Detailed Introduction
SwanLab is an open-source platform for tracking model training lifecycles. It provides training log collection, metric visualization, model versioning, and experiment comparison features, and supports both cloud and self-hosted deployments. SwanLab integrates with PyTorch, Transformers, LLaMA Factory, veRL, Swift, Ultralytics, MMEngine, Keras and more, helping teams improve observability and reproducibility during model development and tuning.
Main Features
- Training tracking: collect key metrics such as loss, accuracy, and resource usage, and display them in real time.
- Visualization dashboard: multi-dimensional charts and comparison views to surface training trends and anomalies.
- Experiment management: model versioning, hyperparameter recording, and experiment comparison for better reproducibility.
- Multi-framework support: adapters for common deep learning frameworks and tooling.
Use Cases
SwanLab is suitable for scenarios that require centralized monitoring and analysis of model training, such as research experiment management, enterprise training pipeline quality monitoring, and production MLOps workflows that incorporate training metrics.
Technical Features
- Open-source and extensible: Apache-2.0 licensed, easy to customize and extend.
- Engineered integrations: adapters to plug into existing CI/CD and training pipelines.
- Deployability: supports cloud and private deployments for compliance and performance needs.
- Real-time observability: emphasizes real-time metric collection and visualization to quickly identify issues.