Documentation: train + RayDMatrix vs XGBoostTrainer + Dataset

[andreostrovsky]

Could you please provide some clarification on the differences and/or how to choose between using xgboost_ray.train + xgboost_ray.RayDMatrix or ray.train.xgboost.XGBoostTrainer + ray.data.Dataset?

My use case is running Ray Tune on Azure Databricks, which operates on Spark. According to the Databricks docs, one creates a Ray Cluster using the Ray on Spark API, and creates a Ray Dataset from Parquet files.

Below are the questions I would like clarification on. Any help you could provide would be greatly appreciated.

Data

According to the README.md one can create a RayDMatrix from either Parquet files or a Ray Dataset:

xgboost_ray/README.md

Lines 450 to 465 in e904925

	### Data sources
	The following data sources can be used with a `RayDMatrix` object.
	| Type | Centralized loading | Distributed loading |
	|------------------------------------------------------------------|---------------------|---------------------|
	| Numpy array | Yes | No |
	| Pandas dataframe | Yes | No |
	| Single CSV | Yes | No |
	| Multi CSV | Yes | Yes |
	| Single Parquet | Yes | No |
	| Multi Parquet | Yes | Yes |
	| [Ray Dataset](https://docs.ray.io/en/latest/data/dataset.html) | Yes | Yes |
	| [Petastorm](https://github.com/uber/petastorm) | Yes | Yes |
	| [Dask dataframe](https://docs.dask.org/en/latest/dataframe.html) | Yes | Yes |
	| [Modin dataframe](https://modin.readthedocs.io/en/latest/) | Yes | Yes |

So if using xgboost_ray, should I

• Create a Ray Dataset from Parquet files, then create a RayDMatrix from that Dataset
  or
• Create the RayDMatrix directly from Parquet files

Training

Should I use Ray Tune with XGBoostTrainer or with xgboost_ray.train, running on this Ray on Spark Cluster?

I also intend to implement CV with early stopping. Since tune-sklearn is now deprecated, I understand that I'll need to implement this myself. As explained in ray-project/ray#21848 (comment), this can be done with ray.tune.stopper.TrialPlateauStopper. But according to #301 we can also use XGBoost's native xgb.callback.EarlyStopping. Which approach would you recommend? Can TrialPlateauStopper be used with xgboost_ray?

Thank you very much for any help you can offer.