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Logging for ML Systems

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Keep records of the important events in our application.
Goku Mohandas
Goku Mohandas
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Intuition

Logging is the process of tracking and recording key events that occur in our applications for the purpose of inspection, debugging, etc. They're a whole lot more powerful than print statements because they allow us to send specific pieces of information to specific locations with custom formatting, shared interfaces, etc. This makes logging a key proponent in being able to surface insightful information from the internal processes of our application.

Components

There are a few overarching concepts to be aware of:

  • Logger: emits the log messages from our application.
  • Handler: sends log records to a specific location.
  • Formatter: formats and styles the log records.

There is so much more to logging such as filters, exception logging, etc. but these basics will allows us to do everything we need for our application.

Levels

Before we create our specialized, configured logger, let's look at what logged messages look like by using the basic configuration. 

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import logging
import sys

# Create super basic logger
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)

# Logging levels (from lowest to highest priority)
logging.debug("Used for debugging your code.")
logging.info("Informative messages from your code.")
logging.warning("Everything works but there is something to be aware of.")
logging.error("There's been a mistake with the process.")
logging.critical("There is something terribly wrong and process may terminate.")


DEBUG:root:Used for debugging your code.
INFO:root:Informative messages from your code.
WARNING:root:Everything works but there is something to be aware of.
ERROR:root:There's been a mistake with the process.
CRITICAL:root:There is something terribly wrong and process may terminate.

These are the basic levels of logging, where DEBUG is the lowest priority and CRITICAL is the highest. We defined our logger using basicConfig to emit log messages to stdout (ie. our terminal console), but we also could've written to any other stream or even a file. We also defined our logging to be sensitive to log messages starting from level DEBUG. This means that all of our logged messages will be displayed since DEBUG is the lowest level. Had we made the level ERROR, then only ERROR and CRITICAL log message would be displayed.

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import logging
import sys

# Create super basic logger
logging.basicConfig(stream=sys.stdout, level=logging.ERROR)

# Logging levels (from lowest to highest priority)
logging.debug("Used for debugging your code.")
logging.info("Informative messages from your code.")
logging.warning("Everything works but there is something to be aware of.")
logging.error("There's been a mistake with the process.")
logging.critical("There is something terribly wrong and process may terminate.")

ERROR:root:There's been a mistake with the process.
CRITICAL:root:There is something terribly wrong and process may terminate.

Configuration

First we'll set the location of our logs in our config.py script:

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# madewithml/config.py
LOGS_DIR = Path(BASE_DIR, "logs")
LOGS_DIR.mkdir(parents=True, exist_ok=True)

Next, we'll configure the logger for our application:

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# madewithml/config.py
import logging
import sys
logging_config = {
    "version": 1,
    "disable_existing_loggers": False,
    "formatters": {
        "minimal": {"format": "%(message)s"},
        "detailed": {
            "format": "%(levelname)s %(asctime)s [%(name)s:%(filename)s:%(funcName)s:%(lineno)d]\n%(message)s\n"
        },
    },
    "handlers": {
        "console": {
            "class": "logging.StreamHandler",
            "stream": sys.stdout,
            "formatter": "minimal",
            "level": logging.DEBUG,
        },
        "info": {
            "class": "logging.handlers.RotatingFileHandler",
            "filename": Path(LOGS_DIR, "info.log"),
            "maxBytes": 10485760,  # 1 MB
            "backupCount": 10,
            "formatter": "detailed",
            "level": logging.INFO,
        },
        "error": {
            "class": "logging.handlers.RotatingFileHandler",
            "filename": Path(LOGS_DIR, "error.log"),
            "maxBytes": 10485760,  # 1 MB
            "backupCount": 10,
            "formatter": "detailed",
            "level": logging.ERROR,
        },
    },
    "root": {
        "handlers": ["console", "info", "error"],
        "level": logging.INFO,
        "propagate": True,
    },
}
  1. [Lines 6-11]: define two different Formatters (determine format and style of log messages), minimal and detailed, which use various LogRecord attributes to create a formatting template for log messages.
  2. [Lines 12-35]: define the different Handlers (details about location of where to send log messages):
    • console: sends log messages (using the minimal formatter) to the stdout stream for messages above level DEBUG (ie. all logged messages).
    • info: send log messages (using the detailed formatter) to logs/info.log (a file that can be up to 1 MB and we'll backup the last 10 versions of it) for messages above level INFO.
    • error: send log messages (using the detailed formatter) to logs/error.log (a file that can be up to 1 MB and we'll backup the last 10 versions of it) for messages above level ERROR.
  3. [Lines 36-40]: attach our different handlers to our root Logger.

We chose to use a dictionary to configure our logger but there are other ways such as Python script, configuration file, etc. Click on the different options below to expand and view the respective implementation.

