Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Clarify multi-GPU training, binary wheels, Pandas integration (#3581)

Browse Source 
* Clarify multi-GPU training, binary wheels, Pandas integration

* Add a note about multi-GPU on gpu/index.rst
This commit is contained in:
Philip Hyunsu Cho
2018-08-11 19:21:28 -07:00
committed by GitHub
parent 7300002516
commit 4202332783
3 changed files with 34 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
doc/gpu/index.rst
View File

@@ -8,7 +8,7 @@ To install GPU support, checkout the :doc:`/build`.
*********************************************
CUDA Accelerated Tree Construction Algorithms
*********************************************
This plugin adds GPU accelerated tree construction and prediction algorithms to XGBoost.
Tree construction (training) and prediction can be accelerated with CUDA-capable GPUs.
Usage
=====
@@ -59,7 +59,11 @@ The device ordinal can be selected using the ``gpu_id`` parameter, which default
Multiple GPUs can be used with the ``gpu_hist`` tree method using the ``n_gpus`` parameter. which defaults to 1. If this is set to -1 all available GPUs will be used. If ``gpu_id`` is specified as non-zero, the gpu device order is ``mod(gpu_id + i) % n_visible_devices`` for ``i=0`` to ``n_gpus-1``. As with GPU vs. CPU, multi-GPU will not always be faster than a single GPU due to PCI bus bandwidth that can limit performance.
This plugin currently works with the CLI, python and R - see :doc:`/build` for details.
.. note:: Enabling multi-GPU training
Default installation may not enable multi-GPU training. To use multiple GPUs, make sure to read :ref:`build_gpu_support`.
The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/build` for details.
.. code-block:: python
:caption: Python example