XGBoost: Difference between revisions
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== Python Module == | == Python Module == | ||
A very common way to use XGBoost is though its python interface, provided as the <code>xgboost</code> 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. | A very common way to use XGBoost is though its python interface, provided as the <code>xgboost</code> 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: | |||
{{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 | |||
}} | |||
</translate> | </translate> | ||
Revision as of 19:34, 11 January 2019
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.
Python Module
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