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 (each GPU's power shown against its cap), a JOB provenance card, and a wall-clock time-budget bar, with the MEM row lighting up amber then red as the working set 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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