slurmwatch

Live per-process CPU / memory / GPU telemetry for a running Slurm job — the facts, so you can judge.

Python 3.10+ MIT License

slurmwatch live TUI: per-process CPU / memory / GPU bars, a JOB provenance card, and a wall-clock time-budget bar, with the alarm strip lighting up as memory climbs toward the OOM guard.

See whether your running job is actually using the CPU, memory, and GPUs you requested — live, in your terminal.

Install

pip install slurmwatch      # or: uv tool install slurmwatch / pipx install slurmwatch

Python 3.10+ on Linux (cgroup v1/v2). GPU monitoring auto-activates on NVIDIA nodes, and is skipped on CPU-only ones.

Usage

slurmwatch 12345    # watch a specific job
slurmwatch          # or auto-discover your running job
sw 12345            # "sw" is a short alias
slurmwatch --help   # everything else

Keys — c/m/g drill into CPU / memory / GPU · type a node number (or ←/→) to switch node · p expand a truncated path · q back/quit.

It counts only your PIDs (a neighbour on a shared node never inflates your numbers), tracks the real working set against your --mem, and flags an idle GPU that's still holding VRAM — the stuff nvidia-smi and htop won't tell you about your job.

Point it at a pending job and, instead of an error, you get why it's waiting, when it'll start and where you sit in line, and where it could run right now — with the exact scontrol update to requeue into a partition that fits.

Run it from a login node or on the node itself — it attaches either way, and falls back to an sstat summary when it can't.

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