From 0fe30ec6db9709736896da92245afc9f8f8bea4e Mon Sep 17 00:00:00 2001 From: rjzamora Date: Mon, 16 Sep 2024 10:18:12 -0700 Subject: [PATCH] another small revision --- docs/source/examples/best-practices.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/examples/best-practices.rst b/docs/source/examples/best-practices.rst index d69c2269..d0ddc510 100644 --- a/docs/source/examples/best-practices.rst +++ b/docs/source/examples/best-practices.rst @@ -44,14 +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. -Spilling from device +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:`Dask-CUDA's spilling documentation ` -page for more details. +for more details. Accelerated Networking ~~~~~~~~~~~~~~~~~~~~~~