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https://github.com/fastai/fastprogress

Simple and flexible progress bar for Jupyter Notebook and console
https://github.com/fastai/fastprogress

developer-tools jupyter-notebook plots python

Last synced: 25 days ago
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Simple and flexible progress bar for Jupyter Notebook and console

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README 

        
# fastprogress



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






## Install



To install simply use

```

pip install fastprogress

```

or:

```

conda install -c fastai fastprogress

```

Note that this requires python 3.6 or later.



## Usage



### Example 1



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.main_bar.comment`

- add a comment in the second bar by changing the value of `mb.child.comment`

- write a line between the two bars with `mb.write('message')`



``` python

from fastprogress.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.main_bar.comment = f'first bar stat'

    mb.write(f'Finished loop {i}.')

    #mb.update_graph(graphs, x_bounds, y_bounds)

```






### Example 2



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 `y_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).



``` python

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.main_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.



### Example 3



Here is an example that a typical machine learning training loop can use. It also demonstrates how to set `y_bounds` dynamically.



```

def plot_loss_update(epoch, epochs, mb, train_loss, valid_loss):

    """ dynamically print the loss plot during the training/validation loop.

        expects epoch to start from 1.

    """

    x = range(1, epoch+1)

    y = np.concatenate((train_loss, valid_loss))

    graphs = [[x,train_loss], [x,valid_loss]]

    x_margin = 0.2

    y_margin = 0.05

    x_bounds = [1-x_margin, epochs+x_margin]

    y_bounds = [np.min(y)-y_margin, np.max(y)+y_margin]



    mb.update_graph(graphs, x_bounds, y_bounds)

```



And here is an emulation of a training loop that uses this function:



```

from fastprogress.fastprogress import master_bar, progress_bar

from time import sleep

import numpy as np

import random



epochs = 5

mb = master_bar(range(1, epochs+1))

# optional: graph legend: if not set, the default is 'train'/'valid'

# mb.names = ['first', 'second']

train_loss, valid_loss = [], []

for epoch in mb:

    # emulate train sub-loop

    for batch in progress_bar(range(2), parent=mb): sleep(0.2)

    train_loss.append(0.5 - 0.06 * epoch + random.uniform(0, 0.04))



    # emulate validation sub-loop

    for batch in progress_bar(range(2), parent=mb): sleep(0.2)

    valid_loss.append(0.5 - 0.03 * epoch + random.uniform(0, 0.04))



    plot_loss_update(epoch, epochs, mb, train_loss, valid_loss)

```



And the output:



Output



----



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.