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()

Core Concepts

1. Projects and Experiments

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

import swanlab

run = swanlab.init(
    project="image-classification",
    experiment_name="resnet18-seed42",
    description="Baseline run on ImageNet subset",
    tags=["baseline", "resnet18"],
    config={
        "model": "resnet18",
        "seed": 42,
        "batch_size": 64,
        "learning_rate": 3e-4,
    },
)

print(run.id)
print(run.config.learning_rate)

2. Configuration Tracking

config = {
    "model": "resnet18",
    "seed": 42,
    "batch_size": 64,
    "learning_rate": 3e-4,
    "epochs": 20,
}

run = swanlab.init(project="my-project", config=config)

learning_rate = run.config.learning_rate
batch_size = run.config.batch_size

3. Metric Logging

# Log scalars
swanlab.log({"loss": 0.42, "accuracy": 0.91})

# Log multiple metrics
swanlab.log(
    {
        "train/loss": train_loss,
        "train/accuracy": train_acc,
        "val/loss": val_loss,
        "val/accuracy": val_acc,
        "lr": current_lr,
        "epoch": epoch,
    }
)

# Log with custom step
swanlab.log({"loss": loss}, step=global_step)

4. Media and Chart Logging

import numpy as np
import swanlab

# Image
image = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
swanlab.log({"examples/image": swanlab.Image(image, caption="Augmented sample")})

# Audio
wave = np.sin(np.linspace(0, 8 * np.pi, 16000)).astype("float32")
swanlab.log({"examples/audio": swanlab.Audio(wave, sample_rate=16000)})

# Text
swanlab.log({"examples/text": swanlab.Text("Training notes for this run.")})

# GIF video
swanlab.log({"examples/video": swanlab.Video("predictions.gif", caption="Validation rollout")})

# Point cloud
points = np.random.rand(128, 3).astype("float32")
swanlab.log({"examples/point_cloud": swanlab.Object3D(points, caption="Point cloud sample")})

# Molecule
swanlab.log({"examples/molecule": swanlab.Molecule.from_smiles("CCO", caption="Ethanol")})

# Custom chart with swanlab.echarts
line = swanlab.echarts.Line()
line.add_xaxis(["epoch-1", "epoch-2", "epoch-3"])
line.add_yaxis("train/loss", [0.92, 0.61, 0.44])
line.set_global_opts(
    title_opts=swanlab.echarts.options.TitleOpts(title="Training Loss")
)

swanlab.log({"charts/loss_curve": line})

See references/visualization.md for more chart and media patterns.

5. Local and Self-Hosted Workflows

import os
import swanlab

# Self-hosted or cloud login
swanlab.login(
    api_key=os.environ["SWANLAB_API_KEY"],
    host="http://your-server:5092",
)

# Local-only logging
run = swanlab.init(
    project="offline-demo",
    mode="local",
    logdir="./swanlog",
)

swanlab.log({"loss": 0.35, "epoch": 1})
run.finish()

# View local logs
swanlab watch -l ./swanlog

# Sync local logs later
swanlab sync ./swanlog

Integration Examples

HuggingFace Transformers

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=8,
    evaluation_strategy="epoch",
    logging_steps=50,
    report_to="swanlab",
    run_name="bert-finetune",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

See references/integrations.md for callback-based setups and additional framework patterns.

PyTorch Lightning

import pytorch_lightning as pl
from swanlab.integration.pytorch_lightning import SwanLabLogger

swanlab_logger = SwanLabLogger(
    project="lightning-demo",
    experiment_name="mnist-classifier",
    config={"batch_size": 64, "max_epochs": 10},
)

trainer = pl.Trainer(
    logger=swanlab_logger,
    max_epochs=10,
    accelerator="auto",
)

trainer.fit(model, train_loader, val_loader)

Fastai

from fastai.vision.all import accuracy, resnet34, vision_learner
from swanlab.integration.fastai import SwanLabCallback

learn = vision_learner(dls, resnet34, metrics=accuracy)
learn.fit(
    5,
    cbs=[
        SwanLabCallback(
            project="fastai-demo",
            experiment_name="pets-classification",
            config={"arch": "resnet34", "epochs": 5},
        )
    ],
)

See references/integrations.md for fuller framework examples.

Best Practices

1. Use Stable Metric Names

# Good: grouped metric namespaces
swanlab.log({
    "train/loss": train_loss,
    "train/accuracy": train_acc,
    "val/loss": val_loss,
    "val/accuracy": val_acc,
})

# Avoid mixing flat and grouped names for the same metric family

2. Initialize Early and Capture Config Once

run = swanlab.init(
    project="image-classification",
    experiment_name="resnet18-baseline",
    config={
        "model": "resnet18",
        "learning_rate": 3e-4,
        "batch_size": 64,
        "seed": 42,