Migrating from Neptune to Lightning AI: Experiment Tracking with LitLogger

Lightning AI presents LitLogger as a free, built-in experiment tracking option for machine learning teams migrating from Neptune, aiming to preserve experiment history while requiring minimal code changes. As part of Lightning AI’s unified platform for training, tracking, and deployment, LitLogger stores metrics, metadata, artifacts, and models alongside workloads, with native access control, sharing, and persistence. It supports standard Python workflows, PyTorch, PyTorch Lightning, and production APIs, while experiments can be inspected, compared, restored, and shared through the Lightning Teamspace interface. Existing Neptune data can be migrated by exporting it with Neptune Exporter, validating the export through a summary command, authenticating with Lightning AI credentials, and loading project data and files into LitLogger, which can create a Lightning AI teamspace for each Neptune project.

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