upvote_count_classification.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChWYc5cL-ic9",
        "outputId": "e88060ab-aa65-471e-8ce2-424c64f5d25d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Apr  1 02:40:55 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 P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    28W / 250W |      0MiB / 16280MiB |      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"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n",
        "! python rapidsai-csp-utils/colab/env-check.py"
      ],
      "metadata": {
        "id": "QBkljTlr-r_J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a2234ab-b013-4602-a4d1-2e6b4d35b3e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'rapidsai-csp-utils'...\n",
            "remote: Enumerating objects: 300, done.\u001b[K\n",
            "remote: Counting objects: 100% (129/129), done.\u001b[K\n",
            "remote: Compressing objects: 100% (108/108), done.\u001b[K\n",
            "remote: Total 300 (delta 72), reused 43 (delta 21), pack-reused 171\u001b[K\n",
            "Receiving objects: 100% (300/300), 86.52 KiB | 4.33 MiB/s, done.\n",
            "Resolving deltas: 100% (134/134), done.\n",
            "***********************************************************************\n",
            "Woo! Your instance has the right kind of GPU, a Tesla P100-PCIE-16GB!\n",
            "***********************************************************************\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! bash rapidsai-csp-utils/colab/update_gcc.sh\n",
        "import os\n",
        "os._exit(00)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myTFHSq9AX9r",
        "outputId": "a5a2c141-05cb-48f7-e2fd-6e6eea71a832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updating your Colab environment.  This will restart your kernel.  Don't Panic!\n",
            "Hit:1 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
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            "Ign:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
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            "Get:12 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [80.8 kB]\n",
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            "Get:24 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,690 kB]\n",
            "Get:25 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic InRelease [20.8 kB]\n",
            "Get:26 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,830 kB]\n",
            "Get:27 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [883 kB]\n",
            "Get:28 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,486 kB]\n",
            "Get:29 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [937 kB]\n",
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            "Fetched 15.7 MB in 4s (3,946 kB/s)\n",
            "Reading package lists... Done\n",
            "Added repo\n",
            "Hit:1 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Hit:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:4 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Ign:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:8 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
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            "Hit:11 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Hit:13 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
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            "Hit:15 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\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 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",
            "The following packages will be upgraded:\n",
            "  libstdc++6\n",
            "1 upgraded, 2 newly installed, 0 to remove and 93 not upgraded.\n",
            "Need to get 641 kB of archives.\n",
            "After this operation, 981 kB of additional disk space will be used.\n",
            "Get:1 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 gcc-11-base amd64 11.1.0-1ubuntu1~18.04.1 [19.0 kB]\n",
            "Get:2 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 libgcc-s1 amd64 11.1.0-1ubuntu1~18.04.1 [41.8 kB]\n",
            "Get:3 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 libstdc++6 amd64 11.1.0-1ubuntu1~18.04.1 [580 kB]\n",
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            "debconf: unable to initialize frontend: Dialog\n",
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            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package gcc-11-base:amd64.\n",
            "(Reading database ... 156210 files and directories currently installed.)\n",
            "Preparing to unpack .../gcc-11-base_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking gcc-11-base:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Setting up gcc-11-base:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Selecting previously unselected package libgcc-s1:amd64.\n",
            "(Reading database ... 156215 files and directories currently installed.)\n",
            "Preparing to unpack .../libgcc-s1_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking libgcc-s1:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Replacing files in old package libgcc1:amd64 (1:8.4.0-1ubuntu1~18.04) ...\n",
            "Setting up libgcc-s1:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "(Reading database ... 156217 files and directories currently installed.)\n",
            "Preparing to unpack .../libstdc++6_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking libstdc++6:amd64 (11.1.0-1ubuntu1~18.04.1) over (8.4.0-1ubuntu1~18.04) ...\n",
            "Setting up libstdc++6:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "restarting Colab...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sq70zeWG_79X",
        "outputId": "1c934960-f5fa-4f3e-dbc9-69c797676a49"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.0.tar.gz (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 5.0 MB/s \n",
            "\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 15.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.63.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.10.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.11.1+cu111)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.21.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.4.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (3.2.5)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 54.1 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.5.1-py3-none-any.whl (77 kB)\n",
            "\u001b[K     |████████████████████████████████| 77 kB 4.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence-transformers) (3.10.0.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 52.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2019.12.20)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.49-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 55.9 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5 MB 42.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (4.11.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.7.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (7.1.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.0-py3-none-any.whl size=120747 sha256=48553806580d82b8f58b4eae0a7e829e70911c2f6d1ead7aebcdfc30ff5b59d8\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/c0/df/b6873ab7aac3f2465aa9144b6b4c41c4391cfecc027c8b07e7\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece, sentence-transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.5.1 pyyaml-6.0 sacremoses-0.0.49 sentence-transformers-2.2.0 sentencepiece-0.1.96 tokenizers-0.11.6 transformers-4.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import condacolab\n",
        "condacolab.install()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rg_TAkK2Ambw",
        "outputId": "9ad1ffd4-313d-43ab-f0f4-3ceb8b6262d3"
      },
      "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 0:00:24\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import condacolab\n",
        "condacolab.check()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-zKacg2tAnpP",
        "outputId": "9b5eff8d-3201-4d10-f089-365d6bcabf08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python rapidsai-csp-utils/colab/install_rapids.py stable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7v-C3oVoA7RO",
        "outputId": "80eb87e7-7515-493e-8c44-59b701c3bde7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: cffi 1.14.5\n",
            "Uninstalling cffi-1.14.5:\n",
            "  Successfully uninstalled cffi-1.14.5\n",
            "Found existing installation: cryptography 3.4.5\n",
            "Uninstalling cryptography-3.4.5:\n",
            "  Successfully uninstalled cryptography-3.4.5\n",
            "Collecting cffi==1.15.0\n",
            "  Downloading cffi-1.15.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (427 kB)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/site-packages (from cffi==1.15.0) (2.20)\n",
            "Installing collected packages: cffi\n",
            "Successfully installed cffi-1.15.0\n",
            "Installing RAPIDS Stable 21.12\n",
            "Starting the RAPIDS install on Colab.  This will take about 15 minutes.\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... WARNING conda.core.solve:_add_specs(611): pinned spec cudatoolkit=11.1 conflicts with explicit specs.  Overriding pinned spec.\n",
            "failed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: ...working... WARNING conda.core.solve:_add_specs(611): pinned spec cudatoolkit=11.1 conflicts with explicit specs.  Overriding pinned spec.\n",
            "failed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): ...working... done\n",
            "Solving environment: ...working... WARNING conda.core.solve:_add_specs(611): pinned spec cudatoolkit=11.1 conflicts with explicit specs.  Overriding pinned spec.\n",
            "done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cudatoolkit=11.2\n",
            "    - dask-sql\n",
            "    - gcsfs\n",
            "    - llvmlite\n",
            "    - openssl\n",
            "    - python=3.7\n",
            "    - rapids=21.12\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    abseil-cpp-20210324.2      |       h9c3ff4c_0        1010 KB  conda-forge\n",
            "    aiohttp-3.8.1              |   py37h5e8e339_0         581 KB  conda-forge\n",
            "    aiosignal-1.2.0            |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    alsa-lib-1.2.3             |       h516909a_0         560 KB  conda-forge\n",
            "    anyio-3.5.0                |   py37h89c1867_0         150 KB  conda-forge\n",
            "    appdirs-1.4.4              |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    argon2-cffi-21.3.0         |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    argon2-cffi-bindings-21.2.0|   py37h5e8e339_1          34 KB  conda-forge\n",
            "    arrow-cpp-5.0.0            |py37hc2523e2_2_cuda        23.3 MB  conda-forge\n",
            "    arrow-cpp-proc-3.0.0       |             cuda          24 KB  conda-forge\n",
            "    asgiref-3.5.0              |     pyhd8ed1ab_0          22 KB  conda-forge\n",
            "    async-timeout-4.0.2        |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    asynctest-0.13.0           |             py_0          24 KB  conda-forge\n",
            "    attrs-21.4.0               |     pyhd8ed1ab_0          49 KB  conda-forge\n",
            "    aws-c-cal-0.5.11           |       h95a6274_0          37 KB  conda-forge\n",
