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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R2cpVp2WdOsp"
+      },
+      "source": [
+        "# NetworkX - Easy Graph Analytics\n",
+        "\n",
+        "NetworkX is the most popular library for graph analytics available in Python, or quite possibly any language. To illustrate this, NetworkX was downloaded more than 71 million times in September of 2024 alone, which is roughly 71 times more than the next most popular graph analytics library! [*](https://en.wikipedia.org/wiki/NetworkX) NetworkX has earned this popularity from its very easy-to-use API, the wealth of documentation and examples available, the large (and friendly) community behind it, and its easy installation which requires nothing more than Python.\n",
+        "\n",
+        "However, NetworkX users are familiar with the tradeoff that comes with those benefits. The pure-Python implementation often results in poor performance when graph data starts to reach larger scales, limiting the usefulness of the library for many real-world problems.\n",
+        "\n",
+        "# Accelerated NetworkX - Easy (and fast!) Graph Analytics\n",
+        "\n",
+        "To address the performance problem, NetworkX 3.0 introduced a mechanism to dispatch algorithm calls to alternate implementations. The NetworkX Python API remains the same but NetworkX will use more capable algorithm implementations provided by one or more backends. This approach means users don't have to give up NetworkX -or even change their code- in order to take advantage of GPU performance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xkg10FrNThrK"
+      },
+      "source": [
+        "# Let's Get the Environment Setup\n",
+        "This notebook will demonstrate NetworkX both with and without GPU acceleration provided by the `nx-cugraph` backend.\n",
+        "\n",
+        "`nx-cugraph` is available as a package installable using `pip`, `conda`, and [from source](https://github.com/rapidsai/nx-cugraph).  Before importing `networkx`, lets install `nx-cugraph` so it can be registered as an available backend by NetworkX when needed.  We'll use `pip` to install.\n",
+        "\n",
+        "NOTES:\n",
+        "* `nx-cugraph` requires a compatible NVIDIA GPU, NVIDIA CUDA and associated drivers, and a supported OS. Details about these and other installation prerequisites can be seen [here](https://docs.rapids.ai/install#system-req).\n",
+        "* The `nx-cugraph` package is currently hosted by NVIDIA and therefore the `--extra-index-url` option must be used.\n",
+        "* `nx-cugraph` is supported on specific 11.x and 12.x CUDA versions, and the major version number must be known in order to install the correct build (this is determined automatically when using `conda`).\n",
+        "\n",
+        "To find the CUDA major version on your system, run the following command:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "NMFwzc1I95BS"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nvcc: NVIDIA (R) Cuda compiler driver\n",
+            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
+            "Built on Wed_Nov_22_10:17:15_PST_2023\n",
+            "Cuda compilation tools, release 12.3, V12.3.107\n",
+            "Build cuda_12.3.r12.3/compiler.33567101_0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!nvcc --version"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i91Yj-yZ-nGS"
+      },
+      "source": [
+        "From the above output we can see we're using CUDA 12.x so we'll be installing `nx-cugraph-cu12`. If we were using CUDA 11.x, the package name would be `nx-cugraph-cu11`. We'll also be adding `https://pypi.nvidia.com` as an `--extra-index-url`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "mYYN9EpnWphu"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com\n",
+            "Requirement already satisfied: nx-cugraph-cu12 in /home/dacosta/anaconda3/lib/python3.11/site-packages (24.8.0)\n",
+            "Requirement already satisfied: cupy-cuda12x>=12.0.0 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from nx-cugraph-cu12) (13.3.0)\n",
+            "Requirement already satisfied: networkx>=3.0 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from nx-cugraph-cu12) (3.3)\n",
+            "Requirement already satisfied: numpy<2.0a0,>=1.23 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from nx-cugraph-cu12) (1.24.3)\n",
+            "Requirement already satisfied: pylibcugraph-cu12==24.8.* in /home/dacosta/anaconda3/lib/python3.11/site-packages (from nx-cugraph-cu12) (24.8.0)\n",
+            "Requirement already satisfied: pylibraft-cu12==24.8.* in /home/dacosta/anaconda3/lib/python3.11/site-packages (from pylibcugraph-cu12==24.8.*->nx-cugraph-cu12) (24.8.1)\n",
+            "Requirement already satisfied: rmm-cu12==24.8.* in /home/dacosta/anaconda3/lib/python3.11/site-packages (from pylibcugraph-cu12==24.8.*->nx-cugraph-cu12) (24.8.2)\n",
+            "Requirement already satisfied: cuda-python<13.0a0,>=12.0 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from pylibraft-cu12==24.8.*->pylibcugraph-cu12==24.8.*->nx-cugraph-cu12) (12.6.0)\n",
+            "Requirement already satisfied: numba>=0.57 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from rmm-cu12==24.8.*->pylibcugraph-cu12==24.8.*->nx-cugraph-cu12) (0.60.0)\n",
+            "Requirement already satisfied: fastrlock>=0.5 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from cupy-cuda12x>=12.0.0->nx-cugraph-cu12) (0.8.2)\n",
+            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /home/dacosta/anaconda3/lib/python3.11/site-packages (from numba>=0.57->rmm-cu12==24.8.*->pylibcugraph-cu12==24.8.*->nx-cugraph-cu12) (0.43.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install nx-cugraph-cu12 --extra-index-url=https://pypi.nvidia.com"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0h1K-7tI_AZH"
+      },
+      "source": [
