From 95ef3675dc08e6d8bede33b6e2af4857cb2859d2 Mon Sep 17 00:00:00 2001 From: "Corey J. Nolet" Date: Tue, 14 Mar 2023 14:10:29 -0400 Subject: [PATCH] Small updates to docs (#1339) RAFT is getting a little more attention and I'm just updating a few things in the docs to make them look more polished. Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Ben Frederickson (https://github.com/benfred) URL: https://github.com/rapidsai/raft/pull/1339 --- README.md | 2 +- docs/source/index.rst | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 773d98e23a..a178d90008 100755 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ RAFT relies heavily on RMM which eases the burden of configuring different alloc ### Multi-dimensional Arrays -The APIs in RAFT currently accept raw pointers to device memory and we are in the process of simplifying the APIs with the [mdspan](https://arxiv.org/abs/2010.06474) multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory. +The APIs in RAFT accept the [mdspan](https://arxiv.org/abs/2010.06474) multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory. The `mdarray` forms a convenience layer over RMM and can be constructed in RAFT using a number of different helper functions: diff --git a/docs/source/index.rst b/docs/source/index.rst index 2418c6a767..814899c36b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,5 @@ -Welcome to RAFT's documentation! -================================= +RAPIDS RAFT: Reusable Accelerated Functions and Tools +===================================================== RAFT contains fundamental widely-used algorithms and primitives for scientific computing, data science and machine learning. The algorithms are CUDA-accelerated and form building-blocks for rapidly composing analytics.