rapids_conda_colab_template.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/epsit03/ML-with-nvidia-rapids/blob/main/rapids_conda_colab_template.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scfLT2i0MLyD"
      },
      "source": [
        "# Environment Sanity Check #\n",
        "\n",
        "Click the _Runtime_ dropdown at the top of the page, then _Change Runtime Type_ and confirm the instance type is _GPU_.\n",
        "\n",
        "Check the output of `!nvidia-smi` to make sure you've been allocated a Tesla T4, P4, or P100."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67T0090Jk2KL",
        "outputId": "050e7cb1-78fd-4482-ab83-037a328b7486",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec  9 23:18:52 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P0    26W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtNdk7PSafKP"
      },
      "source": [
        "#Setup:\n",
        "Set up script installs\n",
        "1. Updates gcc in Colab\n",
        "1. Installs Conda\n",
        "1. Install RAPIDS' current stable version of its libraries, as well as some external libraries including:\n",
        "  1. cuDF\n",
        "  1. cuML\n",
        "  1. cuGraph\n",
        "  1. cuSpatial\n",
        "  1. cuSignal\n",
        "  1. BlazingSQL\n",
        "  1. xgboost\n",
        "1. Copy RAPIDS .so files into current working directory, a neccessary workaround for RAPIDS+Colab integration.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0C8IV5TQnjN",
        "outputId": "013b6de2-22e8-47ce-e7c5-0aeefd3e3675",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# This get the RAPIDS-Colab install files and test check your GPU.  Run this and the next cell only.\n",
        "# Please read the output of this cell.  If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps.\n",
        "!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n",
        "!python rapidsai-csp-utils/colab/env-check.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'rapidsai-csp-utils'...\n",
            "remote: Enumerating objects: 308, done.\u001b[K\n",
            "remote: Counting objects: 100% (137/137), done.\u001b[K\n",
            "remote: Compressing objects: 100% (82/82), done.\u001b[K\n",
            "remote: Total 308 (delta 79), reused 98 (delta 55), pack-reused 171\u001b[K\n",
            "Receiving objects: 100% (308/308), 89.88 KiB | 14.98 MiB/s, done.\n",
            "Resolving deltas: 100% (141/141), done.\n",
            "Traceback (most recent call last):\n",
            "  File \"rapidsai-csp-utils/colab/env-check.py\", line 1, in <module>\n",
            "    import pynvml\n",
            "ModuleNotFoundError: No module named 'pynvml'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sKA_9Dbg4s2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d7e038a-3dd2-409d-f13d-7d5ac4c8e098"
      },
      "source": [
        "# This will update the Colab environment and restart the kernel.  Don't run the next cell until you see the session crash.\n",
        "!bash rapidsai-csp-utils/colab/update_gcc.sh\n",
        "import os\n",
        "os._exit(00)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updating your Colab environment.  This will restart your kernel.  Don't Panic!\n",
            "Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
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            "Hit:8 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Get:9 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [1,073 kB]\n",
            "Hit:10 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
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            "Fetched 14.6 MB in 4s (3,773 kB/s)\n",
            "Reading package lists... Done\n",
            "Added repo\n",
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            "Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
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            "Reading package lists... Done\n",
            "Installing libstdc++\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Selected version '11.1.0-1ubuntu1~18.04.1' (Toolchain test builds:18.04/bionic [amd64]) for 'libstdc++6'\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  gcc-11-base libgcc-s1\n",
            "The following NEW packages will be installed:\n",
            "  gcc-11-base libgcc-s1\n",
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            "  libstdc++6\n",
            "1 upgraded, 2 newly installed, 0 to remove and 45 not upgraded.\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1C4pNiBhYHX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17ea564a-335d-4f59-b2d1-99e71b80f967"
      },
      "source": [
        "# This will install CondaColab.  This will restart your kernel one last time.  Run this cell by itself and only run the next cell once you see the session crash.\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:21\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ko_pJZbhZrM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fec10161-a383-4731-e51d-f92067ac4de9"
      },
      "source": [
        "# you can now run the rest of the cells as normal\n",
        "import condacolab\n",
        "condacolab.check()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_2dancFhf70",
        "outputId": "16427192-cf5f-4a29-ee8b-fafcf2ca1dda",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py <release> <packages>'\n",
        "# The <release> options are 'stable' and 'nightly'.  Leaving it blank or adding any other words will default to stable.\n",
        "!python rapidsai-csp-utils/colab/install_rapids.py stable\n",
        "import os\n",
        "os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'\n",
        "os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'\n",
        "os.environ['CONDA_PREFIX'] = '/usr/local'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "referencing-0.30.2   | 37 KB     | ####3      |  43% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "pywavelets-1.4.1     | 3.5 MB    | ###4       |  35% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "libglib-2.76.4       | 2.6 MB    | #########6 |  97% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "tornado-6.3.3        | 624 KB    | 2          |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libxcb-1.13          | 391 KB    | ########## | 100% \u001b[A\n",
