experiment-tracking-swanlab

You can track ML experiments and visualize results locally by running a self-hosted dashboard that logs metrics, configurations, and media. This approach avoids SaaS dependencies entirely while enabling run comparison and reproducibility.

Yes, experiment tracking integrates directly with PyTorch, Transformers, PyTorch Lightning, and Fastai. You can initialize a run within these frameworks to log metrics, configs, and media for visualization across your training runs.

The best way to compare ML runs across hyperparameters and seeds is by logging metrics and configurations to a local dashboard. This allows you to visualize scalar charts and compare results side-by-side to evaluate model reproducibility.

Yes, you can visualize training media alongside scalar charts in a local dashboard. By logging media through framework integrations, you can monitor training progress and inspect visual outputs across different runs locally or self-hosted.

No, you do not need cloud hosting to compare results. The system supports local or self-hosted deployments, allowing you to organize experiments, compare results, and monitor training entirely on your own infrastructure.