nnlogging

nnlogging is a powerful, modern logging library for neural network and machine learning experiments, combining Rich for beautiful terminal output with Aim for comprehensive experiment tracking.

✨ Features

• 🎨 Beautiful Console Output - Rich-powered colorful logging with integrated progress bars.
• 📊 Effortless Experiment Tracking - Seamless Aim integration for metrics, parameters, and system monitoring.
• 🔧 Simplified API - Get started in one line of code with the global functions.
• 📈 Advanced Progress Tracking - Manage multiple progress bars for training, validation, and other tasks.
• 🎯 ML-Focused Design - Purpose-built for the machine learning workflow.
• 🐍 Modern Python - Fully typed, Python 3.10+ codebase.

🚀 Installation

pip install nnlogging

Requirements: Python 3.10-3.12

⚡ Quick Start

The new nnlogging API is designed for simplicity. You can get a pre-configured, global logger instance and start logging right away.

Basic Logging

Get the global shell or customize your shell from scratch to use logging methods and trackings.

import nnlogging

# Log messages!
nnlogging.info("Starting training...")
nnlogging.warn("Learning rate seems high.")
nnlogging.debug("This is a detailed debug message.")
nnlogging.error("CUDA out of memory.", extra={"show_locals": True})

Experiment Tracking with Aim

Configure the Aim run to track metrics, hyperparameters, and more.

import random
import nnlogging

# 1. Configure the experiment tracker
nnlogging.run_configure(
    experiment="resnet_training",
    log_system_params=True,      # Track CPU/GPU usage
    capture_terminal_logs=True   # Save console output in Aim
)

# 2. Log hyperparameters
config = {"lr": 0.001, "batch_size": 32, "model": "ResNet50"}
nnlogging.update_metadata("hparams", config)
nnlogging.info(f"Using config: {config}")

# 3. Track metrics in your training loop
for epoch in range(10):
    train_loss = 1.0 / (epoch + 1) + random.random() * 0.1
    nnlogging.track(train_loss, name="train_loss", epoch=epoch)
    nnlogging.info(f"Epoch {epoch}: Loss={train_loss:.4f}")

nnlogging.info("Experiment finished!")

To view the results, run aim up in your terminal.

Progress Tracking

Add tasks to the logger to display and update rich progress bars.

import time
import nnlogging

# 1. Add a task to the progress display
nnlogging.task_add("training", desc="Training", total=500)

# 2. Update the task during your training loop
for step in range(500):
    # training_step()
    time.sleep(0.01)
    nnlogging.advance("training", 1)

    if step % 100 == 0:
        nnlogging.info(f"Completed step {step}")

# 3. Remove the task when finished
nnlogging.task_remove("training")
nnlogging.info("Training complete!")

🔄 Workflow

1. Import nnlogging - Import the nnlogging library at the start of your script.
2. run_configure() - (Optional) Configure Aim for experiment tracking.
3. Log & Track - Use nnlogging.info(), nnlogging.track(), and nnlogging.advance() throughout your code.
4. Visualize - Run aim up to launch the Aim UI and analyze your results.

🤝 Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License.