Metadata-Version: 2.1
Name: fastprogress
Version: 1.0.3
Summary: A nested progress with plotting options for fastai
Home-page: https://github.com/fastai/fastprogress
Author: Sylvain Gugger
Author-email: info@fast.ai
License: Apache Software License 2.0
Keywords: jupyter notebook progressbar
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
# fastprogress
A fast and simple progress bar for Jupyter Notebook and console. Created by Sylvain Gugger for fast.ai.
<img src="https://github.com/fastai/fastprogress/raw/master/images/cifar_train.gif" width="600">
## 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)
```
<img src="https://github.com/fastai/fastprogress/raw/master/images/pb_basic.gif" width="600">
### 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}.')
```
<img src="https://github.com/fastai/fastprogress/raw/master/images/pb_cos.gif" width="600">
Here is the rendering in console:
<img src="https://github.com/fastai/fastprogress/raw/master/images/pb_console.gif" width="800">
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:
<img src="https://github.com/fastai/fastprogress/raw/master/images/pb_graph.gif" alt="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.