From a6d85c09bb928cc3c6b93f6e1afdaebc02d1ec02 Mon Sep 17 00:00:00 2001
From: Don Acosta <97529984+acostadon@users.noreply.github.com>
Date: Mon, 28 Nov 2022 19:58:36 -0500
Subject: [PATCH] cuGraph Readme pages and Documentation API structure
refactoring (#2894)
Refactoring the API and adding new landing pages for each cuGraph component
Please just go to: https://github.com/acostadon/cugraph/tree/README_issue_2663 to visualize the changes
closes #2663
Authors:
- Don Acosta (https://github.com/acostadon)
- Brad Rees (https://github.com/BradReesWork)
- Vibhu Jawa (https://github.com/VibhuJawa)
- Alex Barghi (https://github.com/alexbarghi-nv)
Approvers:
- Brad Rees (https://github.com/BradReesWork)
- Taurean Dyer (https://github.com/taureandyernv)
URL: https://github.com/rapidsai/cugraph/pull/2894
---
README.md | 271 ++++++------------
docs/cugraph/source/basics/cugraph_blogs.rst | 55 ++--
img/Stack.png | Bin 0 -> 1855596 bytes
img/Stack2.png | Bin 0 -> 457436 bytes
img/cugraph_logo_2.png | Bin 0 -> 4952011 bytes
img/cugraphops_context.png | Bin 0 -> 24370 bytes
img/gaas_img_1.png | Bin 0 -> 302108 bytes
img/gaas_img_2.png | Bin 0 -> 457371 bytes
img/gnn_blog.png | Bin 0 -> 208941 bytes
img/gnn_context.png | Bin 0 -> 73805 bytes
img/gnn_framework.png | Bin 0 -> 150821 bytes
img/pg_example.png | Bin 0 -> 242398 bytes
python/cugraph/cugraph/gnn/README.md | 0
.../CONTRIBUTING.md | 79 ++---
PRTAGS.md => readme_pages/PRTAGS.md | 3 +-
SOURCEBUILD.md => readme_pages/SOURCEBUILD.md | 5 +-
.../TRANSITIONGUIDE.md | 0
readme_pages/algorithms.md | 85 ++++++
readme_pages/cugraph_dgl.md | 28 ++
readme_pages/cugraph_ops.md | 17 ++
readme_pages/cugraph_pyg.md | 22 ++
readme_pages/cugraph_python.md | 24 ++
readme_pages/cugraph_service.md | 28 ++
readme_pages/data_types.md | 46 +++
readme_pages/getting_cugraph.md | 64 +++++
readme_pages/gnn_support.md | 33 +++
readme_pages/libcugraph.md | 6 +
readme_pages/news.md | 13 +
readme_pages/performance/performance.md | 7 +
readme_pages/property_graph.md | 54 ++++
readme_pages/pylibcugraph.md | 25 ++
31 files changed, 600 insertions(+), 265 deletions(-)
create mode 100644 img/Stack.png
create mode 100644 img/Stack2.png
create mode 100644 img/cugraph_logo_2.png
create mode 100644 img/cugraphops_context.png
create mode 100644 img/gaas_img_1.png
create mode 100644 img/gaas_img_2.png
create mode 100644 img/gnn_blog.png
create mode 100644 img/gnn_context.png
create mode 100644 img/gnn_framework.png
create mode 100644 img/pg_example.png
create mode 100644 python/cugraph/cugraph/gnn/README.md
rename CONTRIBUTING.md => readme_pages/CONTRIBUTING.md (73%)
rename PRTAGS.md => readme_pages/PRTAGS.md (58%)
rename SOURCEBUILD.md => readme_pages/SOURCEBUILD.md (98%)
rename TRANSITIONGUIDE.md => readme_pages/TRANSITIONGUIDE.md (100%)
create mode 100644 readme_pages/algorithms.md
create mode 100644 readme_pages/cugraph_dgl.md
create mode 100644 readme_pages/cugraph_ops.md
create mode 100644 readme_pages/cugraph_pyg.md
create mode 100644 readme_pages/cugraph_python.md
create mode 100644 readme_pages/cugraph_service.md
create mode 100644 readme_pages/data_types.md
create mode 100644 readme_pages/getting_cugraph.md
create mode 100644 readme_pages/gnn_support.md
create mode 100644 readme_pages/libcugraph.md
create mode 100644 readme_pages/news.md
create mode 100644 readme_pages/performance/performance.md
create mode 100644 readme_pages/property_graph.md
create mode 100644 readme_pages/pylibcugraph.md
diff --git a/README.md b/README.md
index 94900568f28..c7d0456cf17 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,89 @@
-# 
cuGraph - GPU Graph Analytics
+
+ 
+
+
+
[](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cugraph/job/branches/job/cugraph-branch-pipeline/)
+
+ 
+
+
+
+ 
+
+
+
+
+
+
+
+
+
+[RAPIDS](https://rapids.ai) cuGraph is a monorepo that represents a collection of packages focused on GPU-accelerated graph analytics, including support for property graphs, remote (graph as a service) operations, and graph neural networks (GNNs). cuGraph supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms.
