SwanLab: a professional AI training analysis platform for metric observation and experiment tracking

What it solves

SwanLab is an AI training analysis and metric observation platform designed for model training teams. It solves the problem of tracking complex training processes by providing a centralized way to visualize metrics, record hyperparameters, and compare experiments, which helps researchers identify training issues faster and accelerate model iteration.

How it works

Users integrate the SwanLab SDK into their machine learning pipeline with a few lines of code. The platform then automatically records training metrics, hyperparameters, and system environment data (such as Git repository and Python libraries). It supports a wide range of data types including scalars, images, audio, text, video, 3D point clouds, and biochemical molecules. The data is then visualized on a professional dashboard where teams can collaborate, share results via URLs, and compare different experimental runs.

Who it’s for

It is built for AI researchers and model training teams who need a professional, modern tool for experiment tracking and analysis across various frameworks and hardware setups.

Highlights

• Broad Framework Integration: Integrates with over 30 mainstream frameworks including PyTorch, HuggingFace Transformers, PyTorch Lightning, LLaMA Factory, and others.
• Comprehensive Hardware Monitoring: Supports real-time monitoring for a vast array of hardware including NVIDIA GPUs, Ascend NPUs, AMD ROCm, Cambricon MLU, and more.
• Extensive Visualization: Offers diverse chart types (line charts, media charts, ECharts custom charts) and specialized components for LLM-generated content with Markdown rendering.
• Flexible Deployment: Available as a cloud-based service or as a self-hosted community edition for offline environments.
• Collaboration Tools: Features project-based organization, team collaboration, and plugin extensions for notifications via Lark, Slack, and Discord.