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

Fix dask API sphinx docstrings (#4507)

Browse Source 
* Fix dask API sphinx docstrings

* Update GPU docs page
This commit is contained in:
Rory Mitchell
2019-05-28 16:39:26 +12:00
committed by GitHub
parent 3f7e5d9c47
commit 972f693eaf
3 changed files with 24 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
doc/gpu/index.rst
View File

@@ -67,11 +67,6 @@ The experimental parameter ``single_precision_histogram`` can be set to True to
The device ordinal can be selected using the ``gpu_id`` parameter, which defaults to 0.
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 selected gpu devices will be from ``gpu_id`` to ``gpu_id+n_gpus``, please note that ``gpu_id+n_gpus`` must be less than or equal to the number of available GPUs on your system. 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.
.. 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.
@@ -82,6 +77,24 @@ The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/bu
param['max_bin'] = 16
param['tree_method'] = 'gpu_hist'
Single Node Multi-GPU
=====================
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 selected gpu devices will be from ``gpu_id`` to ``gpu_id+n_gpus``, please note that ``gpu_id+n_gpus`` must be less than or equal to the number of available GPUs on your system. 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.
.. 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`.
XGBoost supports multi-GPU training on a single machine via specifying the `n_gpus' parameter.
Multi-node Multi-GPU Training
=============================
XGBoost supports fully distributed GPU training using `Dask
<https://dask.org/>`_. See Python documentation :ref:`dask_api` and worked examples `here
<https://github.com/dmlc/xgboost/tree/master/demo/dask>`_.
Objective functions
===================
Most of the objective functions implemented in XGBoost can be run on GPU. Following table shows current support status.
@@ -209,6 +222,7 @@ References
Contributors
=======
Many thanks to the following contributors (alphabetical order):
* Andrey Adinets
* Jiaming Yuan
* Jonathan C. McKinney