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Processing list of dictionaries via python multiprocessing


Processing list of dictionaries via python multiprocessing

By : M.Norullhi
Date : November 20 2020, 03:01 PM
it should still fix some issue I'm processing a list of dictionaries in python like so: , What you're probably looking for is this
code :
from multiprocessing import Pool

def process_results(single_dict):
    first_result, second_result, count = [], [], 0
    first_result.append(single_dict)
    if 'pi' in single_dict:
        second_result.append(single_dict)
        count += 1
    return first_result, second_result

if __name__ == '__main__':
    lst_dict = [{'a':1, 'b':2, 'c':3},{'c':4, 'pi':3.14}, {'pi':'3.14', 'not pi':8.3143}, {'sin(pi)': 0, 'cos(pi)': 1}];
    pool = Pool()
    print pool.map(process_results, lst_dict)
results = []
results = pool.map(process_results, lst_dict)

first_result = [i[0][0] for i in results]
second_result = [i[0][0] for i in results if i[1]]


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python: sharing huge dictionaries using multiprocessing

python: sharing huge dictionaries using multiprocessing


By : user2575238
Date : March 29 2020, 07:55 AM
it helps some times If the dictionaries are read-only, you don't need proxy objects in most operating systems.
Just load the dictionaries before starting the workers, and put them somewhere they'll be reachable; the simplest place is globally to a module. They'll be readable from the workers.
code :
from multiprocessing import Pool

buf = ""

def f(x):
    buf.find("x")
    return 0

if __name__ == '__main__':
    buf = "a" * 1024 * 1024 * 1024
    pool = Pool(processes=1)
    result = pool.apply_async(f, [10])
    print result.get(timeout=5)
autovivificating multiprocessing manager() dictionaries in python

autovivificating multiprocessing manager() dictionaries in python


By : Shubham Lochan
Date : March 29 2020, 07:55 AM
hope this fix your issue The multiprocessing Manager() provdes a proxy to a dictionary or list. Any mutations to a manager.Dict() within a subprocess aren't stored/kept track of. One thus needs to copy the mutations to the proxy-variable that belongs to the Manager. (see also: How does multiprocessing.Manager() work in python?)
This can be solved as folows:
code :
def insert_ngram(queue, managerDict):
    # create a local dictionary with vivification
    localDict = Vividict() 
    # copy the existing manager.dict to the local dict.
    localDict.update(managerDict) 
    #do stuff 
    # copy the local dictionary to the manager dict
    managerDict.update(localDict) 
    return 
Python multiprocessing merge dictionaries of dictionaries from multiple processes

Python multiprocessing merge dictionaries of dictionaries from multiple processes


By : tamani
Date : March 29 2020, 07:55 AM
hop of those help? I am trying to use shared memory across multiple processes to update a dictionary that contains dictionaries which contain dictionaries... I tried using the Manager from the multiprocessing module, but I am having difficulty adding a dictionary to it. Please see the code and the comments below. Essentially this code is supposed to create a copy of the input in another dictionary called "output." Once I get this working, there will be logic to only copy certain "blades" from the input, but the node/cluster/blade hierarchy must be maintained. , According to the python docs,
code :
#output[node] = {}
#output[node]['IP'] = input[node]['IP']
#output[node]['clusters'] = {} These changes are not propagated through the manager

new_node = {}
new_node[node] = {'IP': input[node]['IP'], 'clusters': {}}
output.update(new_node)



#if cluster not in output[node]['clusters']:
    # Raises KeyError: 'clusters'
    #output[node]['clusters'][cluster] = {}
    #output[node]['clusters'][cluster]['blades'] = {}
    #output[node]['clusters'][cluster]['blades'][blade] = input[node]['clusters'][cluster]['blades'][blade]


node_copy = output.copy()
if cluster not in node_copy[node]['clusters']:
    node_copy[node]['clusters'].setdefault(cluster, {'blades': {}})
node_copy[node]['clusters'][cluster]['blades'][blade] = input[node]['clusters'][cluster]['blades'][blade]
output.update(node_copy)
Python Multiprocessing on List of dictionaries

Python Multiprocessing on List of dictionaries


By : user3445788
Date : March 29 2020, 07:55 AM
will be helpful for those in need I have a list of dictionaries. list_of_dict = [{'name' : 'bob', 'weight': 50}, {'name' : 'ramesh', 'weight': 60}] , I have tried with Multiprocessing Pool
code :
from multiprocessing.pool import ThreadPool as Pool

pool_size = 5 

def worker(item1, itme2):
    try:
        print(item1.get('weight'))
        print(itme2)
    except:
        print('error with item')

pool = Pool(pool_size)
items = [{'name' : 'bob', 'weight': 50}, {'name' : 'ramesh','weight': 60}]
for item in items:
    pool.apply_async(worker, (item, 'item2'))

pool.close()
pool.join()
Processing a long list using Multiprocessing

Processing a long list using Multiprocessing


By : Sriram G
Date : March 29 2020, 07:55 AM
This might help you It's a bit hard to suggest what is going wrong with your current code, as the example you've shown has a number of issues (for instance, you're using // to introduce a comment, creating processes that call a finalScore function and pass doc_list as a parameter, neither of which are defined).
Rather than try to figure out what is going on with your code, I'd like to suggest an alternative solution that is likely to be much simpler. If you use multiprocessing.Pool's map method, you'll get your work distributed over however many processes are in the pool.
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