loadingpy 0.1.4

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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).

for deep learning

There is now a new progress bar available for deep learning purposes (I guess it could be leveraged for other stuff as well...). Say, you want to train a model using a dataset $D$ over $e$ epochs. Using TrainBaryou can get a double progress bar (first for the epochs and second for the steps in the current epoch) on a single line. you can check the test or this simple example:

from loadingpy import TrainBar

for data in TrainBar(
        trainloader,
        num_epochs=e,
        base_str="training",
    ):
        inputs, labels = data

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. You can use a global argument in order to disable the verbatim from the loading bars as follows:

from loadingpy import BarConfig

BarConfig["disable loading bar"] = True

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