            "    aws-c-common-0.6.2         |       h7f98852_0         168 KB  conda-forge\n",
            "    aws-c-event-stream-0.2.7   |      h3541f99_13          47 KB  conda-forge\n",
            "    aws-c-io-0.10.5            |       hfb6a706_0         121 KB  conda-forge\n",
            "    aws-checksums-0.1.11       |       ha31a3da_7          50 KB  conda-forge\n",
            "    aws-sdk-cpp-1.8.186        |       hb4091e7_3         4.6 MB  conda-forge\n",
            "    backcall-0.2.0             |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    backports-1.0              |             py_2           4 KB  conda-forge\n",
            "    backports.functools_lru_cache-1.6.4|     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    backports.zoneinfo-0.2.1   |   py37h5e8e339_4          47 KB  conda-forge\n",
            "    beautifulsoup4-4.10.0      |     pyha770c72_0          77 KB  conda-forge\n",
            "    bleach-4.1.0               |     pyhd8ed1ab_0         121 KB  conda-forge\n",
            "    blinker-1.4                |             py_1          13 KB  conda-forge\n",
            "    blosc-1.21.0               |       h9c3ff4c_0         841 KB  conda-forge\n",
            "    bokeh-2.4.0                |   py37h89c1867_0        13.5 MB  conda-forge\n",
            "    boost-1.74.0               |   py37h796e4cb_5         342 KB  conda-forge\n",
            "    boost-cpp-1.74.0           |       h312852a_4        16.3 MB  conda-forge\n",
            "    brotli-1.0.9               |       h7f98852_6          18 KB  conda-forge\n",
            "    brotli-bin-1.0.9           |       h7f98852_6          19 KB  conda-forge\n",
            "    brunsli-0.1                |       h9c3ff4c_0         200 KB  conda-forge\n",
            "    c-blosc2-2.0.4             |       h5f21a17_1         183 KB  conda-forge\n",
            "    ca-certificates-2021.10.8  |       ha878542_0         139 KB  conda-forge\n",
            "    cachetools-5.0.0           |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    cairo-1.16.0               |    h6cf1ce9_1008         1.5 MB  conda-forge\n",
            "    certifi-2021.10.8          |   py37h89c1867_2         145 KB  conda-forge\n",
            "    cfitsio-3.470              |       hb418390_7         1.3 MB  conda-forge\n",
            "    charls-2.2.0               |       h9c3ff4c_0         138 KB  conda-forge\n",
            "    charset-normalizer-2.0.12  |     pyhd8ed1ab_0          35 KB  conda-forge\n",
            "    click-8.1.1                |   py37h89c1867_1         144 KB  conda-forge\n",
            "    click-plugins-1.1.1        |             py_0           9 KB  conda-forge\n",
            "    cligj-0.7.2                |     pyhd8ed1ab_1          10 KB  conda-forge\n",
            "    cloudpickle-2.0.0          |     pyhd8ed1ab_0          24 KB  conda-forge\n",
            "    colorcet-3.0.0             |     pyhd8ed1ab_0         1.5 MB  conda-forge\n",
            "    conda-4.12.0               |   py37h89c1867_0         1.0 MB  conda-forge\n",
            "    cucim-21.12.00             |cuda_11_py37_g6d1f082_0         3.2 MB  rapidsai\n",
            "    cudatoolkit-11.2.72        |       h2bc3f7f_0       933.4 MB  nvidia\n",
            "    cudf-21.12.02              |cuda_11_py37_g06540b9b37_0        12.6 MB  rapidsai\n",
            "    cudf_kafka-21.12.02        |py37_g06540b9b37_0         1.8 MB  rapidsai\n",
            "    cugraph-21.12.00           |cuda11_py37_g3a43e9d0_0         7.2 MB  rapidsai\n",
            "    cuml-21.12.00              |cuda11_py37_g04c4927f3_0         9.4 MB  rapidsai\n",
            "    cupy-9.6.0                 |   py37h07c33ac_0        51.5 MB  conda-forge\n",
            "    curl-7.78.0                |       hea6ffbf_0         148 KB  conda-forge\n",
            "    cusignal-21.12.00          |  py37_g2bf865c_0         1.1 MB  rapidsai\n",
            "    cuspatial-21.12.00         |  py37_gab6748f_0        15.9 MB  rapidsai\n",
            "    custreamz-21.12.02         |py37_g06540b9b37_0          32 KB  rapidsai\n",
            "    cuxfilter-21.12.00         |  py37_g2e0fb5a_0         137 KB  rapidsai\n",
            "    cycler-0.11.0              |     pyhd8ed1ab_0          10 KB  conda-forge\n",
            "    cyrus-sasl-2.1.27          |       h230043b_5         228 KB  conda-forge\n",
            "    cytoolz-0.11.2             |   py37h5e8e339_1         407 KB  conda-forge\n",
            "    dask-2021.11.2             |     pyhd8ed1ab_0           5 KB  conda-forge\n",
            "    dask-core-2021.11.2        |     pyhd8ed1ab_0         783 KB  conda-forge\n",
            "    dask-cuda-21.12.00         |           py37_0         123 KB  rapidsai\n",
            "    dask-cudf-21.12.02         |cuda_11_py37_g06540b9b37_0         112 KB  rapidsai\n",
            "    dask-sql-2022.1.0          |   py37h89c1867_0        19.7 MB  conda-forge\n",
            "    datashader-0.11.1          |     pyh9f0ad1d_0        14.0 MB  conda-forge\n",
            "    datashape-0.5.4            |             py_1          49 KB  conda-forge\n",
            "    debugpy-1.5.1              |   py37hcd2ae1e_0         2.0 MB  conda-forge\n",
            "    decorator-5.1.1            |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    defusedxml-0.7.1           |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    distributed-2021.11.2      |   py37h89c1867_0         1.1 MB  conda-forge\n",
            "    dlpack-0.5                 |       h9c3ff4c_0          12 KB  conda-forge\n",
            "    entrypoints-0.4            |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    expat-2.4.8                |       h27087fc_0         187 KB  conda-forge\n",
            "    faiss-proc-1.0.0           |             cuda          24 KB  rapidsai\n",
            "    fastapi-0.70.1             |     pyhd8ed1ab_0          42 KB  conda-forge\n",
            "    fastavro-1.4.10            |   py37h540881e_0         447 KB  conda-forge\n",
            "    fastrlock-0.8              |   py37hd23a5d3_1          31 KB  conda-forge\n",
            "    fiona-1.8.20               |   py37hb7e2723_2         1.1 MB  conda-forge\n",
            "    flit-core-3.7.1            |     pyhd8ed1ab_0          44 KB  conda-forge\n",
            "    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n",
            "    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n",
            "    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n",
            "    font-ttf-ubuntu-0.83       |       hab24e00_0         1.9 MB  conda-forge\n",
            "    fontconfig-2.13.96         |       h8e229c2_2         306 KB  conda-forge\n",
            "    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge\n",
            "    fonts-conda-forge-1        |                0           4 KB  conda-forge\n",
            "    freetype-2.10.4            |       h0708190_1         890 KB  conda-forge\n",
            "    freexl-1.0.6               |       h7f98852_0          48 KB  conda-forge\n",
            "    frozenlist-1.3.0           |   py37h540881e_1          43 KB  conda-forge\n",
            "    fsspec-2022.2.0            |     pyhd8ed1ab_0          92 KB  conda-forge\n",
            "    gcsfs-2022.2.0             |     pyhd8ed1ab_0          25 KB  conda-forge\n",
            "    gdal-3.3.2                 |   py37hd5a0ba4_3         1.7 MB  conda-forge\n",
            "    geopandas-0.9.0            |     pyhd8ed1ab_1           5 KB  conda-forge\n",
            "    geopandas-base-0.9.0       |     pyhd8ed1ab_1         950 KB  conda-forge\n",
            "    geos-3.9.1                 |       h9c3ff4c_2         1.1 MB  conda-forge\n",
            "    geotiff-1.7.0              |       h08e826d_2         296 KB  conda-forge\n",
            "    gettext-0.19.8.1           |    h0b5b191_1005         3.6 MB  conda-forge\n",
            "    gflags-2.2.2               |    he1b5a44_1004         114 KB  conda-forge\n",
            "    giflib-5.2.1               |       h36c2ea0_2          77 KB  conda-forge\n",
            "    glog-0.5.0                 |       h48cff8f_0         104 KB  conda-forge\n",
            "    google-api-core-2.5.0      |     pyhd8ed1ab_0          69 KB  conda-forge\n",
            "    google-auth-2.6.2          |     pyh6c4a22f_0          84 KB  conda-forge\n",
            "    google-auth-oauthlib-0.5.1 |     pyhd8ed1ab_0          20 KB  conda-forge\n",
            "    google-cloud-core-2.2.2    |     pyh6c4a22f_0          27 KB  conda-forge\n",
            "    google-cloud-storage-2.1.0 |     pyh6c4a22f_0          73 KB  conda-forge\n",
            "    google-crc32c-1.1.2        |   py37hab72019_2          24 KB  conda-forge\n",
            "    google-resumable-media-2.1.0|     pyh6c4a22f_0          41 KB  conda-forge\n",
            "    googleapis-common-protos-1.56.0|   py37h89c1867_0         154 KB  conda-forge\n",
            "    graphite2-1.3.13           |    h58526e2_1001         102 KB  conda-forge\n",
            "    grpc-cpp-1.39.0            |       hf1f433d_2         3.6 MB  conda-forge\n",
            "    grpcio-1.38.1              |   py37hb27c1af_0         2.2 MB  conda-forge\n",
            "    h11-0.13.0                 |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    harfbuzz-2.9.1             |       h83ec7ef_1         2.0 MB  conda-forge\n",
            "    hdf4-4.2.15                |       h10796ff_3         950 KB  conda-forge\n",
            "    hdf5-1.12.1                |nompi_h2750804_100         3.5 MB  conda-forge\n",
            "    heapdict-1.0.1             |             py_0           7 KB  conda-forge\n",
            "    imagecodecs-2021.8.26      |   py37hfe5a812_1         7.2 MB  conda-forge\n",
            "    imageio-2.13.1             |     pyhd8ed1ab_0         3.1 MB  conda-forge\n",
            "    importlib-metadata-4.11.3  |   py37h89c1867_1          33 KB  conda-forge\n",
            "    importlib_metadata-4.11.3  |       hd8ed1ab_1           4 KB  conda-forge\n",
            "    importlib_resources-5.6.0  |     pyhd8ed1ab_0          20 KB  conda-forge\n",
            "    ipykernel-6.11.0           |   py37h25bab4e_0         183 KB  conda-forge\n",
            "    ipython-7.32.0             |   py37h89c1867_0         1.1 MB  conda-forge\n",
            "    ipython_genutils-0.2.0     |             py_1          21 KB  conda-forge\n",
            "    ipywidgets-7.7.0           |     pyhd8ed1ab_0         103 KB  conda-forge\n",
            "    jbig-2.1                   |    h7f98852_2003          43 KB  conda-forge\n",
            "    jedi-0.18.1                |   py37h89c1867_0         997 KB  conda-forge\n",
            "    jinja2-3.1.1               |     pyhd8ed1ab_0          99 KB  conda-forge\n",
            "    joblib-1.1.0               |     pyhd8ed1ab_0         210 KB  conda-forge\n",
            "    jpeg-9e                    |       h7f98852_0         269 KB  conda-forge\n",
            "    jpype1-1.3.0               |   py37h2527ec5_2         492 KB  conda-forge\n",
            "    json-c-0.15                |       h98cffda_0         274 KB  conda-forge\n",
            "    jsonschema-4.4.0           |     pyhd8ed1ab_0          57 KB  conda-forge\n",
            "    jupyter-server-proxy-3.2.1 |     pyhd8ed1ab_0          29 KB  conda-forge\n",
            "    jupyter_client-7.2.1       |     pyhd8ed1ab_0          90 KB  conda-forge\n",
            "    jupyter_core-4.9.2         |   py37h89c1867_0          80 KB  conda-forge\n",
