random.random() generates same number in multiprocessing - python

I'm working on an optimization problem, and you can see a simplified version of my code posted below (the origin code is too complicated for asking such a question, and I hope my simplified code has simulated the original one as much as possible).
My purpose:
use the function foo in the function optimization, but foo can take very long time due to some hard situations. So I use multiprocessing to set a time limit for execution of the function (proc.join(iter_time), the method is from an anwser from this question; How to limit execution time of a function call?).
My problem:
In the while loop, every time the generated values for extra are the same.
The list lst's length is always 1, which means in every iteration in the while loop it starts from an empty list.
My guess: possible reason can be each time I create a process the random seed is counting from the beginning, and each time the process is terminated, there could be some garbage collection mechanism to clean the memory the processused, so the list is cleared.
My question
Anyone know the real reason of such problems?
if not using multiprocessing, is there anyway else that I can realize my purpose while generate different random numbers? btw I have tried func_timeout but it has other problems that I cannot handle...
random.seed(123)
lst = [] # a global list for logging data
def foo(epoch):
...
extra = random.random()
lst.append(epoch + extra)
...
def optimization(loop_time, iter_time):
start = time.time()
epoch = 0
while time.time() <= start + loop_time:
proc = multiprocessing.Process(target=foo, args=(epoch,))
proc.start()
proc.join(iter_time)
if proc.is_alive(): # if the process is not terminated within time limit
print("Time out!")
proc.terminate()
if __name__ == '__main__':
optimization(300, 2)

You need to use shared memory if you want to share variables across processes. This is because child processes do not share their memory space with the parent. Simplest way to do this here would be to use managed lists and delete the line where you set a number seed. This is what is causing same number to be generated because all child processes will take the same seed to generate the random numbers. To get different random numbers either don't set a seed, or pass a different seed to each process:
import time, random
from multiprocessing import Manager, Process
def foo(epoch, lst):
extra = random.random()
lst.append(epoch + extra)
def optimization(loop_time, iter_time, lst):
start = time.time()
epoch = 0
while time.time() <= start + loop_time:
proc = Process(target=foo, args=(epoch, lst))
proc.start()
proc.join(iter_time)
if proc.is_alive(): # if the process is not terminated within time limit
print("Time out!")
proc.terminate()
print(lst)
if __name__ == '__main__':
manager = Manager()
lst = manager.list()
optimization(10, 2, lst)
Output
[0.2035898948744943, 0.07617925389396074, 0.6416754412198231, 0.6712193790613651, 0.419777147554235, 0.732982735576982, 0.7137712131028766, 0.22875414425414997, 0.3181113880578589, 0.5613367673646847, 0.8699685474084119, 0.9005359611195111, 0.23695341111251134, 0.05994288664062197, 0.2306562314450149, 0.15575356275408125, 0.07435292814989103, 0.8542361251850187, 0.13139055891993145, 0.5015152768477814, 0.19864873743952582, 0.2313646288041601, 0.28992667535697736, 0.6265055915510219, 0.7265797043535446, 0.9202923318284002, 0.6321511834038631, 0.6728367262605407, 0.6586979597202935, 0.1309226720786667, 0.563889613032526, 0.389358766191921, 0.37260564565714316, 0.24684684162272597, 0.5982042933298861, 0.896663326233504, 0.7884030244369596, 0.6202229004466849, 0.4417549843477827, 0.37304274232635715, 0.5442716244427301, 0.9915536257041505, 0.46278512685707873, 0.4868394190894778, 0.2133187095154937]
Keep in mind that using managers will affect performance of your code. Alternate to this, you could also use multiprocessing.Array, which is faster than managers but is less flexible in what data it can store, or Queues as well.

