quietml 2.1.0

A lightweight Python tool to silence and simplify noisy ML logs globally or temporarily.

🧘‍♂️ QuietML — Make Machine Learning Logs Calm & Clean

QuietML is a lightweight Python library that helps you silence, simplify, and structure noisy logs from ML frameworks like LightGBM, XGBoost, CatBoost, and TensorFlow — in just one line.

Whether you're a student tired of endless [Info] spam or a developer who likes clean notebooks, QuietML keeps your console peaceful and professional.

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🌟 Why QuietML?

If you’ve ever seen this 👇

[LightGBM] [Info] Number of positive: 4178, number of negative: 4178
[LightGBM] [Info] Auto-choosing row-wise multi-threading...
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

You know how painful it is to find your real output among hundreds of logs.

✅ QuietML fixes that instantly — clean outputs, no spam, one line of code.

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⚙️ Installation

pip install quietml

or if you want the latest build directly from source:

pip install --upgrade --no-cache-dir quietml

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🚀 Quick Start

🧩 Simplest Way — One-Line Setup

from quietml import configure
import lightgbm as lgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Configure QuietML and model in one go
model = configure(lgb.LGBMClassifier(), mode="smart")
model.fit(X_train, y_train)

🧠 Output:

[QuietML] Global mode set to: SMART
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
✅ Model training complete!

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🎛️ Manual Control (Multiple Frameworks)

from quietml import set_mode, apply_to_model
import xgboost as xgb

set_mode("silent")  # or "smart" / "debug"
model = apply_to_model(xgb.XGBClassifier())
model.fit(X_train, y_train)

🧠 Output (silent mode):

[QuietML] Global mode set to: SILENT
✅ Model training complete!

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🔇 Temporary Silence Mode

from quietml import silence
import lightgbm as lgb

model = lgb.LGBMClassifier()

print("Training normally...")
model.fit(X_train, y_train)

with silence("silent"):
    print("Training silently...")
    model.fit(X_train, y_train)

print("Logs restored.")

🧠 Output:

Training normally...
[LightGBM] [Info] ...
Training silently...
✅ (no logs)
Logs restored.

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🧠 Available Modes

Mode	Description	What You See
💤 silent	Suppresses all logs	Only warnings/errors
🤖 smart	Keeps essential info, hides spam	Warnings + important info
🧩 debug	Shows everything	Full developer logs

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🧱 Supported Frameworks

Library	Supported	Controlled Parameter
LightGBM	✅	verbosity
XGBoost	✅	verbosity
CatBoost	✅	logging_level
TensorFlow	✅	TF_CPP_MIN_LOG_LEVEL
Scikit-learn	🔸 (indirectly)	via wrapped models
PyTorch	🔜 (planned)	—
Transformers / LLMs	🔜 (planned)	—

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🧩 API Reference

Function	Purpose
set_mode(mode)	Set global mode (silent, smart, or debug)
get_mode()	Get current global mode
apply_to_model(model)	Apply current mode to a model
configure(model, mode)	Set mode + apply to a model in one call
silence(mode)	Temporarily change mode using a with block

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🧪 Example Output Comparison

Before QuietML:

[LightGBM] [Info] Number of positive: 4178, number of negative: 4178
[LightGBM] [Info] Auto-choosing row-wise multi-threading...
[LightGBM] [Warning] No further splits with positive gain...

After QuietML (smart mode):

[LightGBM] [Warning] No further splits with positive gain...
✅ Training complete.

After QuietML (silent mode):

✅ Training complete.

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📂 Project Structure

quietml/
│
├── quietml/
│   ├── __init__.py          # public API exports
│   ├── core.py              # main logic (modes, apply, silence)
│
├── examples/
│   └── test_suppress.py     # demo file
│
├── README.md
├── LICENSE
└── .gitignore

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🧑‍💻 Contributing

Want to improve QuietML? We’re open to contributions!

Ideas you can work on:

• Add PyTorch or Transformers (LLMs) support
• Add custom mode configurations
• Add auto-detection of ML frameworks

How to contribute:

git clone https://github.com/<your-username>/quietml.git
cd quietml
pip install -e .

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🧡 Author

👨‍💻 Kaustubh Aggarwal Computer Science Engineer, VIT Vellore Passionate about building cleaner, smarter, and more user-friendly ML tools.

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📜 License

This project is licensed under the MIT License — free for personal and commercial use.

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✨ TL;DR for Users

You want to...	Use this
Clean all logs instantly	set_mode("silent")
Keep only key info	set_mode("smart")
Debug your model deeply	set_mode("debug")
Simplify everything	model = configure(model, "smart")
Silence logs temporarily	with silence("silent"):

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🧘‍♂️ QuietML — Because your console deserves peace.