            "    jupyter_server-1.16.0      |     pyhd8ed1ab_1         242 KB  conda-forge\n",
            "    jupyterlab_pygments-0.1.2  |     pyh9f0ad1d_0           8 KB  conda-forge\n",
            "    jupyterlab_widgets-1.1.0   |     pyhd8ed1ab_0         133 KB  conda-forge\n",
            "    jxrlib-1.1                 |       h7f98852_2         235 KB  conda-forge\n",
            "    kealib-1.4.14              |       h87e4c3c_3         186 KB  conda-forge\n",
            "    keyutils-1.6.1             |       h166bdaf_0         115 KB  conda-forge\n",
            "    kiwisolver-1.4.2           |   py37h7cecad7_0          72 KB  conda-forge\n",
            "    krb5-1.19.3                |       h3790be6_0         1.4 MB  conda-forge\n",
            "    lcms2-2.12                 |       hddcbb42_0         443 KB  conda-forge\n",
            "    lerc-3.0                   |       h9c3ff4c_0         216 KB  conda-forge\n",
            "    libaec-1.0.6               |       h9c3ff4c_0          45 KB  conda-forge\n",
            "    libarchive-3.5.2           |       hccf745f_1         1.6 MB  conda-forge\n",
            "    libblas-3.9.0              |13_linux64_openblas          12 KB  conda-forge\n",
            "    libbrotlicommon-1.0.9      |       h7f98852_6          65 KB  conda-forge\n",
            "    libbrotlidec-1.0.9         |       h7f98852_6          33 KB  conda-forge\n",
            "    libbrotlienc-1.0.9         |       h7f98852_6         286 KB  conda-forge\n",
            "    libcblas-3.9.0             |13_linux64_openblas          12 KB  conda-forge\n",
            "    libcrc32c-1.1.2            |       h9c3ff4c_0          20 KB  conda-forge\n",
            "    libcucim-21.12.00          |cuda11_g6d1f082_0         2.8 MB  rapidsai\n",
            "    libcudf-21.12.02           |cuda11_g06540b9b37_0       288.2 MB  rapidsai\n",
            "    libcudf_kafka-21.12.02     |    g06540b9b37_0         126 KB  rapidsai\n",
            "    libcugraph-21.12.00        |cuda11_g3a43e9d0_0       259.4 MB  rapidsai\n",
            "    libcuml-21.12.00           |cuda11_g04c4927f3_0       222.9 MB  rapidsai\n",
            "    libcumlprims-21.12.00      |cuda11_g0a7f19f_0         3.7 MB  nvidia\n",
            "    libcurl-7.78.0             |       h2574ce0_0         335 KB  conda-forge\n",
            "    libcusolver-11.3.4.124     |       h33c3c4e_0        87.0 MB  nvidia\n",
            "    libcuspatial-21.12.00      |cuda11_gab6748f_0         6.4 MB  rapidsai\n",
            "    libdap4-3.20.6             |       hd7c4107_2        11.3 MB  conda-forge\n",
            "    libdeflate-1.8             |       h7f98852_0          67 KB  conda-forge\n",
            "    libevent-2.1.10            |       h9b69904_4         1.1 MB  conda-forge\n",
            "    libfaiss-1.7.0             |cuda112h5bea7ad_8_cuda        98.5 MB  conda-forge\n",
            "    libgcc-ng-11.2.0           |      h1d223b6_14         906 KB  conda-forge\n",
            "    libgcrypt-1.10.0           |       h7f98852_0         705 KB  conda-forge\n",
            "    libgdal-3.3.2              |       h6acdded_3        13.1 MB  conda-forge\n",
            "    libgfortran-ng-11.2.0      |      h69a702a_14          20 KB  conda-forge\n",
            "    libgfortran5-11.2.0        |      h5c6108e_14         1.7 MB  conda-forge\n",
            "    libglib-2.68.4             |       h3e27bee_0         3.0 MB  conda-forge\n",
            "    libgomp-11.2.0             |      h1d223b6_14         429 KB  conda-forge\n",
            "    libgpg-error-1.44          |       h9eb791d_0         294 KB  conda-forge\n",
            "    libgsasl-1.10.0            |       h5b4c23d_0         179 KB  conda-forge\n",
            "    libhwloc-2.3.0             |       h5e5b7d1_1         2.7 MB  conda-forge\n",
            "    libkml-1.3.0               |    h238a007_1014         591 KB  conda-forge\n",
            "    liblapack-3.9.0            |13_linux64_openblas          12 KB  conda-forge\n",
            "    libllvm11-11.1.0           |       hf817b99_3        29.1 MB  conda-forge\n",
            "    libnetcdf-4.8.1            |nompi_hb3fd0d9_101         1.5 MB  conda-forge\n",
            "    libnsl-2.0.0               |       h7f98852_0          31 KB  conda-forge\n",
            "    libntlm-1.4                |    h7f98852_1002          32 KB  conda-forge\n",
            "    libopenblas-0.3.18         |pthreads_h8fe5266_0         9.6 MB  conda-forge\n",
            "    libpng-1.6.37              |       h21135ba_2         306 KB  conda-forge\n",
            "    libpq-13.5                 |       hd57d9b9_1         2.8 MB  conda-forge\n",
            "    libprotobuf-3.16.0         |       h780b84a_0         2.5 MB  conda-forge\n",
            "    librdkafka-1.6.1           |       hc49e61c_1        12.6 MB  conda-forge\n",
            "    librmm-21.12.00            |cuda11_g957ad04_0         688 KB  rapidsai\n",
            "    librttopo-1.1.0            |       h1185371_6         235 KB  conda-forge\n",
            "    libsodium-1.0.18           |       h36c2ea0_1         366 KB  conda-forge\n",
            "    libspatialindex-1.9.3      |       h9c3ff4c_4         4.6 MB  conda-forge\n",
            "    libspatialite-5.0.1        |       h5cf074c_8         4.4 MB  conda-forge\n",
            "    libstdcxx-ng-11.2.0        |      he4da1e4_14         4.2 MB  conda-forge\n",
            "    libthrift-0.14.2           |       he6d91bd_1         4.5 MB  conda-forge\n",
            "    libtiff-4.3.0              |       h6f004c6_2         614 KB  conda-forge\n",
            "    libutf8proc-2.7.0          |       h7f98852_0          98 KB  conda-forge\n",
            "    libuuid-2.32.1             |    h7f98852_1000          28 KB  conda-forge\n",
            "    libuv-1.42.0               |       h7f98852_0         1.0 MB  conda-forge\n",
            "    libwebp-1.2.2              |       h3452ae3_0          85 KB  conda-forge\n",
            "    libwebp-base-1.2.2         |       h7f98852_1         824 KB  conda-forge\n",
            "    libxcb-1.13                |    h7f98852_1004         391 KB  conda-forge\n",
            "    libxgboost-1.5.0dev.rapidsai21.12|       cuda11.2_0       128.2 MB  rapidsai\n",
            "    libxml2-2.9.12             |       h72842e0_0         772 KB  conda-forge\n",
            "    libzip-1.8.0               |       h4de3113_1         126 KB  conda-forge\n",
            "    libzlib-1.2.11             |    h166bdaf_1014          60 KB  conda-forge\n",
            "    libzopfli-1.0.3            |       h9c3ff4c_0         164 KB  conda-forge\n",
            "    llvmlite-0.38.0            |   py37h9d7f4d0_0         2.9 MB  conda-forge\n",
            "    locket-0.2.0               |             py_2           6 KB  conda-forge\n",
            "    mapclassify-2.4.3          |     pyhd8ed1ab_0          36 KB  conda-forge\n",
            "    markdown-3.3.6             |     pyhd8ed1ab_0          67 KB  conda-forge\n",
            "    markupsafe-2.1.1           |   py37h540881e_1          22 KB  conda-forge\n",
            "    matplotlib-base-3.4.3      |   py37h1058ff1_0         7.2 MB  conda-forge\n",
            "    matplotlib-inline-0.1.3    |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    mistune-0.8.4              |py37h5e8e339_1005          54 KB  conda-forge\n",
            "    msgpack-python-1.0.3       |   py37h7cecad7_1          90 KB  conda-forge\n",
            "    multidict-6.0.2            |   py37h5e8e339_0          52 KB  conda-forge\n",
            "    multipledispatch-0.6.0     |             py_0          12 KB  conda-forge\n",
            "    munch-2.5.0                |             py_0          12 KB  conda-forge\n",
            "    nbclient-0.5.13            |     pyhd8ed1ab_0          64 KB  conda-forge\n",
            "    nbconvert-6.4.5            |     pyhd8ed1ab_1           6 KB  conda-forge\n",
            "    nbconvert-core-6.4.5       |     pyhd8ed1ab_1         427 KB  conda-forge\n",
            "    nbconvert-pandoc-6.4.5     |     pyhd8ed1ab_1           4 KB  conda-forge\n",
            "    nbformat-5.2.0             |     pyhd8ed1ab_0         105 KB  conda-forge\n",
            "    nccl-2.12.7.1              |       h0800d71_0       135.5 MB  conda-forge\n",
            "    nest-asyncio-1.5.4         |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    networkx-2.7.1             |     pyhd8ed1ab_0         1.5 MB  conda-forge\n",
            "    nodejs-14.17.4             |       h92b4a50_0        15.8 MB  conda-forge\n",
            "    notebook-6.4.10            |     pyha770c72_0         6.3 MB  conda-forge\n",
            "    numba-0.55.1               |   py37h2d894fd_0         3.8 MB  conda-forge\n",
            "    numpy-1.21.5               |   py37hf2998dd_0         6.1 MB  conda-forge\n",
            "    nvtx-0.2.3                 |   py37h5e8e339_1          55 KB  conda-forge\n",
            "    oauthlib-3.2.0             |     pyhd8ed1ab_0          90 KB  conda-forge\n",
            "    olefile-0.46               |     pyh9f0ad1d_1          32 KB  conda-forge\n",
            "    openjdk-11.0.9.1           |       h5cc2fde_1       173.5 MB  conda-forge\n",
            "    openjpeg-2.4.0             |       hb52868f_1         444 KB  conda-forge\n",
            "    openssl-1.1.1n             |       h166bdaf_0         2.1 MB  conda-forge\n",
            "    orc-1.6.9                  |       h58a87f1_0         746 KB  conda-forge\n",
            "    packaging-21.3             |     pyhd8ed1ab_0          36 KB  conda-forge\n",
            "    pandas-1.3.5               |   py37he8f5f7f_0        12.7 MB  conda-forge\n",
            "    pandoc-2.17.1.1            |       ha770c72_0        11.9 MB  conda-forge\n",
            "    pandocfilters-1.5.0        |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    panel-0.12.4               |     pyhd8ed1ab_0         9.8 MB  conda-forge\n",
            "    param-1.12.1               |     pyh6c4a22f_0          72 KB  conda-forge\n",
            "    parquet-cpp-1.5.1          |                2           3 KB  conda-forge\n",
            "    parso-0.8.3                |     pyhd8ed1ab_0          69 KB  conda-forge\n",
            "    partd-1.2.0                |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    pcre-8.45                  |       h9c3ff4c_0         253 KB  conda-forge\n",
            "    pexpect-4.8.0              |     pyh9f0ad1d_2          47 KB  conda-forge\n",
            "    pickle5-0.0.12             |   py37h5e8e339_0         173 KB  conda-forge\n",
            "    pickleshare-0.7.5          |          py_1003           9 KB  conda-forge\n",
            "    pillow-8.3.1               |   py37h0f21c89_0         692 KB  conda-forge\n",
            "    pixman-0.40.0              |       h36c2ea0_0         627 KB  conda-forge\n",
            "    pooch-1.6.0                |     pyhd8ed1ab_0          44 KB  conda-forge\n",
            "    poppler-21.09.0            |       h0e1ea10_1        16.8 MB  conda-forge\n",
            "    poppler-data-0.4.11        |       hd8ed1ab_0         3.6 MB  conda-forge\n",
            "    postgresql-13.5            |       h2510834_1         5.3 MB  conda-forge\n",
            "    proj-8.1.0                 |       h277dcde_1         2.9 MB  conda-forge\n",
            "    prometheus_client-0.13.1   |     pyhd8ed1ab_0          47 KB  conda-forge\n",
            "    prompt-toolkit-3.0.27      |     pyha770c72_0         252 KB  conda-forge\n",
            "    protobuf-3.16.0            |   py37hcd2ae1e_0         342 KB  conda-forge\n",
