insomniacs01

insomniacs01 / TrainWatch

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Monitor LLM training over SSH from your phone -GPU, loss, ETA, and logs in one mobile-first dashboard.

17
0
100% credibility
Found Mar 15, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

TrainWatch is a mobile-friendly web dashboard for monitoring AI training jobs on remote GPU servers, showing live metrics, progress, and alerts via secure remote connections.

How It Works

1
🔍 Discover TrainWatch

You hear about a handy tool to check your long-running AI training jobs from your phone without sitting at a desk.

2
🚀 Start it up

Run a simple starter script on your computer to launch the dashboard instantly.

3
📱 Open on any device

Visit the web address in your phone or computer browser – it works like a mobile app.

4
🔗 Link your training machine

Enter your server's address, username, and login to connect and start watching live.

5
📊 See jobs in action

Watch progress bars, loss numbers, temperatures, and finish times update every few seconds.

6
Manage shared GPUs
✅
Skip queue

Just monitor existing jobs.

➕
Queue a job

Pick a machine, enter command and GPU needs, join the line.

7
🔔 Stay alerted anywhere

Get warnings on stalls, failures, or overheating right on your phone home screen.

🎉 Training at your fingertips

Relax knowing you can peek at jobs, fix issues fast, from coffee shop or couch.

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Star Growth

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AI-Generated Review

What is TrainWatch?

TrainWatch is a Python-based, mobile-first dashboard that lets you monitor LLM training jobs on remote Linux GPU servers via SSH from your phone or browser. It pulls real-time GPU utilization, VRAM, temps, loss curves, ETA, progress, and log snippets, solving the pain of SSH-ing into servers mid-training or relying on Grafana monitor LLM setups. Fire it up with Docker Compose or a one-line script, add connections via UI, and get a PWA you can pin to your iPhone home screen for how to monitor LLM models on the go.

Why is it gaining traction?

Unlike Grafana monitor LLM or Azure monitor LLM tools that need heavy setup, TrainWatch auto-discovers training processes, correlates them to GPUs, and parses logs for stalled/failed runs with zero config hassle. The shared-GPU FIFO queue auto-launches jobs when resources free up, plus alerts for high temps or OOM errors beat manual monitoring LLMs in production. Devs dig the near-realtime WebSocket updates every 5s without Grafana monitor GitHub Actions complexity.

Who should use this?

ML engineers training LLMs on shared GPU clusters who hate constant SSH checks. Solo researchers monitoring LLM performance, usage, or app costs during long pretrains. Teams needing github monitor control or monitor LLM calls without full observability stacks.

Verdict

Grab it for personal GPU rigs—solid docs, tests, and Docker make it dead simple, despite 17 stars and 1.0% credibility signaling early days. Skip for production until v2 native iOS lands; it's a smart Train Watch alternative now.

(198 words)

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