aschaffer · aschaffer · Feb 25, 2021 · Nov 5, 2020 · Nov 5, 2020 · Nov 5, 2020
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
@@ -1,3 +1,17 @@
+# https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners
+# Order matters - match of highest importance goes last (last match wins)
+
+#doc code owners
+datasets/          @rapidsai/cugraph-doc-codeowners
+notebooks/         @rapidsai/cugraph-doc-codeowners
+docs/              @rapidsai/cugraph-doc-codeowners
+**/*.txt           @rapidsai/cugraph-doc-codeowners
+**/*.md            @rapidsai/cugraph-doc-codeowners
+**/*.rst           @rapidsai/cugraph-doc-codeowners
+**/*.ipynb         @rapidsai/cugraph-doc-codeowners
+**/*.pdf           @rapidsai/cugraph-doc-codeowners
+**/*.png           @rapidsai/cugraph-doc-codeowners
+
 #cpp code owners
 cpp/               @rapidsai/cugraph-cpp-codeowners
 
@@ -9,7 +23,7 @@ python/            @rapidsai/cugraph-python-codeowners
 **/cmake/          @rapidsai/cugraph-cmake-codeowners
 
 #build/ops code owners
-.github/           @rapidsai/ops-codeowners 
+.github/           @rapidsai/ops-codeowners
 ci/                @rapidsai/ops-codeowners
 conda/             @rapidsai/ops-codeowners
 **/Dockerfile      @rapidsai/ops-codeowners

diff --git a/.github/labeler.yml b/.github/labeler.yml
@@ -5,26 +5,33 @@
 python:
   - 'python/**'
   - 'notebooks/**'
+
+benchmarks:
   - 'benchmarks/**'
 
 doc:
   - 'docs/**'
   - '**/*.md'
+  - 'datasets/**'
+  - 'notebooks/**'
+  - '**/*.txt'
+  - '**/*.rst'
+  - '**/*.ipynb'
+  - '**/*.pdf'
+  - '**/*.png'
 
 datasets:
   - 'datasets/**'
 
 cuGraph:
   - 'cpp/**'
-
+  
 CMake:
   - '**/CMakeLists.txt'
   - '**/cmake/**'
-
-Ops:
-  - '.github/**'
-  - 'ci/**'
+
+gpuCI:
+   - 'ci/**'
+
+conda:
   - 'conda/**'
-  - '**/Dockerfile'
-  - '**/.dockerignore'
-  - 'docker/**'
diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml
@@ -0,0 +1,57 @@
+name: Mark inactive issues and pull requests
+
+on:
+  schedule:
+    - cron: "0 * * * *"
+
+jobs:
+  mark-inactive-30d:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Mark 30 day inactive issues and pull requests
+        uses: actions/stale@v3
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: >
+            This issue has been labeled `inactive-30d` due to no recent activity in the past 30 days.
+            Please close this issue if no further response or action is needed.
+            Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.
+            This issue will be labeled `inactive-90d` if there is no activity in the next 60 days.
+          stale-issue-label: "inactive-30d"
+          exempt-issue-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-issue-stale: 30
+          days-before-issue-close: -1
+          stale-pr-message: >
+            This PR has been labeled `inactive-30d` due to no recent activity in the past 30 days.
+            Please close this PR if it is no longer required.
+            Otherwise, please respond with a comment indicating any updates.
+            This PR will be labeled `inactive-90d` if there is no activity in the next 60 days.
+          stale-pr-label: "inactive-30d"
+          exempt-pr-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-pr-stale: 30
+          days-before-pr-close: -1
+          operations-per-run: 50
+  mark-inactive-90d:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Mark 90 day inactive issues and pull requests
+        uses: actions/stale@v3
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: >
+            This issue has been labeled `inactive-90d` due to no recent activity in the past 90 days.
+            Please close this issue if no further response or action is needed.
+            Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.
+          stale-issue-label: "inactive-90d"
+          exempt-issue-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-issue-stale: 90
+          days-before-issue-close: -1
+          stale-pr-message: >
+            This PR has been labeled `inactive-90d` due to no recent activity in the past 90 days.
+            Please close this PR if it is no longer required.
+            Otherwise, please respond with a comment indicating any updates.
+          stale-pr-label: "inactive-90d"
+          exempt-pr-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-pr-stale: 90
+          days-before-pr-close: -1
+          operations-per-run: 50
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,12 +1,14 @@
-
-# cuGraph 0.18.0 (Date TBD)
+# cuGraph 0.19.0 (Date TBD)
 
 ## New Features
 
 ## Improvements
 
 ## Bug Fixes
-- PR #1321 Fix benchmark script trap setup to come after the PATH variable update
+
+# 0.18.0
+
+Please see https://github.com/rapidsai/cugraph/releases/tag/branch-0.18-latest for the latest changes to this development branch.
 
