Is there a way to view progress without external tools?

Yes, the skill can launch terminal-based ASCII dashboards that provide live-updating progress bars and charts directly in your console.

Which logging backends does the monitor skill support?

The skill supports a wide range of backends including TensorBoard, Weights & Biases (W&B), file-based logs, and direct terminal outputs.

How does the skill help with training failures?

It provides a framework for detecting and alerting on common training issues such as loss spikes, NaN values, gradient explosions, and overfitting.

Can I use this to monitor GPU usage?

Yes, the skill includes quick commands to integrate resource monitoring tools like nvidia-smi directly into your workflow.

Live Training Monitor

Name: Live Training Monitor
Author: rHedBull

byrHedBull

データサイエンスとML

Monitors machine learning training progress in real-time by integrating with logging backends and visualization tools.

概要

The Live Training Monitor skill provides a comprehensive framework for tracking machine learning model training within the Claude Code environment. It streamlines the process of visualizing metrics through support for TensorBoard, Weights & Biases, and terminal-based ASCII dashboards. By offering standardized paths for identifying logging backends and setting up alerts for critical issues like NaN values or gradient explosions, this skill ensures that data scientists can maintain high visibility over their experiments and respond quickly to training instabilities.

主な機能

Multi-backend support including TensorBoard and W&B
Integrated hardware monitoring with nvidia-smi and log tailing
Automated anomaly detection for loss spikes and NaNs
Terminal-based ASCII dashboards for lightweight monitoring
0 GitHub stars
Real-time training metrics and progress visualization

ユースケース

Launching and managing visualization dashboards via terminal commands
Tracking loss and learning rate curves during deep learning training sessions
Setting up automated alerts for overfitting or gradient instability

概要

主な機能

Multi-backend support including TensorBoard and W&B
Integrated hardware monitoring with nvidia-smi and log tailing
Automated anomaly detection for loss spikes and NaNs
Terminal-based ASCII dashboards for lightweight monitoring
0 GitHub stars
Real-time training metrics and progress visualization

ユースケース

Launching and managing visualization dashboards via terminal commands
Tracking loss and learning rate curves during deep learning training sessions
Setting up automated alerts for overfitting or gradient instability