experiment-tracking-swanlab

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

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

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

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()