Experiment Tracking Swanlab

SwanLab gives you experiment tracking without the SaaS lock-in. It logs metrics, configs, and media (images, audio, point clouds) and works with PyTorch, Transformers, Lightning, and Fastai out of the box. You can run it locally, self-host, or use their cloud. The skill includes media dependencies pre-configured, so you can start logging visualizations immediately. If you're tired of sending training data to third-party platforms or need to track experiments in air-gapped environments, this is a solid pick. It's open source and the interface handles run comparisons across hyperparameters and seeds cleanly.

skills.sh installs: 961
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill experiment-tracking-swanlab --agent claude-code

Installs into .claude/skills of the current project.

Files

SwanLab: Open-Source Experiment Tracking

When to Use This Skill

Use SwanLab when you need to:

Track ML experiments with metrics, configs, tags, and descriptions
Visualize training with scalar charts and logged media
Compare runs across seeds, checkpoints, and hyperparameters
Work locally or self-hosted instead of depending on managed SaaS
Integrate with PyTorch, Transformers, PyTorch Lightning, or Fastai

Deployment: Cloud, local, or self-hosted | Media: images, audio, text, GIFs, point clouds, molecules | Integrations: PyTorch, Transformers, PyTorch Lightning, Fastai

Installation

# Install SwanLab plus the media dependencies used in this skill
pip install "swanlab>=0.7.11" "pillow>=9.0.0" "soundfile>=0.12.0"

# Add local dashboard support for mode="local" and swanlab watch
pip install "swanlab[dashboard]>=0.7.11"

# Optional framework integrations
pip install transformers pytorch-lightning fastai

# Login for cloud or self-hosted usage
swanlab login

SKILL.md on GitHub