[GraphBolt] add notebooks for OnDiskDataset (#6771)

docs/source/stochastic_training/index.rst

@@ -9,9 +9,8 @@ GraphBolt.
 
 .. toctree::
   :maxdepth: 1
-  :titlesonly:
 
   neighbor_sampling_overview.nblink
   node_classification.nblink
   link_prediction.nblink
-  ondisk-dataset
+  ondisk-dataset.rst

docs/source/stochastic_training/ondisk_dataset_heterograph.nblink

+{
+    "path": "../../../notebooks/stochastic_training/ondisk_dataset_heterograph.ipynb"
+}

docs/source/stochastic_training/ondisk_dataset_homograph.nblink

+{
+    "path": "../../../notebooks/stochastic_training/ondisk_dataset_homograph.ipynb"
+}

notebooks/stochastic_training/ondisk_dataset_heterograph.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "authorship_tag": "ABX9TyM1zJGR6lVdC9JfDbddFLpa"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# OnDiskDataset for Heterogeneous Graph\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dmlc/dgl/blob/master/notebooks/stochastic_training/ondisk_dataset_heterograph.ipynb) [![GitHub](https://img.shields.io/badge/-View%20on%20GitHub-181717?logo=github&logoColor=ffffff)](https://github.com/dmlc/dgl/blob/master/notebooks/stochastic_training/ondisk_dataset_heterograph.ipynb)\n",
+        "\n",
+        "This tutorial shows how to create `OnDiskDataset` for heterogeneous graph that could be used in **GraphBolt** framework.\n",
+        "\n",
+        "By the end of this tutorial, you will be able to\n",
+        "- organize graph structure data.\n",
+        "- organize feature data.\n",
+        "- organize training/validation/test set for specific tasks."
+      ],
+      "metadata": {
+        "id": "FnFhPMaAfLtJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install DGL package"
+      ],
+      "metadata": {
+        "id": "Wlb19DtWgtzq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Install required packages.\n",
+        "import os\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "os.environ['TORCH'] = torch.__version__\n",
+        "os.environ['DGLBACKEND'] = \"pytorch\"\n",
+        "\n",
+        "# Install the CPU version.\n",
+        "device = torch.device(\"cpu\")\n",
+        "!pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html\n",
+        "\n",
+        "try:\n",
+        "    import dgl\n",
+        "    import dgl.graphbolt as gb\n",
+        "    installed = True\n",
+        "except ImportError as error:\n",
+        "    installed = False\n",
+        "    print(error)\n",
+        "print(\"DGL installed!\" if installed else \"DGL not found!\")"
+      ],
+      "metadata": {
+        "id": "UojlT9ZGgyr9"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

notebooks/stochastic_training/ondisk_dataset_homograph.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "authorship_tag": "ABX9TyMnOgpk68ZvpOQVFBgDxDof"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# OnDiskDataset for Homogeneous Graph\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dmlc/dgl/blob/master/notebooks/stochastic_training/ondisk_dataset_homograph.ipynb) [![GitHub](https://img.shields.io/badge/-View%20on%20GitHub-181717?logo=github&logoColor=ffffff)](https://github.com/dmlc/dgl/blob/master/notebooks/stochastic_training/ondisk_dataset_homograph.ipynb)\n",
+        "\n",
+        "This tutorial shows how to create `OnDiskDataset` for homogeneous graph that could be used in **GraphBolt** framework.\n",
+        "\n",
+        "By the end of this tutorial, you will be able to\n",
+        "- organize graph structure data.\n",
+        "- organize feature data.\n",
+        "- organize training/validation/test set for specific tasks."
+      ],
+      "metadata": {
+        "id": "FnFhPMaAfLtJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install DGL package"
+      ],
+      "metadata": {
+        "id": "Wlb19DtWgtzq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Install required packages.\n",
+        "import os\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "os.environ['TORCH'] = torch.__version__\n",
+        "os.environ['DGLBACKEND'] = \"pytorch\"\n",
+        "\n",
+        "# Install the CPU version.\n",
+        "device = torch.device(\"cpu\")\n",
+        "!pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html\n",
+        "\n",
+        "try:\n",
+        "    import dgl\n",
+        "    import dgl.graphbolt as gb\n",
+        "    installed = True\n",
+        "except ImportError as error:\n",
+        "    installed = False\n",
+        "    print(error)\n",
+        "print(\"DGL installed!\" if installed else \"DGL not found!\")"
+      ],
+      "metadata": {
+        "id": "UojlT9ZGgyr9"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}