Related

Share variable in concurrent.futures

I am trying to do a word counter with mapreduce using concurrent.futures, previously I've done a multi threading version, but was so slow because is CPU bound.
I have done the mapping part to divide the words into ['word1',1], ['word2,1], ['word1,1], ['word3',1] and between the processes, so each process will take care of a part of the text file. The next step ("shuffling") is to put these words in a dictionary so that it looks like this: word1: [1,1], word2:[1], word3: [1], but I cannot share the dictionary between the processes because we are using multiprocessing instead of multithreading, so how can I make each process add the "1" to the dictionary shared between all the processes? I'm stuck with this, and I can't continue.
I am at this point:
import sys
import re
import concurrent.futures
import time
# Read text file
def input(index):
try:
reader = open(sys.argv[index], "r", encoding="utf8")
except OSError:
print("Error")
sys.exit()
texto = reader.read()
reader.close()
return texto
# Convert text to list of words
def splitting(input_text):
input_text = input_text.lower()
input_text = re.sub('[,.;:!¡?¿()]+', '', input_text)
words = input_text.split()
n_processes = 4
# Creating processes
with concurrent.futures.ProcessPoolExecutor() as executor:
results = []
for id_process in range(n_processes):
results.append(executor.submit(mapping, words, n_processes, id_process))
for f in concurrent.futures.as_completed(results):
print(f.result())
def mapping(words, n_processes, id_process):
word_map_result = []
for i in range(int((id_process / n_processes) * len(words)),
int(((id_process + 1) / n_processes) * len(words))):
word_map_result.append([words[i], 1])
return word_map_result
if __name__ == '__main__':
if len(sys.argv) == 1:
print("Please, specify a text file...")
sys.exit()
start_time = time.time()
for index in range(1, len(sys.argv)):
print(sys.argv[index], ":", sep="")
text = input(index)
splitting(text)
# for word in result_dictionary_words:
# print(word, ':', result_dictionary_words[word])
print("--- %s seconds ---" % (time.time() - start_time))
I've seen that when doing concurrent programming it is usually best to avoid using shared state as far as possible, so how I can implement Map reduce word count without share the dictionary between processes?
You can create a shared dictionary using a Manager from multiprocessing. I understand from your program that it is your word_map_result you need to share.
You could try something like this
from multiprocessing import Manager
...
def splitting():
...
word_map_result = Manager().dict()
with concurrent.futures.....:
...
results.append(executor.submit(mapping, words, n_processes, id_process, word_map_result)
...
...
def mapping(words, n_processes, id_process, word_map_result):
for ...
# Do not return anything - word_map_result is up to date in your main process
Basically you will remove the local copy of word_map_result from your mapping function and pass it the Manager instance as a parameter. This word_map_result is now shared between all your subprocesses and the main program. Managers add data transfer overhead, though, so this might not help you very much.
In this case you do not return anything from the workers so you do not need the for loop to process results either in your main program - your word_map_result is identical in all subprocesses and the main program.
I may have misunderstood your problem and I am not familiar with the algorithm if it is possible to re-engineer that to work so that you don't need to share anything between processes.
It seems like a misconception to be using multiprocessing at all. First, there is overhead in creating the pool and overhead in passing data to and from the processes. And if you decide to use a shared, managed dictionary that worker function mapping can use to store its results in, know that a managed dictionary uses a proxy, the accessing of which is rather slow. The alternative to using a managed dictionary would be as you currently have it, i.e. mapping returns a list and the main process uses those results to create the keys and values of the dictionary. But what then is the point of mapping returning a list where each element is always a list of two elements where the second element is always the constant value 1? Isn't that rather wasteful of time and space?
I think your performance will be no faster (probably slower) than just implementing splitting as:
# Convert text to list of words
def splitting(input_text):
input_text = input_text.lower()
input_text = re.sub('[,.;:!¡?¿()]+', '', input_text)
words = input_text.split()
results = {}
for word in words:
results[word] = [1]
return results

Factorial calculation with threading takes too long to finish, How to fix it?

