onelog 0.2.0

Simple AI Logging

onelog

onelog is the simplest AI training logging library in the world. Just drop in a few log statements and immediately see your results in a single html file.

Installation

pip install onelog

Usage

Basic Scalar Logging 📊

import onelog

onelog = onelog.Logger()

for dummy_loss in range(20, 1, 0.1):
    onelog.log_scalar(dummy_loss, "dummy_loss")

onelog.finish()

Advanced Logging with Hyperparameters and Images 🖼️

import onelog
from PIL import Image

onelog = onelog.Logger()

# Log hyperparameters ⚙️
onelog.log_hyperparameters({
    "learning_rate": 0.001,
    "batch_size": 32,
    "epochs": 100
})

# Log scalar values 📈
for epoch in range(10):
    loss = 1.0 / (epoch + 1)
    accuracy = 0.8 + epoch * 0.02
    onelog.log_scalar(loss, "training_loss")
    onelog.log_scalar(accuracy, "accuracy")

# Log images 🖼️
sample_image = Image.new('RGB', (100, 100), color='red')
onelog.log_image(sample_image, "sample_output")

# Save as HTML report 📄
onelog.save_html("training_report.html")

Available Methods

• log_scalar(value, name) 📊 - Log numeric values (int/float)
• log_image(image, name) 🖼️ - Log PIL Image objects
• log_hyperparameters(params) ⚙️ - Log training hyperparameters
• finish(html_path=None) ✅ - Finish logging and optionally save HTML
• save_html(target_path) 💾 - Save current data as HTML report