            "    psutil-5.9.0               |   py37h5e8e339_0         344 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h36c2ea0_1001           5 KB  conda-forge\n",
            "    ptxcompiler-0.2.0          |   py37hcfeb250_0         5.6 MB  rapidsai\n",
            "    ptyprocess-0.7.0           |     pyhd3deb0d_0          16 KB  conda-forge\n",
            "    py-xgboost-1.5.0dev.rapidsai21.12|   cuda11.2py37_0         159 KB  rapidsai\n",
            "    pyarrow-5.0.0              |py37hf0016df_2_cuda         2.9 MB  conda-forge\n",
            "    pyasn1-0.4.8               |             py_0          53 KB  conda-forge\n",
            "    pyasn1-modules-0.2.7       |             py_0          60 KB  conda-forge\n",
            "    pyct-0.4.6                 |             py_0           3 KB  conda-forge\n",
            "    pyct-core-0.4.6            |             py_0          13 KB  conda-forge\n",
            "    pydantic-1.9.0             |   py37h5e8e339_0         2.4 MB  conda-forge\n",
            "    pydeck-0.5.0               |     pyh9f0ad1d_0         3.6 MB  conda-forge\n",
            "    pyee-8.1.0                 |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    pygments-2.11.2            |     pyhd8ed1ab_0         796 KB  conda-forge\n",
            "    pyjwt-2.3.0                |     pyhd8ed1ab_1          18 KB  conda-forge\n",
            "    pynvml-11.4.1              |     pyhd8ed1ab_0          40 KB  conda-forge\n",
            "    pyparsing-3.0.7            |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    pyppeteer-1.0.2            |     pyhd8ed1ab_0          63 KB  conda-forge\n",
            "    pyproj-3.1.0               |   py37hdf91f24_4         523 KB  conda-forge\n",
            "    pyrsistent-0.18.1          |   py37h5e8e339_0          90 KB  conda-forge\n",
            "    python-confluent-kafka-1.6.0|   py37h5e8e339_1         125 KB  conda-forge\n",
            "    python-dateutil-2.8.2      |     pyhd8ed1ab_0         240 KB  conda-forge\n",
            "    python-tzdata-2022.1       |     pyhd8ed1ab_0         151 KB  conda-forge\n",
            "    python_abi-3.7             |          2_cp37m           4 KB  conda-forge\n",
            "    pytz-2022.1                |     pyhd8ed1ab_0         242 KB  conda-forge\n",
            "    pytz-deprecation-shim-0.1.0.post0|   py37h89c1867_1          22 KB  conda-forge\n",
            "    pyu2f-0.1.5                |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    pyviz_comms-2.2.0          |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    pywavelets-1.3.0           |   py37h6c7ee08_0         4.4 MB  conda-forge\n",
            "    pyyaml-6.0                 |   py37h540881e_4         178 KB  conda-forge\n",
            "    pyzmq-22.3.0               |   py37h336d617_1         501 KB  conda-forge\n",
            "    rapids-21.12.00            |cuda11.2_py37_gc46440c_94           5 KB  rapidsai\n",
            "    rapids-xgboost-21.12.00    |cuda11.2_py37_gc46440c_94           5 KB  rapidsai\n",
            "    re2-2021.08.01             |       h9c3ff4c_0         220 KB  conda-forge\n",
            "    readline-8.1               |       h46c0cb4_0         295 KB  conda-forge\n",
            "    requests-oauthlib-1.3.1    |     pyhd8ed1ab_0          22 KB  conda-forge\n",
            "    rmm-21.12.00               |cuda11_py37_g957ad04_0_has_cma         943 KB  rapidsai\n",
            "    rsa-4.8                    |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    rtree-0.9.7                |   py37h0b55af0_3          45 KB  conda-forge\n",
            "    s2n-1.0.10                 |       h9b69904_0         442 KB  conda-forge\n",
            "    scikit-image-0.18.1        |   py37hdc94413_0        11.5 MB  conda-forge\n",
            "    scikit-learn-1.0.2         |   py37hf9e9bfc_0         7.8 MB  conda-forge\n",
            "    scipy-1.7.3                |   py37hf2a6cf1_0        21.8 MB  conda-forge\n",
            "    send2trash-1.8.0           |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    setuptools-59.8.0          |   py37h89c1867_1         1.0 MB  conda-forge\n",
            "    shapely-1.8.0              |   py37h48c49eb_0         367 KB  conda-forge\n",
            "    simpervisor-0.4            |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    snappy-1.1.8               |       he1b5a44_3          32 KB  conda-forge\n",
            "    sniffio-1.2.0              |   py37h89c1867_2          15 KB  conda-forge\n",
            "    sortedcontainers-2.4.0     |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    soupsieve-2.3.1            |     pyhd8ed1ab_0          33 KB  conda-forge\n",
            "    spdlog-1.8.5               |       h4bd325d_1         352 KB  conda-forge\n",
            "    sqlite-3.37.0              |       h9cd32fc_0         1.5 MB  conda-forge\n",
            "    starlette-0.16.0           |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    streamz-0.6.3              |     pyh6c4a22f_0          61 KB  conda-forge\n",
            "    tabulate-0.8.9             |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    tblib-1.7.0                |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    terminado-0.13.3           |   py37h89c1867_0          27 KB  conda-forge\n",
            "    testpath-0.6.0             |     pyhd8ed1ab_0          85 KB  conda-forge\n",
            "    threadpoolctl-3.1.0        |     pyh8a188c0_0          18 KB  conda-forge\n",
            "    tifffile-2021.11.2         |     pyhd8ed1ab_0         139 KB  conda-forge\n",
            "    tiledb-2.3.4               |       he87e0bf_0         4.1 MB  conda-forge\n",
            "    toolz-0.11.2               |     pyhd8ed1ab_0          48 KB  conda-forge\n",
            "    tornado-6.1                |   py37h5e8e339_2         642 KB  conda-forge\n",
            "    traitlets-5.1.1            |     pyhd8ed1ab_0          82 KB  conda-forge\n",
            "    treelite-2.1.0             |   py37h4b3d254_0         1.4 MB  conda-forge\n",
            "    typing-extensions-4.1.1    |       hd8ed1ab_0           8 KB  conda-forge\n",
            "    typing_extensions-4.1.1    |     pyha770c72_0          29 KB  conda-forge\n",
            "    tzcode-2022a               |       h166bdaf_0          69 KB  conda-forge\n",
            "    tzdata-2022a               |       h191b570_0         121 KB  conda-forge\n",
            "    tzlocal-4.1                |   py37h89c1867_1          30 KB  conda-forge\n",
            "    ucx-1.11.2+gef2bbcf        |       cuda11.2_0        11.9 MB  rapidsai\n",
            "    ucx-proc-1.0.0             |              gpu           9 KB  rapidsai\n",
            "    ucx-py-0.23.0              |  py37_gef2bbcf_0         353 KB  rapidsai\n",
            "    uvicorn-0.17.6             |   py37h89c1867_0          78 KB  conda-forge\n",
            "    wcwidth-0.2.5              |     pyh9f0ad1d_2          33 KB  conda-forge\n",
            "    webencodings-0.5.1         |             py_1          12 KB  conda-forge\n",
            "    websocket-client-1.3.2     |     pyhd8ed1ab_0          41 KB  conda-forge\n",
            "    websockets-10.2            |   py37h540881e_0         121 KB  conda-forge\n",
            "    widgetsnbextension-3.6.0   |   py37h89c1867_0         1.2 MB  conda-forge\n",
            "    xarray-0.20.2              |     pyhd8ed1ab_0         628 KB  conda-forge\n",
            "    xerces-c-3.2.3             |       h9d8b166_3         1.8 MB  conda-forge\n",
            "    xgboost-1.5.0dev.rapidsai21.12|   cuda11.2py37_0          17 KB  rapidsai\n",
            "    xorg-fixesproto-5.0        |    h7f98852_1002           9 KB  conda-forge\n",
            "    xorg-inputproto-2.3.2      |    h7f98852_1002          19 KB  conda-forge\n",
            "    xorg-kbproto-1.0.7         |    h7f98852_1002          27 KB  conda-forge\n",
            "    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n",
            "    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n",
            "    xorg-libx11-1.7.2          |       h7f98852_0         941 KB  conda-forge\n",
            "    xorg-libxau-1.0.9          |       h7f98852_0          13 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
            "    xorg-libxext-1.3.4         |       h7f98852_1          54 KB  conda-forge\n",
            "    xorg-libxfixes-5.0.3       |    h7f98852_1004          18 KB  conda-forge\n",
            "    xorg-libxi-1.7.10          |       h7f98852_0          46 KB  conda-forge\n",
            "    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n",
            "    xorg-libxtst-1.2.3         |    h7f98852_1002          31 KB  conda-forge\n",
            "    xorg-recordproto-1.14.2    |    h7f98852_1002           8 KB  conda-forge\n",
            "    xorg-renderproto-0.11.1    |    h7f98852_1002           9 KB  conda-forge\n",
            "    xorg-xextproto-7.3.0       |    h7f98852_1002          28 KB  conda-forge\n",
            "    xorg-xproto-7.0.31         |    h7f98852_1007          73 KB  conda-forge\n",
            "    yarl-1.7.2                 |   py37h5e8e339_1         137 KB  conda-forge\n",
            "    zeromq-4.3.4               |       h9c3ff4c_1         351 KB  conda-forge\n",
            "    zfp-0.5.5                  |       h9c3ff4c_8         190 KB  conda-forge\n",
            "    zict-2.1.0                 |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    zipp-3.7.0                 |     pyhd8ed1ab_1          12 KB  conda-forge\n",
            "    zlib-1.2.11                |    h166bdaf_1014          88 KB  conda-forge\n",
            "    zstd-1.5.2                 |       ha95c52a_0         458 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        2.85 GB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  abseil-cpp         conda-forge/linux-64::abseil-cpp-20210324.2-h9c3ff4c_0\n",
            "  aiohttp            conda-forge/linux-64::aiohttp-3.8.1-py37h5e8e339_0\n",
            "  aiosignal          conda-forge/noarch::aiosignal-1.2.0-pyhd8ed1ab_0\n",
            "  alsa-lib           conda-forge/linux-64::alsa-lib-1.2.3-h516909a_0\n",
            "  anyio              conda-forge/linux-64::anyio-3.5.0-py37h89c1867_0\n",
            "  appdirs            conda-forge/noarch::appdirs-1.4.4-pyh9f0ad1d_0\n",
            "  argon2-cffi        conda-forge/noarch::argon2-cffi-21.3.0-pyhd8ed1ab_0\n",
            "  argon2-cffi-bindi~ conda-forge/linux-64::argon2-cffi-bindings-21.2.0-py37h5e8e339_1\n",
            "  arrow-cpp          conda-forge/linux-64::arrow-cpp-5.0.0-py37hc2523e2_2_cuda\n",
            "  arrow-cpp-proc     conda-forge/linux-64::arrow-cpp-proc-3.0.0-cuda\n",
            "  asgiref            conda-forge/noarch::asgiref-3.5.0-pyhd8ed1ab_0\n",
            "  async-timeout      conda-forge/noarch::async-timeout-4.0.2-pyhd8ed1ab_0\n",
            "  asynctest          conda-forge/noarch::asynctest-0.13.0-py_0\n",
            "  attrs              conda-forge/noarch::attrs-21.4.0-pyhd8ed1ab_0\n",
            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.5.11-h95a6274_0\n",
            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.6.2-h7f98852_0\n",
            "  aws-c-event-stream conda-forge/linux-64::aws-c-event-stream-0.2.7-h3541f99_13\n",
            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.10.5-hfb6a706_0\n",
            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.11-ha31a3da_7\n",
            "  aws-sdk-cpp        conda-forge/linux-64::aws-sdk-cpp-1.8.186-hb4091e7_3\n",
