[P] I built a simple gpu-aware single-node job scheduler for researchers / students

A research engineer from a small lab in Asia has open-sourced Ant Scheduler, a practical tool born from the frustration of managing high-volume AI experiments. The developer, who regularly runs dozens to hundreds of training jobs for paper preparation and model development, built the scheduler to eliminate the need for constant GPU availability checks and manual job launches at odd hours. The core problem it solves is inefficient GPU utilization in resource-constrained academic environments, where expensive hardware often sits idle between manually triggered experiments.

Ant Scheduler operates as a lightweight, GPU-aware scheduling engine with a straightforward web interface. Users can paste terminal commands directly into the UI, select their required number of GPUs, and submit jobs to a batch queue. It defaults to using conda environments for dependency management and provides live job monitoring with downloadable logs. By allowing researchers to stack experiments and 'set and forget' them, the tool aims to maximize productivity and hardware ROI, turning a previously manual, interrupt-driven workflow into an automated pipeline. Its simplicity targets individual researchers and small teams who find heavier cluster managers like Slurm or Kubernetes overkill for their single-server setups.