SwanHubX/SwanLab

What it solves

SwanLab is an AI training analysis and metric observation platform designed for model training teams. It solves the difficulty of tracking training progress, analyzing hyperparameters, and collaborating on experiments by providing a centralized dashboard for visualization and logging, replacing fragmented manual tracking with a professional, modern interface.

How it works

Users integrate the SwanLab SDK into their machine learning pipeline with a few lines of code. The platform automatically records metrics, hyperparameters, hardware environment details, and Git repository state. This data is then synced to either a cloud-based service or a self-hosted instance where it is visualized through various chart types (line charts, media charts, 3D point clouds, etc.). It also supports integrating with over 30 popular frameworks like PyTorch, HuggingFace Transformers, and LLaMA Factory.

Who it’s for

It is built for AI researchers and model training teams who need to monitor training stability, compare multiple experimental runs to find optimal hyperparameters, and collaborate with teammates in real-time.

Highlights

• Comprehensive Metric Tracking: Supports scalars, images, audio, text, video, 3D point clouds, and biochemical molecules.
• Extensive Framework Integration: Works with 30+ frameworks including PyTorch, PyTorch Lightning, and various LLM fine-tuning tools.
• Broad Hardware Monitoring: Tracks system-level metrics for NVIDIA, AMD, Ascend, Cambricon, Kunlunxin, and other specialized GPUs/NPUs.
• Flexible Deployment: Offers both a managed cloud version and a self-hosted community edition (via Docker or Kubernetes).
• Collaboration Tools: Includes project sharing via URLs, team-based organization management, and plugin extensions for notifications (e.g., Lark, Slack, Discord).