[HOTFIX] Update cuda-python dependency to 11.7.1 (#2848)

This should resolve a segfault we are seeing with `cuda-python=11.7.0` (rapidsai/cudf#11941). Authors: - Ashwin Srinath (https://github.com/shwina) - Bradley Dice (https://github.com/bdice) - Ray Douglass (https://github.com/raydouglass) Approvers: - Bradley Dice (https://github.com/bdice) - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) - GALI PREM SAGAR (https://github.com/galipremsagar)
rapidsai · Nov 4, 2022 · 6bbdadf · 6bbdadf
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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 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.  
+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.
 
@@ -43,13 +43,13 @@ There are 3 ways to get cuGraph :
 # 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. 
+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.
 
 <div align="left"><img src="img/Scaling.png" width="500px" style="background-color: white;"/>&nbsp;</br>cuGraph Scaling</div>
 </br></br>
 
 ### cuGraph Software Stack
-cuGraph has a new multi-layer software stack that allows users and system integrators to access cuGraph at different layers.  
+cuGraph has a new multi-layer software stack that allows users and system integrators to access cuGraph at different layers.
 
 <div align="left"><img src="img/cugraph-stack.png" width="500px" style="background-color: white;"/>&nbsp;</br>cuGraph Software Stack</div>
 </br></br>
@@ -143,7 +143,7 @@ _Italic_ algorithms are planned for future releases.
 | Other        |                                        |               |                     |
 |              | Renumbering                            | <mark>Multi-GPU</mark> | multiple columns, any data type  |
 |              | Symmetrize                             | <mark>Multi-GPU</mark> |                     |
-|              | Path Extraction                        |               | Extract paths from BFS/SSP results in parallel | 
+|              | Path Extraction                        |               | Extract paths from BFS/SSP results in parallel |
 | Data Generator  |                                     |               |                     |
 |              | RMAT                                   | <mark>Multi-GPU</mark> |                     |
 |              | _Barabasi-Albert_                      |  ---          |                     |
@@ -154,7 +154,7 @@ _Italic_ algorithms are planned for future releases.
 
 ## 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. 
+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.
 
@@ -183,20 +183,15 @@ 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 11.4
-conda install -c nvidia -c rapidsai -c numba -c conda-forge cugraph cudatoolkit=11.4
-
 # CUDA 11.5
-conda install -c nvidia -c rapidsai -c numba -c conda-forge cugraph cudatoolkit=11.5
+conda install -c rapidsai -c numba -c conda-forge -c nvidia cugraph cudatoolkit=11.5
 
-For CUDA > 11.5, please use the 11.5 environment
+# 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.
 
 

diff --git a/ci/benchmark/build.sh b/ci/benchmark/build.sh
@@ -71,7 +71,7 @@ CUGRAPH_DEPS=(cudf rmm)
 LIBCUGRAPH_DEPS=(cudf rmm)
 
 gpuci_logger "Install required packages"
-gpuci_mamba_retry install -c nvidia -c rapidsai -c rapidsai-nightly -c conda-forge \
+gpuci_mamba_retry install -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia \
       "cudf=${MINOR_VERSION}" \
       "rmm=${MINOR_VERSION}" \
       "cudatoolkit=$CUDA_REL" \

diff --git a/conda/environments/cugraph_dev_cuda11.2.yml b/conda/environments/cugraph_dev_cuda11.2.yml
@@ -1,9 +1,9 @@
 name: cugraph_dev
 channels:
 - rapidsai
-- nvidia
 - rapidsai-nightly
 - conda-forge
+- nvidia
 dependencies:
 - c-compiler
 - cxx-compiler
@@ -17,7 +17,7 @@ dependencies:
 - libraft-distance=22.10.*
 - raft-dask=22.10.*
 - pylibraft=22.10.*
-- cuda-python>=11.5,<11.7.1
+- cuda-python>=11.7.1,<12.0
 - dask==2022.9.2
 - distributed==2022.9.2
 - dask-cuda=22.10.*

diff --git a/conda/environments/cugraph_dev_cuda11.4.yml b/conda/environments/cugraph_dev_cuda11.4.yml
@@ -1,9 +1,9 @@
 name: cugraph_dev
 channels:
 - rapidsai
-- nvidia
 - rapidsai-nightly
 - conda-forge
+- nvidia
 dependencies:
 - c-compiler
 - cxx-compiler
@@ -17,7 +17,7 @@ dependencies:
 - libraft-distance=22.10.*
 - raft-dask=22.10.*
 - pylibraft=22.10.*
-- cuda-python>=11.5,<11.7.1
+- cuda-python>=11.7.1,<12.0
 - dask==2022.9.2
 - distributed==2022.9.2
 - dask-cuda=22.10.*

diff --git a/conda/environments/cugraph_dev_cuda11.5.yml b/conda/environments/cugraph_dev_cuda11.5.yml
@@ -1,9 +1,9 @@
 name: cugraph_dev
 channels:
 - rapidsai
-- nvidia
 - rapidsai-nightly
 - conda-forge
+- nvidia
 dependencies:
 - c-compiler
 - cxx-compiler
@@ -17,7 +17,7 @@ dependencies:
 - libraft-distance=22.10.*
 - raft-dask=22.10.*
 - pylibraft=22.10.*
-- cuda-python>=11.5,<11.7.1
+- cuda-python>=11.7.1,<12.0
 - dask==2022.9.2
 - distributed==2022.9.2
 - dask-cuda=22.10.*

diff --git a/conda/recipes/cugraph/meta.yaml b/conda/recipes/cugraph/meta.yaml
@@ -1,7 +1,7 @@
 # Copyright (c) 2018-2022, NVIDIA CORPORATION.
 
 # Usage:
-#   conda build -c nvidia -c rapidsai -c conda-forge  .
+#   conda build -c rapidsai -c conda-forge -c nvidia .
 {% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version =  version.split('.')[0] + '.' + version.split('.')[1] %}
 {% set cuda_version='.'.join(environ.get('CUDA', 'unknown').split('.')[:2]) %}
@@ -63,7 +63,7 @@ requirements:
     - ucx-py {{ ucx_py_version }}
     - ucx-proc=*=gpu
     - {{ pin_compatible('cudatoolkit', max_pin='x', min_pin='x') }}
-    - cuda-python >=11.5,<11.7.1
+    - cuda-python >=11.7.1,<12.0
 
 tests:                                 # [linux64]
   requirements:                        # [linux64]

diff --git a/conda/recipes/libcugraph/meta.yaml b/conda/recipes/libcugraph/meta.yaml
@@ -1,7 +1,7 @@
 # Copyright (c) 2018-2022, NVIDIA CORPORATION.
 
 # Usage:
-#   conda build -c nvidia -c rapidsai -c conda-forge .
+#   conda build -c rapidsai -c conda-forge -c nvidia .
 {% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
 {% set cuda_version = '.'.join(environ.get('CUDA', '11.5').split('.')[:2]) %}

diff --git a/conda/recipes/pylibcugraph/meta.yaml b/conda/recipes/pylibcugraph/meta.yaml
@@ -1,7 +1,7 @@
 # Copyright (c) 2022, NVIDIA CORPORATION.
 
 # Usage:
-#   conda build -c nvidia -c rapidsai -c conda-forge  .
+#   conda build -c rapidsai -c conda-forge -c nvidia .
 {% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version =  version.split('.')[0] + '.' + version.split('.')[1] %}
 {% set cuda_version='.'.join(environ.get('CUDA', 'unknown').split('.')[:2]) %}

diff --git a/conda_build.sh b/conda_build.sh
@@ -1,15 +1,15 @@
 #!/usr/bin/env bash
-# Copyright (c) 2021, NVIDIA CORPORATION
+# Copyright (c) 2021-2022, NVIDIA CORPORATION
 
 set -xe
 
 CUDA_REL=${CUDA_VERSION%.*}
 
 conda install conda-build anaconda-client conda-verify -y
-conda build -c nvidia -c rapidsai -c rapidsai-nightly/label/cuda${CUDA_REL} -c conda-forge --python=${PYTHON} conda/recipes/cugraph
+conda build -c rapidsai -c rapidsai-nightly/label/cuda${CUDA_REL} -c conda-forge -c nvidia --python=${PYTHON} conda/recipes/cugraph
 
 if [ "$UPLOAD_PACKAGE" == '1' ]; then
-    export UPLOADFILE=`conda build -c nvidia -c rapidsai -c conda-forge --python=${PYTHON} conda/recipes/cugraph --output`
+    export UPLOADFILE=`conda build -c rapidsai -c conda-forge -c nvidia --python=${PYTHON} conda/recipes/cugraph --output`
     SOURCE_BRANCH=main
 
     test -e ${UPLOADFILE}