From 4bd939e10032e53ab56fa8bc5ecc346e60cbe81a Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 8 May 2020 15:32:59 -0400 Subject: [PATCH] fixed typos and other gramatical issues --- CONTRIBUTING.md | 1 + README.md | 2 +- SOURCEBUILD.md | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 3cd596737b4..e44d1ccb9c0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -151,6 +151,7 @@ implementation of the issue, ask them in the issue instead of the PR. ### Style Guild All Python code most pass flake8 style checking All C++ code must pass clang style checking +All code must adhere to the [RAPIDS Style Guide](https://docs.rapids.ai/resources/style/) ### Tests All code must have associate test cases. Code without test will not be accepted diff --git a/README.md b/README.md index 7b81a994536..9ccba417363 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![Build Status](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cugraph/job/branches/job/cugraph-branch-pipeline/badge/icon)](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cugraph/job/branches/job/cugraph-branch-pipeline/) -The [RAPIDS](https://rapids.ai) cuGraph library is a collection of GPU accelerated graph algorithms that process data found in [GPU DataFrames](https://github.com/rapidsai/cudf). The vision of cuGraph is _to make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks_. To realize that vision, cuGraph operators, at the Python layer, on GPU DataFrames, allowing for seamless passing of data between ETL tasks in [cuDF](https://github.com/rapidsai/cudf) and machine learning tasks in [cuML](https://github.com/rapidsai/cuml). Data scientist familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, user familiar with NetworkX will quickly reconnize the NetworkX-like API provided in cuGraph, with the goal being to allow existing code to be ported with minimal effort into RAPIDS. For users familar with C/CUDA and graph structures, a C++ API is also provided. However, there is less type and structure checking at the C layer. +The [RAPIDS](https://rapids.ai) cuGraph library is a collection of GPU accelerated graph algorithms that process data found in [GPU DataFrames](https://github.com/rapidsai/cudf). The vision of cuGraph is _to make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks_. To realize that vision, cuGraph operators, at the Python layer, on GPU DataFrames, allowing for seamless passing of data between ETL tasks in [cuDF](https://github.com/rapidsai/cudf) and machine learning tasks in [cuML](https://github.com/rapidsai/cuml). Data scientist familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, user familiar with NetworkX will quickly reconnize the NetworkX-like API provided in cuGraph, with the goal being to allow existing code to be ported with minimal effort into RAPIDS. For users familiar with C++/CUDA and graph structures, a C++ API is also provided. However, there is less type and structure checking at the C layer. For more project details, see [rapids.ai](https://rapids.ai/). diff --git a/SOURCEBUILD.md b/SOURCEBUILD.md index e80ebffd63a..1cefe87f611 100644 --- a/SOURCEBUILD.md +++ b/SOURCEBUILD.md @@ -120,7 +120,7 @@ Note that the libraries will be installed to the location set in `$PREFIX` if se ## Building each section independently -#### Build and Install the C/CUDA `libcugraph` Library +#### Build and Install the C++/CUDA `libcugraph` Library CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. This project uses cmake for building the C/C++ library. To configure cmake, run: