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Clarify the behavior of use_rmm. (#6808)

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* Clarify the `use_rmm` flag in document and demo.
This commit is contained in:
Jiaming Yuan
2021-03-31 15:43:11 +08:00
committed by GitHub
parent 3039dd194b
commit ca998df912
3 changed files with 32 additions and 7 deletions
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demo/rmm_plugin/README.md
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@@ -27,5 +27,21 @@ cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=$CONDA_
cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=/path/to/rmm
```
# Informing XGBoost about RMM pool
When XGBoost is compiled with RMM, most of the large size allocation will go through RMM
allocators, but some small allocations in performance critical areas are using a different
caching allocator so that we can have better control over memory allocation behavior.
Users can override this behavior and force the use of rmm for all allocations by setting
the global configuration ``use_rmm``:
``` python
with xgb.config_context(use_rmm=True):
clf = xgb.XGBClassifier(tree_method="gpu_hist")
```
Depending on the choice of memory pool size or type of allocator, this may have negative
performance impact.
* [Using RMM with a single GPU](./rmm_singlegpu.py)
* [Using RMM with a local Dask cluster consisting of multiple GPUs](./rmm_mgpu_with_dask.py)