runicorn 0.7.0

Local experiment tracking with model versioning - self-hosted W&B alternative with Artifacts

Runicorn

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Local, open-source ML experiment tracking. 100% offline, zero telemetry. A modern self-hosted alternative to W&B.

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✨ Highlights

Feature	Description
🏠 100% Local	Your data never leaves your machine
📊 Real-time Visualization	Live metrics, logs, and GPU monitoring
📦 Model Versioning	Git-like Artifacts with deduplication
🌐 Remote Viewer	Access remote GPU servers via SSH (like VSCode Remote)
🖥️ Desktop App	Native Windows app with auto-backend

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🚀 Quick Start

pip install runicorn
runicorn viewer  # Open http://127.0.0.1:23300

import runicorn as rn

run = rn.init(project="my_project", name="exp_1")

for epoch in range(100):
    loss = train_one_epoch()
    run.log({"loss": loss, "epoch": epoch})

run.finish()

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📦 Model Versioning

# Save
artifact = rn.Artifact("my-model", type="model")
artifact.add_file("model.pth")
run.log_artifact(artifact)  # → v1, v2, v3...

# Load
artifact = run.use_artifact("my-model:latest")
model_path = artifact.download()

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🌐 Remote Viewer

Access remote GPU servers without file sync:

runicorn viewer  # → Click "Remote" → SSH credentials → Done!

	Old Sync (v0.4)	Remote Viewer (v0.5+)
Wait	Minutes~Hours	Seconds
Storage	Required	Zero
Real-time	❌	✅

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📚 Documentation

Resource	Link
User Guide	docs/user-guide/
API Reference	docs/api/
Changelog	CHANGELOG.md

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🆕 v0.7.0 (Latest)

• 🌐 Remote Viewer Hardening — Saved connections, health monitoring, reconnect states, and OpenSSH password support
• 🎨 Web UI Productization — Cleaner navigation, better compare flow, ZIP import/export preview, and unified recycle bin
• 📈 Logs & Monitoring — Virtualized logs, stronger dark-mode consistency, and backend-collected GPU telemetry history
• 🔌 Logging Compatibility — Better support for ImageNet meters, TensorBoard, and tensorboardX
• 🖥️ Desktop Improvements — Native remote-session windows in the current desktop workflow

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License

MIT — see LICENSE

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Version: v0.7.0 | Last Updated: 2026-03-15