mloggers 1.3.4

A collection of loggers well-suited for machine learning experiments.

MLoggers

This package offers a collection of loggers well-suited for machine learning experiments.

Getting started

You can download the package via pip install mloggers. Python version $\geq$ 3.10 is required. Dependencies include:

• aenum
• numpy
• termcolor
• wandb (for integration with Weights & Biases)
• omegaconf (for integration with Hydra via Weights & Biases)

Usage

Example usage (with Hydra integration):

import time

import hydra
from omegaconf import DictConfig

from mloggers import ConsoleLogger, MultiLogger, WandbLogger


@hydra.main(version_base=None, config_path="configs", config_name="train")
def main(config: DictConfig):
    run_id = str(int(time.time()))

    # Create a multi-logger
    logger = MultiLogger(
        [
            ConsoleLogger(),
            WandbLogger(
                config.project_name,
                config.group_name,
                config.experiment_name + "_" + run_id,
                config,
            ),
        ],
        default_mask=[WandbLogger],
    )

    # Run an experiment
    logger.info("Starting the experiment")
    try:
        # `run_experiment` returns a dictionary of results
        results = run_experiment(config, logger)
    except Exception as e:
        logger.error({"Exception occurred during training": e})
        results = {}

    # Log the experiment results
    logger(results, mask=[ConsoleLogger])

Built-in loggers

At this moment, the built-in loggers are:

• Filelogger: records logs to a file.
• ConsoleLogger: records logs to the console.
• WandbLogger: sends logs to a Weights & Biases project; requires an API key.
• MultiLogger: aggregates any/all of the above loggers to record the same messages through multiple channels in a single log() call.

The available methods to log messages are:

• log(message, level): logs a message of a given LogLevel (INFO, WARN, ERROR, DEBUG or a custom level).
• info(message): wrapper to call log(message, LogLevel.INFO).
• warn(message): wrapper to call log(message, LogLevel.WARN).
• error(message): wrapper to call log(message, LogLevel.ERROR).
• debug(message): wrapper to call log(message, LogLevel.DEBUG).

In the case of the MultiLogger, the methods above have the additional optional argument mask, which can be used to prevent the given message from being propagated through the masked loggers.

All logging functions support multiple arguments, similar to the print function. For example, logger.info("The value of x is ", x) will log the message "The value of x is 42" if x = 42. The input messages can also be a series of dictionaries, which will be all logged in separate log entries. If the logger is given both a dictionary and a string, it will fail.

Masks

Masks are used by the MultiLogger to filter loggers which are not supposed to record a given message. At the time of initialization, you can define a default mask to use for all messages for which a mask is not specified when calling MultiLogger.log(message, level, mask) or the level-specific variants. To create a mask, simply pass as argument a list of the class references for the loggers you would like to mask out.

Level filtering

Any logger is initialized with a default_priority argument, which is set to LogLevel.INFO by default. LogLevel elements have an importance attribute, which defines a hierarchy of levels. When a logger is initialized with a given level, it will only log messages with a level of equal or higher importance. For example, if a logger is initialized with LogLevel.WARN, it will log messages with levels WARN and ERROR, but not INFO or DEBUG.

The importance values for the built-in levels are:

• DEBUG: -1
• INFO: 0
• WARN: 1
• ERROR: sys.maxsize (a very large number, as errors should always be logged)

Progress bars

You can make use of a pre-configured wrapper of the progress bars provided by the package rich.progress. The wrapper is provided via the function mloggers.progress.log_progress. Example usage:

import time
from mloggers.progress import log_progress

for _ in log_progress(range(100)):
    time.sleep(0.1)

Customized loggers

You can extend the base class Logger in order to create a custom logger to suit your own needs. Make sure to implement all abstract methods.

Customized log levels

You can register new log levels by using register_level(level, color). Once you register a level "MyLevel", you can use it as logger.log(message, LogLevel.MYLEVEL). The method log also supports a string as a level, which will be upper-cased and given a default color; the level can also be None, which will simply log the message as a stand-alone.

Optional loggers

This library also includes a wrapper around the Logger class called OptionalLogger, which allows you to use a logger which could be None without having to check its validity before every use. Hence, instead of this:

from mloggers import Logger


class MyClass:
    def __init__(self, logger: Logger | None):
        self._logger = logger

    def my_function(self):
        if self._logger is not None:
            self._logger.info("Message")

You can do this:

from mloggers import Logger, OptionalLogger


class MyClass:
    def __init__(self, logger: Logger | None):
        self._logger = OptionalLogger(logger)

    def my_function(self):
        self._logger.info("Message")

If the logger is None, nothing will happen (not even an error!).