I want to execute tasks in parallel and if any of the task exit with an error, all the processes should be stopped also and exit. Here is my code # Run pipeline in parallel with mp.Pool(nwp) as pool: for r in pool.imap_unordered(run_pipeline, coel): if r > 0: logl.append("Error (%d), stop whole thing" % r) ..
If a run a python script where i declare 6 processes using multiprocessing, but i only have 4 CPU cores, what happens to the additional 2 processes which can find a dedicated CPU core. How are they executed? If the two additional processes run as separate threads on the existing Cores, will GIL not stop ..
I am running the below code in Python. The code shows the example of how to use multiprocessing in Python. But it is not printing the result (line 16), Only the dataset (line 9) is getting printed and the kernel keeps on running. I am of the opinion that the results should be produced instantly ..
I could not figure out what are the differences between the two dictionary items in the section below. Why d is OK, but d failed with some strange connection problems? Python 3.8.5 (default, Jul 28 2020, 12:59:40) [GCC 9.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from multiprocessing import Manager ..
I am using pathos.Pool to run multiprocessing. I try to catch multiprocessing errors and specific information of the multiprocessing errors such as the lines of the wrong codes and error types. I want the error information to be specific and detailed that can help me to debug. Is anyone can help? Thanks! Here is the ..
This post makes reference to my previous post : In the previous post, I was explaining that I have a class object called Item. This Item class has a method, call make_request which makes a GET request on a server. Now, I have implemented X Item objects which call make_request. The Item objects gonna call ..
I have plenty (1 million) of relatively small (~50kb) json files I want to concat to a giant csv. The way I did it until now works but is speed inefficient. (I know it’s also memory inefficient but it’s not a problem for my use case) import json import pandas as pd from tqdm.notebook import ..
I have the following two parts of code, first one is the code using multiprocessing and the second one is with mpi4py. Multiporecessing one def simple(data): #(np.arrray) # its a function from other module result = do something with the data return result #(np.arrray) def lesssimple(data, num): num_cores = num inputs = tqdm(x) processed_list = ..
This is a demo of computing cross-correlation between each two signals. I wrote the code below, but the memory usage of that with shared memory is the same as that without the shared memory. In addition, the elapsed time of that with shared memory is much more. Could anyone point out where is the problem? ..
I have build a multiprocessor to speed up the processes (300.000 params), however it would be nice if it shows a progress bar of something like that so I can keep track of the progress. I searched on the stackoverflow for a proper solution however cannot find similar questions. Is it possible to use a ..