atpbar 1.1.4

Progress bars for threading and multiprocessing tasks

atpbar

Progress bars for threading and multiprocessing tasks on terminal and Jupyter Notebook.

 100.00% :::::::::::::::::::::::::::::::::::::::: |     7811 /     7811 |:  task 1
 100.00% :::::::::::::::::::::::::::::::::::::::: |    23258 /    23258 |:  task 0
  65.62% ::::::::::::::::::::::::::               |     8018 /    12219 |:  task 4
  60.89% ::::::::::::::::::::::::                 |    31083 /    51048 |:  task 2
  25.03% ::::::::::                               |    23884 /    95421 |:  task 3

atpbar can display multiple progress bars simultaneously growing to show the progresses of iterations of loops in threading or multiprocessing tasks. atpbar can display progress bars on terminal and Jupyter Notebook. atpbar can be used with Mantichora.

atpbar started its development in 2015 as part of alphatwirl. atpbar prevented physicists from terminating their running analysis codes, which would take many hours to complete, by showing progress bars indicating their codes were actually running. The progress bars have saved the physicists countless hours total. atpbar had been the sub-package progressbar of alphatwirl until it became an independent package, with the name atpbar, in February 2019.

You can try it on Jupyter Notebook online:

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• Requirement
• Install
• User guide
  • Quick start
    • Import libraries
    • One loop
    • Nested loops
    • Threading
    • Multiprocessing
    • With Mantichora
  • Features
    • A break and an exception
    • Progress of starting threads and processes with progress bars
    • On Jupyter Notebook
    • Non TTY device
    • How to disable progress bars
• License
• Contact

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Requirement

• Python 3.6, 3.7, or 3.8

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Install

You can install with conda from conda-forge:

conda install -c conda-forge atpbar

or with pip:

pip install -U atpbar

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User guide

Quick start

I will show here how to use atpbar by simple examples.

Import libraries

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 atpbar

Note: import the object atpbar from the package atpbar rather than importing the package atpbar itself.

One loop

The 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.

Nested loops

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 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.

Threading

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():
    nthreads = 5
    def task(n, name):
        for i in atpbar(range(n), name=name):
            time.sleep(0.0001)
    threads = [ ]
    for i in range(nthreads):
        name = 'thread {}'.format(i)
        n = random.randint(5, 100000)
        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 5 times with threading. atpbar can be used in any threads. 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.

Multiprocessing

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 i 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()
    nprocesses = 4
    ntasks = 10
    reporter = find_reporter()
    queue = multiprocessing.JoinableQueue()
    for i in range(nprocesses):
        p = multiprocessing.Process(target=worker, args=(reporter, task, queue))
        p.start()
    for i in range(ntasks):
        name = 'task {}'.format(i)
        n = random.randint(5, 100000)
        queue.put((n, name))
    for i in range(nprocesses):
        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

With Mantichora

Mantichora provides a simple interface to multiprocessing.

With Mantichora, task() can be concurrently run with multiprocessing with as simple code as the following example:

from mantichora import mantichora

def task(name):
    n = random.randint(1000, 10000)
    for i in atpbar(range(n), name=name):
        time.sleep(0.0001)

with mantichora() as mcore:
    mcore.run(task, 'task 1')
    mcore.run(task, 'task 2')
    mcore.run(task, 'task 3')
    mcore.run(task, 'task 4')
    mcore.run(task, 'task 5')
    returns = mcore.returns()

atpbar can be used in the task function.

 100.00% :::::::::::::::::::::::::::::::::::::::: |     2288 /     2288 |:  task 3
 100.00% :::::::::::::::::::::::::::::::::::::::: |     3964 /     3964 |:  task 4
 100.00% :::::::::::::::::::::::::::::::::::::::: |     4207 /     4207 |:  task 2
 100.00% :::::::::::::::::::::::::::::::::::::::: |     8519 /     8519 |:  task 1
 100.00% :::::::::::::::::::::::::::::::::::::::: |     6595 /     6595 |:  task 5

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Features

A break and an exception

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)
    nthreads = 5
    threads = [ ]
    for i in range(nthreads):
        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

Progress of starting threads and processes with progress bars

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)
    nthreads = 5
    threads = [ ]
    for i in atpbar(range(nthreads)):
        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 indicates that the loops in four threads have already ended before the loop in the main threads ended; the loop in the last thread ended afterwards.

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On Jupyter Notebook

On Jupyter Notebook, atpbar shows progress bars based on widgets.

You can try interactively online:

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Non TTY device

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 

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How to disable progress bars

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.

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License

• atpbar is licensed under the BSD license.

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Contact

• Tai Sakuma - [e-mail]