Resources
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.
Other useful resources
See also the LightGBM integration guide docs.
More about How to Track ML Model Training: LightGBM + neptune.ai Integration
What is a Project in Neptune?
Model Training: Detectron2 + neptune.ai Integration [Example]
Model Training: Prophet + neptune.ai Integration [Example]
Create AzureML Pipeline – Workshop with Aurimas Griciūnas
Explore more resources:
Content type
Area of interest
Area of interest
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.