How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill experiment-tracking-swanlabBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
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 loginpillow 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
python
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
python
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_size3. Metric Logging
python
# 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
python
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")})python
# 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
python
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()bash
# View local logs
swanlab watch -l ./swanlog
# Sync local logs later
swanlab sync ./swanlogIntegration Examples
HuggingFace Transformers
python
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
python
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
python
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
python
# 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 family2. Initialize Early and Capture Config Once
python
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
python
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
python
run = swanlab.init(project="offline-demo", mode="local", logdir="./swanlog")
# ... training code ...
run.finish()
# Inspect later with: swanlab watch -l ./swanlog5. 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
Resources
See Also
- references/integrations.md - Framework-specific examples
- references/visualization.md - Charts and media logging patterns