 # cuGraph 0.17.0 (10 Dec 2020)
 ## New Features
@@ -19,6 +21,7 @@
 - PR #1279 Add self loop check variable in graph
 - PR #1277 SciPy sparse matrix input support for WCC, SCC, SSSP, and BFS
 - PR #1278 Add support for shortest_path_length and fix graph vertex checks
+- PR #1280 Add Multi(Di)Graph support
 
 ## Improvements
 - PR #1227 Pin cmake policies to cmake 3.17 version

diff --git a/README.md b/README.md
@@ -8,7 +8,7 @@ The [RAPIDS](https://rapids.ai) cuGraph library is a collection of GPU accelerat
 
 **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
 import cugraph
@@ -30,6 +30,17 @@ for i in range(len(df_page)):
 		" PageRank is " + str(df_page['pagerank'].iloc[i]))
 ```
 
+## 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)
+<br/><br/>
+
+---
+# Currently Supported Features
+As of Release 0.18 - including 0.18 nightly
+
 
 ## Supported Algorithms
 
@@ -40,6 +51,7 @@ for i in range(len(df_page)):
 |              | Betweenness Centrality                 | Single-GPU   |                     |
 |              | Edge Betweenness Centrality            | Single-GPU   |                     |
 | Community    |                                        |              |                     |
+|              | EgoNet                                 | Single-GPU   |                     |
 |              | Leiden                                 | Single-GPU   |                     |
 |              | Louvain                                | Multi-GPU    |                     |
 |              | Ensemble Clustering for Graphs         | Single-GPU   |                     |
@@ -56,25 +68,27 @@ for i in range(len(df_page)):
 |              | Core Number                            | Single-GPU   |                     |
 | Layout       |                                        |              |                     |
 |              | Force Atlas 2                          | Single-GPU   |                     |
+| Linear Assignment|                                    |              |                     |
+|              | Hungarian                              | Single-GPU   | [README](cpp/src/linear_assignment/README-hungarian.md) |
 | Link Analysis|                                        |              |                     |
 |              | Pagerank                               | Multi-GPU    |                     |
 |              | Personal Pagerank                      | Multi-GPU    |                     |
-|              | HITS                      				| Single-GPU   | leverages Gunrock   |
+|              | HITS                                   | Single-GPU   | leverages Gunrock   |
 | Link Prediction |                                     |              |                     |
 |              | Jaccard Similarity                     | Single-GPU   |                     |
 |              | Weighted Jaccard Similarity            | Single-GPU   |                     |
 |              | Overlap Similarity                     | Single-GPU   |                     |
 | Traversal    |                                        |              |                     |
-|              | Breadth First Search (BFS)             | Multi-GPU    |                     |
+|              | Breadth First Search (BFS)             | Multi-GPU    | with cutoff support |
 |              | Single Source Shortest Path (SSSP)     | Multi-GPU    |                     |
+|              | Traveling Salesperson Problem (TSP)    | Single-GPU   |                     |
 | Structure    |                                        |              |                     |
 |              | Renumbering                            | Single-GPU   | multiple columns, any data type  |
 |              | Symmetrize                             | Multi-GPU    |                     |
 | Other        |                                        |              |                     |
 |              | Hungarian Algorithm                    | Single-GPU   |                     |
 |              | Minimum Spanning Tree                  | Single-GPU   |                     |
 |              | Maximum Spanning Tree                  | Single-GPU   |                     |
-
 |  |  |
 
 </br></br>
@@ -83,13 +97,13 @@ for i in range(len(df_page)):
 | --------------- | --------------------------------------------------- |
 | Graph           | An undirected Graph                                 |
 | DiGraph         | A Directed Graph                                    |
-| _Multigraph_      | _coming in 0.18_                                      |
-| _MultiDigraph_    | _coming in 0.18_                                      |
+| Multigraph      | A Graph with multiple edges between a vertex pair   |
+| MultiDigraph    | A Directed Graph with multiple edges between a vertex pair   |
 |  |  |
 
 </br></br>
 ## Supported Data Types
-cuGraph supports the creation of a graph several data types:
+cuGraph supports graph creation with Source and Destination being expressed as:
 * cuDF DataFrame
 * Pandas DataFrame
 
@@ -123,22 +137,14 @@ The amount of memory required is dependent on the graph structure and the analyt
 
 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
 
+</br></br>
 
-## Getting cuGraph
-### Intro
-There are 3 ways to get cuGraph :
-1. [Quick start with Docker Demo Repo](#quick)
-2. [Conda Installation](#conda)
-3. [Build from Source](#source)
-
-
-
-
+---
 ## Quick Start <a name="quick"></a>
-Please see the [Demo 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.
+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.
 
 
-### Conda <a name="conda"></a>
+## Conda <a name="conda"></a>
 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:
@@ -158,7 +164,7 @@ conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph
 Note: This conda installation only applies to Linux and Python versions 3.7/3.8.
 
 
-### Build from Source and Contributing <a name="source"></a>
+## Build from Source and Contributing <a name="source"></a>
 
 Please see our [guide for building cuGraph from source](SOURCEBUILD.md)</pr>
 

diff --git a/SOURCEBUILD.md b/SOURCEBUILD.md
@@ -1,6 +1,6 @@
 # Building from Source
 
-The following instructions are for users wishing to build cuGraph from source code.  These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments.  Other operating systems _might be_ compatible, but are not currently tested. 
+The following instructions are for users wishing to build cuGraph from source code.  These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments.  Other operating systems _might be_ compatible, but are not currently tested.
 
 The cuGraph package include both a C/C++ CUDA portion and a python portion.  Both libraries need to be installed in order for cuGraph to operate correctly.
 
@@ -9,7 +9,7 @@ The cuGraph package include both a C/C++ CUDA portion and a python portion.  Bot
 __Compiler__:
 * `gcc`         version 5.4+
 * `nvcc`        version 10.0+
-* `cmake`       version 3.12+
+* `cmake`       version 3.18+
 
 __CUDA:__
 * CUDA 10.1+
@@ -97,17 +97,21 @@ There are several other options available on the build script for advanced users
 `build.sh` options:
 ```bash
 build.sh [<target> ...] [<flag> ...]
-   clean            - remove all existing build artifacts and configuration (start over)
-   libcugraph       - build the cugraph C++ code
-   cugraph          - build the cugraph Python package
-
+ where <target> is:
+    clean            - remove all existing build artifacts and configuration (start over)
+    libcugraph       - build the cugraph C++ code
+    cugraph          - build the cugraph Python package
+    docs             - build the docs
  and <flag> is:
    -v               - verbose build mode
    -g               - build for debug
    -n               - no install step
+   --allgpuarch     - build for all supported GPU architectures
    --show_depr_warn - show cmake deprecation warnings
    -h               - print this text
 