            "\n",
            "libxcb-1.13          | 391 KB    | ########## | 100% \u001b[A\n",
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            "ipykernel-6.25.2     | 111 KB    | #4         |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "panel-0.14.1         | 13.5 MB   |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "jupyter-server-proxy | 32 KB     | ########## | 100% \u001b[A\u001b[A\n",
            "\n",
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            "panel-0.14.1         | 13.5 MB   | 5          |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "importlib_resources- | 30 KB     | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "importlib_resources- | 30 KB     | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "jxrlib-1.1           | 235 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "jxrlib-1.1           | 235 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "panel-0.14.1         | 13.5 MB   | #4         |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "dask-core-2023.3.2   | 823 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "dask-core-2023.3.2   | 823 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "panel-0.14.1         | 13.5 MB   | ##6        |  27% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "panel-0.14.1         | 13.5 MB   | ###8       |  38% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "orc-1.8.3            | 909 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "orc-1.8.3            | 909 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "panel-0.14.1         | 13.5 MB   | #####3     |  53% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "panel-0.14.1         | 13.5 MB   | #######4   |  74% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "panel-0.14.1         | 13.5 MB   | #########2 |  92% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "poppler-data-0.4.12  | 2.2 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "poppler-data-0.4.12  | 2.2 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pygments-2.16.1      | 833 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pygments-2.16.1      | 833 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pyproj-3.4.0         | 504 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pyproj-3.4.0         | 504 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "referencing-0.30.2   | 37 KB     | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "referencing-0.30.2   | 37 KB     | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "tornado-6.3.3        | 624 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "tornado-6.3.3        | 624 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "libglib-2.76.4       | 2.6 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "ipykernel-6.25.2     | 111 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "ipykernel-6.25.2     | 111 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pywavelets-1.4.1     | 3.5 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pywavelets-1.4.1     | 3.5 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "panel-0.14.1         | 13.5 MB   | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "\n",
            "\n",
            "The following PRELINK MESSAGES are INCLUDED:\n",
            "\n",
            "\n",
            "  File nvcomp.txt:\n",
            "\n",
            "  By downloading and using the libcudf conda package, you accept the terms\n",
            "  and conditions of the NVIDIA NVCOMP Software License Agreement:\n",
            "    https://developer.download.nvidia.com/compute/nvcomp/2.3/LICENSE.txt\n",
            "\n",
            "\n",
            "Executing transaction: ...working... By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html\n",
            "\n",
            "By downloading and using the CubinLinker conda packages, you accept the terms and conditions of the CubinLinker License Agreement: https://docs.rapids.ai/licenses/CubinLinker.txt\n",
            "\n",
            "done\n",
            "RAPIDS conda installation complete.  Updating Colab's libraries...\n",
            "Copying /usr/local/lib/libcudf.so to /usr/lib/libcudf.so\n",
            "Copying /usr/local/lib/libnccl.so to /usr/lib/libnccl.so\n",
            "Copying /usr/local/lib/libcuml.so to /usr/lib/libcuml.so\n",
            "Copying /usr/local/lib/libcugraph.so to /usr/lib/libcugraph.so\n",
            "Copying /usr/local/lib/libxgboost.so to /usr/lib/libxgboost.so\n",
            "Copying /usr/local/lib/libcuspatial.so to /usr/lib/libcuspatial.so\n",
            "Copying /usr/local/lib/libgeos.so to /usr/lib/libgeos.so\n",
            "Copying /usr/local/lib/libgeos_c.so to /usr/lib/libgeos_c.so\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZJMJ6BulmMn"
      },
      "source": [
        "# RAPIDS is now installed on Colab.  You can copy your code into the cells below.  Enjoy!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nLrk46BllED"
      },
      "source": [
        "import cudf, cuml, cugraph, cuspatial"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cudf.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bvcxjJOCX1tL",
        "outputId": "9f72cd40-17cf-4785-d2f7-c2e2d3a030dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'23.02.00a+164.ga674a638bc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dlsyk9m9NN2K"
      },
      "source": [
        "# Next Steps #\n",
        "\n",
        "For an overview of how you can access and work with your own datasets in Colab, check out [this guide](https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92).\n",
        "\n",
        "For more RAPIDS examples, check out our RAPIDS notebooks repos:\n",
        "1. https://github.com/rapidsai/notebooks\n",
        "2. https://github.com/rapidsai/notebooks-contrib"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q78i8uByXLsJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}