Last Updated on September 12, 2022
You can show progress of tasks in the ProcessPoolExecutor by using a callback function.
In this tutorial you will discover how to show progress of tasks in a Python process pool.
Let’s get started.
Need to Show Progress of Concurrent Tasks
The ProcessPoolExecutor in Python provides a pool of reusable processes for executing ad hoc tasks.
You can submit tasks to the process pool by calling the submit() function and passing in the name of the function you wish to execute on another process.
Calling the submit() function will return a Future object that allows you to check on the status of the task and get the result from the task once it completes.
You may need to show progress of all or a subset of tasks submitted to the process pool with either the submit() function.
This may be for many reasons, such as reporting progress to the user or internally keeping track of how much time or work remains to be completed.
How can we show progress of completed tasks in the ProcessPoolExecutor in a standardized way?
Run loops using all CPUs, download your FREE book to learn how.
How to Show Progress of Concurrent Tasks
You can add a callback function to provide a standard way to track progress for completed tasks.
This can be achieved by calling the add_done_callback() function on the Future object for each task and specifying a custom function to track or report progress.
|
1 2 3 |
... # add a callback function to a task future.add_done_callback(custom_callback) |
Recall that we get a Future object when calling the submit() function on the ProcessPoolExecutor when submitting a task.
The callback function must have a single argument which is the Future object on which it was called.
|
1 2 3 |
# task callback function def custom_callback(future): # do something... |
Using a callback is not the only way to show progress.
For example, you can enumerate tasks as they are completed in the parent process via the as_completed() function.
|
1 2 3 4 |
... # report progress as tasks are completed for future in as_completed(futures): print('Another task has completed!') |
The problem with this approach is that it requires that you mix both result processing and task progress indication code together, which may be less clean than using a callback function.
The callback function will only be called when the task is completed, e.g. “done“.
A task may be completed in one of three ways:
- The task finishes successfully.
- The task is cancelled.
- The task raises an exception that is not handled.
As such, you may want to check the status of the task to see if it was cancelled or if an exception was raised in the callback function before reporting on the progress.
|
1 2 3 4 5 6 7 8 9 |
# task callback function def custom_callback(future): # check if task was cancelled if future.cancelled(): # the task was cancelled elseif future.exception() # the task raised an exception else: # the task finished successfully |
There are many ways to report progress in the callback function.
For example, you can print a character, one for each task that is completed.
|
1 2 3 |
... # show progress for one task print('.', end='', flush=True) |
Now that we know how to show the progress of tasks in a standard way, let’s look at a worked example.
Example Of Showing Progress of Concurrent Tasks
Let’s look at how we might show progress of tasks completed in the ProcessPoolExecutor.
First, let’s define a target task function that blocks for a moment.
|
1 2 3 |
# test that works for moment def task(name): sleep(random()) |
Next, we can define our callback function that will take a Future object and report progress by printing one character for each task that completes.
This is a generic and scalable way of showing progress for tasks, although does not provide an indication of how many tasks remain to be executed.
|
1 2 3 |
# simple progress indicator callback function def progress_indicator(future): print('.', end='', flush=True) |
Next, we can create a process pool with two processes and submit many tasks.
|
1 2 3 4 5 |
... # start the process pool with ProcessPoolExecutor(2) as executor: # send in the tasks futures = [executor.submit(task, i) for i in range(20)] |
We can then register the callback with each task which will be executed after each task has completed.
|
1 2 3 4 |
... # register the progress indicator callback for future in futures: future.add_done_callback(progress_indicator) |
That’s it.
Tying this together, the complete example of showing a progress indicator of completed tasks is listed below.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
# SuperFastPython.com # example of a simple progress indicator for tasks in a process pool from time import sleep from random import random from concurrent.futures import ProcessPoolExecutor from concurrent.futures import wait # simple progress indicator callback function def progress_indicator(future): print('.', end='', flush=True) # mock test that works for moment def task(name): sleep(random()) # entry point if __name__ == '__main__': # start the process pool with ProcessPoolExecutor(2) as executor: # send in the tasks futures = [executor.submit(task, i) for i in range(20)] # register the progress indicator callback for future in futures: future.add_done_callback(progress_indicator) # wait for all tasks to complete print('\nDone!') |
Running the example creates the process pool and submits twenty tasks for execution.
The tasks complete one-by-one reporting the progress of completed tasks with printed dots.
Once all 20 tasks have completed with all 20 dots printed, we can carry on with our program.
|
1 2 |
.................... Done! |
Free Python ProcessPoolExecutor Course
Download your FREE ProcessPoolExecutor PDF cheat sheet and get BONUS access to my free 7-day crash course on the ProcessPoolExecutor API.
Discover how to use the ProcessPoolExecutor class including how to configure the number of workers and how to execute tasks asynchronously.
Further Reading
This section provides additional resources that you may find helpful.
Books
- ProcessPoolExecutor Jump-Start, Jason Brownlee (my book!)
- Concurrent Futures API Interview Questions
- ProcessPoolExecutor PDF Cheat Sheet
I also recommend specific chapters from the following books:
- Effective Python, Brett Slatkin, 2019.
- See Chapter 7: Concurrency and Parallelism
- Python in a Nutshell, Alex Martelli, et al., 2017.
- See: Chapter: 14: Threads and Processes
Guides
- Python ProcessPoolExecutor: The Complete Guide
- Python ThreadPoolExecutor: The Complete Guide
- Python Multiprocessing: The Complete Guide
- Python Pool: The Complete Guide
APIs
References
- Thread (computing), Wikipedia.
- Process (computing), Wikipedia.
- Thread Pool, Wikipedia.
- Futures and promises, Wikipedia.
Overwhelmed by the python concurrency APIs?
Find relief, download my FREE Python Concurrency Mind Maps
Takeaways
You now know how to show progress of tasks in the ProcessPoolExecutor.
Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.
Photo by Nathana Rebouças on Unsplash
Do you have any questions?