+ default action (no args) is to build and install 'libcugraph' then 'cugraph' then 'docs' targets
+
 examples:
 $ ./build.sh clean                        # remove prior build artifacts (start over)
 $ ./build.sh libcugraph -v                # compile and install libcugraph with verbose output
@@ -189,7 +193,7 @@ Run either the C++ or the Python tests with datasets
 
    ```bash
    cd $CUGRAPH_HOME/datasets
-   source get_test_data.sh #This takes about 10 minutes and download 1GB data (>5 GB uncompressed)
+   source get_test_data.sh #This takes about 10 minutes and downloads 1GB data (>5 GB uncompressed)
    ```
 
    Run the C++ tests on large input:

diff --git a/benchmarks/bench_algos.py b/benchmarks/bench_algos.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -32,6 +32,7 @@ def setFixtureParamNames(*args, **kwargs):
 import cugraph
 from cugraph.structure.number_map import NumberMap
 from cugraph.tests import utils
+from cugraph.utilities.utils import is_device_version_less_than
 import rmm
 
 from .params import FIXTURE_PARAMS
@@ -212,6 +213,8 @@ def bench_jaccard(gpubenchmark, graphWithAdjListComputed):
     gpubenchmark(cugraph.jaccard, graphWithAdjListComputed)
 
 
+@pytest.mark.skipif(
+    is_device_version_less_than((7, 0)), reason="Not supported on Pascal")
 def bench_louvain(gpubenchmark, graphWithAdjListComputed):
     gpubenchmark(cugraph.louvain, graphWithAdjListComputed)