slurm-monitor-top 0.1.0

htop-like monitor for Slurm jobs

Slurm htop-style TUI

A terminal dashboard for Slurm clusters, inspired by htop.

Project Links

• GitHub: hforoughmand/slurm-monitor-top
• Issues: github.com/hforoughmand/slurm-monitor-top/issues

Screenshot

Features

• Live jobs table with interactive sort and filters
• Live node/server table (CPU and memory totals, reserved, and free)
• Job statistics split by:
  • all users
  • your jobs
  • other users
• GPU status under nodes:
  • number of GPU types
  • total GPUs
  • active GPUs (running jobs)
  • reserved GPUs (pending jobs)
• Auto refresh every 3 seconds

Requirements

• Python 3.9+
• Slurm CLI commands available in PATH:
  • squeue
  • sinfo
• Python packages: see requirements.txt (main dependencies: textual, rich)

Install

Install from PyPI:

pip install slurm-monitor-top

For local development (editable install), create and activate a virtual environment, then:

pip install -e .

Run

With that environment activated:

slurm-top

Alias command also works:

stop

You can still run it directly during development:

python stop.py

Build Package

python -m pip install --upgrade build twine
python -m build
twine check dist/*

Keybindings

• q quit
• r refresh
• s toggle sort-pick mode
• d asc/desc
• f owner filter (all, me, others)

Panels

• Left panel (Jobs)

  • Interactive sorting and filtering (see keybindings)
  • Includes GPU count per job

• Right panel (Nodes)

  • Uses full right-column height
  • Node state
  • CPU total/allocated/idle
  • Memory:
    • total
    • reserved
    • free
  • Memory is displayed in human-readable units (M, G, T)
  • GPU total per node

• GPU status (under Nodes)

  • Cluster totals: total / active / reserved / free estimate
  • Per GPU type: total / active / reserved / free estimate

• Bottom panel (Job statistics)

  • Per owner bucket: all / me / others
  • For running and pending:
    • jobs count
    • GPU sum
    • CPU sum
    • memory sum

Notes

• Refresh interval is set in stop.py (REFRESH_INTERVAL = 3.0).
• Some Slurm deployments format memory fields differently; if numbers look off, adjust parsing in stop.py.