I got tired of babysitting Colab notebooks, so I built a lightweight runner

Google Colab is great when you just need a quick GPU, but once a notebook turns into a repeated experiment workflow, the manual parts start getting annoying.

For me, the main pain wasn’t just runtime length. It was the friction around the process:

• rerunning the same notebook with slightly different parameters
• repeating setup steps across sessions
• keeping track of outputs/logs from different runs
• recovering cleanly after a reset or reconnect
• remembering which config produced which result

So I built Colab Automation:

https://github.com/jerronl/colab-automation

The goal is to treat a Colab notebook more like a lightweight experiment job, instead of something you have to manage manually in the browser.

It focuses on:

• more repeatable notebook runs
• cleaner parameter/config organization
• easier output tracking
• smoother rerun/recovery workflow
• staying lightweight rather than becoming a full MLOps platform

It’s open-source and still early. I’m mainly building this for research/prototyping/Kaggle-style workflows where Colab is useful, but the browser-based workflow gets messy once you start running many experiments.

Repo: https://github.com/jerronl/colab-automation

Curious how other people handle this:

• Do you still run experiments directly in Colab, or convert notebooks into scripts/jobs?
• How do you keep track of configs and outputs across runs?
• What parts of the Colab workflow become annoying once experiments get more serious?