Studying parallelism in Scala, I have got an impression that Futures and executor tasks are like lightweight threads. You can create more them than there are threads. But I get an OOM exception running the following code
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.{Future, future, Await}
import scala.concurrent.duration._
def fib(n: Int): Future[Int] = future {if (n < 2) n else
List(n-1, n-2) map fib map {Await.result(_, Duration.Inf)} sum};
Await.result(fib(35), Duration.Inf)
What is happening?
Related
Hello I am working on a simulation of a buffer where I need to use threads and locks. So I created two function one so the consumer gets his trail and the second one is once he gets his trail he can go to the next line to get his meal.
However my code never stops running and never goes to the second function were he could get his meal.
from concurrent.futures import thread
import random
import threading
import time
import concurrent.futures
import logging
import traceback
from numpy import number
#Creating the two queues with 50 students
consumers = [x+1 for x in range(50)]
trail = []
meal = []
#putting the locks for both queues
meal_lock = threading.Lock()
trail_lock = threading.Lock()
def trail(x):
global trail_lock
while True:
trail_lock.acquire()
trail.append(x)
if x in trail:
print(f"Consumer {x} Got his trail")
trail_lock.release()
def meal(x):
global meal_lock
while True:
meal_lock.acquire()
if x in trail:
trail.remove(x)
print("Got his meal")
meal.append(x)
meal_lock.release()
break
number_of_meals = 5
number_of_trails = 5
with concurrent.futures.ThreadPoolExecutor(max_workers=number_of_trails) as executor:
executor.map(trail, range(number_of_trails))
with concurrent.futures.ThreadPoolExecutor(max_workers=number_of_meals) as executor:
executor.map(meal, range(1+y,number_of_meals))
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.
Tried to write tfrecord w/ and w/o multithreading, and found the speed difference is not much (w/ 4 threads: 434 seconds; w/o multithread 590 seconds). Not sure if I used it correctly. Is there any better way to write tfrecord faster?
import tensorflow as tf
import numpy as np
import threading
import time
def generate_data(shape=[15,28,60,1]):
return np.random.uniform(size=shape)
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def write_instances_to_tfrecord(tfrecord_file, filenames):
tfrecord_writer = tf.python_io.TFRecordWriter(tfrecord_file)
for i, filename in enumerate(filenames):
curr_MFCC = generate_data()
curr_MFCC_raw = curr_MFCC.tostring()
curr_filename_raw = str(filename)+'-'+str(i)
example = tf.train.Example(features=tf.train.Features(
feature={
'MFCC': _bytes_feature(curr_MFCC_raw),
'filename': _bytes_feature(curr_filename_raw)
})
)
tfrecord_writer.write(example.SerializeToString())
tfrecord_writer.close()
def test():
threading_start = time.time()
coord = tf.train.Coordinator()
threads = []
for thread_index in xrange(4):
args = (str(thread_index), range(200000))
t = threading.Thread(target=write_instances_to_tfrecord, args=args)
t.start()
threads.append(t)
coord.join(threads)
print 'w/ threading takes', time.time()-threading_start
start = time.time()
write_instances_to_tfrecord('5', range(800000))
print 'w/o threading takes', time.time()-start
if __name__ == '__main__':
test()
When using python threading, due to the GIL restriction in the cPython implementation, the CPU utilization will be capped at 1 core. No matter how many threads you add, you will not see a speed up.
A simple solution in your case would be to use the multiprocessing module.
The code is almost exactly the same as what you have, just switch threads to processes:
from multiprocessing import Process
coord = tf.train.Coordinator()
processes = []
for thread_index in xrange(4):
args = (str(thread_index), range(200000))
p = Process(target=write_instances_to_tfrecord, args=args)
p.start()
processes.append(p)
coord.join(processes)
I tested this on my own tfrecord writer code, and got a linear scaling speedup. Total number of processes is limited by memory.
It's better to use Tensorflow computation graph to take advantage of multithreading since each session and graph can be run in different threads. With computation graph, it's about 40 times faster.
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))