From b247dfefe2fb143272532395a239d861e6c214c2 Mon Sep 17 00:00:00 2001
From: BradReesWork <BradReesWork@users.noreply.github.com>
Date: Thu, 13 Feb 2020 13:11:39 -0500
Subject: [PATCH] Updated doc to remove CUDA 9.2

---
 CONTRIBUTING.md | 26 ++++++++++++++------------
 README.md       |  6 ++++--
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index ac3bb3908e6..1fa69b6b59e 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -64,12 +64,12 @@ The following instructions are tested on Linux systems.
 Compiler requirement:
 
 * `gcc`     version 5.4+
-* `nvcc`    version 9.2
+* `nvcc`    version 10.0+
 * `cmake`   version 3.12
 
 CUDA requirement:
 
-* CUDA 9.2+
+* CUDA 10.0+
 * NVIDIA driver 396.44+
 * Pascal architecture or better
 
@@ -115,14 +115,15 @@ To install cuGraph from source, ensure the dependencies are met and follow the s
 
 ```bash
 # create the conda environment (assuming in base `cugraph` directory)
-# for CUDA 9.2
-conda env create --name cugraph_dev --file conda/environments/cugraph_dev92.yml
 
 # for CUDA 10
-conda env create --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.yml
+conda env create --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.0.yml
 
 # for CUDA 10.1
-conda env create --name cugraph_dev --file conda/environments/cugraph_dev_cuda101.yml
+conda env create --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.1.yml
+
+# for CUDA 10.2
+conda env create --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.2.yml
 
 # activate the environment
 conda activate cugraph_dev
@@ -135,14 +136,15 @@ conda deactivate
 
 
 ```bash
-# for CUDA 9.2
-conda env update --name cugraph_dev --file conda/environments/cugraph_dev.yml
 
 # for CUDA 10
-conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.yml
+conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.0.yml
 
 # for CUDA 10.1
-conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda101.yml
+conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.1.yml
+
+# for CUDA 10.2
+conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.2.yml
 
 conda activate cugraph_dev
 ```
@@ -273,8 +275,8 @@ Next the env_vars.sh file needs to be edited
 vi ./etc/conda/activate.d/env_vars.sh
 
 #!/bin/bash
-export PATH=/usr/local/cuda-10.0/bin:$PATH # or cuda-9.2 if using CUDA 9.2
-export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH # or cuda-9.2 if using CUDA 9.2
+export PATH=/usr/local/cuda-10.0/bin:$PATH # or cuda-10.2 if using CUDA 10.2
+export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH # or cuda-10.2 if using CUDA 10.2
 ```
 
 ```
diff --git a/README.md b/README.md
index 29540574fd6..753d5adec99 100644
--- a/README.md
+++ b/README.md
@@ -47,6 +47,7 @@ for i in range(len(gdf_page)):
 | Spectral Clustering - Balanced-Cut            | Single-GPU |                              |
 | Spectral Clustering - Modularity Maximization | Single-GPU |                              |
 | Louvain                                       | Single-GPU |                              |
+| Ensemble Clustering for Graphs (ECG)          | Single-GPU |                              |
 | Renumbering                                   | Single-GPU |                              |
 | Basic Graph Statistics                        | Single-GPU |                              |
 | Weakly Connected Components                   | Single-GPU |                              |
@@ -95,14 +96,15 @@ It is easy to install cuGraph using conda. You can get a minimal conda installat
 Install and update cuGraph using the conda command:
 
 ```bash
-# CUDA 9.2
-conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=9.2
 
 # CUDA 10.0
 conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=10.0
 
 # CUDA 10.1
 conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=10.1
+
+# CUDA 10.2
+conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=10.2
 ```
 
 Note: This conda installation only applies to Linux and Python versions 3.6/3.7.