[FEA] Support categorical features when serving XGBoost models

[gfalcone]

Hello !

XGBoost recently enabled developers to use categorical features in its models (Nvidia did an article on that : https://developer.nvidia.com/blog/categorical-features-in-xgboost-without-manual-encoding/).

From what I understand, we can load a XGBoost model trained on categorical features within the FIL_BACKEND.

However, the FIL_BACKEND only supports float in inputs, which means that we have to do some kind of ensemble (PYTHON_BACKEND + FIL_BACKEND) to accepts strings (steps described here : https://github.com/triton-inference-server/fil_backend/blob/main/notebooks/categorical-fraud-detection/Fraud_Detection_Example.ipynb)

It would make things easier to accept strings in the FIL_BACKEND. Would it be possible to do that ?