littlelogger 1.1.1

A lightweight, zero-setup decorator for logging ML experiments to a JSONL file.

This tool is for the solo data scientist, student, or hobbyist in a Jupyter Notebook who just wants to keep track of their experiments without setting up a database or heavy framework.

Quick Setup

Get started in 30 seconds.

1. Install from PyPI:

pip install littlelogger

2. Import and use the decorator:

from littlelogger import log_run

# NOTE: for this to work, we need the training function to input the parameters that need to be logged
# and output the performance metric e.g., f1_score, accuracy_score, etc.
@log_run(log_file="my_experiments.jsonl")
def train_model(learning_rate, n_estimators):
    # Your training logic...
    f1 = 0.80
    return {"f1_score": f1}

# Just run your function as normal
train_model(0.1, 100)
train_model(0.05, 200)

3. Check your results: A my_experiments.jsonl file will be created, logging every run.

See it in Action

Want to see how it works? Check out the Demo Notebook to see a real-world example of logging experiments from different models into one file.

The Problem

You're tuning a model and your notebook looks like this:

# metrics = train_model(max_depth=5, n_estimators=100) # f1: 0.82
# metrics = train_model(max_depth=7, n_estimators=100) # f1: 0.81
metrics = train_model(max_depth=5, n_estimators=200) # f1: 0.83
print(metrics)

A day later, you've lost track of your best run.

The Solution

Wrap your function with the @log_run decorator.

from littlelogger import log_run

@log_run(log_file="experiment_log.jsonl")
def train_model(max_depth, n_estimators):
    # ... your training logic ...
    f1_score = 0.83 # Your metric
    return {"f1": f1_score}

# Run your experiments
train_model(max_depth=5, n_estimators=100)
train_model(max_depth=7, n_estimators=100)
train_model(max_depth=5, n_estimators=200)

This will create a experiment_log.jsonl file with one line per experiment:

{"timestamp": "2025-11-01T16:30:00...", "runtime_seconds": 12.3, "params": {"max_depth": 5, "n_estimators": 100}, "metrics": {"f1": 0.82}}
{"timestamp": "2025-11-01T16:32:12...", "runtime_seconds": 14.1, "params": {"max_depth": 7, "n_estimators": 100}, "metrics": {"f1": 0.81}}
{"timestamp": "2025-11-01T16:34:45...", "runtime_seconds": 23.5, "params": {"max_depth": 5, "n_estimators": 200}, "metrics": {"f1": 0.83}}

Analyzing Your Results

You can easily load your results back into pandas to find your best run. The key is using load_log() from littlelogger

from littlelogger import load_log

log_df = load_log(PATH_TO_LOG_FILE)

# Find your best run!
log_df.sort_values(by="metric_f1_score", ascending=False)

API Reference

@log_run(log_file="experiment_log.jsonl")

This is the main decorator. You place it above your function definition.

Parameters:

• log_file (str): The path to the .jsonl file where logs will be written.
  • Default: "experiment_log.jsonl"
  • Behavior: The decorator will append to this file. It does not overwrite.

What is Logged?

The decorator will write a single JSON line to the file for each function call, containing:

• timestamp: An ISO 8601 formatted UTC timestamp.
• function_name: The name of the function that was run (e.g., train_model).
• runtime_seconds: The execution time of the function (in seconds).
• params: A dictionary of all arguments (including defaults) passed to your function.
• metrics: The value returned by your function. This is expected to be a dictionary (e.g., {"f1": 0.8, "accuracy": 0.9})

⚠️ Important Limitations

This tool is built for simplicity and has intentional trade-offs:

1. Not Thread-Safe: This logger is NOT designed for concurrency. If you run functions in parallel (using multiprocessing or threading from the same script), they will try to write to the log file at the same time, which will corrupt the file. It is for single-process, iterative experiments only.
2. JSON-Serializable Data Only: The decorator assumes your function arguments and return values are "JSON-serializable" (strings, ints, floats, lists, dicts). If you return a complex object (like a scikit-learn model), the logger will fail to serialize it.
3. Best-Effort Logging (No Retries): If logging fails (due to a non-serializable object or a file permission error), littlelogger will not crash your script. It will print a UserWarning to your console and let your function return its value, prioritizing your main script's execution over logging.

License & Contributing

• License: This project is licensed under the MIT License. See the LICENSE file for details.
• Contributing: Feel free to open an issue or submit a pull request!