multiprocess

I am facing the problem with collecting logs from the following script. Once I set up the SLEEP_TIME to too "small" value, the LoggingThread threads somehow block the logging module. The script freeze on logging request in the action function. If the SLEEP_TIME is about 0.1 the script collect all log messages as I expect. I tried to follow this answer but it does not solve my problem. import multiprocessing import threading import logging import time SLEEP_TIME = 0.000001 logger = logging.getLogger() ch = logging.StreamHandler() ch.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %

I am trying to read a list that contains N number of .csv files stored in a list synchronously. Right now I do the following: import multiprocess Empty list Append list with listdir of .csv's def A() -- even files (list[::2]) def B() -- odd files (list[1::2] Process 1 def A() Process 2 def B() def read_all_lead_files(folder): for files in glob.glob(folder+"*.csv"): file_list.append(files) def read_even(): file_list[::2] def read_odd(): file_list[1::2] p1 = Process(target=read_even) p1.start() p2 = Process(target=read_odd) p2.start() Is there a faster way to split up the partitioning of the

This question already has an answer here: Threading in Python [closed] 7 answers Please consider a class as follow: class Foo: def __init__(self, data): self.data = data def do_task(self): #do something with data In my application I've a list containing several instances of Foo class. The aim is to execute do_task for all Foo objects. A first implementation is simply: #execute tasks of all Foo Object instantiated for f_obj in my_foo_obj_list: f_obj.do_task() I'd like to take advantage of multi-core architecture sharing the for cycle between 4 CPUs of my machine. What's the best way to do it?

Am new to multi threaded/processs programming. So here's what I need to clarify. Process A code pthread_mutex_lock() pthread_create(fooAPI(sharedResource)) //fooAPI creates another thread with shared resource that shares across processes. pthread_mutex_unlock() With the above pseudo code, is process B able to access sharedResource if mutex is not unlocked? How can I access the sharedResource from process B correctly? Any there any clear visual diagram that explains the relationship between mutexes, threads and processes? What you need to do is to call pthread_mutex_lock to secure a mutex, like

We have been told that Google Chrome runs each tab in a separate process. Therefore a crash in one tab would not cause problems in the other tabs. AFAIK, multi-processes are mostly used in programs without a GUI. I have never read any technique that could embed multiple GUI processes into a single one. How does Chrome do that? I am asking this question because I am designing CCTV software which will use video decoding SDKs from multiple camera manufactures, some of which are far from stable. So I prefer to run these SDKs in different processes, which I thought is similar to Chrome. Basically,

I have a python application that grabs a collection of data and for each piece of data in that collection it performs a task. The task takes some time to complete as there is a delay involved. Because of this delay, I don't want each piece of data to perform the task subsequently, I want them to all happen in parallel. Should I be using multiprocess? or threading for this operation? I attempted to use threading but had some trouble, often some of the tasks would never actually fire. If you are truly compute bound, using the multiprocessing module is probably the lightest weight solution (in