A GPU experiment orchestrator for ML research.

Orze runs experiments on GPUs: schedule ideas → train → evaluate → report → repeat. It coordinates GPUs via filesystem locks, works across machines, and gives you a complete leaderboard, notifications, and analysis — out of the box.

Website: orze.ai

curl -sL https://orze.ai/install | bash

That's it. It installs orze, detects your GPUs and codebase, generates training scripts and experiment ideas, and starts running — all in one command.

Pass environment variables for additional options:

# LLM-powered setup
ANTHROPIC_API_KEY=sk-ant-... curl -sL https://orze.ai/install | bash

# With pro (autopilot)
ORZE_PRO_KEY=ORZE-PRO-xxx curl -sL https://orze.ai/install | bash

# Custom project path
curl -sL https://orze.ai/install | bash -s /nfs/my-project

orze is a complete, production-ready tool. orze-pro adds autopilot — so experiments run while you sleep.

After install, orze auto-detects GPUs and starts running experiments.

AI CLI users (Claude Code, Cursor, Codex):

do @ORZE-AGENT.md

# Project lifecycle
orze init [path]              # initialize a new project
orze start                    # start as background daemon
orze stop                     # stop gracefully
orze restart                  # stop + start
orze --check                  # validate config, files, GPUs, API keys
orze --uninstall              # full cleanup, preserves research results

# Operations
orze upgrade                  # reinstall from source + restart daemon
orze admin migrate            # migrate legacy layout to .orze/
orze service install          # auto-restart on crash (systemd)

# Pro
orze pro activate <key>       # activate license
orze pro status               # check license info
orze pro deactivate           # remove license
orze sop list                 # list available SOPs

your-project/
├── orze.yaml                 # Project config (single source of truth)
├── train.py                  # Your training script
├── ideas.md                  # Experiment queue
├── GOAL.md                   # Research objective
├── RESEARCH_RULES.md         # Agent constraints
├── configs/base.yaml         # Default hyperparameters
├── .env                      # API keys (gitignored)
├── ORZE-AGENT.md             # AI CLI instructions
├── ORZE-RULES.md             # Agent guardrails
├── venv/                     # Training dependencies
├── .orze/                    # Runtime state (gitignored)
│   ├── state/version.json    # Layout version
│   ├── logs/                 # Role logs
│   ├── locks/                # Filesystem locks
│   ├── rules/                # Migrated rule files
│   ├── mcp/                  # MCP server configs
│   ├── receipts/             # Execution evidence
│   ├── triggers/             # One-shot role triggers
│   ├── heartbeats/           # Per-host liveness
│   ├── backups/              # ideas.md backups
│   └── feedback/             # Failure feedback
├── procedures/               # User procedure overrides (pro)
├── fsm/runner.py             # FSM orchestrator (pro)
└── orze_results/             # Research outputs
    ├── idea-0001/metrics.json
    ├── methods/              # Generated code
    └── knowledge/            # Analysis insights

Start orze in the same shared folder on any machine — nodes auto-join the research pool.

# Node 1
ssh node1 "cd /nfs/project && orze start"

# Node 2
ssh node2 "cd /nfs/project && orze start"

• Scales to 1M+ Experiments — SQLite-backed job queue with O(log N) scheduling
• Config Inheritance — Child ideas inherit parent configs; specify only what changes
• HP Sweep — lr: [1e-4, 3e-4] auto-expands into all combinations
• Failure Protection — Stops automatically when failure rates spike
• Cross-Experiment Analysis — Detects regressions, tradeoffs, and suggests actions
• Rich Notifications — GPU VRAM, per-dataset breakdown, verified results, target/gap tracking
• Admin Panel — Real-time web dashboard at http://localhost:8787
• Clean Uninstall — orze --uninstall removes runtime files, preserves results

Your training script receives:

python train.py --idea-id idea-001 --results-dir orze_results --ideas-md ideas.md --config base.yaml

Required output: orze_results/{idea_id}/metrics.json:

{"status": "COMPLETED", "test_accuracy": 0.92, "training_time": 142.5}

See SKILL.md for the full technical specification.

Auto-launches at http://localhost:8787. No extra install needed.

notifications:
  enabled: true
  on: [completed, failed, new_best]
  channels:
    - type: telegram
      bot_token: "YOUR_BOT_TOKEN"
      chat_id: "YOUR_CHAT_ID"

orze service install -c orze.yaml    # auto-restart on crash + manage containers
orze service status                  # check health
orze service uninstall               # remove

The watchdog runs every minute (crontab) or every 5 minutes (systemd). It restarts orze on crash/stall and manages Docker containers defined in orze.yaml:

containers:
  paperdog:
    image: orzeai/paperdog:latest
    ports:
      - "8000:8000"

Containers are auto-pulled and recreated when a new image is available.

@article{li2026autoresearching,
  title={Auto Researching, not hyperparameter tuning: Convergence Analysis of 10,000 Experiments},
  author={Li, Xiaoyi},
  journal={arXiv preprint arXiv:2603.15916},
  year={2026}
}

Apache 2.0 — orze is and will always be free and open source.

orze-pro (autopilot features) is commercially licensed.