Agent skill · data analytics · zechenzhangagi
experiment-tracking-swanlab
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
Why this skill is useful
Provides specific commands and patterns for tracking ML experiments with SwanLab that the AI wouldn't reliably generate on its own.
What it needs
Requires pillow, soundfile, swanlab installed locally. About 4k tokens when loaded. Last updated 2026-06-16. 11,472 stars on the source repository.
What this skill does
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 pillow and soundfile are the media dependencies used by the Image and Audio examples in this skill. swanlab[dashboard] adds the local dashboard dependency required by mode="local" and swanlab watch. Quick Start Basic Experiment Tracking With PyTorch Core Concepts 1. Projects and Experiments Project: Collection of related experiments Experiment: Single execution of a training or evaluation workflow 2. Configuration Tracking 3. Metric Logging 4. Media and Chart Logging See references/visualization.md for more chart and media patterns. 5. Local and Self-Hosted Workflows Integration Examples HuggingFace Transformers See references/integrations.md for callback-based setups and additional framework patterns. PyTorch Lightning Fastai See references/integrations.md for fuller framework examples. Best Practices 1. Use Stable Metric Names 2. Initialize Early and Capture Config Once 3. Save Checkpoints Locally 4. Use Local Mode for Offline-First Workflows 5. Keep Advanced Patterns in References Use references/visualization.md for advanced chart and media patterns Use references/integrations.md for callback-based and framework-specific integration details Resources Official docs (Chinese) Official docs (English) GitHub repo Self-hosted repo See Also references/integrations.md - Framework-specific examples references/visualization.md - Charts and media logging patterns
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills zechenzhangagi/swanlab