How to Track ML Model Training: XGBoost + neptune.ai Integration
Important: This video was created in July 2021. For the most up-to-date code examples, please refer to the Neptune-XGBoost integration docs.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework.
Neptune + XGBoost integration, lets you automatically log many types of metadata during training:
- Metrics
- Parameters
- Learning rate
- Pickled model
- Visualizations (feature importance chart and tree visualizations)
- Hardware consumption (CPU, GPU, memory)
- Stdout and stderr logs
- Training code
- 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
Check also XGBoost integration docs.
More about How to Track ML Model Training: XGBoost + neptune.ai Integration
See also
Train, Track, and Deploy Your Models: Neptune + Modelbit Integration
See also
.upload(“product_updates_september_2023”)
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
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.
