- Neptune tutorial – set up Neptune and learn how to use it in your workflow.
- Neptune tutorial for R – set up and learn our R client package.
ML use cases#
- Tracking and organizing model-training runs – in this tutorial, we train a classifier model and explore the central experiment-tracking features in Neptune.
- Monitoring model training live – how to use Neptune to monitor metrics during training.
- The Data versioning tutorials walk you through dataset versioning with the help of Neptune artifact tracking.
- Tracking distributed training jobs – learn how to track metadata from single-node, multi-node, or multi-GPU jobs.
- Tracking and visualizing cross-validation results – see how to use Neptune namespaces to organize cross-validation metadata.
- Tracking HPO jobs – use Neptune to track metadata using either a single run or separate runs for each trial.
- Re-running a failed training – model training runs don't always go as planned. Learn how to fetch the parameters and metadata of a failed Neptune run and use them for a new run.
Integrations – see how to use Neptune with specific libraries and tools.