Custom Logger for Machine Learning. Inspired by NVIDIA/dllogger, this logging utility provides seamless logging to stdout, file, CSVs, and TensorBoard. loggerplus can also append to previous logging files in the event that training is resumed from a checkpoint.

From GitHub:

$ pip install git+https://github.com/gpauloski/loggerplus

For local development:

$ git clone https://github.com/gpauloski/loggerplus.git
$ cd loggerplus
$ pip install -e .

import loggerplus as logger

logger.init(
    handlers=[
        logger.StreamHandler(),
        logger.FileHandler('logs/output.log', overwrite=True),
        logger.TorchTensorboardHandler('logs/'),
        logger.CSVHandler('logs/metrics.csv', overwrite=False),
    ]
)

metrics = {'LR': 0.001, 'loss': 2.356, 'accuracy': 0.768}
logger.log('TRAIN', step=0, **metrics)

logger.info('Finished Logging')

• StreamHandler: logs to standard out
• FileHandler: logs to a file, can append to existing files
• TorchTensorboardHandler: TensorBoard metric logging
• CSVHandler: CSV metric logging

All handlers have a verbose flag to disable logging (e.g. when using multiple processes so only the main process writes to files).

loggerplus.log(tag, step, **kwargs) is the primary function for logging training metrics.

loggerplus.info(message) is essentially just a print and will log to both StreamHandler and FileHandler. TorchTensorboardHandler and CSVHandler only log metrics and will ignore calls to info().