            "  backcall           conda-forge/noarch::backcall-0.2.0-pyh9f0ad1d_0\n",
            "  backports          conda-forge/noarch::backports-1.0-py_2\n",
            "  backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n",
            "  backports.zoneinfo conda-forge/linux-64::backports.zoneinfo-0.2.1-py37h5e8e339_4\n",
            "  beautifulsoup4     conda-forge/noarch::beautifulsoup4-4.10.0-pyha770c72_0\n",
            "  bleach             conda-forge/noarch::bleach-4.1.0-pyhd8ed1ab_0\n",
            "  blinker            conda-forge/noarch::blinker-1.4-py_1\n",
            "  blosc              conda-forge/linux-64::blosc-1.21.0-h9c3ff4c_0\n",
            "  bokeh              conda-forge/linux-64::bokeh-2.4.0-py37h89c1867_0\n",
            "  boost              conda-forge/linux-64::boost-1.74.0-py37h796e4cb_5\n",
            "  boost-cpp          conda-forge/linux-64::boost-cpp-1.74.0-h312852a_4\n",
            "  brotli             conda-forge/linux-64::brotli-1.0.9-h7f98852_6\n",
            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.0.9-h7f98852_6\n",
            "  brunsli            conda-forge/linux-64::brunsli-0.1-h9c3ff4c_0\n",
            "  c-blosc2           conda-forge/linux-64::c-blosc2-2.0.4-h5f21a17_1\n",
            "  cachetools         conda-forge/noarch::cachetools-5.0.0-pyhd8ed1ab_0\n",
            "  cairo              conda-forge/linux-64::cairo-1.16.0-h6cf1ce9_1008\n",
            "  cfitsio            conda-forge/linux-64::cfitsio-3.470-hb418390_7\n",
            "  charls             conda-forge/linux-64::charls-2.2.0-h9c3ff4c_0\n",
            "  charset-normalizer conda-forge/noarch::charset-normalizer-2.0.12-pyhd8ed1ab_0\n",
            "  click              conda-forge/linux-64::click-8.1.1-py37h89c1867_1\n",
            "  click-plugins      conda-forge/noarch::click-plugins-1.1.1-py_0\n",
            "  cligj              conda-forge/noarch::cligj-0.7.2-pyhd8ed1ab_1\n",
            "  cloudpickle        conda-forge/noarch::cloudpickle-2.0.0-pyhd8ed1ab_0\n",
            "  colorcet           conda-forge/noarch::colorcet-3.0.0-pyhd8ed1ab_0\n",
            "  cucim              rapidsai/linux-64::cucim-21.12.00-cuda_11_py37_g6d1f082_0\n",
            "  cudatoolkit        nvidia/linux-64::cudatoolkit-11.2.72-h2bc3f7f_0\n",
            "  cudf               rapidsai/linux-64::cudf-21.12.02-cuda_11_py37_g06540b9b37_0\n",
            "  cudf_kafka         rapidsai/linux-64::cudf_kafka-21.12.02-py37_g06540b9b37_0\n",
            "  cugraph            rapidsai/linux-64::cugraph-21.12.00-cuda11_py37_g3a43e9d0_0\n",
            "  cuml               rapidsai/linux-64::cuml-21.12.00-cuda11_py37_g04c4927f3_0\n",
            "  cupy               conda-forge/linux-64::cupy-9.6.0-py37h07c33ac_0\n",
            "  curl               conda-forge/linux-64::curl-7.78.0-hea6ffbf_0\n",
            "  cusignal           rapidsai/noarch::cusignal-21.12.00-py37_g2bf865c_0\n",
            "  cuspatial          rapidsai/linux-64::cuspatial-21.12.00-py37_gab6748f_0\n",
            "  custreamz          rapidsai/linux-64::custreamz-21.12.02-py37_g06540b9b37_0\n",
            "  cuxfilter          rapidsai/linux-64::cuxfilter-21.12.00-py37_g2e0fb5a_0\n",
            "  cycler             conda-forge/noarch::cycler-0.11.0-pyhd8ed1ab_0\n",
            "  cyrus-sasl         conda-forge/linux-64::cyrus-sasl-2.1.27-h230043b_5\n",
            "  cytoolz            conda-forge/linux-64::cytoolz-0.11.2-py37h5e8e339_1\n",
            "  dask               conda-forge/noarch::dask-2021.11.2-pyhd8ed1ab_0\n",
            "  dask-core          conda-forge/noarch::dask-core-2021.11.2-pyhd8ed1ab_0\n",
            "  dask-cuda          rapidsai/linux-64::dask-cuda-21.12.00-py37_0\n",
            "  dask-cudf          rapidsai/linux-64::dask-cudf-21.12.02-cuda_11_py37_g06540b9b37_0\n",
            "  dask-sql           conda-forge/linux-64::dask-sql-2022.1.0-py37h89c1867_0\n",
            "  datashader         conda-forge/noarch::datashader-0.11.1-pyh9f0ad1d_0\n",
            "  datashape          conda-forge/noarch::datashape-0.5.4-py_1\n",
            "  debugpy            conda-forge/linux-64::debugpy-1.5.1-py37hcd2ae1e_0\n",
            "  decorator          conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0\n",
            "  defusedxml         conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0\n",
            "  distributed        conda-forge/linux-64::distributed-2021.11.2-py37h89c1867_0\n",
            "  dlpack             conda-forge/linux-64::dlpack-0.5-h9c3ff4c_0\n",
            "  entrypoints        conda-forge/noarch::entrypoints-0.4-pyhd8ed1ab_0\n",
            "  expat              conda-forge/linux-64::expat-2.4.8-h27087fc_0\n",
            "  faiss-proc         rapidsai/linux-64::faiss-proc-1.0.0-cuda\n",
            "  fastapi            conda-forge/noarch::fastapi-0.70.1-pyhd8ed1ab_0\n",
            "  fastavro           conda-forge/linux-64::fastavro-1.4.10-py37h540881e_0\n",
            "  fastrlock          conda-forge/linux-64::fastrlock-0.8-py37hd23a5d3_1\n",
            "  fiona              conda-forge/linux-64::fiona-1.8.20-py37hb7e2723_2\n",
            "  flit-core          conda-forge/noarch::flit-core-3.7.1-pyhd8ed1ab_0\n",
            "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0\n",
            "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0\n",
            "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0\n",
            "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-hab24e00_0\n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.13.96-h8e229c2_2\n",
            "  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0\n",
            "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.4-h0708190_1\n",
            "  freexl             conda-forge/linux-64::freexl-1.0.6-h7f98852_0\n",
            "  frozenlist         conda-forge/linux-64::frozenlist-1.3.0-py37h540881e_1\n",
            "  fsspec             conda-forge/noarch::fsspec-2022.2.0-pyhd8ed1ab_0\n",
            "  gcsfs              conda-forge/noarch::gcsfs-2022.2.0-pyhd8ed1ab_0\n",
            "  gdal               conda-forge/linux-64::gdal-3.3.2-py37hd5a0ba4_3\n",
            "  geopandas          conda-forge/noarch::geopandas-0.9.0-pyhd8ed1ab_1\n",
            "  geopandas-base     conda-forge/noarch::geopandas-base-0.9.0-pyhd8ed1ab_1\n",
            "  geos               conda-forge/linux-64::geos-3.9.1-h9c3ff4c_2\n",
            "  geotiff            conda-forge/linux-64::geotiff-1.7.0-h08e826d_2\n",
            "  gettext            conda-forge/linux-64::gettext-0.19.8.1-h0b5b191_1005\n",
            "  gflags             conda-forge/linux-64::gflags-2.2.2-he1b5a44_1004\n",
            "  giflib             conda-forge/linux-64::giflib-5.2.1-h36c2ea0_2\n",
            "  glog               conda-forge/linux-64::glog-0.5.0-h48cff8f_0\n",
            "  google-api-core    conda-forge/noarch::google-api-core-2.5.0-pyhd8ed1ab_0\n",
            "  google-auth        conda-forge/noarch::google-auth-2.6.2-pyh6c4a22f_0\n",
            "  google-auth-oauth~ conda-forge/noarch::google-auth-oauthlib-0.5.1-pyhd8ed1ab_0\n",
            "  google-cloud-core  conda-forge/noarch::google-cloud-core-2.2.2-pyh6c4a22f_0\n",
            "  google-cloud-stor~ conda-forge/noarch::google-cloud-storage-2.1.0-pyh6c4a22f_0\n",
            "  google-crc32c      conda-forge/linux-64::google-crc32c-1.1.2-py37hab72019_2\n",
            "  google-resumable-~ conda-forge/noarch::google-resumable-media-2.1.0-pyh6c4a22f_0\n",
            "  googleapis-common~ conda-forge/linux-64::googleapis-common-protos-1.56.0-py37h89c1867_0\n",
            "  graphite2          conda-forge/linux-64::graphite2-1.3.13-h58526e2_1001\n",
            "  grpc-cpp           conda-forge/linux-64::grpc-cpp-1.39.0-hf1f433d_2\n",
            "  grpcio             conda-forge/linux-64::grpcio-1.38.1-py37hb27c1af_0\n",
            "  h11                conda-forge/noarch::h11-0.13.0-pyhd8ed1ab_0\n",
            "  harfbuzz           conda-forge/linux-64::harfbuzz-2.9.1-h83ec7ef_1\n",
            "  hdf4               conda-forge/linux-64::hdf4-4.2.15-h10796ff_3\n",
            "  hdf5               conda-forge/linux-64::hdf5-1.12.1-nompi_h2750804_100\n",
            "  heapdict           conda-forge/noarch::heapdict-1.0.1-py_0\n",
            "  imagecodecs        conda-forge/linux-64::imagecodecs-2021.8.26-py37hfe5a812_1\n",
            "  imageio            conda-forge/noarch::imageio-2.13.1-pyhd8ed1ab_0\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-4.11.3-py37h89c1867_1\n",
            "  importlib_metadata conda-forge/noarch::importlib_metadata-4.11.3-hd8ed1ab_1\n",
            "  importlib_resourc~ conda-forge/noarch::importlib_resources-5.6.0-pyhd8ed1ab_0\n",
            "  ipykernel          conda-forge/linux-64::ipykernel-6.11.0-py37h25bab4e_0\n",
            "  ipython            conda-forge/linux-64::ipython-7.32.0-py37h89c1867_0\n",
            "  ipython_genutils   conda-forge/noarch::ipython_genutils-0.2.0-py_1\n",
            "  ipywidgets         conda-forge/noarch::ipywidgets-7.7.0-pyhd8ed1ab_0\n",
            "  jbig               conda-forge/linux-64::jbig-2.1-h7f98852_2003\n",
            "  jedi               conda-forge/linux-64::jedi-0.18.1-py37h89c1867_0\n",
            "  jinja2             conda-forge/noarch::jinja2-3.1.1-pyhd8ed1ab_0\n",
            "  joblib             conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0\n",
            "  jpeg               conda-forge/linux-64::jpeg-9e-h7f98852_0\n",
            "  jpype1             conda-forge/linux-64::jpype1-1.3.0-py37h2527ec5_2\n",
            "  json-c             conda-forge/linux-64::json-c-0.15-h98cffda_0\n",
            "  jsonschema         conda-forge/noarch::jsonschema-4.4.0-pyhd8ed1ab_0\n",
            "  jupyter-server-pr~ conda-forge/noarch::jupyter-server-proxy-3.2.1-pyhd8ed1ab_0\n",
            "  jupyter_client     conda-forge/noarch::jupyter_client-7.2.1-pyhd8ed1ab_0\n",
            "  jupyter_core       conda-forge/linux-64::jupyter_core-4.9.2-py37h89c1867_0\n",
            "  jupyter_server     conda-forge/noarch::jupyter_server-1.16.0-pyhd8ed1ab_1\n",
            "  jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.1.2-pyh9f0ad1d_0\n",
            "  jupyterlab_widgets conda-forge/noarch::jupyterlab_widgets-1.1.0-pyhd8ed1ab_0\n",
            "  jxrlib             conda-forge/linux-64::jxrlib-1.1-h7f98852_2\n",
            "  kealib             conda-forge/linux-64::kealib-1.4.14-h87e4c3c_3\n",
            "  keyutils           conda-forge/linux-64::keyutils-1.6.1-h166bdaf_0\n",
            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.2-py37h7cecad7_0\n",
            "  lcms2              conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n",
            "  lerc               conda-forge/linux-64::lerc-3.0-h9c3ff4c_0\n",
            "  libaec             conda-forge/linux-64::libaec-1.0.6-h9c3ff4c_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-13_linux64_openblas\n",
            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.0.9-h7f98852_6\n",
            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.0.9-h7f98852_6\n",
            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.0.9-h7f98852_6\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-13_linux64_openblas\n",
            "  libcrc32c          conda-forge/linux-64::libcrc32c-1.1.2-h9c3ff4c_0\n",
            "  libcucim           rapidsai/linux-64::libcucim-21.12.00-cuda11_g6d1f082_0\n",
            "  libcudf            rapidsai/linux-64::libcudf-21.12.02-cuda11_g06540b9b37_0\n",
