Verwendung
- Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
- Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
- Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill experiment-tracking-swanlabVor dem Start
- Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
- Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument
Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.
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
