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Document for device ordinal. (#9398)

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- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots.
- Add GPU demos into the sphinx gallery.
- Add RMM demos into the sphinx gallery.
- Test for firing threads with different device ordinals.
This commit is contained in:
Jiaming Yuan
2023-07-22 15:26:29 +08:00
committed by GitHub
parent 22b0a55a04
commit 275da176ba
32 changed files with 351 additions and 398 deletions
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doc/gpu/index.rst
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@@ -23,20 +23,19 @@ The GPU algorithms currently work with CLI, Python, R, and JVM packages. See :do
:caption: Python example
params = dict()
params["device"] = "cuda:0"
params["device"] = "cuda"
params["tree_method"] = "hist"
Xy = xgboost.QuantileDMatrix(X, y)
xgboost.train(params, Xy)
.. code-block:: python
:caption: With Scikit-Learn interface
:caption: With the Scikit-Learn interface
XGBRegressor(tree_method="hist", device="cuda")
GPU-Accelerated SHAP values
=============================
XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as a backend for computing shap values when the GPU predictor is selected.
XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as a backend for computing shap values when the GPU is used.
.. code-block:: python
@@ -44,12 +43,12 @@ XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as
shap_values = booster.predict(dtrain, pred_contribs=True)
shap_interaction_values = model.predict(dtrain, pred_interactions=True)
See examples `here <https://github.com/dmlc/xgboost/tree/master/demo/gpu_acceleration>`__.
See :ref:`sphx_glr_python_gpu-examples_tree_shap.py` for a worked example.
Multi-node Multi-GPU Training
=============================
XGBoost supports fully distributed GPU training using `Dask <https://dask.org/>`_, ``Spark`` and ``PySpark``. For getting started with Dask see our tutorial :doc:`/tutorials/dask` and worked examples `here <https://github.com/dmlc/xgboost/tree/master/demo/dask>`__, also Python documentation :ref:`dask_api` for complete reference. For usage with ``Spark`` using Scala see :doc:`/jvm/xgboost4j_spark_gpu_tutorial`. Lastly for distributed GPU training with ``PySpark``, see :doc:`/tutorials/spark_estimator`.
XGBoost supports fully distributed GPU training using `Dask <https://dask.org/>`_, ``Spark`` and ``PySpark``. For getting started with Dask see our tutorial :doc:`/tutorials/dask` and worked examples :doc:`/python/dask-examples/index`, also Python documentation :ref:`dask_api` for complete reference. For usage with ``Spark`` using Scala see :doc:`/jvm/xgboost4j_spark_gpu_tutorial`. Lastly for distributed GPU training with ``PySpark``, see :doc:`/tutorials/spark_estimator`.
Memory usage
@@ -67,7 +66,8 @@ If you are getting out-of-memory errors on a big dataset, try the or :py:class:`
CPU-GPU Interoperability
========================
XGBoost models trained on GPUs can be used on CPU-only systems to generate predictions. For information about how to save and load an XGBoost model, see :doc:`/tutorials/saving_model`.
The model can be used on any device regardless of the one used to train it. For instance, a model trained using GPU can still work on a CPU-only machine and vice versa. For more information about model serialization, see :doc:`/tutorials/saving_model`.
Developer notes