Python script 
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import logging
from rich.logging import RichHandler

# Get root logger
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

# Create handlers
console_handler = RichHandler(markup=True)
console_handler.setLevel(logging.DEBUG)
info_handler = logging.handlers.RotatingFileHandler(
    filename=Path(LOGS_DIR, "info.log"),
    maxBytes=10485760,  # 1 MB
    backupCount=10,
)
info_handler.setLevel(logging.INFO)
error_handler = logging.handlers.RotatingFileHandler(
    filename=Path(LOGS_DIR, "error.log"),
    maxBytes=10485760,  # 1 MB
    backupCount=10,
)
error_handler.setLevel(logging.ERROR)

# Create formatters
minimal_formatter = logging.Formatter(fmt="%(message)s")
detailed_formatter = logging.Formatter(
    fmt="%(levelname)s %(asctime)s [%(name)s:%(filename)s:%(funcName)s:%(lineno)d]\n%(message)s\n"
)

# Hook it all up
console_handler.setFormatter(fmt=minimal_formatter)
info_handler.setFormatter(fmt=detailed_formatter)
error_handler.setFormatter(fmt=detailed_formatter)
logger.addHandler(hdlr=console_handler)
logger.addHandler(hdlr=info_handler)
logger.addHandler(hdlr=error_handler)
Configuration file

  1. Place this inside a logging.config file: 

    [formatters]
keys=minimal,detailed

[formatter_minimal]
format=%(message)s

[formatter_detailed]
format=
    %(levelname)s %(asctime)s [%(name)s:%(filename)s:%(funcName)s:%(lineno)d]
    %(message)s

[handlers]
keys=console,info,error

[handler_console]
class=StreamHandler
level=DEBUG
formatter=minimal
args=(sys.stdout,)

[handler_info]
class=handlers.RotatingFileHandler
level=INFO
formatter=detailed
backupCount=10
maxBytes=10485760
args=("logs/info.log",)

[handler_error]
class=handlers.RotatingFileHandler
level=ERROR
formatter=detailed
backupCount=10
maxBytes=10485760
args=("logs/error.log",)

[loggers]
keys=root

[logger_root]
level=INFO
handlers=console,info,error

  2. Place this inside your Python script: 

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    import logging
import logging.config
from rich.logging import RichHandler

# Use config file to initialize logger
logging.config.fileConfig(Path(CONFIG_DIR, "logging.config"))
logger = logging.getLogger()
logger.handlers[0] = RichHandler(markup=True)  # set rich handler

We can load our logger configuration dict like so:

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# madewithml/config.py
import logging

# Logger
logging_config = {...}
logging.config.dictConfig(logging_config)
logger = logging.getLogger()

# Sample messages (note that we use configured `logger` now)
logger.debug("Used for debugging your code.")
logger.info("Informative messages from your code.")
logger.warning("Everything works but there is something to be aware of.")
logger.error("There's been a mistake with the process.")
logger.critical("There is something terribly wrong and process may terminate.")

DEBUG    Used for debugging your code.                                 config.py:71
INFO     Informative messages from your code.                          config.py:72
WARNING  Everything works but there is something to be aware of.       config.py:73
ERROR    There's been a mistake with the process.                      config.py:74
CRITICAL There is something terribly wrong and process may terminate.  config.py:75

Our logged messages become stored inside the respective files in our logs directory:

logs/
    ├── info.log
    └── error.log

And since we defined a detailed formatter, we would see informative log messages like these:

INFO 2020-10-21 11:18:42,102 [config.py:module:72]
Informative messages from your code.

Implementation

In our project, we can replace all of our print statements into logging statements:

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print("✅ Training complete!")
────   becomes:   ──── 
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from config import logger
logger.info("✅ Training complete!")

All of our log messages are at the INFO level but while developing we may have had to use DEBUG levels and we also add some ERROR or CRITICAL log messages if our system behaves in an unintended manner.

  • what: log all the necessary details you want to surface from our application that will be useful during development and afterwards for retrospective inspection.

  • where: a best practice is to not clutter our modular functions with log statements. Instead we should log messages outside of small functions and inside larger workflows. For example, there are no log messages inside any of our scripts except the main.py and train.py files. This is because these scripts use the smaller functions defined in the other scripts (data.py, evaluate.py, etc.). If we ever feel that we the need to log within our other functions, then it usually indicates that the function needs to be broken down further.

When it comes to saving our logs, we could simply upload our logs to a cloud blog storage (ex. S3 or Google Cloud Storage). Or for a more production-grade logging option, we could consider the Elastic stack.

In the next lesson, we'll learn how to document our code and automatically generate high quality docs for our application.

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To cite this content, please use:

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@article{madewithml,
    author       = {Goku Mohandas},
    title        = { Logging - Made With ML },
    howpublished = {\url{https://madewithml.com/}},
    year         = {2023}
}