Iroy30 · Iroy30 · May 12, 2020 · May 12, 2020 · May 12, 2020 · May 12, 2020
diff --git a/.gitignore b/.gitignore
@@ -58,11 +58,22 @@ cpp/thirdparty/googletest/
 *.ipr
 *.iws
 
+## Datasets
 datasets/*
+!datasets/cyber.csv
+!datasets/karate-data.csv
+!datasets/karate_undirected.csv
+!datasets/netscience.csv
+
+
 .pydevproject
 
 # Jupyter Notebooks
 .ipynb_checkpoints
 
 ## Doxygen
 cpp/doxygen/html
+
+# Raft symlink
+python/cugraph/raft
+python/_external_repositories/
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,32 +1,77 @@
-# cuGraph 0.14.0 (Date TBD)
+# cuGraph 0.14.0 (03 Jun 2020)
 
 ## New Features
+- PR #756 Add Force Atlas 2 layout
 - PR #822 Added new functions in python graph class, similar to networkx
+- PR #840 OPG degree
+- PR #875 UVM notebook
+- PR #881 Raft integration infrastructure
 
 ## Improvements
+- PR #917 Remove gunrock option from Betweenness Centrality
 - PR #764 Updated sssp and bfs with GraphCSR, removed gdf_column, added nullptr weights test for sssp
 - PR #765 Remove gdf_column from connected components
 - PR #780 Remove gdf_column from cuhornet features
 - PR #781 Fix compiler argument syntax for ccache
 - PR #782 Use Cython's `new_build_ext` (if available)
 - PR #788 Added options and config file to enable codecov
 - PR #793 Fix legacy cudf imports/cimports
-- PR #802 Removed use of gdf_column from db code
 - PR #798 Edit return graph type in algorithms return graphs
+- PR #799 Refactored graph class with RAII
+- PR #802 Removed use of gdf_column from db code
 - PR #803 Enable Ninja build
 - PR #804 Cythonize in parallel
 - PR #807 Updating the Python docs
+- PR #817 Add native Betweenness Centrality with sources subset
+- PR #818 Initial version of new "benchmarks" folder
 - PR #820 OPG infra and all-gather smoke test
+- PR #823 Remove gdf column from nvgraph
 - PR #829 Updated README and CONTRIBUTIOIN docs
+- PR #831 Updated Notebook - Added K-Truss, ECG, and Betweenness Centrality
+- PR #832 Removed RMM ALLOC from db subtree
+- PR #833 Update graph functions to use new Graph class
+- PR #834 Updated local gpuci build
+- PR #836 Remove SNMG code
+- PR #845 Add .clang-format & format all files
+- PR #859 Updated main docs
+- PR #862 Katz Centrality : Auto calculation of alpha parameter if set to none
+- PR #865 Added C++ docs
+- PR #866 Use RAII graph class in KTruss
+- PR #867 Updates to support the latest flake8 version
+- PR #874 Update setup.py to use custom clean command
+- PR #876 Add BFS C++ tests
+- PR #878 Updated build script
+- PR #879 Add docs build script to repository
+- PR #880 Remove remaining gdf_column references
+- PR #882 Add Force Atlas 2 to benchmarks
+- PR #891 A few gdf_column stragglers
+- PR #893 Add external_repositories dir and raft symlink to .gitignore
+- PR #897 Remove RMM ALLOC calls
+- PR #899 Update include paths to remove deleted cudf headers
+- PR #906 Update Louvain notebook
 
 ## Bug Fixes
+- PR #927 Update scikit learn dependency
+- PR #916 Fix CI error on Force Atlas 2 test
 - PR #763 Update RAPIDS conda dependencies to v0.14
 - PR #795 Fix some documentation
 - PR #800 Fix bfs error in optimization path
 - PR #825 Fix outdated CONTRIBUTING.md
-- PR #827 Fix indexing CI errors due to cudf updates 
-
-# cuGraph 0.13.0 (Date TBD)
+- PR #827 Fix indexing CI errors due to cudf updates
+- PR #844 Fixing tests, converting __getitem__ calls to .iloc
+- PR #851 Removed RMM from tests
+- PR #852 Fix BFS Notebook
+- PR #855 Missed a file in the original SNMG PR
+- PR #860 Fix all Notebooks
+- PR #870 Fix Louvain
+- PR #889 Added missing conftest.py file to benchmarks dir
+- PR #896 opg dask infrastructure fixes
+- PR #907 Fix bfs directed missing vertices
+- PR #911 Env and changelog update
+- PR #923 Updated pagerank with @afender 's temp fix for double-free crash
+- PR #928 Fix scikit learn test install to work with libgcc-ng 7.3
+
+# cuGraph 0.13.0 (31 Mar 2020)
 
 ## New Features
 - PR #736 cuHornet KTruss integration

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -8,9 +8,14 @@ cuGraph, and all of RAPIDS in general, is an open-source project where we encour
 If you are ready to contribute, jump right to the [Contribute Code](#code) section.
 
