# Experiment Tracking Swanlab

Provides guidance for experiment tracking with SwanLab.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill experiment-tracking-swanlab -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill experiment-tracking-swanlab -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill experiment-tracking-swanlab -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill experiment-tracking-swanlab -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```bash
# 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
```

`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

```python
import swanlab

run = swanlab.init(
    project="my-project",
    experiment_name="baseline",
    config={
        "learning_rate": 1e-3,
        "epochs": 10,
        "batch_size": 32,
        "model": "resnet18",
    },
)

for epoch in range(run.config.epochs):
    train_loss = train_epoch()
    val_loss = validate()

    swanlab.log(
        {
            "train/loss": train_loss,
            "val/loss": val_loss,
            "epoch": epoch,
        }
    )

run.finish()
```

### With PyTorch

```python
import torch
import torch.nn as nn
import torch.optim as optim
import swanlab

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/13-mlops/swanlab/SKILL.md