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Define the new device parameter. (#9362)

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Jiaming Yuan
2023-07-13 19:30:25 +08:00
committed by GitHub
parent 2d0cd2817e
commit 04aff3af8e
63 changed files with 827 additions and 477 deletions
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doc/gpu/index.rst
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@@ -22,7 +22,8 @@ Supported parameters
GPU accelerated prediction is enabled by default for the above mentioned ``tree_method`` parameters but can be switched to CPU prediction by setting ``predictor`` to ``cpu_predictor``. This could be useful if you want to conserve GPU memory. Likewise when using CPU algorithms, GPU accelerated prediction can be enabled by setting ``predictor`` to ``gpu_predictor``.
The device ordinal (which GPU to use if you have many of them) can be selected using the
``gpu_id`` parameter, which defaults to 0 (the first device reported by CUDA runtime).
``device`` parameter, which defaults to 0 when "CUDA" is specified(the first device reported by CUDA
runtime).
The GPU algorithms currently work with CLI, Python, R, and JVM packages. See :doc:`/install` for details.
@@ -30,13 +31,13 @@ The GPU algorithms currently work with CLI, Python, R, and JVM packages. See :do
.. code-block:: python
:caption: Python example
param['gpu_id'] = 0
param["device"] = "cuda:0"
param['tree_method'] = 'gpu_hist'
.. code-block:: python
:caption: With Scikit-Learn interface
XGBRegressor(tree_method='gpu_hist', gpu_id=0)
XGBRegressor(tree_method='gpu_hist', device="cuda")
GPU-Accelerated SHAP values
@@ -45,7 +46,7 @@ XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as
.. code-block:: python
model.set_param({"gpu_id": "0", "tree_method": "gpu_hist"})
model.set_param({"device": "cuda:0", "tree_method": "gpu_hist"})
shap_values = model.predict(dtrain, pred_contribs=True)
shap_interaction_values = model.predict(dtrain, pred_interactions=True)