How to Track ML Model Training: Catalyst + Integration

1 min
Prince Canuma
5th April, 2022

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

What will you get with this integration?

Catalyst is a PyTorch framework for Deep Learning R&D. It is built with three main requirements in mind: reproducibility, rapid experimentation, and codebase reuse. Catalyst is a part of the PyTorch Ecosystem.

Neptune is integrated with Catalyst, so that you can automatically log:

  • Metrics
  • Hparams (hyper-parameters)
  • Images
  • Artifacts (videos, audio, model checkpoints, files, etc.)
  • Hardware consumption statistics
  • Stdout and stderr logs
  • Training code and git commit information 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 the docs explaining how to use Catalyst with Neptune.

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

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