COR Brief
Infrastructure & MLOps

Swanlab

SwanLab is an open-source AI experiment tracking and visualization tool designed to support researchers and teams in managing deep learning model training. It offers a platform to track, record, and compare experiments with support for over 30 mainstream AI training frameworks, including HuggingFace Transformers and PyTorch Lightning. SwanLab provides both cloud and offline usage modes, enabling flexibility for different development environments. The tool includes a Python API for logging hyperparameters, metrics, and multimedia content such as images and audio, facilitating detailed experiment documentation. The platform features an interactive dashboard with charts for visualizing training metrics, system resources, and experiment comparisons. It supports multi-user collaboration through online sharing within organizations and integrates with development environments via a VSCode plugin. SwanLab's open-source nature allows users to self-host and customize their setup, with local visualization available through an additional dashboard extension.

Updated Feb 12, 2026open-source

SwanLab is an open-source AI experiment tracking tool supporting cloud and offline modes with multi-framework integration and collaboration features.

Pricing
open-source
Category
Infrastructure & MLOps
Company
Interactive PresentationOpen Fullscreen ↗
01
Automatically logs hyperparameters and training metrics during model training loops using a Python API.
02
Provides charts for line plots, system resource monitoring, and experiment comparisons accessible both online and locally.
03
Compatible with over 30 AI training frameworks including HuggingFace Transformers, PyTorch Lightning, Hydra, Swift, and Axolotl.
04
Enables experiment sharing via links and supports collaboration within teams and organizations.
05
Supports cloud usage with API key login and local/offline modes including self-hosting without cloud dependency.
06
Allows recording of images, audio, text, and configuration files alongside experiment data.
07
Integrates directly into VSCode for streamlined experiment tracking within the development environment.

AI Research Experiment Tracking

Researchers conducting deep learning experiments can log hyperparameters, metrics, and multimedia data to monitor training progress and compare results.

Team Collaboration on Model Development

Teams can share experiment results via links and collaborate on model training projects using SwanLab's multi-user features.

Offline Experiment Management

Users working in environments without reliable internet can use SwanLab's local and offline modes to track and visualize experiments.

1
Install SwanLab
Run pip install swanlab to install the core package.
2
Login with API Key
Use swanlab login -k api-key or swanlab.login(api_key="Your API Key") with a key obtained from https://swanlab.cn settings.
3
Initialize an Experiment
Create an experiment instance with run = swanlab.init(project="my-project", config={"learning_rate": 0.01}).
4
Log Metrics During Training
Within the training loop, log metrics using run.log({"loss": loss}).
5
View Experiment Dashboard
Access the dashboard online at https://swanlab.cn or locally with swanlab watch ./swanlog after installing the dashboard extension.
📊

Strategic Context for Swanlab

Get weekly analysis on market dynamics, competitive positioning, and implementation ROI frameworks with AI Intelligence briefings.

Try Intelligence Free →
7 days free · No credit card
Pricing
Model: open-source

SwanLab is open-source software with free cloud usage available after API key login. Local and offline usage options do not have specified costs.

Assessment
Strengths
  • Open-source with option for source installation to access latest features.
  • Supports over 30 AI training frameworks including Swift and Axolotl.
  • Offers offline and local modes without requiring cloud dependency.
  • Includes a VSCode plugin for integrated experiment tracking.
  • Enables multi-user collaboration and experiment sharing via links.
Limitations
  • Requires separate installation of a dashboard extension for full local visualization capabilities.
  • Cloud mode requires API key setup and may encounter import errors if using package versions below 0.3.0.