From dadf905cbbac1531233508de860ac4278659a8b0 Mon Sep 17 00:00:00 2001 From: Yuchao Zhang <418121364@qq.com> Date: Sat, 13 Jan 2024 03:27:23 +0800 Subject: [PATCH] Fix typo in document (#41965) Signed-off-by: Yuchao Zhang <418121364@qq.com> --- doc/source/data/overview.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/data/overview.rst b/doc/source/data/overview.rst index d57dbf84993b..bd068016aad5 100644 --- a/doc/source/data/overview.rst +++ b/doc/source/data/overview.rst @@ -147,7 +147,7 @@ How does Ray Data compare to X for ML training ingest? .. dropdown:: NVTabular * **Supported data types:** `NVTabular `__ only supports tabular (Parquet, CSV, Avro) data, while Ray Data supports many other file formats. - * **Lower overhead:** Datasets is lower overhead: it supports zero-copy exchange between processes, in contrast to the multi-processing-based pipelines used by Petastorm. + * **Lower overhead:** Datasets is lower overhead: it supports zero-copy exchange between processes, in contrast to the multi-processing-based pipelines used by NVTabular. * **Heterogeneous compute:** NVTabular doesn't support mixing heterogeneous resources in dataset transforms (e.g. both CPU and GPU transformations), while Ray Data supports this. ML ingest case studies