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Building a Machine Learning Pipeline from Scratch

Logging

Explore the role of logging in machine learning pipelines and learn how to use Python's logging module to track execution events, set logging levels, and manage output destinations. This lesson helps you improve debugging and monitoring by organizing log messages for better pipeline maintenance.

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An introduction to logging

Logging is the means by which we keep track of events that occur during the execution of a program. Whether we realize it or not, we’ve all done logging in our programs in one way or another. We all know about the Python print statement. Every Python programmer starts off debugging by writing print statements throughout their code to print informational and error messages and display variable values. Let’s take a look at an example.

Python 3.8
import sys
def sum_list(a):
    print("Computing sum...")
    if a == []:
        print("ERROR: List is empty.", file=sys.stderr)
        raise Exception("List is empty")
    sum_ = sum(a)
    print("Sum computing done.")
    return sum_
if __name__ == "__main__":
    try:
        sum_ = sum_list([1, 2, 3, 4, 5])
        print(sum_)
        sum_ = sum_list([])
        print(sum_)
    except:
        print("ERROR: Sum computing failed.", file=sys.stderr)

In this code, we have a function that computes the sum of all elements in a list. There are a couple of informational messages, Computing sum... and Sum computing done. in lines 5 and 13, respectively, that are printed to the default standard output stream. There are error messages that are printed to the standard error stream (stderr) when the input list is empty. There are also debug statements for printing the sum. When we run the program, all relevant print statements are executed, and we see the output. This is ...