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Co-authored-by: Peter Andreas Entschev <[email protected]>
rapidsai · Sep 10, 2024 · 262e52f · 262e52f
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diff --git a/docs/source/examples/best-practices.rst b/docs/source/examples/best-practices.rst
@@ -47,7 +47,7 @@ Additionally, when using `Accelerated Networking`_ , we only need to register a
 Spilling from Device
 ~~~~~~~~~~~~~~~~~~~~
 
-Dask CUDA offers several different ways to enable automatic spilling from device memory.
+Dask-CUDA offers several different ways to enable automatic spilling from device memory.
 The best method often depends on the specific workflow. For classic ETL workloads with
 `Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, cuDF spilling is usually
 the best place to start. See `spilling`_ for more details.

diff --git a/docs/source/spilling.rst b/docs/source/spilling.rst
@@ -169,4 +169,4 @@ Although cuDF spilling is the best option for most Dask cuDF ETL workflows,
 it will be much less effective if that workflow converts between ``cudf.DataFrame``
 and other data formats (e.g. ``cupy.ndarray``). Once the underlying device buffers
 are "exposed" to external memory references, they become "unspillable" by cuDF.
-In cases like this (e.g. Dask CUDA + XGBoost), JIT-Unspill is usually a better choice.
+In cases like this (e.g., Dask-CUDA + XGBoost), JIT-Unspill is usually a better choice.