Mixing
Python Partials With Objects Embedded In Dictionary
I'm trying to parallelize a set of code in which I need to pass in some constant parameters that iterate over a list. Based on the nature of the constants it makes sense to pass it in using a couple dictionaries. When I run the code below it works fine if I were to iterate my mymod.myfuncname(a,b,x) where x is an iteration of c in a for loop (single threaded).
When trying to parallelize the code, the culprit is the dictionary "b" in which an ODBC connection object is passed in as one of the values assigned to a key in that dictionary. Rather than passing in the connection string and creating the connection object each time the function executes, does anyone know of any other ways to parallelize with multi-input arguments that allow more complex objects to be passed in. Alternatively, is there something else I should be doing using functools.partial to handle something like an ODBC connection object embedded in a dictionary?
#Import Stuff
import multiprocessing as mp
import functools as ft
import pyodbc
import mymod
#Initialize Stuff
value1 = 0
cnxn = pyodbc.connect(cnxnstr)
#Initialize Constant Stuff Into Dictionaries
a = {'Param1':value1}
b = {'DBConn':cnxn}
#Initialize List
c = ['f1','f2','f3','f4','f5','f6','f7','f8']
#Setup Pool
p1 = mp.Pool(16)
#Create Partial Object
func = ft.partial(mymod.myfuncname,a,b)
#Parallelize Across List "c"
p1.map_async(func,c)
python multiprocessing pyodbc functools
asked Nov 20 '18 at 4:55
Coder117Coder117
11
add a comment |
I'm trying to parallelize a set of code in which I need to pass in some constant parameters that iterate over a list. Based on the nature of the constants it makes sense to pass it in using a couple dictionaries. When I run the code below it works fine if I were to iterate my mymod.myfuncname(a,b,x) where x is an iteration of c in a for loop (single threaded).
When trying to parallelize the code, the culprit is the dictionary "b" in which an ODBC connection object is passed in as one of the values assigned to a key in that dictionary. Rather than passing in the connection string and creating the connection object each time the function executes, does anyone know of any other ways to parallelize with multi-input arguments that allow more complex objects to be passed in. Alternatively, is there something else I should be doing using functools.partial to handle something like an ODBC connection object embedded in a dictionary?
#Import Stuff
import multiprocessing as mp
import functools as ft
import pyodbc
import mymod
#Initialize Stuff
value1 = 0
cnxn = pyodbc.connect(cnxnstr)
#Initialize Constant Stuff Into Dictionaries
a = {'Param1':value1}
b = {'DBConn':cnxn}
#Initialize List
c = ['f1','f2','f3','f4','f5','f6','f7','f8']
#Setup Pool
p1 = mp.Pool(16)
#Create Partial Object
func = ft.partial(mymod.myfuncname,a,b)
#Parallelize Across List "c"
p1.map_async(func,c)
python multiprocessing pyodbc functools
asked Nov 20 '18 at 4:55
Coder117Coder117
11
add a comment |
I'm trying to parallelize a set of code in which I need to pass in some constant parameters that iterate over a list. Based on the nature of the constants it makes sense to pass it in using a couple dictionaries. When I run the code below it works fine if I were to iterate my mymod.myfuncname(a,b,x) where x is an iteration of c in a for loop (single threaded).
When trying to parallelize the code, the culprit is the dictionary "b" in which an ODBC connection object is passed in as one of the values assigned to a key in that dictionary. Rather than passing in the connection string and creating the connection object each time the function executes, does anyone know of any other ways to parallelize with multi-input arguments that allow more complex objects to be passed in. Alternatively, is there something else I should be doing using functools.partial to handle something like an ODBC connection object embedded in a dictionary?
#Import Stuff
import multiprocessing as mp
import functools as ft
import pyodbc
import mymod
#Initialize Stuff
value1 = 0
cnxn = pyodbc.connect(cnxnstr)
#Initialize Constant Stuff Into Dictionaries
a = {'Param1':value1}
b = {'DBConn':cnxn}
#Initialize List
c = ['f1','f2','f3','f4','f5','f6','f7','f8']
#Setup Pool
p1 = mp.Pool(16)
#Create Partial Object
func = ft.partial(mymod.myfuncname,a,b)
#Parallelize Across List "c"
p1.map_async(func,c)
python multiprocessing pyodbc functools
asked Nov 20 '18 at 4:55
Coder117Coder117
11
I'm trying to parallelize a set of code in which I need to pass in some constant parameters that iterate over a list. Based on the nature of the constants it makes sense to pass it in using a couple dictionaries. When I run the code below it works fine if I were to iterate my mymod.myfuncname(a,b,x) where x is an iteration of c in a for loop (single threaded).
When trying to parallelize the code, the culprit is the dictionary "b" in which an ODBC connection object is passed in as one of the values assigned to a key in that dictionary. Rather than passing in the connection string and creating the connection object each time the function executes, does anyone know of any other ways to parallelize with multi-input arguments that allow more complex objects to be passed in. Alternatively, is there something else I should be doing using functools.partial to handle something like an ODBC connection object embedded in a dictionary?
#Import Stuff
import multiprocessing as mp
import functools as ft
import pyodbc
import mymod
#Initialize Stuff
value1 = 0
cnxn = pyodbc.connect(cnxnstr)
#Initialize Constant Stuff Into Dictionaries
a = {'Param1':value1}
b = {'DBConn':cnxn}
#Initialize List
c = ['f1','f2','f3','f4','f5','f6','f7','f8']
#Setup Pool
p1 = mp.Pool(16)
#Create Partial Object
func = ft.partial(mymod.myfuncname,a,b)
#Parallelize Across List "c"
p1.map_async(func,c)
python multiprocessing pyodbc functools
python multiprocessing pyodbc functools
asked Nov 20 '18 at 4:55
Coder117Coder117
11
asked Nov 20 '18 at 4:55
Coder117Coder117
11
asked Nov 20 '18 at 4:55
Coder117Coder117
11
asked Nov 20 '18 at 4:55
Coder117Coder117
11
asked Nov 20 '18 at 4:55
Coder117Coder117
11
11
add a comment |
add a comment |
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