multiprocessing-manager

Python 3.6+: Nested multiprocessing managers cause FileNotFoundError

由 主宰稳场 提交于 2021-01-27 06:31:38
问题 So I'm trying to use multiprocessing Manager on a dict of dicts, this was my initial try: from multiprocessing import Process, Manager def task(stat): test['z'] += 1 test['y']['Y0'] += 5 if __name__ == '__main__': test = Manager().dict({'x': {'X0': 10, 'X1': 20}, 'y': {'Y0': 0, 'Y1': 0}, 'z': 0}) p = Process(target=task, args=(test,)) p.start() p.join() print(test) of course when I run this, the output is not what I expect, z updates correctly while y is unchanged! This is the output: {'x': {

Sharing mutable global variable in Python multiprocessing.Pool

由 ＊爱你＆永不变心＊ 提交于 2020-05-16 22:05:20
问题 I'm trying to update a shared object (a dict ) using the following code. But it does not work. It gives me the input dict as an output. Edit : Exxentially, What I'm trying to achieve here is to append items in the data (a list) to the dict's list. Data items give indices in the dict. Expected output : {'2': [2], '1': [1, 4, 6], '3': [3, 5]} Note: Approach 2 raise error TypeError: 'int' object is not iterable Approach 1 from multiprocessing import * def mapTo(d,tree): for idx, item in

Sharing mutable global variable in Python multiprocessing.Pool

由 人盡茶涼 提交于 2020-05-16 22:04:20
问题 I'm trying to update a shared object (a dict ) using the following code. But it does not work. It gives me the input dict as an output. Edit : Exxentially, What I'm trying to achieve here is to append items in the data (a list) to the dict's list. Data items give indices in the dict. Expected output : {'2': [2], '1': [1, 4, 6], '3': [3, 5]} Note: Approach 2 raise error TypeError: 'int' object is not iterable Approach 1 from multiprocessing import * def mapTo(d,tree): for idx, item in

Sharing mutable global variable in Python multiprocessing.Pool

由 断了今生、忘了曾经 提交于 2020-05-16 22:03:10
问题 I'm trying to update a shared object (a dict ) using the following code. But it does not work. It gives me the input dict as an output. Edit : Exxentially, What I'm trying to achieve here is to append items in the data (a list) to the dict's list. Data items give indices in the dict. Expected output : {'2': [2], '1': [1, 4, 6], '3': [3, 5]} Note: Approach 2 raise error TypeError: 'int' object is not iterable Approach 1 from multiprocessing import * def mapTo(d,tree): for idx, item in

Unable to update nested dictionary value in multiprocessing's manager.dict()

由 天涯浪子 提交于 2020-05-10 14:48:25
问题 I am trying to update a key in a nested dictionary of multiprocessing module's manager.dict() but not able to do so. It doesn't update the value and doesn't throw any error too. Code: import time import random from multiprocessing import Pool, Manager def spammer_task(d, token, repeat): success = 0 fail = 0 while success+fail<repeat: time.sleep(random.random()*2.0) if (random.random()*100)>98.0: fail+=1 else: success+=1 d[token] = { 'status': 'ongoing', 'fail': fail, 'success': success,

Sharing object (class instance) in python using Managers

由 跟風遠走 提交于 2019-12-17 18:07:40
问题 I need to share an object and its methods between several processes in python. I am trying to use Managers (in module multiprocessing) but it crashes. Here is a silly example of producer-consumer where the shared object between the two processes is just a list of numbers with four methods. from multiprocessing import Process, Condition, Lock from multiprocessing.managers import BaseManager import time, os lock = Lock() waitC = Condition(lock) waitP = Condition(lock) class numeri(object): def

How python manager.dict() locking works:

由 ε祈祈猫儿з 提交于 2019-12-11 00:53:07
问题 A managers.dict() allow to share a dictionary across process and perform thread-safe operation. In my case each a coordinator process create the shared dict with m elements and n worker processes read and write to/from a single dict key. Do managers.dict() have one single lock for the dict or m locks, one for every key in it? Is there an alternative way to share m elements to n workers, other than a shared dict, when the workers do not have to communicate with each other? Related python

Python manager.dict() is very slow compared to regular dict

由 ℡╲_俬逩灬. 提交于 2019-12-04 19:23:28
问题 I have a dict to store objects: jobs = {} job = Job() jobs[job.name] = job now I want to convert it to use manager dict because I want to use multiprocessing and need to share this dict amonst processes mgr = multiprocessing.Manager() jobs = mgr.dict() job = Job() jobs[job.name] = job just by converting to use manager.dict() things got extremely slow. For example, if using native dict, it only took .65 seconds to create 625 objects and store it into the dict. The very same task now takes 126

Python manager.dict() is very slow compared to regular dict

由 你离开我真会死。 提交于 2019-12-03 12:51:51
I have a dict to store objects: jobs = {} job = Job() jobs[job.name] = job now I want to convert it to use manager dict because I want to use multiprocessing and need to share this dict amonst processes mgr = multiprocessing.Manager() jobs = mgr.dict() job = Job() jobs[job.name] = job just by converting to use manager.dict() things got extremely slow. For example, if using native dict, it only took .65 seconds to create 625 objects and store it into the dict. The very same task now takes 126 seconds! Any optimization i can do to keep manager.dict() on par with python {}? The problem is that

Python IPC with matplotlib

由 穿精又带淫゛_ 提交于 2019-11-29 12:53:28
Project description: Connect existing "C" program (main control) to Python GUI/Widget. For this I'm using a FIFO. The C program is designed look at frame based telemetry. The Python GUI performs two functions: Runs/creates plots (probably created through matplotlib) via GUI widget as the user desires (individual .py files, scripts written by different users) Relays the frame number to the python plotting scripts after they have been created so they can "update" themselves after being given the frame number from the master program. I have several questions--understanding the pros and cons from
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