Progress bars for threading and multiprocessing tasks on the terminal and Jupyter Notebook.
atpbar can display multiple progress bars simultaneously growing to show the progress of each iteration of loops in threading or multiprocessing tasks. atpbar can display progress bars on the terminal and Jupyter Notebook.
atpbar started its development in 2015 and was the sub-package progressbar of alphatwirl. It became an independent package in 2019.
You can try it on Jupyter Notebook online:
You can install with pip from PyPI:
pip install -U atpbarTo install with Jupyter Notebook support, use the following command:
pip install -U atpbar[jupyter]I will show you how to use the atpbar using simple examples.
To create simple loops in the examples, we use two Python standard
libraries, time and
random. Import the
two packages as well as atpbar.
import time, random
from atpbar import atpbarThe object atpbar is an iterable that can wrap another iterable and shows the
progress bars for the iterations. (The idea of making the interface iterable
was inspired by tqdm.)
n = random.randint(1000, 10000)
for i in atpbar(range(n)):
time.sleep(0.0001)The task in the above code is to sleep for 0.0001 seconds in each iteration
of the loop. The number of the iterations of the loop is randomly selected from
between 1000 and 10000.
A progress bar will be shown by atpbar.
51.25% :::::::::::::::::::: | 4132 / 8062 |: range(0, 8062)
In order for atpbar to show a progress bar, the wrapped iterable needs to
have a length. If the length cannot be obtained by len(), atpbar won't show
a progress bar.
atpbar can show progress bars for nested loops as in the following example.
for i in atpbar(range(4), name='Outer'):
n = random.randint(1000, 10000)
for j in atpbar(range(n), name='Inner {}'.format(i)):
time.sleep(0.0001)The outer loop iterates 4 times. The inner loop is similar to the loop
in the previous example---sleeps for 0.0001 seconds. You can
optionally give the keyword argument name to specify the label on
the progress bar.
100.00% :::::::::::::::::::::::::::::::::::::::: | 3287 / 3287 |: Inner 0
100.00% :::::::::::::::::::::::::::::::::::::::: | 5850 / 5850 |: Inner 1
50.00% :::::::::::::::::::: | 2 / 4 |: Outer
34.42% ::::::::::::: | 1559 / 4529 |: Inner 2
In the snapshot of the progress bars above, the outer loop is in its 3rd iteration. The inner loop has been completed twice and is running the third. The progress bars for the completed tasks move up. The progress bars for the active tasks are growing at the bottom.
atpbar can show multiple progress bars for loops concurrently iterating in
different threads.
The function run_with_threading() in the following code shows an
example.
from atpbar import flush
import threading
def run_with_threading():
def task(n, name):
for _ in atpbar(range(n), name=name):
time.sleep(0.0001)
n_threads = 5
threads = []
for i in range(n_threads):
name = 'Thread {}'.format(i)
n = random.randint(5, 10000)
t = threading.Thread(target=task, args=(n, name))
t.start()
threads.append(t)
for t in threads:
t.join()
flush()
run_with_threading()The task to sleep for 0.0001 seconds is defined as the function task. The
task is concurrently run five times with threading. atpbar can be used in
any thread. Five progress bars growing simultaneously will be shown. The
function flush() returns when the progress bars have finished updating.
100.00% :::::::::::::::::::::::::::::::::::::::: | 8042 / 8042 |: Thread 3
33.30% ::::::::::::: | 31967 / 95983 |: Thread 0
77.41% :::::::::::::::::::::::::::::: | 32057 / 41411 |: Thread 1
45.78% :::::::::::::::::: | 31816 / 69499 |: Thread 2
39.93% ::::::::::::::: | 32373 / 81077 |: Thread 4
As a task completes, the progress bar for the task moves up. The progress bars for active tasks are at the bottom.
atpbar can be used with multiprocessing.
