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Fix typo in spilling documentation #1384

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Sep 16, 2024
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5 changes: 3 additions & 2 deletions docs/source/examples/best-practices.rst
Original file line number Diff line number Diff line change
Expand Up @@ -49,8 +49,9 @@ Spilling from Device

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 using
`Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, cuDF spilling is usually the
best place to start. See :ref:`Spilling from device <spilling-from-device>` for more details.
`Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, native cuDF spilling is usually
the best place to start. See :ref:`Dask-CUDA's spilling documentation <spilling-from-device>`
for more details.

Accelerated Networking
~~~~~~~~~~~~~~~~~~~~~~
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2 changes: 1 addition & 1 deletion docs/source/spilling.rst
Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ cuDF Spilling

When executing an ETL workflow with `Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_
(i.e. Dask DataFrame), it is usually best to leverage `native spilling support in cuDF
<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>`.
<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>`_.
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This is the only "required" change in this PR.


Native cuDF spilling has an important advantage over the other methodologies mentioned
above. When JIT-unspill or default spilling are used, the worker is only able to spill
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