python-multiprocessing

It is unclear how to properly timeout workers of joblib's Parallel in python. Others have had similar questions here , here , here and here . In my example I am utilizing a pool of 50 joblib workers with threading backend. Parallel Call (threading): output = Parallel(n_jobs=50, backend = 'threading') (delayed(get_output)(INPUT) for INPUT in list) Here, Parallel hangs without errors as soon as len(list) <= n_jobs but only when n_jobs => -1 . In order to circumvent this issue, people give instructions on how to create a timeout decorator to the Parallel function ( get_output(INPUT) ) in the

When I run predict_proba on a dataframe without multiprocessing I get the expected behavior. The code is as follows: probabilities_data = classname.perform_model_prob_predictions_nc(prediction_model, vectorized_data) where: perform_model_prob_predictions_nc is: def perform_model_prob_predictions_nc(model, dataFrame): try: return model.predict_proba(dataFrame) except AttributeError: logging.error("AttributeError occurred",exc_info=True) But when I try to run the same function using chunks and multiprocessing: probabilities_data = classname.perform_model_prob_predictions(prediction_model, chunks

I need to say that multiprocessing is something new to me. I read some about it but it makes me more confused. I want to understand it on a simple example. Let's assume that we have 2 functions in first one I just increment 'a' variable and then assign it to 'number' variable, in second I start first function and each every one second I want to print 'number' variable. It should looks like: global number def what_number(): a=1 while True: a+=1 number=a def read_number(): while True: --> #here I need to start 'what_number' function <-- time.sleep(1) print(number) if __name__ == "__main__": read

In my project, I'm using argprse to pass arguments and somewhere in script I'm using multiprocessing to do rest of the calculations. Script is working fine if I call it from command prompt for ex. " python complete_script.py --arg1=xy --arg2=yz " . But after converting it to exe using Pyinstaller using command "pyinstaller --onefile complete_script.py" it throws error " error: unrecognized arguments: --multiprocessing-fork 1448" Any suggestions how could I make this work. Or any other alternative. My goal is to create an exe application which I can call in other system where Python is not

I am running this code from multiprocessing.Pool import ThreadPool from selenium import webdriver driver = webdriver.Firefox() driver.get(url) elements = driver.find_elements_by_class_name("class-name") pool = ThreadPool(4) async = [pool.apply_async(fn_which_calls_get_attribute,(element,)) for element in elements] results = [result.get() for result in async] which works fine for some of the results, but throws an error of ResponseNotReady for other results. It runs as expected if I use "pool.apply" instead of the async version. Is it a problem that I am making multiple calls to the selenium

import multiprocessing import numpy as np import multiprocessing as mp import ctypes class Test(): def __init__(self): shared_array_base = multiprocessing.Array(ctypes.c_double, 100, lock=False) self.a = shared_array = np.ctypeslib.as_array(shared_array_base) def my_fun(self,i): self.a[i] = 1 if __name__ == "__main__": num_cores = multiprocessing.cpu_count() t = Test() def my_fun_wrapper(i): t.my_fun(i) with mp.Pool(num_cores) as p: p.map(my_fun_wrapper, np.arange(100)) print(t.a) In the code above, I'm trying to write a code to modify an array, using multiprocessing . The function my_fun() ,