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[doc] Add notes about RMM and device ordinal. [skip ci] (#10562)

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- Remove the experimental tag, we have been running it for a long time now.
- Add notes about avoiding set CUDA device.
- Add link in parameter.
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Jiaming Yuan 2024-07-10 13:00:57 +08:00 committed by GitHub
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18
demo/rmm_plugin/README.rst
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@ -1,5 +1,5 @@
Using XGBoost with RAPIDS Memory Manager (RMM) plugin (EXPERIMENTAL)
====================================================================
Using XGBoost with RAPIDS Memory Manager (RMM) plugin
=====================================================
`RAPIDS Memory Manager (RMM) <https://github.com/rapidsai/rmm>`__ library provides a
collection of efficient memory allocators for NVIDIA GPUs. It is now possible to use
@ -47,5 +47,15 @@ the global configuration ``use_rmm``:
with xgb.config_context(use_rmm=True):
clf = xgb.XGBClassifier(tree_method="hist", device="cuda")
Depending on the choice of memory pool size or type of allocator, this may have negative
performance impact.
Depending on the choice of memory pool size and the type of the allocator, this can add
more consistency to memory usage but with slightly degraded performance impact.
*******************************
No Device Ordinal for Multi-GPU
*******************************
Since with RMM the memory pool is pre-allocated on a specific device, changing the CUDA
device ordinal in XGBoost can result in memory error ``cudaErrorIllegalAddress``. Use the
``CUDA_VISIBLE_DEVICES`` environment variable instead of the ``device="cuda:1"`` parameter
for selecting device. For distributed training, the distributed computing frameworks like
``dask-cuda`` are responsible for device management.

6
doc/parameter.rst
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@ -25,7 +25,11 @@ Global Configuration
The following parameters can be set in the global scope, using :py:func:`xgboost.config_context()` (Python) or ``xgb.set.config()`` (R).
* ``verbosity``: Verbosity of printing messages. Valid values of 0 (silent), 1 (warning), 2 (info), and 3 (debug).
* ``use_rmm``: Whether to use RAPIDS Memory Manager (RMM) to allocate GPU memory. This option is only applicable when XGBoost is built (compiled) with the RMM plugin enabled. Valid values are ``true`` and ``false``.
* ``use_rmm``: Whether to use RAPIDS Memory Manager (RMM) to allocate cache GPU
memory. The primary memory is always allocated on the RMM pool when XGBoost is built
(compiled) with the RMM plugin enabled. Valid values are ``true`` and ``false``. See
:doc:`/python/rmm-examples/index` for details.
******************
General Parameters