I built gflow, a single-node GPU job scheduler as a lightweight alternative to SLURM. If you've ever shared a multi-GPU machine with teammates and dealt
   with GPU conflicts, or just wanted a simple way to queue up training jobs overnight, this is for that.

  The problem: SLURM is designed for clusters. Setting it up on a single machine is overkill, and most ML teams sharing a workstation end up with ad-hoc
  solutions — checking nvidia-smi, shouting in Slack, or writing hacky bash scripts.

  gflow gives you:

  - Job queue with GPU-aware scheduling — auto-detects GPUs via NVML, handles allocation and sets CUDA_VISIBLE_DEVICES for you
  - Job dependencies — with AND/OR logic, so you can chain training → eval → export
  - Job arrays — for hyperparameter sweeps
  - tmux-based execution — jobs run in tmux sessions, so you can attach to see live output, and they survive terminal disconnects
  - Conda environment support — specify the env per job
  - Webhook notifications — get pinged when jobs finish or fail
  - SLURM-inspired CLI — gbatch, gqueue, gcancel, gjob — familiar if you've used SLURM, simpler if you haven't

  It's a single Rust binary. Install via pip install runqd (pre-built binaries), cargo install, or grab a release from GitHub. Run gflowd init to set up,
  gflowd up to start, and you're scheduling jobs.

  Example:
  gbatch --gpus 2 --conda myenv python train.py --lr 0.001
  gqueue
  gjob log 1

  Tech stack: Rust, Tokio, Axum (REST API), NVML, MessagePack for state persistence.

  GitHub: https://github.com/AndPuQing/gflow

  Happy to answer any questions.