 
+__Style Formatting Tools:__
+* `clang-format`  version 8.01+
+* `flake8`        version 3.5.0+
+
+
 <a name="issue"></a>
 ## 1) File an Issue for the RAPIDS cuGraph team to work
-To file an issue, go to the RAPIDS cuGraph [issue](https://github.com/rapidsai/cugraph/issues/new/choose) page an select the appropiate issue type.  Once an issue is filed the RAPIDS cuGraph team will evaluate and triage the issue.  If you believe the issue needs priority attention, please include that in the issue to notify the team.
+To file an issue, go to the RAPIDS cuGraph [issue](https://github.com/rapidsai/cugraph/issues/new/choose) page an select the appropriate issue type.  Once an issue is filed the RAPIDS cuGraph team will evaluate and triage the issue.  If you believe the issue needs priority attention, please include that in the issue to notify the team.
 
 ***Bug Report***</pr>
 If you notice something not working please file an issue
@@ -49,20 +54,19 @@ We love when people want to get involved, and if you have a suggestion for a new
 
 If you need more context on a particular issue, please ask.
 
-
+----
 <a name="code"></a>
-
-# So You Want to Contribute Code
+# So you want to contribute code
 
 **TL;DR General Development Process**
 1. Read the documentation on [building from source](SOURCEBUILD.md) to learn how to setup, and validate, the development environment
 2. Read the RAPIDS [Code of Conduct](https://docs.rapids.ai/resources/conduct/)
-3. Find or submit an issue to work on (include a comment that you are workign issue)
+3. Find or submit an issue to work on (include a comment that you are working issue)
 4. Fork the cuGraph [repo](#fork) and Code (make sure to add unit tests)!
 5. When done, and code passes local CI, create your pull request (PR)
    1. Update the CHANGELOG.md with PR number - see [Changelog formatting](https://docs.rapids.ai/resources/changelog/)
    2. Ensure that the PR has the proper [tags](PRTAGS.md)
-   3. Ensure the code matches out [style guilde](https://docs.rapids.ai/resources/style/) 
+   3. Ensure the code matches out [style guide](https://docs.rapids.ai/resources/style/) 
 6. Verify that cuGraph CI passes all [status checks](https://help.github.com/articles/about-status-checks/). Fix if needed
 7. Wait for other developers to review your code and update code as needed
 8. Once reviewed and approved, a RAPIDS developer will merge your pull request
@@ -144,15 +148,14 @@ others know you are working on it. If you have any questions related to the
 implementation of the issue, ask them in the issue instead of the PR.
 
 
-### Style Guild
+### Style Guide
 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
@@ -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/).
 
@@ -52,6 +52,8 @@ for i in range(len(gdf_page)):
 |              | K-Core                                 | Single-GPU   |                     |
 |              | Core Number                            | Single-GPU   |                     |
 |              | K-Truss                                | Single-GPU   |                     |
+| Layout       |                                        |              |                     |
+|              | Force Atlas 2                          | Single-GPU   |                     |
 | Link Analysis|                                        |              |                     |
 |              | Pagerank                               | Single-GPU   |  Multi-GPU on DGX avaible  |
 |              | Personal Pagerank                      | Single-GPU   |                     |
@@ -86,6 +88,16 @@ cuGraph provides an auto-renumbering feature, enabled by default, during Graph c
 
 cuGraph is constantly being updatred 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        | Recomended GPU Memory |
+|-------------------|-----------------------|
+| 500 million edges	|  32GB    |
+| 250 million edges |	16 GB  |
+
+
 
 
 ## Getting cuGraph

diff --git a/SOURCEBUILD.md b/SOURCEBUILD.md
@@ -4,27 +4,33 @@ The following instructions are for users wishing to build cuGraph from source co
 
 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.
 
