experiment-tracking-swanlab

To track PyTorch experiment metrics locally, you can log runs, metrics, and configurations using SwanLab. This provides rich visual dashboards for comparing runs and ensures reproducibility across your local or self-hosted environments.

Logging training configurations for reproducibility involves capturing metadata and metrics end-to-end across your runs. This approach ensures auditability and provides visual dashboards to compare different experimental setups.

Yes, you can use it for logging media in Fastai workflows. It supports framework integrations for Fastai, PyTorch, Transformers, and PyTorch Lightning to streamline logging and analysis of your training media.

Yes, SwanLab supports self-hosted experiment tracking. It guides researchers to implement end-to-end tracking across local or self-hosted environments, offering visual dashboards to compare runs and configurations.

Visual dashboards compare runs across PyTorch Lightning experiments by capturing logged metrics, configurations, and metadata. This allows you to analyze differences side-by-side and ensure reproducibility across your training runs.