fastprogress

A fast and simple progress bar for Jupyter Notebook and console. Created by Sylvain Gugger for fast.ai.

Copyright 2017 onwards, fast.ai. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.

Install

To install simply use

pip install fastprogress

or:

conda install -c fastai fastprogress

Note that this requires python 3.6 or later.

Usage

Here is a simple example. Each bar takes an iterator as a main argument, and we can specify the second bar is nested with the first by adding the argument parent=mb. We can then

add a comment in the first bar by changing the value of mb.first_bar.comment
add a comment in the first bar by changing the value of mb.child.comment
write a line between the two bars with mb.write('message')

from fastprogress import master_bar, progress_bar
from time import sleep
mb = master_bar(range(10))
for i in mb:
    for j in progress_bar(range(100), parent=mb):
        sleep(0.01)
        mb.child.comment = f'second bar stat'
    mb.first_bar.comment = f'first bar stat'
    mb.write(f'Finished loop {i}.')
    #mb.update_graph(graphs, x_bounds, y_bounds)

To add a graph that get plots as the training goes, just use the command mb.update_graphs. It will create the figure on its first use. Arguments are:

graphs: a list of graphs to be plotted (each of the form [x,y])
x_bounds: the min and max values of the x axis (if None, it will those given by the graphs)
y_bounds: the min and max values of the y axis (if None, it will those given by the graphs)

Note that it's best to specify x_bounds and _bounds otherwise the box will change as the loop progresses.

Additionally, we can give the label of each graph via the command mb.names (should have as many elements as the graphs argument).

import numpy as np
mb = master_bar(range(10))
mb.names = ['cos', 'sin']
for i in mb:
    for j in progress_bar(range(100), parent=mb):
        if j%10 == 0:
            k = 100 * i + j
            x = np.arange(0, 2*k*np.pi/1000, 0.01)
            y1, y2 = np.cos(x), np.sin(x)
            graphs = [[x,y1], [x,y2]]
            x_bounds = [0, 2*np.pi]
            y_bounds = [-1,1]
            mb.update_graph(graphs, x_bounds, y_bounds)
            mb.child.comment = f'second bar stat'
    mb.first_bar.comment = f'first bar stat'
    mb.write(f'Finished loop {i}.')

Here is the rendering in console:

If the script using this is executed with a redirect to a file, only the results of the .write method will be printed in that file.

Metadata

Release files for fastprogress 0.1.16

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for fastprogress 0.1.16
File	Size	Uploaded
fastprogress-0.1.16.tar.gz	5.7 kB	Nov 27, 2018	Details

Built distribution (wheel)

Table of built distributions (wheels) for fastprogress 0.1.16
File	Interpreter	ABI	Platform	Reset
fastprogress-0.1.16-py3-none-any.whl 6.1 kB Nov 27, 2018	Python 3	none	any	Details

Total release size: 11.8 kB

Release files / fastprogress-0.1.16.tar.gz

Download URL	fastprogress-0.1.16.tar.gz
Size	5.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`e51acc6f3648351e7c3556690e0dda1cccc54fe01715602e7abc8372e5765043`
BLAKE2b-256 checksum How to use checksums	`15a32f087c7cf8d1cb101e691073bb98e2427d0f9b4b35ba262ccdd0ae3aee7f`
Upload date	Nov 27, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/39.1.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.6.5`

Release files / fastprogress-0.1.16-py3-none-any.whl

Download URL	fastprogress-0.1.16-py3-none-any.whl
Size	6.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`a2ebbaa3e5f1d1848f923992f55265bd3a85c4716d9fd7e73be05c529e297340`
BLAKE2b-256 checksum How to use checksums	`ea93b35cabbab4d25a2fdc5cd196114fbe1160451df5cf1459a80781893f3b0f`
Upload date	Nov 27, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/39.1.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.6.5`