multiprocessing ExampleUse the multiprocessing module to run a method on multiple processes. Pass an argument to each method.
This page was last reviewed on Feb 25, 2024.
Multiprocessing. For long-running tasks, using separate processes to allow parallelism can yield significant speed ups. While Python has limitations with threads, it supports separate processes.
With map in multiprocessing.Pool, we can run a method on different processes with minimal setup code. An argument (like a string) can be passed to the methods.
Example. To begin, we import the multiprocessing module at the top (along with time, which is used to call the time.sleep method).
Step 1 We create a string list that contains the arguments to the methods we want to run on multiple processes.
String List
Step 2 With time.time() we record the current time—this is done to ensure the processes are all run in parallel.
Step 3 We use a with-statement to access multiprocessing.Pool, and then call map on the pool to specify the method we want to run in parallel.
Step 4 We print a message indicating a method was started, and also print the string argument to the method.
Step 5 After sleeping for 1 second, we print a message indicating the method is done executing.
Step 6 We print the elapsed time for all 3 methods to run, and the total time is 1 second, which means all methods ran in parallel.
import multiprocessing, time def example(name): # Step 4: print the start message. print(name + " started") # Step 5: sleep for a while and print a done message. time.sleep(1) print(name + " done") # Step 1: argument to the function we will call on multiple threads. all_names = ["A", "B", "C"] # Step 2: start measuring time. start = time.time() # Step 3: call pool.map on all the list elements with multiprocessing.Pool. with multiprocessing.Pool() as pool: pool.map(example, all_names) # Step 6: print elapsed time. print(time.time() - start)
A started B started C started B done A done C done 1.0340125560760498
With multiprocessing.Pool, we can use the map() method to call a function (with differing arguments) on multiple processes. This is an effective way to introduce parallel in Python programs.
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This page was last updated on Feb 25, 2024 (new).
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