How to Track ML Model Training: LightGBM + neptune.ai Integration

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

What will you get with this integration?

LightGBM is a gradient boosting framework that uses tree-based learning algorithms.

Neptune + LightGBM integration, lets you:

• Automatically log many types of metadata during trainingrn
  • Training and validation metrics
  • Parameters
  • Feature names, num_features and num_rows for the train set
  • Hardware consumption (CPU, GPU, memory)
  • Stdout and stderr logs
  • Training code and git commit information
• Log model summary after trainingrn
  • Pickled model
  • Feature importance chart (gain and split)
  • Visualized trees
  • Trees saved as DataFrame
  • Confusion matrix (only for classification problems)

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.  

More about How to Track ML Model Training: LightGBM + neptune.ai Integration

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Model Training: Prophet + neptune.ai Integration [Example]

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Create AzureML Pipeline – Workshop with Aurimas Griciūnas

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