loggerml 0.3.1

Log your ml training in the console in an attractive way.

LoggerML - Machine Learning Logger in the console

Log your Machine Learning training in the console in a beautiful way using rich✨ with useful information but with minimal code.

Installation

In a new virtual environment, install simply the package via pipy:

pip install loggerml

Supported platforms

This package is supported on Linux, macOS and Windows.

Be careful, notebooks are not supported (but PR are welcome!).

Quick start

Minimal usage

Integrate the LogML logger in your training loops! For instance for 4 epochs, 20 batches per epoch:

import time

from logml import Logger

logger = Logger(n_epochs=4, n_batches=20)

for _ in range(4):
    logger.new_epoch()  # Indicate the start of a new epoch
    for _ in range(20):
        logger.new_batch()  # Indicate the start of a new batch

        time.sleep(0.1)  # Simulate a training step

        # Log whatever you want (int, float, str, bool):
        logger.log({'loss': 0.54321256, 'accuracy': 0.85244777, 'loss name': 'MSE',
                    'improve baseline': True})

Yields:

Epoch 1/4, batch 20/20
[================================================][100%]
[global 00:00:02 > 00:00:06 | epoch 00:00:02 > 00:00:00]
  loss  | accuracy | loss name | improve baseline
 0.5432 |  0.8524  |    MSE    |       True

Epoch 2/4, batch 8/20
[=================>                              ][ 40%]
[global 00:00:03 > 00:00:05 | epoch 00:00:01 > 00:00:01]
  loss  | accuracy | loss name | improve baseline
 0.5432 |  0.8524  |    MSE    |       True

And continue until the end of the loop.

Advanced usage

Now you can add a validation logger, customize the logger with your own styles and colors, compute the average of some values over batch, add a dynamic message at each batch, update the value only every some batches and more!

At initialization you can set default configuration for the logger that will be eventually overwritten by the configuration passed to the log method.

An example with more features:

train_logger = Logger(
    n_epochs=2,
    n_batches=20,
    log_interval=2,
    name='Training',
    name_style='dark_orange',
    styles='yellow',  # Default style for all values
    sizes={'accuracy': 4},  # only 4 characters for 'accuracy'
    average=['loss'],  # 'loss' will be averaged over the current epoch
    bold_keys=True,  # Bold the keys
    show_time=False,  # Remove the time bar
)
val_logger = Logger(
    n_epochs=2,
    n_batches=10,
    name='Validation',
    name_style='cyan',
    styles='blue',
    bold_keys=True,
    show_time=False,
)
for _ in range(2):
    train_logger.new_epoch()
    for _ in range(20):
        train_logger.new_batch()
        time.sleep(0.1)
        # Overwrite the default style for "loss" and add a message
        train_logger.log(
            {'loss': 0.54321256, 'accuracy': 85.244777},
            styles={'loss': 'italic red'},
            message="Training is going well?\nYes!",
        )
    val_logger.new_epoch()
    for _ in range(10):
        val_logger.new_batch()
        time.sleep(0.1)
        val_logger.log({'val loss': 0.65422135, 'val accuracy': 81.2658775})

Yields:

Training
Epoch 1/2, batch 20/20
[================================================][100%]
  loss  | accuracy
 0.5432 |    85.2
Training is going well?
Yes!

Validation
Epoch 1/2, batch 3/10
[=================>                              ][ 30%]
 val loss | val accuracy
  0.6542  |    81.265

With colors! See the gif here.

Don't know the number of batches in advance?

If you don't have the number of batches in advance, you can initialize the logger with n_batches=None. The progress bar is replaced by a cyclic animation. The eta times are not know at the first epoch but was estimated after the second epoch.

How to contribute

For development, install the package dynamically and dev requirements with:

pip install -e .
pip install -r requirements-dev.txt

Everyone can contribute to LogML, and we value everyone’s contributions. Please see our contributing guidelines for more information 🤗

License

Copyright (C) 2023 Valentin Goldité

This program is free software: you can redistribute it and/or modify it under the terms of the MIT License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

This project is free to use for COMMERCIAL USE, MODIFICATION, DISTRIBUTION and PRIVATE USE as long as the original license is include as well as this copy right notice at the top of the modified files.