Experiment Tracking Swanlab Agent Skill
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill experiment-tracking-swanlabThe instruction itself
24 sections, as written by the authorSwanLab: 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
Resources
See Also
- references/integrations.md - Framework-specific examples
- references/visualization.md - Charts and media logging patterns
Other skills for the same job
different authors, same section of the catalogue
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
How to use it
Take orchestra-research/experiment-tracking-swanlab from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.