+
+
+
+[Getting cuGraph](./readme_pages/getting_cugraph.md) *
+[Graph Algorithms](./readme_pages/algorithms.md) *
+[Graph Service](./readme_pages/cugraph_service.md) *
+[Property Graph](./readme_pages/property_graph.md) *
+[GNN Support](./readme_pages/gnn_support.md)
+
+
+
+-----
+
+## Table of content
+- Getting packages
+ - [Getting cuGraph Packages](./readme_pages/getting_cugraph.md)
+ - [Contributing to cuGraph](./readme_pages/CONTRIBUTING.md)
+- General
+ - [Latest News](./readme_pages/news.md)
+ - [Current list of algorithms](./readme_pages/algorithms.md)
+ - [Blogs and Presentation](./docs/cugraph/source/basics/cugraph_blogs.rst)
+ - [Performance](./readme_pages/performance/performance.md)
+- Packages
+ - [cuGraph Python](./readme_pages/cugraph_python.md)
+ - [Property Graph](./readme_pages/property_graph.md)
+ - [External Data Types](./readme_pages/data_types.md)
+ - [pylibcugraph](./readme_pages/pylibcugraph.md)
+ - [libcugraph (C/C++/CUDA)](./readme_pages/libcugraph.md)
+ - [cugraph-service](./readme_pages/cugraph_service.md)
+ - [cugraph-dgl](./readme_pages/cugraph_dgl.md)
+ - [cugraph-ops](./readme_pages/cugraph_ops.md)
+- API Docs
+ - Python
+ - [Python Nightly](https://docs.rapids.ai/api/cugraph/nightly/)
+ - [Python Stable](https://docs.rapids.ai/api/cugraph/stable/)
+ - C++
+ - [C++ Nightly](https://docs.rapids.ai/api/libcugraph/nightly/)
+ - [C++ Stable](https://docs.rapids.ai/api/libcugraph/stable/)
+- References
+ - [RAPIDS](https://rapids.ai/)
+ - [ARROW](https://arrow.apache.org/)
+ - [DASK](https://www.dask.org/)
+
+
+
+-----
+
+
+
+---
-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 operates, at the Python layer, on GPU DataFrames, thereby 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 scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS.
-While the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.
+[RAPIDS](https://rapids.ai) cuGraph is a collection of GPU-accelerated graph algorithms and services. At the Python layer, cuGraph operates on [GPU DataFrames](https://github.com/rapidsai/cudf), thereby 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 scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. To similfy integration, cuGraph also support data found in [Pandas DataFrame](https://pandas.pydata.org/), [NetworkX Graph Objects](https://networkx.org/) and several other formats.
- For more project details, see [rapids.ai](https://rapids.ai/).
+While the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.
