How to Track ML Model Training: PyTorch + Integration

1 min
Prince Canuma
11th April, 2022

Important: This video was created in June 2021. For the most up-to-date code examples, please refer to the Neptune-PyTorch integration docs

What will you learn?

You will learn how to use Neptune + PyTorch to help you keep track of your model training metadata.

With Neptune + PyTorch you can:

  • Log model configuration
  • Log hyperparameters
  • Log loss & metrics
  • Log training code and git information
  • Log images and the predictions
  • Log artifacts (i.e. model weights, dataset version)
  • Log 2-D/3-D tensors as images or 1-D tensors as metrics 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

See also PyTorch integration guide. 

Couldn’t find the use case you were looking for?

Just get in touch, and our ML team will create a custom demo for you.