            "  libcudf_kafka      rapidsai/linux-64::libcudf_kafka-21.12.02-g06540b9b37_0\n",
            "  libcugraph         rapidsai/linux-64::libcugraph-21.12.00-cuda11_g3a43e9d0_0\n",
            "  libcuml            rapidsai/linux-64::libcuml-21.12.00-cuda11_g04c4927f3_0\n",
            "  libcumlprims       nvidia/linux-64::libcumlprims-21.12.00-cuda11_g0a7f19f_0\n",
            "  libcusolver        nvidia/linux-64::libcusolver-11.3.4.124-h33c3c4e_0\n",
            "  libcuspatial       rapidsai/linux-64::libcuspatial-21.12.00-cuda11_gab6748f_0\n",
            "  libdap4            conda-forge/linux-64::libdap4-3.20.6-hd7c4107_2\n",
            "  libdeflate         conda-forge/linux-64::libdeflate-1.8-h7f98852_0\n",
            "  libevent           conda-forge/linux-64::libevent-2.1.10-h9b69904_4\n",
            "  libfaiss           conda-forge/linux-64::libfaiss-1.7.0-cuda112h5bea7ad_8_cuda\n",
            "  libgcrypt          conda-forge/linux-64::libgcrypt-1.10.0-h7f98852_0\n",
            "  libgdal            conda-forge/linux-64::libgdal-3.3.2-h6acdded_3\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-11.2.0-h69a702a_14\n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-11.2.0-h5c6108e_14\n",
            "  libglib            conda-forge/linux-64::libglib-2.68.4-h3e27bee_0\n",
            "  libgpg-error       conda-forge/linux-64::libgpg-error-1.44-h9eb791d_0\n",
            "  libgsasl           conda-forge/linux-64::libgsasl-1.10.0-h5b4c23d_0\n",
            "  libhwloc           conda-forge/linux-64::libhwloc-2.3.0-h5e5b7d1_1\n",
            "  libkml             conda-forge/linux-64::libkml-1.3.0-h238a007_1014\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-13_linux64_openblas\n",
            "  libllvm11          conda-forge/linux-64::libllvm11-11.1.0-hf817b99_3\n",
            "  libnetcdf          conda-forge/linux-64::libnetcdf-4.8.1-nompi_hb3fd0d9_101\n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.0-h7f98852_0\n",
            "  libntlm            conda-forge/linux-64::libntlm-1.4-h7f98852_1002\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.18-pthreads_h8fe5266_0\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n",
            "  libpq              conda-forge/linux-64::libpq-13.5-hd57d9b9_1\n",
            "  libprotobuf        conda-forge/linux-64::libprotobuf-3.16.0-h780b84a_0\n",
            "  librdkafka         conda-forge/linux-64::librdkafka-1.6.1-hc49e61c_1\n",
            "  librmm             rapidsai/linux-64::librmm-21.12.00-cuda11_g957ad04_0\n",
            "  librttopo          conda-forge/linux-64::librttopo-1.1.0-h1185371_6\n",
            "  libsodium          conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1\n",
            "  libspatialindex    conda-forge/linux-64::libspatialindex-1.9.3-h9c3ff4c_4\n",
            "  libspatialite      conda-forge/linux-64::libspatialite-5.0.1-h5cf074c_8\n",
            "  libthrift          conda-forge/linux-64::libthrift-0.14.2-he6d91bd_1\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.3.0-h6f004c6_2\n",
            "  libutf8proc        conda-forge/linux-64::libutf8proc-2.7.0-h7f98852_0\n",
            "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h7f98852_1000\n",
            "  libuv              conda-forge/linux-64::libuv-1.42.0-h7f98852_0\n",
            "  libwebp            conda-forge/linux-64::libwebp-1.2.2-h3452ae3_0\n",
            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.2.2-h7f98852_1\n",
            "  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1004\n",
            "  libxgboost         rapidsai/linux-64::libxgboost-1.5.0dev.rapidsai21.12-cuda11.2_0\n",
            "  libzip             conda-forge/linux-64::libzip-1.8.0-h4de3113_1\n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.11-h166bdaf_1014\n",
            "  libzopfli          conda-forge/linux-64::libzopfli-1.0.3-h9c3ff4c_0\n",
            "  llvmlite           conda-forge/linux-64::llvmlite-0.38.0-py37h9d7f4d0_0\n",
            "  locket             conda-forge/noarch::locket-0.2.0-py_2\n",
            "  mapclassify        conda-forge/noarch::mapclassify-2.4.3-pyhd8ed1ab_0\n",
            "  markdown           conda-forge/noarch::markdown-3.3.6-pyhd8ed1ab_0\n",
            "  markupsafe         conda-forge/linux-64::markupsafe-2.1.1-py37h540881e_1\n",
            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.4.3-py37h1058ff1_0\n",
            "  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0\n",
            "  mistune            conda-forge/linux-64::mistune-0.8.4-py37h5e8e339_1005\n",
            "  msgpack-python     conda-forge/linux-64::msgpack-python-1.0.3-py37h7cecad7_1\n",
            "  multidict          conda-forge/linux-64::multidict-6.0.2-py37h5e8e339_0\n",
            "  multipledispatch   conda-forge/noarch::multipledispatch-0.6.0-py_0\n",
            "  munch              conda-forge/noarch::munch-2.5.0-py_0\n",
            "  nbclient           conda-forge/noarch::nbclient-0.5.13-pyhd8ed1ab_0\n",
            "  nbconvert          conda-forge/noarch::nbconvert-6.4.5-pyhd8ed1ab_1\n",
            "  nbconvert-core     conda-forge/noarch::nbconvert-core-6.4.5-pyhd8ed1ab_1\n",
            "  nbconvert-pandoc   conda-forge/noarch::nbconvert-pandoc-6.4.5-pyhd8ed1ab_1\n",
            "  nbformat           conda-forge/noarch::nbformat-5.2.0-pyhd8ed1ab_0\n",
            "  nccl               conda-forge/linux-64::nccl-2.12.7.1-h0800d71_0\n",
            "  nest-asyncio       conda-forge/noarch::nest-asyncio-1.5.4-pyhd8ed1ab_0\n",
            "  networkx           conda-forge/noarch::networkx-2.7.1-pyhd8ed1ab_0\n",
            "  nodejs             conda-forge/linux-64::nodejs-14.17.4-h92b4a50_0\n",
            "  notebook           conda-forge/noarch::notebook-6.4.10-pyha770c72_0\n",
            "  numba              conda-forge/linux-64::numba-0.55.1-py37h2d894fd_0\n",
            "  numpy              conda-forge/linux-64::numpy-1.21.5-py37hf2998dd_0\n",
            "  nvtx               conda-forge/linux-64::nvtx-0.2.3-py37h5e8e339_1\n",
            "  oauthlib           conda-forge/noarch::oauthlib-3.2.0-pyhd8ed1ab_0\n",
            "  olefile            conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n",
            "  openjdk            conda-forge/linux-64::openjdk-11.0.9.1-h5cc2fde_1\n",
            "  openjpeg           conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n",
            "  orc                conda-forge/linux-64::orc-1.6.9-h58a87f1_0\n",
            "  packaging          conda-forge/noarch::packaging-21.3-pyhd8ed1ab_0\n",
            "  pandas             conda-forge/linux-64::pandas-1.3.5-py37he8f5f7f_0\n",
            "  pandoc             conda-forge/linux-64::pandoc-2.17.1.1-ha770c72_0\n",
            "  pandocfilters      conda-forge/noarch::pandocfilters-1.5.0-pyhd8ed1ab_0\n",
            "  panel              conda-forge/noarch::panel-0.12.4-pyhd8ed1ab_0\n",
            "  param              conda-forge/noarch::param-1.12.1-pyh6c4a22f_0\n",
            "  parquet-cpp        conda-forge/noarch::parquet-cpp-1.5.1-2\n",
            "  parso              conda-forge/noarch::parso-0.8.3-pyhd8ed1ab_0\n",
            "  partd              conda-forge/noarch::partd-1.2.0-pyhd8ed1ab_0\n",
            "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0\n",
            "  pexpect            conda-forge/noarch::pexpect-4.8.0-pyh9f0ad1d_2\n",
            "  pickle5            conda-forge/linux-64::pickle5-0.0.12-py37h5e8e339_0\n",
            "  pickleshare        conda-forge/noarch::pickleshare-0.7.5-py_1003\n",
            "  pillow             conda-forge/linux-64::pillow-8.3.1-py37h0f21c89_0\n",
            "  pixman             conda-forge/linux-64::pixman-0.40.0-h36c2ea0_0\n",
            "  pooch              conda-forge/noarch::pooch-1.6.0-pyhd8ed1ab_0\n",
            "  poppler            conda-forge/linux-64::poppler-21.09.0-h0e1ea10_1\n",
            "  poppler-data       conda-forge/noarch::poppler-data-0.4.11-hd8ed1ab_0\n",
            "  postgresql         conda-forge/linux-64::postgresql-13.5-h2510834_1\n",
            "  proj               conda-forge/linux-64::proj-8.1.0-h277dcde_1\n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.13.1-pyhd8ed1ab_0\n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.27-pyha770c72_0\n",
            "  protobuf           conda-forge/linux-64::protobuf-3.16.0-py37hcd2ae1e_0\n",
            "  psutil             conda-forge/linux-64::psutil-5.9.0-py37h5e8e339_0\n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001\n",
            "  ptxcompiler        rapidsai/linux-64::ptxcompiler-0.2.0-py37hcfeb250_0\n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0\n",
            "  py-xgboost         rapidsai/linux-64::py-xgboost-1.5.0dev.rapidsai21.12-cuda11.2py37_0\n",
            "  pyarrow            conda-forge/linux-64::pyarrow-5.0.0-py37hf0016df_2_cuda\n",
            "  pyasn1             conda-forge/noarch::pyasn1-0.4.8-py_0\n",
            "  pyasn1-modules     conda-forge/noarch::pyasn1-modules-0.2.7-py_0\n",
            "  pyct               conda-forge/noarch::pyct-0.4.6-py_0\n",
            "  pyct-core          conda-forge/noarch::pyct-core-0.4.6-py_0\n",
            "  pydantic           conda-forge/linux-64::pydantic-1.9.0-py37h5e8e339_0\n",
            "  pydeck             conda-forge/noarch::pydeck-0.5.0-pyh9f0ad1d_0\n",
            "  pyee               conda-forge/noarch::pyee-8.1.0-pyhd8ed1ab_0\n",
            "  pygments           conda-forge/noarch::pygments-2.11.2-pyhd8ed1ab_0\n",
            "  pyjwt              conda-forge/noarch::pyjwt-2.3.0-pyhd8ed1ab_1\n",
            "  pynvml             conda-forge/noarch::pynvml-11.4.1-pyhd8ed1ab_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-3.0.7-pyhd8ed1ab_0\n",
            "  pyppeteer          conda-forge/noarch::pyppeteer-1.0.2-pyhd8ed1ab_0\n",
            "  pyproj             conda-forge/linux-64::pyproj-3.1.0-py37hdf91f24_4\n",
            "  pyrsistent         conda-forge/linux-64::pyrsistent-0.18.1-py37h5e8e339_0\n",
            "  python-confluent-~ conda-forge/linux-64::python-confluent-kafka-1.6.0-py37h5e8e339_1\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\n",
            "  python-tzdata      conda-forge/noarch::python-tzdata-2022.1-pyhd8ed1ab_0\n",
            "  pytz               conda-forge/noarch::pytz-2022.1-pyhd8ed1ab_0\n",
            "  pytz-deprecation-~ conda-forge/linux-64::pytz-deprecation-shim-0.1.0.post0-py37h89c1867_1\n",
            "  pyu2f              conda-forge/noarch::pyu2f-0.1.5-pyhd8ed1ab_0\n",
            "  pyviz_comms        conda-forge/noarch::pyviz_comms-2.2.0-pyhd8ed1ab_0\n",
            "  pywavelets         conda-forge/linux-64::pywavelets-1.3.0-py37h6c7ee08_0\n",
            "  pyyaml             conda-forge/linux-64::pyyaml-6.0-py37h540881e_4\n",
            "  pyzmq              conda-forge/linux-64::pyzmq-22.3.0-py37h336d617_1\n",
            "  rapids             rapidsai/linux-64::rapids-21.12.00-cuda11.2_py37_gc46440c_94\n",
            "  rapids-xgboost     rapidsai/linux-64::rapids-xgboost-21.12.00-cuda11.2_py37_gc46440c_94\n",
            "  re2                conda-forge/linux-64::re2-2021.08.01-h9c3ff4c_0\n",
            "  requests-oauthlib  conda-forge/noarch::requests-oauthlib-1.3.1-pyhd8ed1ab_0\n",
            "  rmm                rapidsai/linux-64::rmm-21.12.00-cuda11_py37_g957ad04_0_has_cma\n",
            "  rsa                conda-forge/noarch::rsa-4.8-pyhd8ed1ab_0\n",
            "  rtree              conda-forge/linux-64::rtree-0.9.7-py37h0b55af0_3\n",
            "  s2n                conda-forge/linux-64::s2n-1.0.10-h9b69904_0\n",
