From e633d10476018ff6c694a574216b6946bac34e88 Mon Sep 17 00:00:00 2001 From: rjzamora Date: Wed, 11 Sep 2024 11:35:31 -0700 Subject: [PATCH] remove hyphen again --- docs/source/examples/best-practices.rst | 4 ++-- docs/source/spilling.rst | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/examples/best-practices.rst b/docs/source/examples/best-practices.rst index 03e26bf5..fbfd8f0c 100644 --- a/docs/source/examples/best-practices.rst +++ b/docs/source/examples/best-practices.rst @@ -48,8 +48,8 @@ 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 with -`Dask-cuDF `_, cuDF spilling is usually the +The best method often depends on the specific workflow. For classic ETL workloads using +`Dask cuDF `_, cuDF spilling is usually the best place to start. See :ref:`Spilling from device ` for more details. Accelerated Networking diff --git a/docs/source/spilling.rst b/docs/source/spilling.rst index 04cfa05c..cfc6cfcf 100644 --- a/docs/source/spilling.rst +++ b/docs/source/spilling.rst @@ -112,9 +112,9 @@ to enable compatibility mode, which automatically calls ``unproxy()`` on all fun cuDF Spilling ------------- -When executing a `Dask-cuDF `_ -(i.e. Dask DataFrame) ETL workflow, it is usually best to leverage `native spilling support in -cuDF `. +When executing an ETL workflow with `Dask cuDF `_ +(i.e. Dask DataFrame), it is usually best to leverage `native spilling support in cuDF +`. 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 @@ -148,7 +148,7 @@ Statistics When cuDF spilling is enabled, it is also possible to have cuDF collect basic spill statistics. Collecting this information can be a useful way to understand -the performance of Dask-cuDF workflows with high memory utilization. +the performance of memory-intensive workflows using cuDF. When deploying a ``LocalCUDACluster``, cuDF spilling can be enabled with the ``cudf_spill_stats`` argument: @@ -179,7 +179,7 @@ for more information on the available spill-statistics options. Limitations ~~~~~~~~~~~ -Although cuDF spilling is the best option for most Dask-cuDF ETL workflows, +Although cuDF spilling is the best option for most ETL workflows using Dask cuDF, 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.