":
pool = Pool(cpu_count())
results = pool.map(request(data))
pool.close() # 'TERM'
pool.join() # 'KILL'
Multiprocessing has overhead. It has to start the process and transfer function data via interprocess mechanism. Just running a single function in another process vs. running that same function normally is always going to be slower. The advantage is actually doing parallelism with significant work in the functions that makes the overhead minimal.
You can call multiprocessing.current_process().name to see the process name change.
I am trying to update a variable of a class by calling a function of the class from a different function which is being run on multi-process.
To achieve the desired result, process (p1) needs to update the variable "transaction" and which should get then modified by process (p2)
I tried the below code and I know i should use Multiprocess.value or manager to achieve the desired result and I am not sure of how to do it as my variable to be updated is in another class
Below is the code:
from multiprocessing import Process
from helper import Helper
camsource = ['a','b']
Pros = []
def sub(i):
HC.trail_func(i)
def main():
for i in camsource:
print ("Camera Thread {} Started!".format(i))
p = Process(target=sub, args=(i))
Pros.append(p)
p.start()
# block until all the threads finish (i.e. block until all function_x calls finish)
for t in Pros:
t.join()
if __name__ == "__main__":
HC = Helper()
main()
Here is the helper code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
class Helper():
def __init__(self):
self.transactions = []
def trail_func(self,preview):
if preview == 'a':
self.transactions.append({"Apple":1})
else:
if self.transactions[0]['Apple'] == 1:
self.transactions[0]['Apple'] = self.transactions[0]['Apple'] + 1
print (self.transactions)
Desired Output:
p1:
transactions = {"Apple":1}
p2:
transactions = {"Apple":2}
I've recently released this module that can help you with your code, as all data frames (data models that can hold any type of data), have locks on them, in order to solve concurrency issues. Anyway, take a look at the README file and the examples.
I've made an example here too, if you'd like to check.
I am following the principles laid down in this post to safely output the results which will eventually be written to a file. Unfortunately, the code only print 1 and 2, and not 3 to 6.
import os
import argparse
import pandas as pd
import multiprocessing
from multiprocessing import Process, Queue
from time import sleep
def feed(queue, parlist):
for par in parlist:
queue.put(par)
print("Queue size", queue.qsize())
def calc(queueIn, queueOut):
while True:
try:
par=queueIn.get(block=False)
res=doCalculation(par)
queueOut.put((res))
queueIn.task_done()
except:
break
def doCalculation(par):
return par
def write(queue):
while True:
try:
par=queue.get(block=False)
print("response:",par)
except:
break
if __name__ == "__main__":
nthreads = 2
workerQueue = Queue()
writerQueue = Queue()
considerperiod=[1,2,3,4,5,6]
feedProc = Process(target=feed, args=(workerQueue, considerperiod))
calcProc = [Process(target=calc, args=(workerQueue, writerQueue)) for i in range(nthreads)]
writProc = Process(target=write, args=(writerQueue,))
feedProc.start()
feedProc.join()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writProc.start()
writProc.join()
On running the code it prints,
$ python3 tst.py
Queue size 6
response: 1
response: 2
Also, is it possible to ensure that the write function always outputs 1,2,3,4,5,6 i.e. in the same order in which the data is fed into the feed queue?
The error is somehow with the task_done() call. If you remove that one, then it works, don't ask me why (IMO that's a bug). But the way it works then is that the queueIn.get(block=False) call throws an exception because the queue is empty. This might be just enough for your use case, a better way though would be to use sentinels (as suggested in the multiprocessing docs, see last example). Here's a little rewrite so your program uses sentinels:
import os
import argparse
import multiprocessing
from multiprocessing import Process, Queue
from time import sleep
def feed(queue, parlist, nthreads):
for par in parlist:
queue.put(par)
for i in range(nthreads):
queue.put(None)
print("Queue size", queue.qsize())
def calc(queueIn, queueOut):
while True:
par=queueIn.get()
if par is None:
break
res=doCalculation(par)
queueOut.put((res))
def doCalculation(par):
return par
def write(queue):
while not queue.empty():
par=queue.get()
print("response:",par)
if __name__ == "__main__":
nthreads = 2
workerQueue = Queue()
writerQueue = Queue()
considerperiod=[1,2,3,4,5,6]
feedProc = Process(target=feed, args=(workerQueue, considerperiod, nthreads))
calcProc = [Process(target=calc, args=(workerQueue, writerQueue)) for i in range(nthreads)]
writProc = Process(target=write, args=(writerQueue,))
feedProc.start()
feedProc.join()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writProc.start()
writProc.join()
A few things to note:
the sentinel is putting a None into the queue. Note that you need one sentinel for every worker process.
for the write function you don't need to do the sentinel handling as there's only one process and you don't need to handle concurrency (if you would do the empty() and then get() thingie in your calc function you would run into a problem if e.g. there's only one item left in the queue and both workers check empty() at the same time and then both want to do get() and then one of them is locked forever)
you don't need to put feed and write into processes, just put them into your main function as you don't want to run it in parallel anyway.
how can I have the same order in output as in input? [...] I guess multiprocessing.map can do this
Yes map keeps the order. Rewriting your program into something simpler (as you don't need the workerQueue and writerQueue and adding random sleeps to prove that the output is still in order:
from multiprocessing import Pool
import time
import random
def calc(val):
time.sleep(random.random())
return val
if __name__ == "__main__":
considerperiod=[1,2,3,4,5,6]
with Pool(processes=2) as pool:
print(pool.map(calc, considerperiod))
I'm struggling with this code copied from a tutorial about multiprocessing:
(source: https://tutorialedge.net/post/python/python-multiprocessing-tutorial/ )
from multiprocessing import Process, Queue
import random
def rand_num():
num = random.random()
print(num)
if __name__ == "__main__":
queue = Queue()
processes = [Process(target=rand_num, args=()) for x in range(4)]
for p in processes:
p.start()
for p in processes:
p.join()
Testing in Python 3.6.1 on W7 pc.
In Idle it comes back to the prompt without printing the expected numbers.
When doubleclicked in Windows the py.exe screen comes up for a very short time and closes again.
Can anybody tell me where it goes wrong?
I'm using Python 3.5.2 in Windows 32bits and aware that asyncio call_at is not threadsafe, hence following code won't print 'bomb' unless I uncomment the line loop._write_to_self().
import asyncio
import threading
def bomb(loop):
loop.call_later(1, print, 'bomb')
print('submitted')
# loop._write_to_self()
if __name__ == '__main__':
loop = asyncio.get_event_loop()
threading.Timer(2, bomb, args=(loop,)).start()
loop.run_forever()
However I couldn't find a piece of information about why call_at_threadsafe and call_later_threadsafe is implemented. Is the reason ever exists?
Simply use loop.call_soon_threadsafe to schedule loop.call_later:
loop.call_soon_threadsafe(loop.call_later, 1, print, 'bomb')