What metrics are visualized in the overview?

It typically generates a 2x2 grid covering training/validation loss, the learning rate schedule, and gradient norms to ensure numerical stability.

How does it help with overfitting?

It automatically detects the onset of overfitting by monitoring the gap between training and validation loss, providing a clear signal of when the model starts losing generalization.

Can I use this to compare different model versions?

Yes, the skill can process multiple logs simultaneously to create overlay plots and comparison tables, helping you evaluate which version performed best.

What log formats does the Training Curve Interpreter support?

The skill supports a wide range of formats including CSV, JSON, TensorBoard, Weights & Biases (W&B), and raw pasted values from your terminal.

Training Curve Interpreter

Name: Training Curve Interpreter
Author: rHedBull

byrHedBull

0•

Data Science & ML

Analyzes machine learning training logs to visualize loss curves, detect training issues, and provide diagnostic insights.

The Training Curve Interpreter is designed for data scientists and ML engineers to automate the tedious process of log analysis. By parsing data from CSVs, TensorBoard, or W&B, it generates comprehensive 2x2 grid visualizations and applies heuristic patterns to identify critical training states such as overfitting, loss plateaus, and gradient divergence. It provides a detailed statistical summary and comparative analysis between different experiments, offering actionable recommendations to improve model convergence and performance.

Key Features

01Statistical summary tables with key performance metrics and dynamics

02Cross-run comparative analysis with overlay plots and best-loss tables

03Multi-source log ingestion supporting CSV, JSON, TensorBoard, and W&B

04Intelligent pattern detection for overfitting, instability, and divergence

05Automated 2x2 grid plotting for loss, learning rate, and gradient norms

060 GitHub stars

Use Cases

01Comparing multiple hyperparameter tuning experiments to identify the best run

02Determining the exact onset of overfitting to optimize early stopping

03Diagnosing convergence issues and gradient instability during model training

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add rhedbull/ai-trainer interpret-curves

For use in Claude.ai and ChatGPT

Download Skill