python-multithreading

I'm trying to spawn multiple webdriver instances with the code from: http://www.ibm.com/developerworks/aix/library/au-threadingpython/ import time import Queue import urllib2 import threading from selenium import webdriver from BeautifulSoup import BeautifulSoup hosts = ["http://yahoo.com", "http://google.com", "http://amazon.com", "http://ibm.com", "http://apple.com"] queue = Queue.Queue out_queue = Queue.Queue class Login_Driver(threading.Thread): def __init__(self, queue, out_queue, driver): threading.Thread.__init__(self) self.queue = queue self.out_queue = out_queue self.driver = driver

In a multithreaded Python program, one thread sometimes asks for console input using the built-in raw_input() . I'd like to be able to be able to close the program while at a raw_input prompt by typing ^C at the shell (i.e., with a SIGINT signal). However, when the child thread is executing raw_input, typing ^C does nothing -- the KeyboardInterrupt is not raised until I hit return (leaving raw_input). For example, in the following program: import threading class T(threading.Thread): def run(self): x = raw_input() print x if __name__ == '__main__': t = T() t.start() t.join() Typing ^C does

This question already has an answer here: Is there a simple process-based parallel map for python? 4 answers I wrote code like this: def process(data): #create file using data all = ["data1", "data2", "data3"] I want to execute process function on my all list in parallel, because they are creating small files so I am not concerned about disk write but the processing takes long, so I want to use all of my cores. How can I do this using default modules in python 2.7? You may try a basic example like: from threading import Thread def process(data): print "processing %s" % data all = ["data1",

I'm looking to run a long-running python analysis process on a few Amazon EC2 instances. The code already runs using the python multiprocessing module and can take advantage of all cores on a single machine. The analysis is completely parellel and each instance does not need to communicate with any of the others. All of the work is "file-based" and each process works on each file indivually ... so I was planning on just mounting the same S3 volume across all of the nodes. I was wondering if anyone knew of any tutorials (or had any suggestions) for setting up the multiprocessing environment so

First off I wasn't sure if I should post this as a Ubuntu question or here. But I'm guessing it's more of an Python question than a OS one. My Python application is running on top of Ubuntu on a 64 core AMD server. It pulls images from 5 GigE cameras over the network by calling out to a .so through ctypes and then processes them. I am seeing frequent pauses in my application causing frames from the cameras to be dropped by the external camera library. To debug this I've used the popular psutil Python package with which I log out CPU stats every 0.2 seconds in a separate thread. I sleep for 0.2