[RMP] Support Tree Ranking Models (like XGBoost) in Merlin Models and Systems #105
Description
Problem
Gradient-boosted decision trees (GBDTs) are commonly used in the industry as part of the scoring phase of recommender systems. Supporting serving of these models and integrating with the Merlin ecosystem will help facilitate usage of these models in these systems.
The Triton Inference Server has a backend called FIL (Forest Inference Library) to facilitate GPU accelerated serving of these models.
Random forests (RF) and gradient-boosted decision trees (GBDTs) have become workhorse models of applied machine learning. XGBoost and LightGBM, popular packages implementing GBDT models, consistently rank among the most commonly used tools by data scientists on the Kaggle platform. We see similar interest in forest-based models in industry, where they are applied to problems ranging from inventory forecasting, to ad ranking, to medical diagnostics.
RAPIDS Forest Inference Library: Prediction at 100 million rows per second
Goals
- Enable the use of Tree based models (e.g. GBDTs, Random Forests) in a Merlin Systems ensemble.
- Support the training of XGBoost models from a Merlin Dataset.
Constraints
- Use the Triton FIL backend in Merlin systems
Starting Point
Merlin-models (Data Scientist)
NVTabular (Data Scientist)
- [NA] Operators for batch prediction with these models
- Note: Batch prediction is not in scope for this development
Merlin-systems (Product Engineer)
- FIL integration in ensemble API.
Supports the following models: XGBoost, LightGBM, Scikit-Learn (Random Forest), cuML (Random Forest)
Examples and Docs (Everyone)
- Example of training tree models ( End to End example )
- Example of serving tree models ( End to End example ) models#575
Aha! Link: https://nvaiinfa.aha.io/features/MERLIN-828