**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cugraph/blob/main/README.md) ensure you are on the latest branch.
+
+
As an example, the following Python snippet loads graph data and computes PageRank:
```python
@@ -32,179 +106,11 @@ df_page.sort_values('pagerank', ascending=False).head(10)
```
-## Getting cuGraph
-There are 3 ways to get cuGraph :
-1. [Quick start with Docker Repo](#quick)
-2. [Conda Installation](#conda)
-3. [Build from Source](#source)
-
-
----
-# cuGraph News
-
-### Scaling to 1 Trillion Edges
-At GTC Spring '22 we presented results of running cuGraph on the [Selene](https://top500.org/system/179842/) supercomputer using 2,048 GPUs and processing a graph with `1.1 Trillion edges`. Synthetic data created with the RMAT generator found in cuGraph.
-
-
cuGraph Scaling
cuGraph Software Stack
[C++ README](cpp/src/traversal/README.md#BFS) |
-| | Single Source Shortest Path (SSSP) | Multi-GPU | [C++ README](cpp/src/traversal/README.md#SSSP) |
-| | _ASSP / APSP_ | | |
-| Tree | | | |
-| | Minimum Spanning Tree | Single-GPU | |
-| | Maximum Spanning Tree | Single-GPU | |
-| Other | | | |
-| | Renumbering | Multi-GPU | multiple columns, any data type |
-| | Symmetrize | Multi-GPU | |
-| | Path Extraction | | Extract paths from BFS/SSP results in parallel |
-| Data Generator | | | |
-| | RMAT | Multi-GPU | |
-| | _Barabasi-Albert_ | --- | |
-| | |
-
-
-
-
-## cuGraph Notice
-
-Vertex IDs are expected to be contiguous integers starting from 0. If your data doesn't match that restriction, we have a solution. cuGraph provides the renumber function, which is by default automatically called when data is added to a graph. Input vertex IDs for the renumber function can be any type, can be non-contiguous, can be multiple columns, and can start from an arbitrary number. The renumber function maps the provided input vertex IDs to either 32- or 64-bit contiguous integers starting from 0.
-
-Additionally, when using the auto-renumbering feature, vertices are automatically un-renumbered in results.
-
-cuGraph is constantly being updated and improved. Please see the [Transition Guide](TRANSITIONGUIDE.md) if errors are encountered with newer versions
-
-## Graph Sizes and GPU Memory Size
-The amount of memory required is dependent on the graph structure and the analytics being executed. As a simple rule of thumb, the amount of GPU memory should be about twice the size of the data size. That gives overhead for the CSV reader and other transform functions. There are ways around the rule but using smaller data chunks.
-
-| Size | Recommended GPU Memory |
-|-------------------|------------------------|
-| 500 million edges | 32 GB |
-| 250 million edges | 16 GB |
-
-The use of managed memory for oversubscription can also be used to exceed the above memory limitations. See the recent blog on _Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs_: https://medium.com/rapids-ai/tackling-large-graphs-with-rapids-cugraph-and-unified-virtual-memory-b5b69a065d4
-
-
-
----
-## Quick Start
-Please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph.
+[Why cuGraph does not support Method Cascading]()
-## Conda
-It is easy to install cuGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download).
-
-Install and update cuGraph using the conda command:
-
-```bash
-# CUDA 11.5
-conda install -c rapidsai -c numba -c conda-forge -c nvidia cugraph cudatoolkit=11.5
-
-# CUDA 11.4
-conda install -c rapidsai -c numba -c conda-forge -c nvidia cugraph cudatoolkit=11.4
-```
-
-For CUDA > 11.5, please use the 11.5 environment.
-
-Note: This conda installation only applies to Linux and Python versions 3.8/3.9.
-
-
-## Build from Source and Contributing
-
-Please see our [guide for building cuGraph from source](SOURCEBUILD.md)
-
-Please see our [guide for contributing to cuGraph](CONTRIBUTING.md).
-
-
-
-## Documentation
-Python API documentation can be generated from [docs](docs) directory.
------
# Projects that use cuGraph
@@ -212,19 +118,26 @@ Python API documentation can be generated from [docs](docs) directory.
