I need to do some multiprocessing with my Python scripts and I decided to give it a try with Google's collaboratory.
I've connected to local runtime and tried to run the following script:
import multiprocessing
def spawn(num):
print('Spawned! {}'.format(num))
if __name__ == '__main__':
for i in range(5):
p = multiprocessing.Process(target=spawn, args=(i,))
p.start()
However, when I run this, nothing happens. Absolutely nothing, no errors, no prints, it just executes instantly and that's it.
Am I missing something? Does multiprocessing work with Google Colab local runtime?
Thanks in advance.
Run this instead
import multiprocessing
def spawn(num):
print('Spawned! {}'.format(num))
for i in range(5):
p = multiprocessing.Process(target=spawn, args=(i,))
p.start()
Related
I have a mac (MacOs 10.15.4, Python ver 3.82) and need to work in multiprocessing, but on my pc the procedures doesn’t work.
For example, I have copied a simple parallel python program
import multiprocessing as mp
import time
def test_function(i):
print("function starts" + str(i))
time.sleep(1)
print("function ends" + str(i))
if __name__ == '__main__':
pool = mp.Pool(mp.cpu_count())
pool.map(test_function, [i for i in range(4)])
pool.close()
pool.join()
What I expect to see in the output:
function starts0
function ends0
function starts1
function ends1
function starts2
function ends2
function starts3
function ends3
Or similar...
What I actually see:
= RESTART: /Users/Simulazioni/prova.py
>>>
Just nothing, no errors and no informations, just nothing. I have already try mamy procedure without results. The main problem, I could see, is the call of the function, in fact the instruction:
if __name__ == '__main__':
doesn’t call the function,
def test_function(i):
I tried many example of that kind without results.
Is ti possible and/or what's is the easiest way to parallelize in macOs?
I know this is a bit of an old question, but I just faced the same issue and I solved it by using a different multithreading package's version, which is:
import multiprocess
instead of:
import multiprocessing
Reference:
https://pypi.org/project/multiprocess/
I use Rust to speed up a data processing pipeline, but I have to run some existing Python code as-is, which I want to parallelize. Following discussion in another question, creating multiple Python processes is a possible approach given my project's specific constraints. However, running the code below gives an infinite loop. I can't quite understand why.
use cpython::Python;
fn main() {
let gil = Python::acquire_gil();
let py = gil.python();
py.run(r#"
import sys
from multiprocessing import Process
def f(name):
print('hello', name)
if __name__ == '__main__':
print('start')
sys.argv=['']
p = Process(target=f, args=('bob',))
p.start()
p.join()
"#, None,None).unwrap();
}
Output (continues until Ctrl-C):
start
start
start
start
start
start
start
start
EDIT
As mentioned in the comments below, I gave up on trying to create processes from the Python code. The interference between Windows, the Python multiprocessing module, and how processes are created with Rust are too obscure to manage properly.
So instead I will create and manage them from Rust. The code is therefore more textbook:
use std::process::Command;
fn main() {
let mut cmd = Command::new("python");
cmd.args(&["-c", "print('test')"]);
let process = cmd.spawn().expect("Couldn't spawn process.");
println!("{:?}", process.wait_with_output().unwrap());
}
I can't reproduce this; for me it just prints start and then hello bob as expected. For whatever reason, it seems that in your case, __name__ is always equal to "__main__" and you get this infinite recursion. I'm using the cpython crate version v0.4.1 and Python 3.8.1 on Arch Linux.
A workaround is to not depend on __name__ at all, but to instead define your Python code as a module with a main() function and then call that function:
use cpython::{Python, PyModule};
fn main() {
let gil = Python::acquire_gil();
let py = gil.python();
let module = PyModule::new(py, "bob").unwrap();
py.run(r#"
import sys
from multiprocessing import Process
def f(name):
print('hello', name)
def main():
print('start')
sys.argv=['']
p = Process(target=f, args=('bob',))
p.start()
p.join()
"#, Some(&module.dict(py)), None).unwrap();
module.call(py, "main", cpython::NoArgs, None).unwrap();
}
I am trying to set up the very basic example of multiprocessing below. However, the execution only prints here and <_MainProcess(MainProcess, started)> and pool.apply() never even calls the function cube(). Instead, the execution just keeps running indefinitely without termination.
import multiprocessing as mp
def cube(x):
print('in function')
return x**3
if __name__ == '__main__':
pool = mp.Pool(processes=4)
print('here')
print(mp.current_process())
results = [pool.apply(cube, args=(x,)) for x in range(1,7)]
print('now here')
pool.close()
pool.join()
print(results)
I have tried various other basic examples including pool.map() but keep running into the same problem. I am using Python 3.7 on Windows 10. Since I am out of ideas, does anybody know what is wrong here or how I can debug this further?
