loadingpy 0.0.0

fancy progress bar

loadingpy

In this repository, we provide a custom made progress bar for python iterables. This library can be used as is or modified for any purposes (see licence).

Example

You can install with pip pip install loadingpy and use as follows

from loadingpy import pybar

loss = 0.0
accuracy = 0.0
for inputs, labels in pybar(dataset, monitoring=[loss, accuracy], naming=["loss", "accuracy"], base_str="training"):
    # do whatever you please
    loss += 0.0 # update monitoring variables in place
    accuracy += 0.0 # update monitoring variables in place

For a more detailed exampel (in torch) check this tutorial.

Arguments

Here is a list of the arguments and their description

argument	description	type
iterable	python object that can be iterated over	can be a list, tuple, range, np.ndarray, torch.Tensor, dataset,...
monitoring	a python object (or list of python objects) that will be printed after each iteration using the following format f'{monitoring}'. IF they are updated during the loop, make sure to update inplace, in order to see the changes	an be a tensor, float or list of these
naming	if you want to add a descritpion prefix to the monitoring variables	str or list of str
total_steps	number of iterations to perform (if you set it to a lower value than the length of the iterable, then the process will stop after the given total_steps)	int
base_str	prefix description of the loop we are iterating over	str
color	which color to use for the loading bar	str