Promote Datasets to stable and clean-up unit tests

.github/workflows/build.yaml

@@ -27,6 +27,7 @@ concurrency:
 
 jobs:
  cpp-build:
+ node_type: cpu32
  secrets: inherit
  uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.08
  with:

.github/workflows/pr.yaml

@@ -36,7 +36,7 @@ jobs:
  uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.08
  with:
  build_type: pull-request
- node_type: cpu16
+ node_type: cpu32
  conda-cpp-tests:
  needs: conda-cpp-build
  secrets: inherit

notebooks/algorithms/centrality/Betweenness.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -13,10 +14,12 @@
  "| Brad Rees | 04/24/2019 | created | 0.15 | GV100, CUDA 11.0\n",
  "| Brad Rees | 08/16/2020 | tested / updated | 21.10 nightly | RTX 3090 CUDA 11.4\n",
  "| Don Acosta | 07/05/2022 | tested / updated | 22.08 nightly | DGX Tesla V100 CUDA 11.5\n",
- "| Ralph Liu | 07/26/2022 | updated | 22.08 nightly | DGX Tesla V100 CUDA 11.5"
+ "| Ralph Liu | 07/26/2022 | updated | 22.08 nightly | DGX Tesla V100 CUDA 11.5\n",
+ "| | 06/29/2023 | updated | 23.08 nightly | DGX Tesla V100 CUDA 12.0"
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -28,6 +31,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -37,6 +41,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -71,6 +76,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -82,6 +88,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -98,6 +105,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -115,7 +123,7 @@
  "import cudf\n",
  "\n",
  "# Import a built-in dataset\n",
- "from cugraph.experimental.datasets import karate"
+ "from cugraph.datasets import karate"
  ]
  },
  {
@@ -129,6 +137,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -142,10 +151,11 @@
  "outputs": [],
  "source": [
  "# Create a graph using the imported Dataset object\n",
- "G = karate.get_graph(fetch=True)"
+ "G = karate.get_graph(download=True)"
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -173,6 +183,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -203,13 +214,15 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "---"
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -267,18 +280,20 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "As mentioned, the scores are different but the ranking is the same."
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "___\n",
- "Copyright (c) 2019-2022, NVIDIA CORPORATION.\n",
+ "Copyright (c) 2019-2023, NVIDIA CORPORATION.\n",
  "\n",
  "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 http://www.apache.org/licenses/LICENSE-2.0\n",
  "\n",
@@ -303,7 +318,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.12"
  },
  "vscode": {
  "interpreter": {

notebooks/algorithms/centrality/Centrality.ipynb

@@ -1,13 +1,15 @@
 {
  "cells": [
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "# Centrality"
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -18,10 +20,12 @@
  "| Brad Rees | 04/16/2021 | created | 0.19 | GV100, CUDA 11.0\n",
  "| Brad Rees | 08/05/2021 | tested / updated | 21.10 nightly | RTX 3090 CUDA 11.4\n",
  "| Don Acosta | 07/29/2022 | tested / updated | 22.08 nightly | DGX Tesla V100 CUDA 11.5\n",
+ "| Ralph Liu | 06/29/2023 | updated | 23.08 nightly | DGX Tesla V100 CUDA 12.0\n",
  " "
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -86,13 +90,15 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "---"
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -116,7 +122,7 @@
  "# Import the cugraph modules\n",
  "import cugraph\n",
  "import cudf\n",
- "from cugraph.experimental.datasets import karate"
+ "from cugraph.datasets import karate"
  ]
  },
  {
@@ -132,6 +138,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -194,6 +201,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -203,6 +211,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -216,10 +225,11 @@
  "outputs": [],
  "source": [
  "# Create a graph using the imported Dataset object\n",
- "G = karate.get_graph(fetch=True)"
+ "G = karate.get_graph(download=True)"
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -236,6 +246,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -254,6 +265,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -312,6 +324,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -320,6 +333,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -369,6 +383,7 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -385,13 +400,15 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "There are differences in how each centrality measure ranks the nodes. In some cases, every algorithm returns similar results, and in others, the results are different. Understanding how the centrality measure is computed and what edge represent is key to selecting the right centrality metric."
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
@@ -420,7 +437,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.9.15"
+ "version": "3.10.12"
  },
  "vscode": {
  "interpreter": {