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:
- 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.
Other useful resources
Check also XGBoost integration docs.