(alphabetical order)
* ArangoDB - a free and open-source native multi-model database system - https://www.arangodb.com/
* CuPy - "NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python" - https://cupy.dev/
-* Memgraph - In-memory database - https://memgraph.com/
+* Memgraph - In-memory Graph database - https://memgraph.com/
* ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/
+
+(please post an issue if you have a project to add to this list)
------
+
+
+## 
+
+
+For more project details, see [rapids.ai](https://rapids.ai/).
-### Apache Arrow on GPU
+
+### Apache Arrow on GPU
-The GPU version of [Apache Arrow](https://arrow.apache.org/) is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.
+The GPU version of [Apache Arrow](https://arrow.apache.org/) is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.
\ No newline at end of file
diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/basics/cugraph_blogs.rst
index 2d3e751a4b6..368dbcce4f8 100644
--- a/docs/cugraph/source/basics/cugraph_blogs.rst
+++ b/docs/cugraph/source/basics/cugraph_blogs.rst
@@ -1,5 +1,5 @@
-cuGraph BLOGS and Presentations
+cuGraph Blogs and Presentations
************************************************
The RAPIDS team blogs at https://medium.com/rapids-ai, and many of
@@ -7,21 +7,17 @@ these blog posts provide deeper dives into features from cuGraph.
Here, we've selected just a few that are of particular interest to cuGraph users:
-BLOGS & Conferences
+Blogs & Conferences
====================
-2018
--------
- * `GTC18 Fall - RAPIDS: Benchmarking Graph Analytics on the DGX-2 `_
-
+2022
+------
+ * `GTC: State of cuGraph (video & slides) `_
+ * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_
+ * `KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph `_
-2019
--------
- * `RAPIDS cuGraph `_
- * `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_
- * `RAPIDS cuGraph : multi-GPU PageRank `_
- * `Similarity in graphs: Jaccard versus the Overlap Coefficient `_
- * `GTC19 Spring - Accelerating Graph Algorithms with RAPIDS `_
- * `GTC19 Fall - Multi-Node Multi-GPU Machine Learning and Graph Analytics with RAPIDS `_
+2021
+------
+ * `GTC 21 - State of RAPIDS cuGraph and what's comming next `_
2020
------
@@ -31,16 +27,19 @@ BLOGS & Conferences
* `Large Graph Visualization with RAPIDS cuGraph `_
* `GTC 20 Fall - cuGraph Goes Big `_
-2021
-------
- * `GTC 21 - State of RAPIDS cuGraph and what's comming next `_
+2019
+-------
+ * `RAPIDS cuGraph `_
+ * `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_
+ * `RAPIDS cuGraph : multi-GPU PageRank `_
+ * `Similarity in graphs: Jaccard versus the Overlap Coefficient `_
+ * `GTC19 Spring - Accelerating Graph Algorithms with RAPIDS `_
+ * `GTC19 Fall - Multi-Node Multi-GPU Machine Learning and Graph Analytics with RAPIDS `_
+2018
+-------
+ * `GTC18 Fall - RAPIDS: Benchmarking Graph Analytics on the DGX-2 `_
-2022
-------
- * `GTC: State of cuGraph (video & slides) `_
- * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_
- * `KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph `_
Media
@@ -51,16 +50,16 @@ Media
Academic Papers
===============
+ * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press
+
* S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020
* Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows`. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
* Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504).
- * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press
-
-Other BLOGS
+Other Blogs
========================
* `4 graph algorithms on steroids for data scientists with cugraph `_
* `Where should I walk `_
@@ -69,4 +68,8 @@ Other BLOGS
* `Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms `_
* `Dev Blog Repost: Similarity in Graphs: Jaccard Versus the Overlap Coefficient `_
-
+RAPIDS Event Notebooks
+======================
+* `KDD 2022 Notebook that demonstates using cuDF for ETL/data cleaning and XGBoost for training a fraud predection model. `_
+* `SciPy 22 Notebook comparing cuGraph to NetworkX `_
+* `KDD 2020 Tutorial Notebooks - Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS `_
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