nomad-hpc 1.2.4

A lightweight HPC monitoring and predictive analytics tool

NØMAD-HPC

NØde Monitoring And Diagnostics — Lightweight HPC monitoring, visualization, and predictive analytics.

"Travels light, adapts to its environment, and doesn't need permanent infrastructure."

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📖 Full Documentation — Installation guides, configuration, CLI reference, network methodology, ML framework, and more.

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Quick Start

pip install nomad-hpc
nomad demo                    # Try with synthetic data

For production:

nomad init                    # Configure for your cluster
nomad collect                 # Start data collection
nomad dashboard               # Launch web interface

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Features

Feature	Description	Command
Dashboard	Real-time multi-cluster monitoring with partition views	nomad dashboard
Educational Analytics	Track computational proficiency development	nomad edu explain <job>
Alerts	Threshold + predictive alerts (email, Slack, webhook)	nomad alerts
ML Prediction	Job failure prediction using similarity networks	nomad predict
Community Export	Anonymized datasets for cross-institutional research	nomad community export
Interactive Sessions	Monitor RStudio/Jupyter sessions	nomad report-interactive
Derivative Analysis	Detect accelerating trends before thresholds	Built into alerts

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Architecture

┌────────────────────────────────────────────────────────────┐
│                         NØMAD                              │
├──────────────┬──────────────┬──────────────┬───────────────┤
│  Collectors  │   Analysis   │     Viz      │    Alerts     │
├──────────────┼──────────────┼──────────────┼───────────────┤
│ disk         │ derivatives  │ dashboard    │ thresholds    │
│ iostat       │ similarity   │ network 3D   │ predictive    │
│ slurm        │ ML ensemble  │ partitions   │ email/slack   │
│ gpu          │ edu scoring  │ edu views    │ webhooks      │
│ nfs          │              │              │               │
└──────────────┴──────────────┴──────────────┴───────────────┘
                              │
                    ┌─────────┴─────────┐
                    │  SQLite Database  │
                    └───────────────────┘

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CLI Reference

Core Commands

nomad init                    # Setup wizard
nomad collect                 # Start collectors
nomad dashboard               # Web interface
nomad demo                    # Demo mode
nomad status                  # System status

Educational Analytics

nomad edu explain <job_id>    # Job analysis with recommendations
nomad edu trajectory <user>   # User proficiency over time
nomad edu report <group>      # Course/group report

Analysis & Prediction

nomad disk /path              # Filesystem trends
nomad jobs --user <user>      # Job history
nomad similarity              # Network analysis
nomad train                   # Train ML models
nomad predict                 # Run predictions

Community & Alerts

nomad community export        # Export anonymized data
nomad community preview       # Preview export
nomad alerts                  # View alerts
nomad alerts --unresolved     # Unresolved only

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Installation

From PyPI

pip install nomad-hpc

From Source

git clone https://github.com/jtonini/nomad-hpc
cd nomad && pip install -e .

Requirements

• Python 3.9+
• SQLite 3.35+
• sysstat package (iostat, mpstat)
• Optional: SLURM, nvidia-smi, nfsiostat

System Check

nomad syscheck

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Documentation

📖 jtonini.github.io/nomad-hpc

• Installation & Configuration
• System Install (--system)
• Dashboard Guide
• Educational Analytics
• Network Methodology
• ML Framework
• Proficiency Scoring
• CLI Reference
• Configuration Options

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License

Dual-licensed:

• AGPL v3 — Free for academic, educational, and open-source use
• Commercial License — Available for proprietary deployments

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Citation

@software{nomad2026,
  author = {Tonini, João Filipe Riva},
  title = {NØMAD: Lightweight HPC Monitoring with Machine Learning-Based Failure Prediction},
  year = {2026},
  url = {https://github.com/jtonini/nomad-hpc},
  doi = {10.5281/zenodo.18614517}
}

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Contributing

See CONTRIBUTING.md for guidelines.

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Contact

• Author: João Tonini
• Email: [e-mail]
• Issues: GitHub Issues