Thanks!
Thank you, upgrading to Python 3.7.3 solved the issue.
Last year I wrote a simple code to mosaic hundreds of raster data. It went well.
Last week I picked up this code to do the same work, but it didn't work.
I copy the official demo:
from multiprocessing import Process
def f(name):
print 'hello', name
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()
But there is no feedback, no report error. If I switch multiprocessing module to Thread module, it goes well.
Then I find out that the the demo can be ran in Python console but not in IPython console.
PS, I use WinPython-64bit, and have tried 2.7 and 3.5 version, both have the same problem. And I cannot use multiprocessing module in the ArcGIS python console.
Thanks
I have not understand your question that much.
but, if you want to use Thread, you can try this.
from multiprocessing.dummy import Pool as ThreadPool
def f(name):
print 'hello', name
if __name__ == '__main__':
pool = ThreadPool(4) # 4 is number of your CPU core, 4 times faster
names = ['bob','jack','newt'] #prepare your name list
results = pool.map(f, names)
pool.close()
pool.join()
I'm currently going through some pre-existing code with the goal of speeding it up. There's a few places that are extremely good candidates for parallelization. Since Python has the GIL, I thought I'd use the multiprocess module.
However from my understanding the only way this will work on windows is if I call the function that needs multiple processes from the highest-level script with the if __name__=='__main__' safeguard. However, this particular program was meant to be distributed and imported as a module, so it'd be kind of clunky to have the user copy and paste that safeguard and is something I'd really like to avoid doing.
Am I out of luck or misunderstanding something as far as multiprocessing goes? Or is there any other way to do it with Windows?
For everyone still searching:
inside module
from multiprocessing import Process
def printing(a):
print(a)
def foo(name):
var={"process":{}}
if name == "__main__":
for i in range(10):
var["process"][i] = Process(target=printing , args=(str(i)))
var["process"][i].start()
for i in range(10):
var["process"][i].join
inside main.py
import data
name = __name__
data.foo(name)
output:
>>2
>>6
>>0
>>4
>>8
>>3
>>1
>>9
>>5
>>7
I am a complete noob so please don't judge the coding OR presentation but at least it works.
As explained in comments, perhaps you could do something like
#client_main.py
from mylib.mpSentinel import MPSentinel
#client logic
if __name__ == "__main__":
MPSentinel.As_master()
#mpsentinel.py
class MPSentinel(object):
_is_master = False
#classmethod
def As_master(cls):
cls._is_master = True
#classmethod
def Is_master(cls):
return cls._is_master
It's not ideal in that it's effectively a singleton/global but it would work around window's lack of fork. Still you could use MPSentinel.Is_master() to use multiprocessing optionally and it should prevent Windows from process bombing.
On ms-windows, you should be able to import the main module of a program without side effects like starting a process.
When Python imports a module, it actually runs it.
So one way of doing that is in the if __name__ is '__main__' block.
Another way is to do it from within a function.
The following won't work on ms-windows:
from multiprocessing import Process
def foo():
print('hello')
p = Process(target=foo)
p.start()
This is because it tries to start a process when importing the module.
The following example from the programming guidelines is OK:
from multiprocessing import Process, freeze_support, set_start_method
def foo():
print('hello')
if __name__ == '__main__':
freeze_support()
set_start_method('spawn')
p = Process(target=foo)
p.start()
Because the code in the if block doesn't run when the module is imported.
But putting it in a function should also work:
from multiprocessing import Process
def foo():
print('hello')
def bar()
p = Process(target=foo)
p.start()
When this module is run, it will define two new functions, not run then.
i've been developing an instagram images scraper so in order to get the download & save operations run faster i've implemented multiprocesing in one auxiliary module, note that this code it's inside an auxiliary module and not inside the main module.
The solution I found is adding this line:
if __name__ != '__main__':
pretty simple but it's actually working!
def multi_proces(urls, profile):
img_saved = 0
if __name__ != '__main__': # line needed for the sake of getting this NOT to crash
processes = []
for url in urls:
try:
process = multiprocessing.Process(target=download_save, args=[url, profile, img_saved])
processes.append(process)
img_saved += 1
except:
continue
for proce in processes:
proce.start()
for proce in processes:
proce.join()
return img_saved
def download_save(url, profile,img_saved):
file = requests.get(url, allow_redirects=True) # Download
open(f"scraped_data\{profile}\{profile}-{img_saved}.jpg", 'wb').write(file.content) # Save