SwanLab is an open-source training tracking and visualization platform that helps machine learning teams monitor, compare, and reproduce model training runs. It integrates natively with popular frameworks including PyTorch, Transformers, LLaMA Factory, veRL, Swift, Ultralytics, MMEngine, and Keras, providing a unified dashboard for experiment management across both cloud and self-hosted environments.
Key Features
- Automatic collection of training metrics (loss, accuracy, GPU utilization) with real-time multi-dimensional charts
- Experiment management including hyperparameter logging, model versioning, and side-by-side run comparison
- Built-in framework adapters requiring only a single line of code to integrate with major deep learning frameworks
- Dual deployment modes supporting both cloud-hosted and private self-hosted dashboards
- Configurable storage backends for experiment artifacts and checkpoints
Use Cases
- Tracking and comparing experiments during model development, replacing manual logging with automated metric collection
- Monitoring job health and resource usage at scale in enterprise ML training pipelines
- Maintaining visibility over training runs across staging and production environments in CI/CD workflows
- Collaborative experiment review across distributed research teams
Technical Details
- Licensed under Apache-2.0 with extensible SDK adapters that plug into existing training loops without code restructuring
- Supports real-time metric streaming to both cloud and self-hosted dashboards
- Emphasizes lightweight integration and fast setup, enabling teams to add observability in minutes
- Configurable artifact storage backends for flexible experiment data management