How to track ML Model Training: Colab + Integration

1 min
Siddhant Sadangi
11th April, 2022

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

What will you get with this integration?

Google Colab is a temporary runtime environment. This means you lose all your data (unless saved externally) once you restart your kernel.

This is where you can leverage Neptune. By running model training on Google Colab and keeping track of it with Neptune you can log and download things like:

  • Parameters
  • Metrics and losses
  • Images, interactive charts, and other media
  • Hardware consumption
  • Model checkpoints and other artifacts

By doing that you can keep your run metadata safe even when the Google Colab kernel has died. 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

Read the docs on how to use Neptune in Google Colab.

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