[[Category:Software]][[Category:AI and Machine Learning]] '''[https://xgboost.readthedocs.io/en/latest/ 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 [https://xgboost.readthedocs.io/en/latest/get_started.html xgboost documentation]. There is a [https://xgboost.readthedocs.io/en/latest/gpu/index.html 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; readers can should familiarize themselves with the use of [[Python#Creating_and_using_a_virtual_environment | Python virtual environments]] before starting an XGBoost project. Currently, version 0.81 of XGBoost is available. The following commands illustrate the needed package and module: {{Commands |prompt=(myvenv) name@server $ |module load nixpkgs/16.09 intel/2018.3 cuda/10.0.130 |module load nccl/2.3.5 |pip install xgboost{{=}}{{=}}0.81 --no-index }}