I am trying to calculate whether a given number is prime or not with the formula :
(n-1)! mod n =? (n-1)
I must calculate the factorial with different threads and make them work simultaneously and control if they're all finished and if so then join them. By doing so I will be calculating factorial with different threads and then be able to take the modulo. However even though my code works fine with the small prime numbers it is taking too long to execute when the number is too big. I searched my code and couldn't really find alternative that can slow down the execution time. Here is my code :
import threading
import time
# GLOBAL VARIABLE
result = 1
# worker class in order to multiply on threads
class Worker:
# initiating the worker class
def __init__(self):
super().__init__()
self.jobs = []
# the function that does the actual multiplying
def multiplier(self,beg,end):
global result
for i in range(beg,end+1):
result*= i
#print("\tresult updated with *{}:".format(i),result)
#print("Calculating from {} to {}".format(beg,end)," : ",result)
# appending threads to the object
def append_job(self,job):
self.jobs.append(job)
# function that is to see the threads
def see_jobs(self):
return self.jobs
# initiating the threads
def initiate(self):
for j in self.jobs:
j.start()
# finalizing and joining the threads
def finalize(self):
for j in self.jobs:
j.join()
# controlling the threads by blocking them untill all threads are asleep
def work(self):
while True:
if 0 == len([t for t in self.jobs if t.is_alive()]):
self.finalize()
break
# this is the function to split the factorial into several threads
def splitUp(n,t):
# defining the remainder and the whole
remainder, whole = (n-1) % t, (n-1) // t
# deciding to tuple count
tuple_count = whole if remainder == 0 else whole + 1
# empty result list
result = []
# iterating
beginning = 1
end = (n-1) // t
for i in range(1,tuple_count+1):
if i == tuple_count:
result.append((beginning,n-1)) # if we are at the end, just append all to end
else:
result.append((beginning,end*i))
beginning = end*i + 1
return result
if __name__ == "__main__":
threads = 64
number = 743
splitted = splitUp(number,threads)
worker = Worker()
#print(worker.see_jobs())
s = time.time()
# creating the threads
for arg in splitted:
thread = threading.Thread(target=worker.multiplier(arg[0],arg[1]))
worker.append_job(thread)
worker.initiate()
worker.work()
e = time.time()
print("result found with {} threads in {} secs\n".format(threads,e-s))
if result % number == number-1:
print("PRIME")
else:
print("NOT PRIME")
"""
-------------------- REPORT ------------------------
result found with 2 threads in 6.162530899047852 secs
result found with 4 threads in 0.29897499084472656 secs
result found with 16 threads in 0.009003162384033203 secs
result found with 32 threads in 0.0060007572174072266 secs
result found with 64 threads in 0.0029952526092529297 secs
note that: these results may differ from machine to machine
-------------------------------------------------------
"""
Thanks in advance.
First and foremost, you have a critical error in your code that you haven't reported or tried to trace:
======================== RESTART: ========================
result found with 2 threads in 5.800899267196655 secs
NOT PRIME
>>>
======================== RESTART: ========================
result found with 64 threads in 0.002002716064453125 secs
PRIME
>>>
As the old saying goes, "if the program doesn't work, it doesn't matter how fast it is".
The only test case you've given is 743; if you want help to diagnose the logic error, please determine the minimal test and parallelism that causes the error, and post a separate question.
I suspect that it's with your multplier function, as you're working with an ill-advised global variable in parallel processing, and your multiply operation is not thread-safe.
In assembly terms, you have an unguarded region:
LOAD result
MUL i
STORE result
If this is interleaved with the same work from another process, the result is virtually guaranteed to be wrong. You have to make this a critical region.
Once you fix that, you still have your speed problem. Factorial is the poster-child for recursion acceleration. I see two obvious accelerants:
Instead of that horridly slow multiplication loop, use functools.reduce to blast through your multiplication series.
If you're going to loop the program with a series of inputs, then short-cut most of the calculations with memoization. The example on the linked page benefits greatly from multiple-recursion; since factorial is linear, you'd need repeated application to take advantage of the technique.

