Quick start

Minimal usage

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

import time

from logml import Logger

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

for _ in range(4):
    for _ in logger.tqdm(range(20)):
        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:

Note that the expected remaining time of the overall train is displayed as well as the one for the epoch. The logger also provides also the possibility to average the logged values over an epoch or a full training.

Pause and resume

You can also pause and resume the logger internal time with logger.pause() and logger.resume(). You can check the internal time with logger.get_current_time(). Note that the resume method continues the time from the last pause. it means that if you pause the training logger at 10 seconds, then resume it at 20 seconds, the logger will display 10 seconds of training time. The global and the epoch time will be updated accordingly. You can also find examples in the documentation.

Save the logs

In Linux you can use tee to save the logs in a file and display them in the console. However you need to use unbuffer to keep the style:

unbuffer python main.py --color=auto | tee output.log

See here for details.

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
)
val_logger = Logger(
    n_epochs=2,
    n_batches=10,
    name='Validation',
    name_style='cyan',
    styles='blue',
    bold_keys=True,
    show_time=False,  # Remove the time bar
)
for _ in range(2):
    train_logger.new_epoch()  # Manually declare a new epoch
    for _ in range(20):
        train_logger.new_batch()  # Manually declare a 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})
    val_logger.detach()  # End the live display to print something else after

Yields:

Further features are described in the Features section.