From 16aaf856dc41d353176994e01d2839cabb7afe15 Mon Sep 17 00:00:00 2001 From: rjzamora Date: Mon, 16 Sep 2024 10:16:39 -0700 Subject: [PATCH] roll back name change --- docs/source/examples/best-practices.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/source/examples/best-practices.rst b/docs/source/examples/best-practices.rst index b89cefd7..d69c2269 100644 --- a/docs/source/examples/best-practices.rst +++ b/docs/source/examples/best-practices.rst @@ -44,13 +44,14 @@ We also recommend allocating most, though not all, of the GPU memory space. We d Additionally, when using `Accelerated Networking`_ , we only need to register a single IPC handle for the whole pool (which is expensive, but only done once) since from the IPC point of viewer there's only a single allocation. As opposed to just using RMM without a pool where each new allocation must be registered with IPC. -Memory Spilling -~~~~~~~~~~~~~~~ +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 `_, native cuDF spilling is usually -the best place to start. See :ref:`Spilling from device ` for more details. +the best place to start. See :ref:`Dask-CUDA's spilling documentation ` +page for more details. Accelerated Networking ~~~~~~~~~~~~~~~~~~~~~~