multiprocessing

问题 I've got a mod_wsgi server setup with 5 processes and a celery worker queue (2 of them) all on the same VM. I'm running into problems where the loggers are stepping on each other and while it appears there are some solutions if you are using python multiprocessing, I don't see how that applies to mod_wsgi processes combined also with celery processes. What is everyone else doing with this problem? The celery tasks are using code that logs in the same files as the webserver code. Do I somehow

问题 I have been trying to improve my code (with numba and multiprocessing), but I cannot quite get it, because my function has a lot of arguments. I have already simplified it with other functions (see below)... As each agent (a class instance) is independent of each other for these actions, I would like to replace the for with Pool . So I would get a large function pooling() that I would call and pass the list of agents from multiprocessing import Pool p = Pool(4) p.map(pooling, list(agents))

问题 I recently saw a answer/comment about how functions are objects in python. So, I wondering why when I take that example, and create a class around it while initializing a variable, it doesn't work the same way. (The class example receives a pickling error): PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup __builtin__.instancemethod failed Does anyone know why this is? Example code from the link: import multiprocessing as mp def f(x): f.q.put('Doing: ' + str(x)) return x*x

问题 Can this be done? What i have in mind is the following: i ll have a dict, and each child process will add a new key:value combination to the dict. Can this be done with multiprocessing? Are there any limitations? Thanks! 回答1: In case you want to just read in the data at the child processes and each child will add single key value pair you can use Pool: import multiprocessing def worker(x): return x, x ** 2 if __name__ == '__main__': multiprocessing.freeze_support() pool = multiprocessing.Pool

问题 I have a genetic algorithm which I would like to speed up. I'm thinking the easiest way to achieve this is by pythons multiprocessing module. After running cProfile on my GA, I found out that most of the computational time takes place in the evaluation function. def evaluation(): scores = [] for chromosome in population: scores.append(costly_function(chromosome)) How would I go about to parallelize this method? It is important that all the scores append in the same order as they would if the

问题 I have python script linked to gui and which runs in background.Based on the input from the GUI the python script should send messages accordingly.But as soon as I link my script( where I am using multiprocessing) to GUI,Screen freezes.Is there anything that I am doing wrong?Please provide me a solution. from multiprocessing import Process slider_perc= [0.0 ,10.0,20.0,30.0,40.0,50,60.0,70.0,80.0,90.0,100] slider_output_msg=["a","b","c","d","e","f","g","h","i","j"] Slider_dictionary=dict(zip

问题 I have a python script that would traverse a list(>1000 elements), find the variable in a large file and then output the result. I am reading the entire file >1000 times. I tried using multiprocessing, but not of much help. Here's what I am trying to do: import gzip from multiprocessing.pool import ThreadPool as Pool def getForwardIP(clientIP, requestID): with gzip.open("xyz.log") as infile: for lines in infile: line= lines.split(" ") myRequestID= line[0] forwardIP= line[1] if myRequestID=

问题 I have a multiprocessing program where I'm unable to work with global variables. I have a program which starts like this:- from multiprocessing import Process ,Pool print ("Initializing") someList = [] ... ... ... Which means I have someList variables which get initialized before my main is called. Later on in the code someList is set to some value and then I create 4 processes to process it pool = Pool(4) combinedResult = pool.map(processFn, someList) pool.close() pool.join() Before spawning

问题 I use Django to create a browser-based GUI for a multicore scientific computing library. The workflow is roughly as follows: Press "run analysis" in browser. Call django view. In the django view, call the library. For simplicity let's say like that: execfile('/path/to/library.py') Result: library.py runs only on a single core. However, when library.py is called from the python console (i.e. not from the browser) it uses multiple cores. Note that library.py contains all the multicore logic

问题 I am trying to send several parallel requests to a XMLRPC server (mosesserver). To launch the XMLRPC call, a ServerProxy object is needed (this object contains the URL of the server, among other things) In serial execution, I can create this object at the beginning of the program (with a call to xmlrpclib.ServerProxy(server_URL)), store it in a variable and use it whenever I need it. But when using a pool of processes, each process needs a different instance of this object. The task of each