XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It is a popular package used for a wide variety of machine learning and datascience tasks, serving the role of a convenient, domain-agnostic black box classifier. XGBoost provides GPU accelerated learning for some problems, and Compute Canada provides a GPU enabled build.

For detailed documentation on using the library, please consult the xgboost documentation. There is a separate section for GPU-enabled training.

Python Module Installation

A very common way to use XGBoost is though its python interface, provided as the xgboost python module. Compute Canada provides an optimized, multi-GPU enabled build as a Python wheel. The reader is recommended to familiarize oneself with the basics of creating a python environment before starting and XGBoost project.

Currently, version 0.81 of XGBoost is available. The following commands illustrate the needed package and module:

(myvenv) name@server $ module load nixpkgs/16.09 intel/2018.3 cuda/10.0.130
(myvenv) name@server $ module load nccl/2.3.5