-### Prerequisites
+## Prerequisites
 
-Compiler requirement:
-
-* `gcc`     version 5.4+
-* `nvcc`    version 10.0+
-* `cmake`   version 3.12
-
-CUDA requirement:
+__Compiler__:
+* `gcc`         version 5.4+
+* `nvcc`        version 10.0+
+* `cmake`       version 3.12+
 
+__CUDA:__
 * CUDA 10.0+
 * NVIDIA driver 396.44+
 * Pascal architecture or better
 
+__Other__
+* `git`
+
+
+
 You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads).
 
-### Build and Install the C/C++ CUDA components
 
-To install cuGraph from source, ensure the dependencies are met and follow the steps below:
 
-1) A cloned version of the repository
+## Building cuGraph
+To install cuGraph from source, ensure the dependencies are met.
+
+
+### Clone Repo and Configure Conda Environment
+__GIT clone a version of the repository__
 
   ```bash
   # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME
@@ -36,7 +42,7 @@ To install cuGraph from source, ensure the dependencies are met and follow the s
   cd $CUGRAPH_HOME
   ```
 
-2) Create the conda development environment
+__Create the conda development environment__
 
 ```bash
 # create the conda environment (assuming in base `cugraph` directory)
@@ -74,9 +80,50 @@ conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda10
 conda activate cugraph_dev
 ```
 
-3) Build and install `libcugraph`. 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:
+### Build and Install Using the `build.sh` Script
+Using the `build.sh` script make compiling and installig cuGraph a breeze.  To build and install, simply do:
+
+```bash
+$ cd $CUGRAPH_HOME
+$ ./build.sh clean
+$ ./build.sh libcugraph
+$ ./build.sh cugraph
+```
+
+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
+
+ and <flag> is:
+   -v               - verbose build mode
+   -g               - build for debug
+   -n               - no install step
+   --show_depr_warn - show cmake deprecation warnings
+   -h               - print this text
+
+examples:
+$ ./build.sh clean                        # remove prior build artifacts (start over)
+$ ./build.sh libcugraph -v                # compile and install libcugraph with verbose output
+$ ./build.sh libcugraph -g                # compile and install libcugraph for debug
+$ ./build.sh libcugraph -n                # compile libcugraph but do not install
+
+# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4)
+$ PARALLEL_LEVEL=4 ./build.sh libcugraph
+
+Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`.
+```
+
+
+## Building each section independently
+#### 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:
 
   ```bash
   # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME
@@ -94,16 +141,10 @@ conda activate cugraph_dev
   ```
 The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/cugraph` respectively.
 
-As a convenience, a `build.sh` script is provided in `$CUGRAPH_HOME`. To execute the same build commands above, run the script as shown below.  Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`.
-```bash
-$ cd $CUGRAPH_HOME
-$ ./build.sh libcugraph  # build the cuGraph libraries and install them to
-                         # $PREFIX if set, otherwise $CONDA_PREFIX
-```
 
 ### Building and installing the Python package
 
-5. Install the Python package to your Python path:
+2) Install the Python package to your Python path:
 
 ```bash
 cd $CUGRAPH_HOME
@@ -112,26 +153,11 @@ python setup.py build_ext --inplace
 python setup.py install    # install cugraph python bindings
 ```
 
-Like the `libcugraph` build step above, `build.sh` can also be used to build the `cugraph` python package, as shown below:
-```bash
-$ cd $CUGRAPH_HOME
-$ ./build.sh cugraph  # build the cuGraph python bindings and install them
-                      # to $PREFIX if set, otherwise $CONDA_PREFIX
-```
 
-Note: other `build.sh` options include:
-```bash
-$ cd $CUGRAPH_HOME
-$ ./build.sh clean                        # remove any prior build artifacts and configuration (start over)
-$ ./build.sh libcugraph -v                # compile and install libcugraph with verbose output
-$ ./build.sh libcugraph -g                # compile and install libcugraph for debug
-$ PARALLEL_LEVEL=4 ./build.sh libcugraph  # compile and install libcugraph limiting parallel build jobs to 4 (make -j4)
-$ ./build.sh libcugraph -n                # compile libcugraph but do not install
-```
 
-### Run tests
+## Run tests
 
-6. Run either the C++ or the Python tests with datasets
+Run either the C++ or the Python tests with datasets
 
   - **Python tests with datasets**
 
@@ -234,4 +260,4 @@ cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DCMAKE_CXX11_ABI=OFF
 ```
 
 ## Attribution
-Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md
+Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md