dlprog 1.1.0

A progress bar that aggregates the values of each iteration.

dlprog

Deep Learning Progress

A Python library for progress bars with the function of aggregating each iteration's value.
It helps manage the loss of each epoch in deep learning or machine learning training.

Installation

pip install dlprog

General Usage

Setup

from dlprog import Progress
prog = Progress()

Example

import random
import time
n_epochs = 5
n_iter = 10

prog.start(n_epochs=n_epochs, n_iter=n_iter) # Initialize start time and epoch.
for _ in range(n_epochs):
    for _ in range(n_iter):
        time.sleep(0.1)
        value = random.random()
        prog.update(value) # Update progress bar and aggregate value.

Output

1/5: ######################################## 100% [00:00:01.05] value: 0.45692 
2/5: ######################################## 100% [00:00:01.05] value: 0.48990 
3/5: ######################################## 100% [00:00:01.06] value: 0.56601 
4/5: ######################################## 100% [00:00:01.06] value: 0.54549 
5/5: ######################################## 100% [00:00:01.05] value: 0.34751 

Get each epoch's value

>>> prog.values
[0.4569237062691406,
 0.4898950231979676,
 0.5660061074197436,
 0.545491839810675,
 0.3475107169240045]

In machine learning training

Setup

from dlprog import train_progress
prog = train_progress()

Example. Case of training a deep learning model with PyTorch.

n_epochs = 5
n_iter = len(dataloader)

prog.start(n_epochs=n_epochs, n_iter=n_iter)
for _ in range(n_epochs):
    for x, label in dataloader:
        optimizer.zero_grad()
        y = model(x)
        loss = criterion(y, label)
        loss.backward()
        optimizer.step()
        prog.update(loss.item())

Output

1/5: ######################################## 100% [00:00:03.08] loss: 0.34099 
2/5: ######################################## 100% [00:00:03.12] loss: 0.15259 
3/5: ######################################## 100% [00:00:03.14] loss: 0.10684 
4/5: ######################################## 100% [00:00:03.15] loss: 0.08008 
5/5: ######################################## 100% [00:00:03.20] loss: 0.06347 

If you want to obtain exact values considering batch size:

prog.update(loss.item(), weight=len(x))

Optional Arguments

leave_freq

The frequency of leaving the progress bar.

n_epochs = 12
n_iter = 10
prog.start(n_epochs=n_epochs, n_iter=n_iter, leave_freq=4)
for _ in range(n_epochs):
    for _ in range(n_iter):
        time.sleep(0.1)
        value = random.random()
        prog.update(value)

Output

 4/12: ######################################## 100% [00:00:01.06] loss: 0.34203 
 8/12: ######################################## 100% [00:00:01.05] loss: 0.47886 
12/12: ######################################## 100% [00:00:01.05] loss: 0.40241 

unit

Multiple epochs as a unit.

n_epochs = 12
n_iter = 10
prog.start(n_epochs=n_epochs, n_iter=n_iter, unit=4)
for _ in range(n_epochs):
    for _ in range(n_iter):
        time.sleep(0.1)
        value = random.random()
        prog.update(value)

Output

  1-4/12: ######################################## 100% [00:00:04.21] value: 0.49179 
  5-8/12: ######################################## 100% [00:00:04.20] value: 0.51518 
 9-12/12: ######################################## 100% [00:00:04.18] value: 0.54546 

Version History

1.0.0 (2023-07-13)

• Add Progress class.
• Add train_progress function.

1.1.0 (2023-07-13, Latest)

• Add values attribute.
• Add leave_freq argument.
• Add unit argument.