experiment-tracking-swanlab

Files (3)

• SKILL.md
• integrations.md · references/
• visualization.md · references/

SKILL.md (first lines)

# 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

run = swanlab.init(
    project="pytorch-demo",
    experiment_name="mnist-mlp",
    config={
        "learning_rate": 1e-3,
        "batch_size": 64,
        "epochs": 10,
        "hidden_size": 128,
    },
)

model = nn.Sequential(
    nn.Flatten(),
    nn.Linear(28 * 28, run.config.hidden_size),
    nn.ReLU(),
    nn.Linear(run.config.hidden_size, 10),
)
optimizer = optim.Adam(model.parameters(), lr=run.config.learning_rate)
criterion = nn.CrossEntropyLoss()

for epoch in range(run.config.epochs):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        logits = model(data)
        loss = criterion(logits, target)
        loss.backward()
        optimizer.step()

        if batch_idx % 100 == 0:
            swanlab.log(
                {
                    "train/loss": loss.item(),
                    "train/epoch": epoch,
                    "train/batch": batch_idx,
                }
            )

run.finish()
```

## Core Concepts

### 1. Projects and Experiments

**Project**: Collection of related experiments  
**Experiment**: Single execution of a training or evaluation workflow

From Orchestra-Research/AI-Research-SKILLs, MIT.

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