In this video, we show how to keep track of your model-training metadata when using PyTorch Lightning. Parth Tiwary explains:
- How to use the Neptune-Lightning integration,
- How to log your Lightning training metadata to Neptune,
- How to analyze the data in the Neptune app,
- And more.
Important: This video was created in November 2021. For the most up-to-date code examples, please refer to the Neptune-Lightning integration docs.
If you want to try out the integration on your own, check this Neptune-Lightning Colab notebook or check this Neptune-Lightning GitHub repo.
neptune.ai is an MLOps stack component for experiment tracking. So we’re constantly working on making it easy to integrate with other parts of the workflow.
It is already integrated with 25+ tools and libraries, and the list is growing. You can check our roadmap to see what’s currently under development.
Other useful resources
Watch the webinar Computer Vision Projects With PyTorch Lightning and neptune.ai: Deep Dive.
See a Lightning example project in the Neptune app (no registration needed).
More about How to Track ML Model Training: Lightning + neptune.ai Integration
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
See also
Create AzureML Pipeline – Workshop with Aurimas Griciūnas
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