The function run_with_multiprocessing() in the following code shows an
example.
import multiprocessing
multiprocessing.set_start_method('fork', force=True)
from atpbar import register_reporter, find_reporter, flush
def run_with_multiprocessing():
def task(n, name):
for _ in atpbar(range(n), name=name):
time.sleep(0.0001)
def worker(reporter, task, queue):
register_reporter(reporter)
while True:
args = queue.get()
if args is None:
queue.task_done()
break
task(*args)
queue.task_done()
n_processes = 4
processes = []
reporter = find_reporter()
queue = multiprocessing.JoinableQueue()
for i in range(n_processes):
p = multiprocessing.Process(target=worker, args=(reporter, task, queue))
p.start()
processes.append(p)
n_tasks = 10
for i in range(n_tasks):
name = 'Task {}'.format(i)
n = random.randint(5, 10000)
queue.put((n, name))
for i in range(n_processes):
queue.put(None)
queue.join()
flush()
run_with_multiprocessing()It starts four workers in subprocesses with multiprocessing and have
them run ten tasks.
In order to use atpbar in a subprocess, the reporter, which can be
found in the main process by the function find_reporter(), needs to
be brought to the subprocess and registered there by the function
register_reporter().
Simultaneously growing progress bars will be shown.
100.00% :::::::::::::::::::::::::::::::::::::::: | 44714 / 44714 |: Task 3
100.00% :::::::::::::::::::::::::::::::::::::::: | 47951 / 47951 |: Task 2
100.00% :::::::::::::::::::::::::::::::::::::::: | 21461 / 21461 |: Task 5
100.00% :::::::::::::::::::::::::::::::::::::::: | 73721 / 73721 |: Task 1
100.00% :::::::::::::::::::::::::::::::::::::::: | 31976 / 31976 |: Task 4
100.00% :::::::::::::::::::::::::::::::::::::::: | 80765 / 80765 |: Task 0
58.12% ::::::::::::::::::::::: | 20133 / 34641 |: Task 6
20.47% :::::::: | 16194 / 79126 |: Task 7
47.71% ::::::::::::::::::: | 13072 / 27397 |: Task 8
76.09% :::::::::::::::::::::::::::::: | 9266 / 12177 |: Task 9
To use atpbar with
multiprocessing.Pool,
use find_reporter as the initializer and give the reporter as an argument
to the initializer.
def task(n, name):
for _ in atpbar(range(n), name=name):
time.sleep(0.0001)
def run_with_multiprocessing_pool():
n_processes = 4
reporter = find_reporter()
n_tasks = 10
args = [(random.randint(5, 10000), 'Task {}'.format(i)) for i in range(n_tasks)]
with multiprocessing.Pool(n_processes, register_reporter, (reporter,)) as pool:
pool.starmap(task, args)
flush()
run_with_multiprocessing_pool()An example with concurrent.futures.ThreadPoolExecutor:
def task(n, name):
for _ in atpbar(range(n), name=name):
time.sleep(0.0001)
def run_with_thread_pool():
n_workers = 5
n_tasks = 10
with ThreadPoolExecutor(max_workers=n_workers) as executor:
for i in range(n_tasks):
name = 'Task {}'.format(i)
n = random.randint(5, 1000)
executor.submit(task, n, name)
flush()
run_with_thread_pool()An example with concurrent.futures.ProcessPoolExecutor:
def task(n, name):
for _ in atpbar(range(n), name=name):
time.sleep(0.0001)
def run_with_process_pool():
n_workers = 5
n_tasks = 10
reporter = find_reporter()
with ProcessPoolExecutor(
max_workers=n_workers, initializer=register_reporter, initargs=(reporter,)
) as executor:
for i in range(n_tasks):
name = 'Task {}'.format(i)
n = random.randint(5, 1000)
executor.submit(task, n, name)
flush()
run_with_process_pool()When the loop ends with a break or an exception, the progress bar stops with
the last complete iteration.
For example, the loop in the following code breaks during the 1235th iteration.
for i in atpbar(range(2000)):
if i == 1234:
break
time.sleep(0.0001)Since i starts with 0, when i is 1234, the loop is in its 1235th
iteration. The last complete iteration is 1234. The progress bar stops at 1234.
