I am trying to use multiprocessing module to initialize each column of a dataframe using a separate CPU core in Python 3.6 but my code doesn't work. Does anybody know the issue with this code? I appreciate your help.
My laptop has Windows 10 and its CPU is Core i7 8th Gen:
import time
import pandas as pd
import numpy as np
import multiprocessing
df=pd.DataFrame(index=range(10),columns=["A","B","C","D"])
def multiprocessing_func(col):
for i in range(0,df.shape[0]):
df.iloc[i,col]=np.random(4)
print("column "+str(col)+ " is completed" )
if __name__ == '__main__':
starttime = time.time()
processes = []
for i in range(0,df.shape[1]):
p = multiprocessing.Process(target=multiprocessing_func, args=(i,))
processes.append(p)
p.start()
for process in processes:
process.join()
print('That took {} seconds'.format(time.time() - starttime))
When you start a Process, it is basically a copy of the parent process. (I'm skipping over some details here, but they shouldn't matter for the explanation).
Unlike threads, processes don't share data. (Processes can use shared memory, but this is not automatic. To the best of my knowledge, the mechanisms in multiprocessing for sharing data cannot handle a dataframe.)
So what happens is that each of the worker processes is modifying its own copy of the dataframe, not the dataframe in the parent process.
For this to work, you'd have to send the new data back to the parent process. You could do that by e.g. return-ing it from the worker function, and then putting the returned data into the original dataframe.
It only makes sense to use multiprocessing like this if the work of generating the data takes significantly longer then launching a new worker process, sending the data back to the parent process and putting it into the dataframe. Since you are basically filling the columns with random data, I don't think that is the case here.
So I don't see why you would use multiprocessing here.
Edit: Based on your comment that it takes days to calculate each column, I would propose the following.
Use Proces like you have been doing, but have each of the worker processes save the numbers they produce in a file where the filename includes the value of i. Have the workers return a status code so you can determine that thay have succeeded or failed. In case of failure, also return some kind of index of the amount of data successfully completed, so you don't have to re-calculate that again.
The file format should be simple and preferable readable. E.g. one number per line.
Wait for all processes to finish, read the files and fill the dataframe.
Related
I am measuring the metrics of an encryption algorithm that I designed. I have declared 2 functions and a brief sample is as follows:
import sys, random, timeit, psutil, os, time
from multiprocessing import Process
from subprocess import check_output
pid=0
def cpuUsage():
global running
while pid == 0:
time.sleep(1)
running=true
p = psutil.Process(pid)
while running:
print(f'PID: {pid}\t|\tCPU Usage: {p.memory_info().rss/(1024*1024)} MB')
time.sleep(1)
def Encryption()
global pid, running
pid = os.getpid()
myList=[]
for i in range(1000):
myList.append(random.randint(-sys.maxsize,sys.maxsize)+random.random())
print('Now running timeit function for speed metrics.')
p1 = Process(target=metric_collector())
p1.start()
p1.join()
number=1000
unit='msec'
setup = '''
import homomorphic,random,sys,time,os,timeit
myList={myList}
'''
enc_code='''
for x in range(len(myList)):
myList[x] = encryptMethod(a, b, myList[x], d)
'''
dec_code='''
\nfor x in range(len(myList)):
myList[x] = decryptMethod(myList[x])
'''
time=timeit.timeit(setup=setup,
stmt=(enc_code+dec_code),
number=number)
running=False
print(f'''Average Time:\t\t\t {time/number*.0001} seconds
Total time for {number} Iters:\t\t\t {time} {unit}s
Total Encrypted/Decrypted Values:\t {number*len(myList)}''')
sys.exit()
if __name__ == '__main__':
print('Beginning Metric Evaluation\n...\n')
p2 = Process(target=Encryption())
p2.start()
p2.join()
I am sure there's an implementation error in my code, I'm just having trouble grabbing the PID for the encryption method and I am trying to make the overhead from other calls as minimal as possible so I can get an accurate reading of just the functionality of the methods being called by timeit. If you know a simpler implementation, please let me know. Trying to figure out how to measure all of the metrics has been killing me softly.
I've tried acquiring the pid a few different ways, but I only want to measure performance when timeit is run. Good chance I'll have to break this out separately and run it that way (instead of multiprocessing) to evaluate the function properly, I'm guessing.
There are at least three major problems with your code. The net result is that you are not actually doing any multiprocessing.
The first problem is here, and in a couple of other similar places:
p2 = Process(target=Encryption())
What this code passes to Process is not the function Encryption but the returned value from Encryption(). It is exactly the same as if you had written:
x = Encryption()
p2 = Process(target=x)
What you want is this:
p2 = Process(target=Encryption)
This code tells Python to create a new Process and execute the function Encryption() in that Process.