            "  scikit-image       conda-forge/linux-64::scikit-image-0.18.1-py37hdc94413_0\n",
            "  scikit-learn       conda-forge/linux-64::scikit-learn-1.0.2-py37hf9e9bfc_0\n",
            "  scipy              conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0\n",
            "  send2trash         conda-forge/noarch::send2trash-1.8.0-pyhd8ed1ab_0\n",
            "  shapely            conda-forge/linux-64::shapely-1.8.0-py37h48c49eb_0\n",
            "  simpervisor        conda-forge/noarch::simpervisor-0.4-pyhd8ed1ab_0\n",
            "  snappy             conda-forge/linux-64::snappy-1.1.8-he1b5a44_3\n",
            "  sniffio            conda-forge/linux-64::sniffio-1.2.0-py37h89c1867_2\n",
            "  sortedcontainers   conda-forge/noarch::sortedcontainers-2.4.0-pyhd8ed1ab_0\n",
            "  soupsieve          conda-forge/noarch::soupsieve-2.3.1-pyhd8ed1ab_0\n",
            "  spdlog             conda-forge/linux-64::spdlog-1.8.5-h4bd325d_1\n",
            "  starlette          conda-forge/noarch::starlette-0.16.0-pyhd8ed1ab_0\n",
            "  streamz            conda-forge/noarch::streamz-0.6.3-pyh6c4a22f_0\n",
            "  tabulate           conda-forge/noarch::tabulate-0.8.9-pyhd8ed1ab_0\n",
            "  tblib              conda-forge/noarch::tblib-1.7.0-pyhd8ed1ab_0\n",
            "  terminado          conda-forge/linux-64::terminado-0.13.3-py37h89c1867_0\n",
            "  testpath           conda-forge/noarch::testpath-0.6.0-pyhd8ed1ab_0\n",
            "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.1.0-pyh8a188c0_0\n",
            "  tifffile           conda-forge/noarch::tifffile-2021.11.2-pyhd8ed1ab_0\n",
            "  tiledb             conda-forge/linux-64::tiledb-2.3.4-he87e0bf_0\n",
            "  toolz              conda-forge/noarch::toolz-0.11.2-pyhd8ed1ab_0\n",
            "  tornado            conda-forge/linux-64::tornado-6.1-py37h5e8e339_2\n",
            "  traitlets          conda-forge/noarch::traitlets-5.1.1-pyhd8ed1ab_0\n",
            "  treelite           conda-forge/linux-64::treelite-2.1.0-py37h4b3d254_0\n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-4.1.1-hd8ed1ab_0\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-4.1.1-pyha770c72_0\n",
            "  tzcode             conda-forge/linux-64::tzcode-2022a-h166bdaf_0\n",
            "  tzdata             conda-forge/noarch::tzdata-2022a-h191b570_0\n",
            "  tzlocal            conda-forge/linux-64::tzlocal-4.1-py37h89c1867_1\n",
            "  ucx                rapidsai/linux-64::ucx-1.11.2+gef2bbcf-cuda11.2_0\n",
            "  ucx-proc           rapidsai/linux-64::ucx-proc-1.0.0-gpu\n",
            "  ucx-py             rapidsai/linux-64::ucx-py-0.23.0-py37_gef2bbcf_0\n",
            "  uvicorn            conda-forge/linux-64::uvicorn-0.17.6-py37h89c1867_0\n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n",
            "  webencodings       conda-forge/noarch::webencodings-0.5.1-py_1\n",
            "  websocket-client   conda-forge/noarch::websocket-client-1.3.2-pyhd8ed1ab_0\n",
            "  websockets         conda-forge/linux-64::websockets-10.2-py37h540881e_0\n",
            "  widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.6.0-py37h89c1867_0\n",
            "  xarray             conda-forge/noarch::xarray-0.20.2-pyhd8ed1ab_0\n",
            "  xerces-c           conda-forge/linux-64::xerces-c-3.2.3-h9d8b166_3\n",
            "  xgboost            rapidsai/linux-64::xgboost-1.5.0dev.rapidsai21.12-cuda11.2py37_0\n",
            "  xorg-fixesproto    conda-forge/linux-64::xorg-fixesproto-5.0-h7f98852_1002\n",
            "  xorg-inputproto    conda-forge/linux-64::xorg-inputproto-2.3.2-h7f98852_1002\n",
            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h7f98852_1002\n",
            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0\n",
            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000\n",
            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h7f98852_0\n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0\n",
            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n",
            "  xorg-libxfixes     conda-forge/linux-64::xorg-libxfixes-5.0.3-h7f98852_1004\n",
            "  xorg-libxi         conda-forge/linux-64::xorg-libxi-1.7.10-h7f98852_0\n",
            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n",
            "  xorg-libxtst       conda-forge/linux-64::xorg-libxtst-1.2.3-h7f98852_1002\n",
            "  xorg-recordproto   conda-forge/linux-64::xorg-recordproto-1.14.2-h7f98852_1002\n",
            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h7f98852_1002\n",
            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h7f98852_1002\n",
            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h7f98852_1007\n",
            "  yarl               conda-forge/linux-64::yarl-1.7.2-py37h5e8e339_1\n",
            "  zeromq             conda-forge/linux-64::zeromq-4.3.4-h9c3ff4c_1\n",
            "  zfp                conda-forge/linux-64::zfp-0.5.5-h9c3ff4c_8\n",
            "  zict               conda-forge/noarch::zict-2.1.0-pyhd8ed1ab_0\n",
            "  zipp               conda-forge/noarch::zipp-3.7.0-pyhd8ed1ab_1\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2020.12.5-ha878542_0 --> 2021.10.8-ha878542_0\n",
            "  certifi                          2020.12.5-py37h89c1867_1 --> 2021.10.8-py37h89c1867_2\n",
            "  conda                                4.9.2-py37h89c1867_0 --> 4.12.0-py37h89c1867_0\n",
            "  krb5                                    1.17.2-h926e7f8_0 --> 1.19.3-h3790be6_0\n",
            "  libarchive                               3.5.1-h3f442fb_1 --> 3.5.2-hccf745f_1\n",
            "  libcurl                                 7.75.0-hc4aaa36_0 --> 7.78.0-h2574ce0_0\n",
            "  libgcc-ng                               9.3.0-h2828fa1_18 --> 11.2.0-h1d223b6_14\n",
            "  libgomp                                 9.3.0-h2828fa1_18 --> 11.2.0-h1d223b6_14\n",
            "  libstdcxx-ng                            9.3.0-h6de172a_18 --> 11.2.0-he4da1e4_14\n",
            "  libxml2                                 2.9.10-h72842e0_3 --> 2.9.12-h72842e0_0\n",
            "  openssl                                 1.1.1j-h7f98852_0 --> 1.1.1n-h166bdaf_0\n",
            "  python_abi                                    3.7-1_cp37m --> 3.7-2_cp37m\n",
            "  readline                                   8.0-he28a2e2_2 --> 8.1-h46c0cb4_0\n",
            "  setuptools                          49.6.0-py37h89c1867_3 --> 59.8.0-py37h89c1867_1\n",
            "  sqlite                                  3.34.0-h74cdb3f_0 --> 3.37.0-h9cd32fc_0\n",
            "  zlib                                 1.2.11-h516909a_1010 --> 1.2.11-h166bdaf_1014\n",
            "  zstd                                     1.4.9-ha95c52a_0 --> 1.5.2-ha95c52a_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\n",
            "pynvml-11.4.1        | 40 KB     |            |   0%\n",
            "pynvml-11.4.1        | 40 KB     | ####       |  40%\n",
            "pynvml-11.4.1        | 40 KB     | ########## | 100%\n",
            "\n",
            "pyjwt-2.3.0          | 18 KB     |            |   0%\n",
            "pyjwt-2.3.0          | 18 KB     | ########## | 100%\n",
            "\n",
            "multidict-6.0.2      | 52 KB     |            |   0%\n",
            "multidict-6.0.2      | 52 KB     | ########## | 100%\n",
            "\n",
            "libhwloc-2.3.0       | 2.7 MB    |            |   0%\n",
            "libhwloc-2.3.0       | 2.7 MB    | ########3  |  84%\n",
            "libhwloc-2.3.0       | 2.7 MB    | ########## | 100%\n",
            "\n",
            "hdf5-1.12.1          | 3.5 MB    |            |   0%\n",
            "hdf5-1.12.1          | 3.5 MB    | ########## | 100%\n",
            "hdf5-1.12.1          | 3.5 MB    | ########## | 100%\n",
            "\n",
            "xorg-libxfixes-5.0.3 | 18 KB     |            |   0%\n",
            "xorg-libxfixes-5.0.3 | 18 KB     | ########## | 100%\n",
            "\n",
            "soupsieve-2.3.1      | 33 KB     |            |   0%\n",
            "soupsieve-2.3.1      | 33 KB     | ########## | 100%\n",
            "\n",
            "xgboost-1.5.0dev.rap | 17 KB     |            |   0%\n",
            "xgboost-1.5.0dev.rap | 17 KB     | #########2 |  93%\n",
            "xgboost-1.5.0dev.rap | 17 KB     | ########## | 100%\n",
            "\n",
            "expat-2.4.8          | 187 KB    |            |   0%\n",
            "expat-2.4.8          | 187 KB    | ########## | 100%\n",
            "\n",
            "libllvm11-11.1.0     | 29.1 MB   |            |   0%\n",
            "libllvm11-11.1.0     | 29.1 MB   | ###1       |  31%\n",
            "libllvm11-11.1.0     | 29.1 MB   | #######4   |  75%\n",
            "libllvm11-11.1.0     | 29.1 MB   | ########## | 100%\n",
            "libllvm11-11.1.0     | 29.1 MB   | ########## | 100%\n",
            "\n",
            "cuml-21.12.00        | 9.4 MB    |            |   0%\n",
            "cuml-21.12.00        | 9.4 MB    |            |   0%\n",
            "cuml-21.12.00        | 9.4 MB    | #6         |  16%\n",
            "cuml-21.12.00        | 9.4 MB    | ########## | 100%\n",
            "cuml-21.12.00        | 9.4 MB    | ########## | 100%\n",
            "\n",
            "libprotobuf-3.16.0   | 2.5 MB    |            |   0%\n",
            "libprotobuf-3.16.0   | 2.5 MB    | ########## | 100%\n",
            "libprotobuf-3.16.0   | 2.5 MB    | ########## | 100%\n",
            "\n",
            "rsa-4.8              | 31 KB     |            |   0%\n",
            "rsa-4.8              | 31 KB     | ########## | 100%\n",
            "\n",
            "munch-2.5.0          | 12 KB     |            |   0%\n",
            "munch-2.5.0          | 12 KB     | ########## | 100%\n",
            "\n",
            "partd-1.2.0          | 18 KB     |            |   0%\n",
            "partd-1.2.0          | 18 KB     | ########## | 100%\n",
            "\n",
            "postgresql-13.5      | 5.3 MB    |            |   0%\n",
            "postgresql-13.5      | 5.3 MB    | ########## | 100%\n",
            "postgresql-13.5      | 5.3 MB    | ########## | 100%\n",
            "\n",
            "websockets-10.2      | 121 KB    |            |   0%\n",
            "websockets-10.2      | 121 KB    | ########## | 100%\n",
            "\n",
            "msgpack-python-1.0.3 | 90 KB     |            |   0%\n",
            "msgpack-python-1.0.3 | 90 KB     | ########## | 100%\n",
            "\n",
            "widgetsnbextension-3 | 1.2 MB    |            |   0%\n",
            "widgetsnbextension-3 | 1.2 MB    | ######8    |  69%\n",
            "widgetsnbextension-3 | 1.2 MB    | ########## | 100%\n",
            "widgetsnbextension-3 | 1.2 MB    | ########## | 100%\n",
            "\n",
            "ptyprocess-0.7.0     | 16 KB     |            |   0%\n",
            "ptyprocess-0.7.0     | 16 KB     | ########## | 100%\n",
            "\n",
            "lcms2-2.12           | 443 KB    |            |   0%\n",
            "lcms2-2.12           | 443 KB    | ########## | 100%\n",
            "lcms2-2.12           | 443 KB    | ########## | 100%\n",
            "\n",
            "packaging-21.3       | 36 KB     |            |   0%\n",
            "packaging-21.3       | 36 KB     | ########## | 100%\n",
            "\n",
            "sniffio-1.2.0        | 15 KB     |            |   0%\n",
            "sniffio-1.2.0        | 15 KB     | ########## | 100%\n",
            "\n",
            "prometheus_client-0. | 47 KB     |            |   0%\n",
            "prometheus_client-0. | 47 KB     | ########## | 100%\n",