Set the nr of executions per second using Python's multiprocessing

I wrote a script in Python 3.6 initially using a for loop which called an API, then putting all results into a pandas dataframe and writing them to a SQL database. (approximately 9,000 calls are made to that API every time the script runs).
Realising the calls inside the for loop were processed one-by-one, I decided to use the multiprocessing module to speed things up.
Therefore, I created a module level function called parallel_requests and now I call that instead of having the for loop:
list_of_lists = multiprocessing.Pool(processes=4).starmap(parallel_requests, zip(....))
Side note: I use starmap instead of map only because my parallel_requests function takes multiple arguments which I need to zip.
The good: this approach works and is much faster.
The bad: this approach works but is too fast. By using 4 processes (I tried that because I have 4 cores), parallel_requests is getting executed too fast. More than 15 calls per second are made to the API, and I'm getting blocked by the API itself.
In fact, it only works if I use 1 or 2 processes, otherwise it's too damn fast.
Essentially what I want is to keep using 4 processes, but also to limit the execution of my parallel_requests function to only 15 times per second overall.
Is there any parameter of multiprocessing.Pool that would help with this, or it's more complicated than that?
For this case I'd use a leaky bucket. You can have one process that fills a queue at the proscribed rate, with a maximum size that indicates how many requests you can "bank" if you don't make them at the maximum rate; the worker processes then just need to get from the queue before doing its work.
import time
def make_api_request(this, that, rate_queue):
rate_queue.get()
print("DEBUG: doing some work at {}".format(time.time()))
return this * that
def throttler(rate_queue, interval):
try:
while True:
if not rate_queue.full(): # avoid blocking
rate_queue.put(0)
time.sleep(interval)
except BrokenPipeError:
# main process is done
return
if __name__ == '__main__':
from multiprocessing import Pool, Manager, Process
from itertools import repeat
rq = Manager().Queue(maxsize=15) # conservative; no banking
pool = Pool(4)
Process(target=throttler, args=(rq, 1/15.)).start()
pool.starmap(make_api_request, zip(range(100), range(100, 200), repeat(rq)))
I'll look at the ideas posted here, but in the meantime I've just used a simple approach of opening and closing a Pool of 4 processes for every 15 requests and appending all the results in a list_of_lists.
Admittedly, not the best approach, since it takes time/resources to open/close a Pool, but it was the most handy solution for now.
# define a generator for use below
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
list_of_lists = []
for current_chunk in chunks(all_data, 15): # 15 is the API's limit of requests per second
pool = multiprocessing.Pool(processes=4)
res = pool.starmap(parallel_requests, zip(current_chunk, [to_symbol]*len(current_chunk), [query]*len(current_chunk), [start]*len(current_chunk), [stop]*len(current_chunk)) )
sleep(1) # Sleep for 1 second after every 15 API requests
list_of_lists.extend(res)
pool.close()
flatten_list = [item for sublist in list_of_lists for item in sublist] # use this to construct a `pandas` dataframe
PS: This solution is really not at all that fast due to the multiple opening/closing of pools. Thanks Nathan Vērzemnieks for suggesting to open just one pool, it's much faster, plus your processor won't look like it's running a stress test.
One way to do is to use Queue, which can share details about api-call timestamps with other processes.
Below is an example how this could work. It takes the oldest entry in queue, and if it is younger than one second, sleep functions is called for the duration of the difference.
from multiprocessing import Pool, Manager, queues
from random import randint
import time
MAX_CONNECTIONS = 10
PROCESS_COUNT = 4
def api_request(a, b):
time.sleep(randint(1, 9) * 0.03) # simulate request
return a, b, time.time()
def parallel_requests(a, b, the_queue):
try:
oldest = the_queue.get()
time_difference = time.time() - oldest
except queues.Empty:
time_difference = float("-inf")
if 0 < time_difference < 1:
time.sleep(1-time_difference)
else:
time_difference = 0
print("Current time: ", time.time(), "...after sleeping:", time_difference)
the_queue.put(time.time())
return api_request(a, b)
if __name__ == "__main__":
m = Manager()
q = m.Queue(maxsize=MAX_CONNECTIONS)
for _ in range(0, MAX_CONNECTIONS): # Fill the queue with zeroes
q.put(0)
p = Pool(PROCESS_COUNT)
# Create example data
data_length = 100
data1 = range(0, data_length) # Just some dummy-data
data2 = range(100, data_length+100) # Just some dummy-data
queue_iterable = [q] * (data_length+1) # required for starmap -function
list_of_lists = p.starmap(parallel_requests, zip(data1, data2, queue_iterable))
print(list_of_lists)