The second problem has to do with the way Python handles memory for Processes. Each Process lives in its own memory space. Each Process has its own local copy of global variables, so you cannot set a global variable in one Process and have another Process be aware of this change. There are mechanisms to handle this important situation, documented in the multiprocessing module. See the section titled "Sharing state between processes." The bottom line here is that you cannot simply set a global variable inside a Process and expect other Processes to see the change, as you are trying to do with pid. You have to use one of the approaches described in the documentation.
The third problem is this code pattern, which occurs for both p1 and p2.
p2 = Process(target=Encryption)
p2.start()
p2.join()
This tells Python to create a Process and to start it. Then you immediately wait for it to finish, which means that your current Process must stop at that point until the new Process is finished. You never allow two Processes to run at once, so there is no performance benefit. The only reason to use multiprocessing is to run two things at the same time, which you never do. You might as well not bother with multiprocessing at all since it is only making your life more difficult.
Finally I am not sure why you have decided to try to use multiprocessing in the first place. The functions that measure memory usage and execution time are almost certainly very fast, and I would expect them to be much faster than any method of synchronizing one Process to another. If you're worried about errors due to the time used by the diagnostic functions themselves, I doubt that you can make things better by multiprocessing. Why not just start with a simple program and see what results you get?
I cannot understand if what I want to do in Dask is possible...
Currently, I have a long list of heavy files.
I am using multiprocessing library to process every entry of the list. My function opens and entry, operates on it, saves the result in a binary file to disk, and returns None. Everything works fine. I did this essentially to reduce RAM usage.
I would like to do "the same" in Dask, but I cannot figure out how to save binary data in parallel. In my mind, it should be something like:
for element in list:
new_value = func(element)
new_value.tofile('filename.binary')
where there can only be N elements loaded at once, where N is the number of workers, and each element is used and forgotten at the end of each cycle.
Is it possible?
Thanks a lot for any suggestion!
That does sound like a feasible task:
from dask import delayed, compute
#delayed
def myfunc(element):
new_value = func(element)
new_value.tofile('filename.binary') # you might want to
# change the destination for each element...
delayeds = [myfunc(e) for e in list]
results = compute(delayeds)
If you want fine control over tasks, you might want to explicitly specify the number of workers by starting a LocalCluster:
from dask.distributed import Client, LocalCluster
cluster = LocalCluster(n_workers=3)
client = Client(cluster)
There is a lot more that can be done to customize the settings/workflow, but perhaps the above will work for your use case.
I am trying to parallelize a function on my pandas dataframe and I'm running into an issue where it seems that the multiprocessing library is hanging. I am doing this all within a Jupyter notebook with myFunction() existing in a separate .py file. Can someone point out what I am doing wrong here?
Surprisingly, this piece of code has worked previously on my Windows 7 machine with the same version of python. I have just copied the file over to my Mac laptop.
I also use tqdm so I can monitor the progress, the behavior is the same with or without it.
#This function hands the multiprocessing
from multiprocessing import Pool, cpu_count
import numpy as np
import tqdm
def parallelize_dataframe(df, func):
num_partitions = cpu_count()*2 # number of partitions to split dataframe
num_cores = cpu_count() # number of cores on your machine
df_split = np.array_split(df, num_partitions)
pool = Pool(num_cores)
return pd.concat(list(tqdm.tqdm_notebook(pool.imap(func, df_split),total=num_partitions)))
#My function that I am applying to the dataframe is in another file
#myFunction retrieves a JSON from an API for each ID in myDF and converts it to a dataframe
from myFuctions import myFunction
#Code that calls the parallelize function
finalDF = parallelize_dataframe(myDF,myFunction)
The expected result is a concatenation of a list of dataframes that have been retrieved by myFunction(). This is worked in the past, but now the process seems to hang indefinitely without any error messages.
Q : Can someone point out what I am doing wrong here?
You just expected the MacOS to use the same mechanism for process-instantiations as the WinOS did in past.
The multiprocessing module does not do the same set of things on either of the supported O/S-es and even reported some methods to be dangerous and also had changed the default behaviour on MacOS- and Linux-based systems.
Next steps to try to move forward :
re-read how to do the explicit setup of the call-signatures in multiprocessing documentation ( avoid hidden dependency of the code-behaviour on "new" default values )
test if may avoid the cases where multiprocessing will spawn the full-copy of the python-interpreter process, that many times as you instruct ( memory allocations could soon get devastatingly large, if many replicas try to get instantiated beyond the localhost RAM-footprint, just due to a growing number of CPU-cores )
test if the "worker"-code is not computing intensive but rather network-remote API-call latency driven. In such a case asyncio/await decorated tools will help more with latency-masking than going into in the case of IO-latency dominated use-cases inefficient multiprocessing spawned and rather expensive full-copy concurrency of many python-processes (that just stay waitin for receiving remote-API answers ).
last but not least - performance-sensitive code best runs outside any mediating-ecosystem, like the interactivity-focused Jupyter-notebooks are.