61.70% :::::::::::::::::::::::: | 1234 / 2000 |: range(0, 2000)
As an example of an exception, in the following code, an exception is thrown during the 1235th iteration.
for i in atpbar(range(2000)):
if i == 1234:
raise Exception
time.sleep(0.0001)The progress bar stops at the last complete iteration, 1234.
61.70% :::::::::::::::::::::::: | 1234 / 2000 |: range(0, 2000)
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
Exception
This feature works as well with nested loops, threading, and multiprocessing. For example, in the following code, the loops in five threads break at 1235th iteration.
from atpbar import flush
import threading
def run_with_threading():
def task(n, name):
for i in atpbar(range(n), name=name):
if i == 1234:
break
time.sleep(0.0001)
n_threads = 5
threads = []
for i in range(n_threads):
name = 'Thread {}'.format(i)
n = random.randint(3000, 10000)
t = threading.Thread(target=task, args=(n, name))
t.start()
threads.append(t)
for t in threads:
t.join()
flush()
run_with_threading()All progress bars stop at 1234.
18.21% ::::::: | 1234 / 6777 |: Thread 0
15.08% :::::: | 1234 / 8183 |: Thread 2
15.25% :::::: | 1234 / 8092 |: Thread 1
39.90% ::::::::::::::: | 1234 / 3093 |: Thread 4
19.67% ::::::: | 1234 / 6274 |: Thread 3
atpbar can be used for a loop that starts sub-threads or sub-processes in
which atpbar is also used.
from atpbar import flush
import threading
def run_with_threading():
def task(n, name):
for i in atpbar(range(n), name=name):
time.sleep(0.0001)
n_threads = 5
threads = []
for i in atpbar(range(n_threads)):
name = 'Thread {}'.format(i)
n = random.randint(200, 1000)
t = threading.Thread(target=task, args=(n, name))
t.start()
threads.append(t)
time.sleep(0.1)
for t in threads:
t.join()
flush()
run_with_threading() 100.00% :::::::::::::::::::::::::::::::::::::::: | 209 / 209 |: Thread 1
100.00% :::::::::::::::::::::::::::::::::::::::: | 699 / 699 |: Thread 0
100.00% :::::::::::::::::::::::::::::::::::::::: | 775 / 775 |: Thread 2
100.00% :::::::::::::::::::::::::::::::::::::::: | 495 / 495 |: Thread 3
100.00% :::::::::::::::::::::::::::::::::::::::: | 5 / 5 |: range(0, 5)
100.00% :::::::::::::::::::::::::::::::::::::::: | 647 / 647 |: Thread 4
The atpbar sensibly works regardless of the order in which multiple instances
of atpbar in multiple threads and multiple processes start and end. The
progress bars in the example above indicate that the loops in four threads
have already ended before the loop in the main threads ended; the loop in the
last thread ended afterward.
On Jupyter Notebook, atpbar shows progress bars based on
widgets.
You can try interactively online:
If neither on Jupyter Notebook or on a TTY device, atpbar is not able to show
progress bars. atpbar occasionally prints the status.
03/04 09:17 : 1173 / 7685 ( 15.26%): Thread 0
03/04 09:17 : 1173 / 6470 ( 18.13%): Thread 3
03/04 09:17 : 1199 / 1199 (100.00%): Thread 4
03/04 09:18 : 1756 / 2629 ( 66.79%): Thread 2
03/04 09:18 : 1757 / 7685 ( 22.86%): Thread 0
03/04 09:18 : 1757 / 6470 ( 27.16%): Thread 3
03/04 09:19 : 2342 / 2629 ( 89.08%): Thread 2
The function disable() disables atpbar; progress bars will not be shown.
from atpbar import disable
disable()This function needs to be called before atpbar or find_reporter() is used,
typically at the beginning of the program.
- atpbar is licensed under the BSD license.