python parallel calculations in a while loop

I have been looking around for some time, but haven't had luck finding an example that could solve my problem. I have added an example from my code. As one can notice this is slow and the 2 functions could be done separately.
My aim is to print every second the latest parameter values. At the same time the slow processes can be calculated in the background. The latest value is shown and when any process is ready the value is updated.
Can anybody recommend a better way to do it? An example would be really helpful.
Thanks a lot.
import time
def ProcessA(parA):
# imitate slow process
time.sleep(5)
parA += 2
return parA
def ProcessB(parB):
# imitate slow process
time.sleep(10)
parB += 5
return parB
# start from here
i, parA, parB = 1, 0, 0
while True: # endless loop
print(i)
print(parA)
print(parB)
time.sleep(1)
i += 1
# update parameter A
parA = ProcessA(parA)
# update parameter B
parB = ProcessB(parB)
I imagine this should do it for you. This has the benefit of you being able to add extra parallel funcitons up to a total equal to the number of cores you have. Edits are welcome.
#import time module
import time
#import the appropriate multiprocessing functions
from multiprocessing import Pool
#define your functions
#whatever your slow function is
def slowFunction(x):
return someFunction(x)
#printingFunction
def printingFunction(new,current,timeDelay):
while new == current:
print current
time.sleep(timeDelay)
#set the initial value that will be printed.
#Depending on your function this may take some time.
CurrentValue = slowFunction(someTemporallyDynamicVairable)
#establish your pool
pool = Pool()
while True: #endless loop
#an asynchronous function, this will continue
# to run in the background while your printing operates.
NewValue = pool.apply_async(slowFunction(someTemporallyDynamicVairable))
pool.apply(printingFunction(NewValue,CurrentValue,1))
CurrentValue = NewValue
#close your pool
pool.close()

Optimizing Multiprocessing in Python (Follow Up: using Queues)

This is a followup question to this. User Will suggested using a queue, I tried to implement that solution below. The solution works just fine with j=1000, however, it hangs as I try to scale to larger numbers. I am stuck here and cannot determine why it hangs. Any suggestions would be appreciated. Also, the code is starting to get ugly as I keep messing with it, I apologize for all the nested functions.
def run4(j):
"""
a multicore approach using queues
"""
from multiprocessing import Process, Queue, cpu_count
import os
def bazinga(uncrunched_queue, crunched_queue):
"""
Pulls the next item off queue, generates its collatz
length and
"""
num = uncrunched_queue.get()
while num != 'STOP': #Signal that there are no more numbers
length = len(generateChain(num, []) )
crunched_queue.put([num , length])
num = uncrunched_queue.get()
def consumer(crunched_queue):
"""
A process to pull data off the queue and evaluate it
"""
maxChain = 0
biggest = 0
while not crunched_queue.empty():
a, b = crunched_queue.get()
if b > maxChain:
biggest = a
maxChain = b
print('%d has a chain of length %d' % (biggest, maxChain))
uncrunched_queue = Queue()
crunched_queue = Queue()
numProcs = cpu_count()
for i in range(1, j): #Load up the queue with our numbers
uncrunched_queue.put(i)
for i in range(numProcs): #put sufficient stops at the end of the queue
uncrunched_queue.put('STOP')
ps = []
for i in range(numProcs):
p = Process(target=bazinga, args=(uncrunched_queue, crunched_queue))
p.start()
ps.append(p)
p = Process(target=consumer, args=(crunched_queue, ))
p.start()
ps.append(p)
for p in ps: p.join()
You're putting 'STOP' poison pills into your uncrunched_queue (as you should), and having your producers shut down accordingly; on the other hand your consumer only checks for emptiness of the crunched queue:
while not crunched_queue.empty():
(this working at all depends on a race condition, btw, which is not good)
When you start throwing non-trivial work units at your bazinga producers, they take longer. If all of them take long enough, your crunched_queue dries up, and your consumer dies. I think you may be misidentifying what's happening - your program doesn't "hang", it just stops outputting stuff because your consumer is dead.
You need to implement a smarter methodology for shutting down your consumer. Either look for n poison pills, where n is the number of producers (who accordingly each toss one in the crunched_queue when they shut down), or use something like a Semaphore that counts up for each live producer and down when one shuts down.

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