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
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
See the docs explaining how to use Catalyst with Neptune.
More about How to Track ML Model Training: Catalyst + 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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