| (reposting in my main account because anonymous account cannot post here.) Hi everyone! I’m a research engineer from a small lab in Asia, and I wanted to share a small project I’ve been using daily for the past few months. During paper prep and model development, I often end up running dozens (sometimes hundreds) of experiments. I found myself constantly checking whether GPUs were free, and even waking up at random hours just to launch the next job so my server wouldn’t sit idle. I got tired of that pretty quickly (and honestly, I was too lazy to keep writing one-off scripts for each setup), so I built a simple scheduling tool for myself. It’s basically a lightweight scheduling engine for researchers:
Nothing fancy, just something that made my life way easier. Figured it might help others here too. If you run a lot of experiments, I’d love for you to give it a try (and any feedback would be super helpful). Github Link: https://github.com/gjamesgoenawan/ant-scheduler [link] [comments] |
[P] I built a simple gpu-aware single-node job scheduler for researchers / students
Reddit r/MachineLearning / 4/1/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
Key Points
- The author shares a personal project: a lightweight, GPU-aware single-node job scheduler aimed at researchers and students running many experiments.
- The tool provides a web UI where users paste terminal commands, select how many GPUs to use, and submit jobs to reduce manual GPU availability checks.
- It includes practical features such as batch queueing, live monitoring, and built-in logging accessible via browser or downloadable files.
- By default, the scheduler is designed to work with conda environments, helping streamline reproducible experiment setups.
- The project is offered as open source on GitHub, with the author inviting community feedback and adoption for similar workloads.