+        "Of course, we'll also be using `networkx`, which is already provided in the Colab environment. This notebook will be using features added in version 3.3, so we'll import it here to verify we have a compatible version."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "YTV0ZTME2tV6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'3.3'"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import networkx as nx\n",
+        "nx.__version__"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "oFHwNqqsNsqS"
+      },
+      "outputs": [],
+      "source": [
+        "nx.config.backend_priority=[\"cugraph\"]  # NETWORKX_BACKEND_PRIORITY=cugraph\n",
+        "nx.config.cache_converted_graphs=True   # NETWORKX_CACHE_CONVERTED_GRAPHS=True"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Namb5JLvwS-q"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\", message=\"Using cached graph for 'cugraph' backend\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mefjUEAnZ4pq"
+      },
+      "source": [
+        "# Working with Big Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These are the two files we will use and the location/commands to get them."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "lyYF0LbtFwjh"
+      },
+      "outputs": [],
+      "source": [
+        "# !wget -P ../data/ https://s3.amazonaws.com/data.patentsview.org/download/g_us_patent_citation.tsv.zip\n",
+        "# !unzip -d ../data/ ../data/g_us_patent_citation.tsv.zip \n",
+        "# !wget -P ../data/ https://s3.amazonaws.com/data.patentsview.org/download/g_patent.tsv.zip\n",
+        "# !unzip -d ../data/ ../data/g_patent.tsv.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will do all the dataframes using cudf pandas. Anything we can do in the dataframe will be very fast"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "kjGINYphQSQ2"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext cudf.pandas\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This loads the US patent citation data. It contains an edge for relationship where a patent cites another patent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# load the citation graph\n",
+        "citation_df = pd.read_csv(\"../data/g_us_patent_citation.tsv\",\n",
+        "                sep='\\t',\n",
+        "                header=0,\n",
+        "                usecols=[0,2],\n",
+        "                names=[\"source\", \"target\"],\n",
+        "                dtype={\"source\":str,\"target\":str},\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Since the dataframe is using pandas accelerated with cudf, accessing it is fast !!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "142183260"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(citation_df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This will take a few minutes. It is using NetworkX to create a 142 million edge graph on the cpu. This is a necessary overhead for loading the graph that will be later transformed into the cuGraph GPU-resident graph that will be reused in each algorithm we call, accelerating those algorithms dramatically."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 4min 44s, sys: 6.68 s, total: 4min 50s\n",
+            "Wall time: 4min 47s\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%time\n",
+        "G = nx.from_pandas_edgelist(citation_df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is being done on the cpu as well. It will take longer than counting the dataframe above."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "141943194"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "G.number_of_edges()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Running this first algorithm wraps in the overhead of building the cuGraph GPU-resident graph.\n",
+        "Subsequent algoritms will be faster."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pr_results = nx.pagerank(G, backend=\"cugraph\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Running the same algorithm...but with the graph cached, it takes only seconds."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
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+              " '5200895': 8.593584139555631e-08,\n",
+              " '5242190': 8.742881853545992e-08,\n",
+              " '5285878': 1.4927541022064342e-07,\n",
+              " '5293968': 1.2916691656612495e-07,\n",
+              " '5299488': 2.0191691186574021e-07,\n",
+              " '5337863': 2.8562894634385774e-08,\n",
+              " '5350187': 1.7580600844315137e-07,\n",
+              " '5350983': 1.3134430318337252e-07,\n",
+              " '5360089': 1.9020255485069512e-07,\n",
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+              " '5363945': 2.2099301629287409e-07,\n",
+              " ...}"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "nx.pagerank(G, backend=\"cugraph\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This call finds the patent with the highest pagerank score."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'D732697'"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "mip = sorted(pr_results.items(), key=lambda x: x[1], reverse=True)[2:3][0]\n",
+        "most_important_patent = mip[0]\n",
+        "mip[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "clusters = nx.community.louvain_communities(G,seed =1, backend=\"cugraph\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "for cluster in clusters:\n",
+        "    if most_important_patent in cluster:\n",
+        "        save_cluster = cluster"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
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+              " '8853900',\n",
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+              " ...}"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "save_cluster"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This loads the enrichment data. In this case it contains the title of each patent. The [patents view site](https://patentsview.org/download/data-download-tables) contains many other files that could be similarly loaded and merged."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "8890049"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "title_df = pd.read_csv(\"../data/g_patent.tsv\",\n",
