experiment-tracking-swanlab

Teaches your assistant SwanLab, an open-source tracker for training runs: it logs metrics, settings, images and audio, and compares runs side by side. Runs can go to SwanLab's cloud, to a server you host yourself, or stay on your own machine. It is not part of any of the collection's plugins, so it is installed on its own.

WHEN TO USE

SCOPE

USE FOR

Log loss, accuracy and settings from a PyTorch training loop.
Track a Hugging Face Transformers run by setting it to report to SwanLab.
Add tracking to PyTorch Lightning or Fastai through their built-in hooks.
Log sample images, audio clips, text and 3D point clouds next to the metrics.
Record runs offline and upload them later, or browse them in a local dashboard.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (9)

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,
    },
)

3. Save Checkpoints Locally

import torch
import swanlab

checkpoint_path = "checkpoints/best.pth"
torch.save(model.state_dict(), checkpoint_path)

swanlab.log(
    {
        "best/val_accuracy": best_val_accuracy,
        "artifacts/checkpoint_path": swanlab.Text(checkpoint_path),
    }
)

4. Use Local Mode for Offline-First Workflows

run = swanlab.init(project="offline-demo", mode="local", logdir="./swanlog")
# ... training code ...
run.finish()

# Inspect later with: swanlab watch -l ./swanlog

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

SwanLab: Open-Source Experiment Tracking

When to Use This Skill

Installation

Quick Start

Basic Experiment Tracking

With PyTorch

Core Concepts

1. Projects and Experiments

2. Configuration Tracking

3. Metric Logging

4. Media and Chart Logging

5. Local and Self-Hosted Workflows

Integration Examples

HuggingFace Transformers

PyTorch Lightning

Fastai

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

Resources

See Also

Account or credentials required

What's inside

experiment-tracking-swanlab

SwanLab: Open-Source Experiment Tracking

When to Use This Skill

Installation

Quick Start

Basic Experiment Tracking

With PyTorch

Core Concepts

1. Projects and Experiments

2. Configuration Tracking

3. Metric Logging

4. Media and Chart Logging

5. Local and Self-Hosted Workflows

Integration Examples

HuggingFace Transformers

PyTorch Lightning

Fastai

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

Resources

See Also

Account or credentials required

What's inside

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