I write Python 3 code, in which I have 2 functions. The first function insertBlock() inserts data in MongoDB collection 1, the second function insertTransactionData() takes data from collection 1 and inserts it into collection 2. Data is in very large amount so I use threading to increase performance. But when I use threading it is taking more time to insert data than without threading. I am so confused that exactly how threading will work in my code and how to increase performance? Here is the main function :
if __name__ == '__main__':
t1 = threading.Thread(target=insertBlock())
t1.start()
t2 = threading.Thread(target=insertTransactionData())
t2.start()
From the python documentation for threading:
target is the callable object to be invoked by the run() method. Defaults to None, meaning nothing is called.
So the correct usage is
threading.Thread(target=insertBlock)
(without the () after insertBlock), because otherwise insertBlock is called, executed normally (blocking the main thread) and target is set to it's return value None. This causes t1.start() not to do anything and you don't get any performance improvement.
Warning:
Be aware that multithreading gives you no guarantee on what the order of execution in different threads will be. You can not rely on the data that insertBlock has inserted into the database inside the insertTransactionData function, because at the time insertTransactionData uses this data, you can not be sure that it was already inserted. So, maybe multithreading does not work at all for this code or you need to restructure your code and only parallelize those parts that do not depend on each other.
I solved this problem by merging these two functionalities into one new function
insertBlockAndTransaction(startrange,endrange). As these two functionalities depend on each other so what I did is I insert transaction information immediately below where block information is inserted (block number was common and needed for both functionalities).Then did multithreading by creating 10 threads for single function:
for i in range(10):
print('thread:',i)
t1 = threading.Thread(target=insertBlockAndTransaction,args(5000000+i*10000,5000000+(i+1)*10000))
t1.start()
It helps me to deal with increasing execution time for more than 1lakh data.
I have a program that randomly selects 13 cards from a full pack and analyses the hands for shape, point count and some other features important to the game of bridge. The program will select and analyse 10**7 hands in about 5 minutes. Checking the Activity Monitor shows that during execution the CPU (which s a 6 Core processor) is devoting about 9% of its time to the program and ~90% of its time it is idle. So it looks like a prime candidate for multiprocessing and I created a multiprocessing version using a Queue to pass information from each process back to the main program. Having navigated the problems of IDLE not working will multiprocessing (I now run it using PyCharm) and that doing a join on a process before it has finished freezes the program, I got it to work.
However, it doesn’t matter how many processes I use 5,10, 25 or 50 the result is always the same. The CPU devotes about 18% of its time to the program and has ~75% of its time idle and the execution time is slightly more than double at a bit over 10 minutes.
Can anyone explain how I can get the processes to take up more of the CPU time and how I can get the execution time to reflect this? Below are the relevant sections fo the program:
import random
import collections
import datetime
import time
from math import log10
from multiprocessing import Process, Queue
NUM_OF_HANDS = 10**6
NUM_OF_PROCESSES = 25
def analyse_hands(numofhands, q):
#code remove as not relevant to the problem
q.put((distribution, points, notrumps))
if __name__ == '__main__':
processlist = []
q = Queue()
handsperprocess = NUM_OF_HANDS // NUM_OF_PROCESSES
print(handsperprocess)
# Set up the processes and get them to do their stuff
start_time = time.time()
for _ in range(NUM_OF_PROCESSES):
p = Process(target=analyse_hands, args=((handsperprocess, q)))
processlist.append(p)
p.start()
# Allow q to get a few items
time.sleep(.05)
while not q.empty():
while not q.empty():
#code remove as not relevant to the problem
# Allow q to be refreshed so allowing all processes to finish before
# doing a join. It seems that doing a join before a process is
# finished will cause the program to lock
time.sleep(.05)
counter['empty'] += 1
for p in processlist:
p.join()
while not q.empty():
# This is never executed as all the processes have finished and q
# emptied before the join command above.
#code remove as not relevant to the problem
finish_time = time.time()
I have no answer to the reason why IDLE will not run a multiprocessor start instruction correctly but I believe the answer to the doubling of the execution times lies in the type of problem I am dealing with. Perhaps others can comment but it seems to me that the overhead involved with adding and removing items to and from the Queue is quite high so that performance improvements will be best achieved when the amount of data being passed via the Queue is small compared with the amount of processing required to obtain that data.
In my program I am creating and passing 10**7 items of data and I suppose it is the overhead of passing this number of items via the Queue that kills any performance improvement from getting the data via separate Processes. By using a map it seems all 10^7 items of data will need to be stored in the map before any further processing can be done. This might improve performance depending on the overhead of using the map and dealing with that amount of data but for the time being I will stick with my original vanilla, single processed code.