From 1ce3edde343f3651461cc70efcd9e6dcb8e633ec Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Tue, 7 Nov 2023 00:58:17 -0600 Subject: [PATCH 1/4] Updates README to accommodate new users. --- python/nx-cugraph/README.md | 79 +++++++++++++++++++++++++++++-------- 1 file changed, 63 insertions(+), 16 deletions(-) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index ab267e5a756..cb0d78954bd 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -2,31 +2,78 @@ ## Description [RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends) -with minimal dependencies (`networkx`, `cupy`, and `pylibcugraph`) to run graph algorithms on the GPU. +to run supported algorithms with GPU acceleration. -### Contribute +## System Requirements -Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.12 -and [building from source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/), then build nx-cugraph in develop (i.e., editable) mode: -``` -$ ./build.sh nx-cugraph --pydevelop -``` +Using nx-cugraph with this notebook requires the following: + + * NVIDIA GPU, Pascal architecture or later + * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0 + * Python versions 3.9, 3.10, or 3.11 + * NetworkX >= version 3.2 + +More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).. + +## Installation -### Run tests +nx-cugraph can be installed using either conda or pip. -Run nx-cugraph tests from `cugraph/python/nx-cugraph` directory: +### conda ``` -$ pytest +conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph ``` -Run nx-cugraph benchmarks: +### pip ``` -$ pytest --bench +python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com ``` -Run networkx tests (requires networkx version 3.2): +Notes: + + * Nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above. + * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start). + +## Enabling nx-cugraph + +### `NETWORKX_AUTOMATIC_BACKENDS` environment variable. +The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. +Set `NETWORKX_AUTOMATIC_BACKENDS=cugraph` to use nx-cugraph to GPU accelerate supported APIs with no code changes. +Example: ``` -$ ./run_nx_tests.sh +bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph python my_networkx_script.py ``` -Additional arguments may be passed to pytest such as: + +### `backend=` keyword argument +To explicitly specify a particular backend for an API, use the `backend=` +keyword argument. This argument takes precedence over the +`NETWORKX_AUTOMATIC_BACKENDS` environment variable. This requires anyone +running code that uses the `backend=` keyword argument to have the specified +backend installed. + +Example: +``` +nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph") +``` + +### Type-based dispatching + +NetworkX also supports automatically dispatching to backends associated with +specific graph types. Like the `backend=` keyword argument example above, this +requires the user to write code for a specific backend, and therefore requires +the backend to be installed, but has the advantage of ensuring a particular +behavior without the potential for runtime conversions. + +To use type-based dispatching with nx-cugraph, the user must import the backend +directly in their code to access the utilities provided to create a Graph +instance specifically for the nx-cugraph backend. + +Example: ``` -$ ./run_nx_tests.sh -x --sw -k betweenness +import networkx as nx +import nx_cugraph as nxcg + +G = nx.Graph() +... +nxcg_G = nxcg.from_networkx(G) # conversion happens once here +nx.betweenness_centrality(nxcg_G, k=1000) # nxcg Graph type causes cugraph backend + # to be used, no conversion necessary ``` From 4614f1e285007f2a2ab05c5f66925dd8008d09ad Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Tue, 7 Nov 2023 01:07:40 -0600 Subject: [PATCH 2/4] Removed DGL and PyG from diagram since they are 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zFJ0fz8u@iY_o^LU;lKFlW*?T@u_x`$D7o_YU-g@({OWnUZ0Qu=JG8c;Yp7TM1^lV1 MXkN%TfBVt@0fYxuh5!Hn From 270a1dec6290ce6dd933a609b56a3e7f4717bee8 Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Tue, 7 Nov 2023 01:54:35 -0600 Subject: [PATCH 3/4] Added nx-cugraph and News section. --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 560d1483242..5a4ffa64d22 100644 --- a/README.md +++ b/README.md @@ -35,8 +35,13 @@ ----- +## News -## Table of content +[nx-cugraph](./python/nx-cugraph/README.md) is a NetworkX backend that's available to provide GPU acceleration to NetworkX. + +----- + +## Table of contents - Installation - [Getting cuGraph Packages](./docs/cugraph/source/installation/getting_cugraph.md) - [Building from Source](./docs/cugraph/source/installation/source_build.md) @@ -52,6 +57,7 @@ - [External Data Types](./readme_pages/data_types.md) - [pylibcugraph](./readme_pages/pylibcugraph.md) - [libcugraph (C/C++/CUDA)](./readme_pages/libcugraph.md) + - [nx-cugraph](./python/nx-cugraph/README.md) - [cugraph-service](./readme_pages/cugraph_service.md) - [cugraph-dgl](./readme_pages/cugraph_dgl.md) - [cugraph-ops](./readme_pages/cugraph_ops.md) From 793b5d7f7b294472648548f198b4e092a3d42716 Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Tue, 7 Nov 2023 23:37:58 -0600 Subject: [PATCH 4/4] Removes mention of notebook, adds table of supported algos, updated news section to add an example, adds NetworkX to the list of projects using cuGraph. --- README.md | 8 ++++- python/nx-cugraph/README.md | 58 ++++++++++++++++++++++++++++++++++++- 2 files changed, 64 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5a4ffa64d22..8026e4feb64 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,12 @@ ----- ## News -[nx-cugraph](./python/nx-cugraph/README.md) is a NetworkX backend that's available to provide GPU acceleration to NetworkX. +___NEW!___ _[nx-cugraph](./python/nx-cugraph/README.md)_, a NetworkX backend that provides GPU acceleration to NetworkX with zero code change. +``` +> pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com +> export NETWORKX_AUTOMATIC_BACKENDS=cugraph +``` +That's it. NetworkX now leverages cuGraph for accelerated graph algorithms. ----- @@ -122,6 +127,7 @@ df_page.sort_values('pagerank', ascending=False).head(10) * 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 Graph database - https://memgraph.com/ +* NetworkX (via [nx-cugraph](./python/nx-cugraph/README.md) backend) - an extremely popular, free and open-source package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks - https://networkx.org/ * PyGraphistry - free and open-source GPU graph ETL, AI, and visualization, including native RAPIDS & cuGraph support - http://github.com/graphistry/pygraphistry * ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/ diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index cb0d78954bd..148fb2f0993 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -6,7 +6,7 @@ to run supported algorithms with GPU acceleration. ## System Requirements -Using nx-cugraph with this notebook requires the following: +nx-cugraph requires the following: * NVIDIA GPU, Pascal architecture or later * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0 @@ -34,6 +34,9 @@ Notes: ## Enabling nx-cugraph +NetworkX will use nx-cugraph as the graph analytics backend if any of the +following are are used: + ### `NETWORKX_AUTOMATIC_BACKENDS` environment variable. The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. Set `NETWORKX_AUTOMATIC_BACKENDS=cugraph` to use nx-cugraph to GPU accelerate supported APIs with no code changes. @@ -77,3 +80,56 @@ nxcg_G = nxcg.from_networkx(G) # conversion happens once here nx.betweenness_centrality(nxcg_G, k=1000) # nxcg Graph type causes cugraph backend # to be used, no conversion necessary ``` + +## Supported Algorithms + +The nx-cugraph backend to NetworkX connects +[pylibcugraph](../../readme_pages/pylibcugraph.md) (cuGraph's low-level python +interface to its CUDA-based graph analytics library) and +[CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's +familiar and easy-to-use API. + +Below is the list of algorithms (many listed using pylibcugraph names), +available today in pylibcugraph or implemented using CuPy, that are or will be +supported in nx-cugraph. + +| feature/algo | release/target version | +| ----- | ----- | +| analyze_clustering_edge_cut | ? | +| analyze_clustering_modularity | ? | +| analyze_clustering_ratio_cut | ? | +| balanced_cut_clustering | ? | +| betweenness_centrality | 23.10 | +| bfs | ? | +| core_number | ? | +| degree_centrality | 23.12 | +| ecg | ? | +| edge_betweenness_centrality | 23.10 | +| ego_graph | ? | +| eigenvector_centrality | 23.12 | +| get_two_hop_neighbors | ? | +| hits | 23.12 | +| in_degree_centrality | 23.12 | +| induced_subgraph | ? | +| jaccard_coefficients | ? | +| katz_centrality | 23.12 | +| k_core | ? | +| k_truss_subgraph | 23.12 | +| leiden | ? | +| louvain | 23.10 | +| node2vec | ? | +| out_degree_centrality | 23.12 | +| overlap_coefficients | ? | +| pagerank | 23.12 | +| personalized_pagerank | ? | +| sorensen_coefficients | ? | +| spectral_modularity_maximization | ? | +| sssp | 23.12 | +| strongly_connected_components | ? | +| triangle_count | ? | +| uniform_neighbor_sample | ? | +| uniform_random_walks | ? | +| weakly_connected_components | ? | + +To request nx-cugraph backend support for a NetworkX API that is not listed +above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph).