Merge remote-tracking branch 'upstream/branch-24.12' into branch-24.1…

…2_remove-edge-renumber-map

.pre-commit-config.yaml

@@ -53,6 +53,11 @@ repos:
               meta[.]yaml$|
               setup[.]cfg$
       - id: verify-alpha-spec
+  - repo: https://github.com/sphinx-contrib/sphinx-lint
+    rev: v1.0.0
+    hooks:
+      - id: sphinx-lint
+        args: ["--enable=all", "--disable=line-too-long"]
   - repo: https://github.com/rapidsai/dependency-file-generator
     rev: v1.16.0
     hooks:

README.md

@@ -34,16 +34,6 @@
 
 </div>
 
------
-## News
-
-___NEW!___   _[nx-cugraph](https://rapids.ai/nx-cugraph/)_, 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.
-
 -----
 
 ## Table of contents

cpp/src/prims/detail/sample_and_compute_local_nbr_indices.cuh

@@ -597,8 +597,6 @@ rmm::device_uvector<edge_t> compute_uniform_sampling_index_without_replacement(
   raft::random::RngState& rng_state,
   size_t K)
 {
-  assert(cugraph::invalid_edge_id_v<edge_t> == cugraph::ops::graph::INVALID_ID<edge_t>);
-
   edge_t mid_partition_degree_range_last = static_cast<edge_t>(K * 10);  // tuning parameter
   assert(mid_partition_degree_range_last > K);
   size_t high_partition_oversampling_K = K * 2;  // tuning parameter
@@ -1567,10 +1565,7 @@ uniform_sample_and_compute_local_nbr_indices(
   size_t K,
   bool with_replacement)
 {
-  using edge_t = typename GraphViewType::edge_type;
-
-  assert(cugraph::invalid_edge_id_v<edge_t> == cugraph::ops::graph::INVALID_ID<edge_t>);
-
+  using edge_t   = typename GraphViewType::edge_type;
   using vertex_t = typename GraphViewType::vertex_type;
   using key_t    = typename thrust::iterator_traits<KeyIterator>::value_type;
 

cpp/src/sampling/neighbor_sampling_impl.hpp

@@ -224,7 +224,7 @@ neighbor_sample_impl(raft::handle_t const& handle,
   std::vector<size_t> level_sizes{};
   std::vector<size_t> level_sizes_edge_types{};
 
-  for (auto hop = 0; hop < num_hops; hop++) {
+  for (size_t hop = 0; hop < num_hops; ++hop) {
     for (auto edge_type_id = 0; edge_type_id < num_edge_types; edge_type_id++) {
       auto k_level = fan_out[(hop * num_edge_types) + edge_type_id];
       rmm::device_uvector<vertex_t> srcs(0, handle.get_stream());

cpp/tests/community/balanced_edge_test.cpp

@@ -15,7 +15,7 @@
 #include <rmm/device_vector.hpp>
 #include <rmm/exec_policy.hpp>
 
-TEST(balanced_edge, DISABLED_success)
+TEST(balanced_edge, success)
 {
   std::vector<int> off_h = {0,  16,  25,  35,  41,  44,  48,  52,  56,  61,  63, 66,
                             67, 69,  74,  76,  78,  80,  82,  84,  87,  89,  91, 93,

docs/cugraph/source/api_docs/cugraph-ops/index.rst

@@ -1,7 +1,7 @@
 cugraph-ops API reference
 =========================
 
-This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace.
+This page provides a list of all publicly accessible modules, methods and classes through ``pylibcugraphops.*`` namespace.
 
 .. toctree::
     :maxdepth: 2

docs/cugraph/source/api_docs/cugraph-ops/python/index.rst

@@ -1,7 +1,7 @@
 cugraph-ops Python API reference
 ================================
 
-This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace.
+This page provides a list of all publicly accessible modules, methods and classes through ``pylibcugraphops.*`` namespace.
 
 .. toctree::
     :maxdepth: 2

docs/cugraph/source/nx_cugraph/how-it-works.md

@@ -10,7 +10,7 @@ While NetworkX is a pure-Python implementation, backends may be written to use o
 
 ## Enabling nx-cugraph
 
-It is recommended to use `networkx>=3.4` for optimal zero code change performance, but `nx-cugraph` will also work with `networkx 3.0+`.
+It is recommended to use `networkx>=3.4` for optimal zero code change performance, but `nx-cugraph` will also work with `networkx 3.2+`.
 
 NetworkX will use `nx-cugraph` as the backend if any of the following are used:
 

docs/cugraph/source/nx_cugraph/index.rst

@@ -49,7 +49,7 @@ Users can have GPU-based, large-scale performance **without** changing their fam
 +--------------------------------------------------------------------------------------------------------+
 | **Run the same code on CPU or GPU**                                                                    |
 |                                                                                                        |
-| Nothing changes, not even your `import` statements, when going from CPU to GPU.                        |
+| Nothing changes, not even your ``import`` statements, when going from CPU to GPU.                      |
 +--------------------------------------------------------------------------------------------------------+
 
 

docs/cugraph/source/nx_cugraph/installation.md

@@ -10,7 +10,7 @@ This guide describes how to install ``nx-cugraph`` and use it in your workflows.
  - **Volta architecture or later NVIDIA GPU, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+**
  - **[CUDA](https://docs.nvidia.com/cuda/index.html) 11.2, 11.4, 11.5, 11.8, 12.0, 12.2, or 12.5**
  - **Python >= 3.10**
- - **[NetworkX](https://networkx.org/documentation/stable/install.html#) >= 3.0 (version 3.4 or higher recommended)**
+ - **[NetworkX](https://networkx.org/documentation/stable/install.html#) >= 3.2 (version 3.4 or higher recommended)**
 
 More details about system requirements can be found in the [RAPIDS System Requirements Documentation](https://docs.rapids.ai/install#system-req).
 

docs/cugraph/source/nx_cugraph/supported-algorithms.rst

@@ -180,7 +180,7 @@ Algorithms
 +---------------------------------------+
 
 +---------------------------+
-| **Traversal**       		|
+| **Traversal**             |
 +===========================+
 | bfs_edges                 |
 +---------------------------+

docs/cugraph/source/tutorials/cugraph_blogs.rst

@@ -65,9 +65,9 @@ Academic Papers
 
  * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics <https://books.google.com/books?hl=en&lr=&id=QspxEAAAQBAJ&oi=fnd&pg=PT8&dq=book:%22Massive+Graph+Analytics%22&ots=3HAGJ0njKO&sig=8e4v0azmzA6LTQNUNgPw-uTLkoc#v=onepage&q&f=false>`_  Bader, D. (Editor) CRC Press
 
- * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020
+ * S Kang, A. Fender, J. Eaton, B. Rees. `Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters <https://ieeexplore.ieee.org/abstract/document/9286216>`_. In IEEE HPEC, Sep. 2020
 
- * Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows`. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
+ * Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows <https://ieeexplore.ieee.org/abstract/document/9286224>`_. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
 
  * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504).
 

python/cugraph/cugraph/cores/core_number.py

@@ -23,29 +23,27 @@
 def core_number(G, degree_type="bidirectional"):
     """
     Compute the core numbers for the nodes of the graph G. A k-core of a graph
-    is a maximal subgraph that contains nodes of degree k or more.
-    A node has a core number of k if it belongs a k-core but not to k+1-core.
-    This call does not support a graph with self-loops and parallel
-    edges.
+    is a maximal subgraph that contains nodes of degree k or more.  A node has
+    a core number of k if it belongs to a k-core but not to k+1-core.  This
+    call does not support a graph with self-loops and parallel edges.
 
     Parameters
     ----------
     G : cuGraph.Graph or networkx.Graph
-        The graph should contain undirected edges where undirected edges are
-        represented as directed edges in both directions. While this graph
-        can contain edge weights, they don't participate in the calculation
+        The current implementation only supports undirected graphs.  The graph
+        can contain edge weights, but they don't participate in the calculation
         of the core numbers.
-        The current implementation only supports undirected graphs.
 
         .. deprecated:: 24.12
            Accepting a ``networkx.Graph`` is deprecated and will be removed in a
            future version.  For ``networkx.Graph`` use networkx directly with
            the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
 
     degree_type: str, (default="bidirectional")
-        This option determines if the core number computation should be based
-        on input, output, or both directed edges, with valid values being
-        "incoming", "outgoing", and "bidirectional" respectively.
+        This option is currently ignored.  This option may eventually determine
+        if the core number computation should be based on input, output, or
+        both directed edges, with valid values being "incoming", "outgoing",
+        and "bidirectional" respectively.
 
     Returns
     -------
@@ -63,19 +61,28 @@ def core_number(G, degree_type="bidirectional"):
     >>> from cugraph.datasets import karate
     >>> G = karate.get_graph(download=True)
     >>> df = cugraph.core_number(G)
-
+    >>> df.head()
+       vertex  core_number
+    0      33            4
+    1       0            4
+    2      32            4
+    3       2            4
+    4       1            4
     """
 
     G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.is_directed():
         raise ValueError("input graph must be undirected")
 
-    if degree_type not in ["incoming", "outgoing", "bidirectional"]:
-        raise ValueError(
-            f"'degree_type' must be either incoming, "
-            f"outgoing or bidirectional, got: {degree_type}"
-        )
+    # degree_type is currently ignored until libcugraph supports directed
+    # graphs for core_number. Once supporteed, degree_type should be checked
+    # like so:
+    # if degree_type not in ["incoming", "outgoing", "bidirectional"]:
+    #     raise ValueError(
+    #         f"'degree_type' must be either incoming, "
+    #         f"outgoing or bidirectional, got: {degree_type}"
+    #     )
 
     vertex, core_number = pylibcugraph_core_number(
         resource_handle=ResourceHandle(),

python/cugraph/cugraph/dask/cores/core_number.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -53,15 +53,15 @@ def core_number(input_graph, degree_type="bidirectional"):
     Parameters
     ----------
     input_graph : cugraph.graph
-        cuGraph graph descriptor, should contain the connectivity information,
-        (edge weights are not used in this algorithm).
-        The current implementation only supports undirected graphs.
+        The current implementation only supports undirected graphs.  The graph
+        can contain edge weights, but they don't participate in the calculation
+        of the core numbers.
 
     degree_type: str, (default="bidirectional")
-        This option determines if the core number computation should be based
-        on input, output, or both directed edges, with valid values being
-        "incoming", "outgoing", and "bidirectional" respectively.
-
+        This option is currently ignored.  This option may eventually determine
+        if the core number computation should be based on input, output, or
+        both directed edges, with valid values being "incoming", "outgoing",
+        and "bidirectional" respectively.
 
     Returns
     -------
@@ -77,11 +77,14 @@ def core_number(input_graph, degree_type="bidirectional"):
     if input_graph.is_directed():
         raise ValueError("input graph must be undirected")
 
-    if degree_type not in ["incoming", "outgoing", "bidirectional"]:
-        raise ValueError(
-            f"'degree_type' must be either incoming, "
-            f"outgoing or bidirectional, got: {degree_type}"
-        )
+    # degree_type is currently ignored until libcugraph supports directed
+    # graphs for core_number. Once supporteed, degree_type should be checked
+    # like so:
+    # if degree_type not in ["incoming", "outgoing", "bidirectional"]:
+    #     raise ValueError(
+    #         f"'degree_type' must be either incoming, "
+    #         f"outgoing or bidirectional, got: {degree_type}"
+    #     )
 
     # Initialize dask client
     client = default_client()

python/cugraph/cugraph/datasets/dataset.py

@@ -352,7 +352,9 @@ def get_dask_graph(
             If True, stores the transpose of the adjacency matrix.  Required
             for certain algorithms.
         """
-        if self._edgelist is None:
+        if self._edgelist is None or not isinstance(
+            self._edgelist, dask_cudf.DataFrame
+        ):
             self.get_dask_edgelist(download=download)
 
         if create_using is None:

python/cugraph/cugraph/tests/core/test_core_number.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, 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
@@ -32,11 +32,15 @@ def setup_function():
 # =============================================================================
 # Pytest fixtures
 # =============================================================================
-degree_type = ["incoming", "outgoing"]
+# FIXME: degree_type is currently unsupported (ignored)
+# degree_type = ["incoming", "outgoing"]
 
+# fixture_params = gen_fixture_params_product(
+#     (UNDIRECTED_DATASETS, "graph_file"),
+#     (degree_type, "degree_type"),
+# )
 fixture_params = gen_fixture_params_product(
     (UNDIRECTED_DATASETS, "graph_file"),
-    (degree_type, "degree_type"),
 )
 
 
@@ -46,7 +50,9 @@ def input_combo(request):
     This fixture returns a dictionary containing all input params required to
     run a Core number algo
     """
-    parameters = dict(zip(("graph_file", "degree_type"), request.param))
+    # FIXME: degree_type is not supported so do not test with different values
+    # parameters = dict(zip(("graph_file", "degree_type"), request.param))
+    parameters = {"graph_file": request.param[0]}
 
     graph_file = parameters["graph_file"]
     G = graph_file.get_graph()
@@ -69,7 +75,8 @@ def input_combo(request):
 def test_core_number(input_combo):
     G = input_combo["G"]
     Gnx = input_combo["Gnx"]
-    degree_type = input_combo["degree_type"]
+    # FIXME: degree_type is currently unsupported (ignored)
+    # degree_type = input_combo["degree_type"]
     nx_core_number_results = cudf.DataFrame()
 
     dic_results = nx.core_number(Gnx)
@@ -80,7 +87,7 @@ def test_core_number(input_combo):
     )
 
     core_number_results = (
-        cugraph.core_number(G, degree_type)
+        cugraph.core_number(G)
         .sort_values("vertex")
         .reset_index(drop=True)
         .rename(columns={"core_number": "cugraph_core_number"})
@@ -109,8 +116,3 @@ def test_core_number_invalid_input(input_combo):
 
     with pytest.raises(ValueError):
         cugraph.core_number(G)
-
-    invalid_degree_type = "invalid"
-    G = input_combo["G"]
-    with pytest.raises(ValueError):
-        cugraph.core_number(G, invalid_degree_type)