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Update GPU doc. (#4953)

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Jiaming Yuan 2019-10-16 05:54:09 -04:00 committed by GitHub
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2 changed files with 17 additions and 8 deletions
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15
doc/gpu/index.rst
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@ -46,6 +46,8 @@ Supported parameters
+--------------------------------+--------------+ +--------------------------------+--------------+
| ``max_bin`` | |tick| | | ``max_bin`` | |tick| |
+--------------------------------+--------------+ +--------------------------------+--------------+
| ``gamma`` | |tick| |
+--------------------------------+--------------+
| ``gpu_id`` | |tick| | | ``gpu_id`` | |tick| |
+--------------------------------+--------------+ +--------------------------------+--------------+
| ``n_gpus`` (deprecated) | |tick| | | ``n_gpus`` (deprecated) | |tick| |
@ -75,9 +77,13 @@ The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/bu
:caption: Python example :caption: Python example
param['gpu_id'] = 0 param['gpu_id'] = 0
param['max_bin'] = 16
param['tree_method'] = 'gpu_hist' param['tree_method'] = 'gpu_hist'
.. code-block:: python
:caption: With Scikit-Learn interface
XGBRegressor(tree_method='gpu_hist', gpu_id=0)
Single Node Multi-GPU Single Node Multi-GPU
===================== =====================
@ -85,9 +91,10 @@ Single Node Multi-GPU
Multi-node Multi-GPU Training Multi-node Multi-GPU Training
============================= =============================
XGBoost supports fully distributed GPU training using `Dask XGBoost supports fully distributed GPU training using `Dask <https://dask.org/>`_. For
<https://dask.org/>`_. See Python documentation :ref:`dask_api` and worked examples `here getting started see our tutorial :doc:`/tutorials/dask` and worked examples `here
<https://github.com/dmlc/xgboost/tree/master/demo/dask>`_. <https://github.com/dmlc/xgboost/tree/master/demo/dask>`_, also Python documentation
:ref:`dask_api` for complete reference.
Objective functions Objective functions

10
python-package/xgboost/sklearn.py
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@ -101,8 +101,8 @@ class XGBModel(XGBModelBase):
.. note:: .. note::
Using gblinear booster with shotgun updater is Using gblinear booster with shotgun updater is nondeterministic as
nondeterministic as it uses Hogwild algorithm. it uses Hogwild algorithm.
missing : float, optional missing : float, optional
Value in the data which needs to be present as a missing value. If Value in the data which needs to be present as a missing value. If
@ -960,8 +960,10 @@ class XGBRanker(XGBModel):
random_state : int random_state : int
Random number seed. Random number seed.
.. note:: Using gblinear booster with shotgun updater is .. note::
nondeterministic as it uses Hogwild algorithm.
Using gblinear booster with shotgun updater is nondeterministic as
it uses Hogwild algorithm.
missing : float, optional missing : float, optional
Value in the data which needs to be present as a missing value. If Value in the data which needs to be present as a missing value. If