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CLI is not most developed interface. Putting them into correct directory can help new users to avoid it as most of the use cases are from a language binding.
Distributed XGBoost Training
This is an tutorial of Distributed XGBoost Training. Currently xgboost supports distributed training via CLI program with the configuration file. There is also plan push distributed python and other language bindings, please open an issue if you are interested in contributing.
Build XGBoost with Distributed Filesystem Support
To use distributed xgboost, you only need to turn the options on to build with distributed filesystems(HDFS or S3) in cmake.
cmake <path/to/xgboost> -DUSE_HDFS=ON -DUSE_S3=ON -DUSE_AZURE=ON
Step by Step Tutorial on AWS
Checkout this tutorial for running distributed xgboost.
Model Analysis
XGBoost is exchangeable across all bindings and platforms. This means you can use python or R to analyze the learnt model and do prediction. For example, you can use the plot_model.ipynb to visualize the learnt model.