+        "                sep='\\t',\n",
+        "                header=0,\n",
+        "                usecols=[0,3],\n",
+        "                names=[\"patent_id\", \"patent_title\"],\n",
+        "                dtype={\"patent_id\":\"str\",\"patent_title\":str},\n",
+        ")\n",
+        "# title_df[0] = pd.to_numeric(title_df[0],errors='coerce')\n",
+        "len(title_df)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This code merges the ids in the cluster containing the highest ranking node with the patent titles to create a dataframe enriched with those titles and shows the first 20 in that dataframe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>patent_id</th>\n",
+              "      <th>patent_title</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>Methods for making optical components, optical...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000075</td>\n",
+              "      <td>Multilayer imaging with a high-gloss clear ink...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000150</td>\n",
+              "      <td>Lighting circuit and vehicle lamp</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000151</td>\n",
+              "      <td>Motor vehicle lighting device</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000160</td>\n",
+              "      <td>Vehicle article carrier with integrated camera...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>10000289</td>\n",
+              "      <td>Temperature control gasper apparatus</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>10000426</td>\n",
+              "      <td>Marking coating</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>10000695</td>\n",
+              "      <td>Method of manufacturing fluoride phosphor, whi...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>10000696</td>\n",
+              "      <td>Light-emitting ceramic, light-emitting element...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10000697</td>\n",
+              "      <td>Magnesium alumosilicate-based phosphor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>10000864</td>\n",
+              "      <td>Group 13 element nitride crystal layer and fun...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>10000888</td>\n",
+              "      <td>Applique to provide a design on a fabric</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>10001138</td>\n",
+              "      <td>Powered ventilators</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>10001248</td>\n",
+              "      <td>Light bar</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>10001249</td>\n",
+              "      <td>Shell integrated light-emitting diode assembly...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>10001251</td>\n",
+              "      <td>Lantern</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>10001252</td>\n",
+              "      <td>Expanding multi-faced work light flashlight</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>10001253</td>\n",
+              "      <td>Lamp for vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>10001255</td>\n",
+              "      <td>Custom printed lamp shade</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>10001257</td>\n",
+              "      <td>Gradient optics for even light distribution of...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   patent_id                                       patent_title\n",
+              "0   10000015  Methods for making optical components, optical...\n",
+              "1   10000075  Multilayer imaging with a high-gloss clear ink...\n",
+              "2   10000150                  Lighting circuit and vehicle lamp\n",
+              "3   10000151                      Motor vehicle lighting device\n",
+              "4   10000160  Vehicle article carrier with integrated camera...\n",
+              "5   10000289               Temperature control gasper apparatus\n",
+              "6   10000426                                    Marking coating\n",
+              "7   10000695  Method of manufacturing fluoride phosphor, whi...\n",
+              "8   10000696  Light-emitting ceramic, light-emitting element...\n",
+              "9   10000697             Magnesium alumosilicate-based phosphor\n",
+              "10  10000864  Group 13 element nitride crystal layer and fun...\n",
+              "11  10000888           Applique to provide a design on a fabric\n",
+              "12  10001138                                Powered ventilators\n",
+              "13  10001248                                          Light bar\n",
+              "14  10001249  Shell integrated light-emitting diode assembly...\n",
+              "15  10001251                                            Lantern\n",
+              "16  10001252        Expanding multi-faced work light flashlight\n",
+              "17  10001253                                   Lamp for vehicle\n",
+              "18  10001255                          Custom printed lamp shade\n",
+              "19  10001257  Gradient optics for even light distribution of..."
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cluster_df = pd.DataFrame(save_cluster, columns=['patent_id'])\n",
+        "enriched_df = title_df.merge(cluster_df, on='patent_id', how='inner')\n",
+        "enriched_df.iloc[0:20]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k2DfAaZaDIBj"
+      },
+      "source": [
+        "---\n",
+        "U.S. Patent and Trademark Office. “Data Download Tables.” PatentsView. Accessed [10/06/2024]. \n",
+        "\n",
+        "https://patentsview.org/download/data-download-tables.\n",
+        "\n",
+        "Data used is licensed under Creative Commons 4.0 \n",
+        "\n",
+        " You may obtain a copy of the License at https://creativecommons.org/licenses/by/4.0/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "___\n",
+        "Copyright (c) 2024, 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",
+        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
+        "___"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}