            "\n",
            "llvmlite-0.38.0      | 2.9 MB    |            |   0%\n",
            "llvmlite-0.38.0      | 2.9 MB    |            |   1%\n",
            "llvmlite-0.38.0      | 2.9 MB    | ########## | 100%\n",
            "llvmlite-0.38.0      | 2.9 MB    | ########## | 100%\n",
            "\n",
            "param-1.12.1         | 72 KB     |            |   0%\n",
            "param-1.12.1         | 72 KB     | ########## | 100%\n",
            "\n",
            "orc-1.6.9            | 746 KB    |            |   0%\n",
            "orc-1.6.9            | 746 KB    | ########## | 100%\n",
            "orc-1.6.9            | 746 KB    | ########## | 100%\n",
            "\n",
            "charset-normalizer-2 | 35 KB     |            |   0%\n",
            "charset-normalizer-2 | 35 KB     | ########## | 100%\n",
            "\n",
            "pyu2f-0.1.5          | 31 KB     |            |   0%\n",
            "pyu2f-0.1.5          | 31 KB     | ########## | 100%\n",
            "\n",
            "cucim-21.12.00       | 3.2 MB    |            |   0%\n",
            "cucim-21.12.00       | 3.2 MB    |            |   0%\n",
            "cucim-21.12.00       | 3.2 MB    | #########9 | 100%\n",
            "cucim-21.12.00       | 3.2 MB    | ########## | 100%\n",
            "\n",
            "gflags-2.2.2         | 114 KB    |            |   0%\n",
            "gflags-2.2.2         | 114 KB    | ########## | 100%\n",
            "\n",
            "libbrotlienc-1.0.9   | 286 KB    |            |   0%\n",
            "libbrotlienc-1.0.9   | 286 KB    | ########## | 100%\n",
            "\n",
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            "\n",
            "dask-core-2021.11.2  | 783 KB    |            |   0%\n",
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            "\n",
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            "\n",
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            "ipykernel-6.11.0     | 183 KB    |            |   0%\n",
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            "cuxfilter-21.12.00   | 137 KB    |            |   0%\n",
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            "\n",
            "nccl-2.12.7.1        | 135.5 MB  |            |   0%\n",
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            "\n",
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            "\n",
            "libntlm-1.4          | 32 KB     |            |   0%\n",
            "libntlm-1.4          | 32 KB     | ########## | 100%\n",
            "\n",
            "beautifulsoup4-4.10. | 77 KB     |            |   0%\n",
            "beautifulsoup4-4.10. | 77 KB     | ########## | 100%\n",
            "\n",
            "boost-1.74.0         | 342 KB    |            |   0%\n",
            "boost-1.74.0         | 342 KB    | ########## | 100%\n",
            "boost-1.74.0         | 342 KB    | ########## | 100%\n",
            "\n",
            "xorg-libxi-1.7.10    | 46 KB     |            |   0%\n",
            "xorg-libxi-1.7.10    | 46 KB     | ########## | 100%\n",
            "\n",
            "jbig-2.1             | 43 KB     |            |   0%\n",
            "jbig-2.1             | 43 KB     | ########## | 100%\n",
            "\n",
            "xorg-libxrender-0.9. | 32 KB     |            |   0%\n",
            "xorg-libxrender-0.9. | 32 KB     | ########## | 100%\n",
            "\n",
            "pandoc-2.17.1.1      | 11.9 MB   |            |   0%\n",
            "pandoc-2.17.1.1      | 11.9 MB   | ###        |  31%\n",
            "pandoc-2.17.1.1      | 11.9 MB   | ########## | 100%\n",
            "pandoc-2.17.1.1      | 11.9 MB   | ########## | 100%\n",
            "\n",
            "libgfortran-ng-11.2. | 20 KB     |            |   0%\n",
            "libgfortran-ng-11.2. | 20 KB     | ########## | 100%\n",
            "\n",
            "google-auth-oauthlib | 20 KB     |            |   0%\n",
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            "\n",
            "heapdict-1.0.1       | 7 KB      |            |   0%\n",
            "heapdict-1.0.1       | 7 KB      | ########## | 100%\n",
            "\n",
            "asgiref-3.5.0        | 22 KB     |            |   0%\n",
            "asgiref-3.5.0        | 22 KB     | ########## | 100%\n",
            "\n",
            "python-tzdata-2022.1 | 151 KB    |            |   0%\n",
            "python-tzdata-2022.1 | 151 KB    | ########## | 100%\n",
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            "\n",
            "faiss-proc-1.0.0     | 24 KB     |            |   0%\n",
            "faiss-proc-1.0.0     | 24 KB     | ######7    |  68%\n",
            "faiss-proc-1.0.0     | 24 KB     | ########## | 100%\n",
            "\n",
            "cytoolz-0.11.2       | 407 KB    |            |   0%\n",
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            "\n",
            "debugpy-1.5.1        | 2.0 MB    |            |   0%\n",
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            "\n",
            "pyparsing-3.0.7      | 79 KB     |            |   0%\n",
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            "\n",
            "parso-0.8.3          | 69 KB     |            |   0%\n",
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            "\n",
            "font-ttf-source-code | 684 KB    |            |   0%\n",
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            "\n",
            "bleach-4.1.0         | 121 KB    |            |   0%\n",
            "bleach-4.1.0         | 121 KB    | ########## | 100%\n",
            "\n",
            "libnetcdf-4.8.1      | 1.5 MB    |            |   0%\n",
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            "\n",
            "freetype-2.10.4      | 890 KB    |            |   0%\n",
            "freetype-2.10.4      | 890 KB    | ########## | 100%\n",
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            "\n",
            "importlib_metadata-4 | 4 KB      |            |   0%\n",
            "importlib_metadata-4 | 4 KB      | ########## | 100%\n",
            "\n",
            "libuv-1.42.0         | 1.0 MB    |            |   0%\n",
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            "\n",
            "libzlib-1.2.11       | 60 KB     |            |   0%\n",
            "libzlib-1.2.11       | 60 KB     | ########## | 100%\n",
            "\n",
            "ptxcompiler-0.2.0    | 5.6 MB    |            |   0%\n",
            "ptxcompiler-0.2.0    | 5.6 MB    |            |   0%\n",
            "ptxcompiler-0.2.0    | 5.6 MB    | ########## | 100%\n",
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            "\n",
            "freexl-1.0.6         | 48 KB     |            |   0%\n",
            "freexl-1.0.6         | 48 KB     | ########## | 100%\n",
            "\n",
            "locket-0.2.0         | 6 KB      |            |   0%\n",
            "locket-0.2.0         | 6 KB      | ########## | 100%\n",
            "\n",
            "ucx-py-0.23.0        | 353 KB    |            |   0%\n",
            "ucx-py-0.23.0        | 353 KB    | 4          |   5%\n",
            "ucx-py-0.23.0        | 353 KB    | ########## | 100%\n",
            "\n",
            "libxgboost-1.5.0dev. | 128.2 MB  |            |   0%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  |            |   0%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | 9          |  10%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | #4         |  14%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ##3        |  24%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ##8        |  29%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ####       |  41%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ####7      |  47%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | #####5     |  56%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ######2    |  63%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ######9    |  69%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | #######5   |  75%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ########4  |  84%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | #########1 |  92%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | #########8 |  98%\n",
            "libxgboost-1.5.0dev. | 128.2 MB  | ########## | 100%\n",
            "\n",
            "nest-asyncio-1.5.4   | 9 KB      |            |   0%\n",
            "nest-asyncio-1.5.4   | 9 KB      | ########## | 100%\n",
            "\n",
            "libdap4-3.20.6       | 11.3 MB   |            |   0%\n",
            "libdap4-3.20.6       | 11.3 MB   | #####1     |  52%\n",
            "libdap4-3.20.6       | 11.3 MB   | ########## | 100%\n",
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            "\n",
            "pcre-8.45            | 253 KB    |            |   0%\n",
            "pcre-8.45            | 253 KB    | ########## | 100%\n",
            "\n",
            "rapids-xgboost-21.12 | 5 KB      |            |   0%\n",
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            "\n",
            "rapids-21.12.00      | 5 KB      |            |   0%\n",
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            "\n",
            "cudatoolkit-11.2.72  | 933.4 MB  |            |   0%\n",
            "cudatoolkit-11.2.72  | 933.4 MB  | ########## | 100%\n",
            "cudatoolkit-11.2.72  | 933.4 MB  | ########## | 100%\n",
            "\n",
            "ca-certificates-2021 | 139 KB    |            |   0%\n",
            "ca-certificates-2021 | 139 KB    | #1         |  12%\n",
            "ca-certificates-2021 | 139 KB    | ########## | 100%\n",
            "\n",
            "cairo-1.16.0         | 1.5 MB    |            |   0%\n",
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            "\n",
            "pyct-core-0.4.6      | 13 KB     |            |   0%\n",
            "pyct-core-0.4.6      | 13 KB     | ########## | 100%\n",
            "\n",
            "cugraph-21.12.00     | 7.2 MB    |            |   0%\n",
            "cugraph-21.12.00     | 7.2 MB    |            |   0%\n",
            "cugraph-21.12.00     | 7.2 MB    | ##5        |  26%\n",
            "cugraph-21.12.00     | 7.2 MB    | ########## | 100%\n",
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            "\n",
            "aws-c-io-0.10.5      | 121 KB    |            |   0%\n",
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            "\n",
            "libcumlprims-21.12.0 | 3.7 MB    |            |   0%\n",
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            "\n",
            "libwebp-1.2.2        | 85 KB     |            |   0%\n",
            "libwebp-1.2.2        | 85 KB     | ########## | 100%\n",
            "\n",
            "libcuspatial-21.12.0 | 6.4 MB    |            |   0%\n",
            "libcuspatial-21.12.0 | 6.4 MB    |            |   0%\n",
            "libcuspatial-21.12.0 | 6.4 MB    | ######1    |  61%\n",
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            "libcuspatial-21.12.0 | 6.4 MB    | ########## | 100%\n",
            "\n",
            "importlib-metadata-4 | 33 KB     |            |   0%\n",
            "importlib-metadata-4 | 33 KB     | ########## | 100%\n",
            "\n",
            "fastavro-1.4.10      | 447 KB    |            |   0%\n",
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            "\n",
            "graphite2-1.3.13     | 102 KB    |            |   0%\n",
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            "\n",
            "treelite-2.1.0       | 1.4 MB    |            |   0%\n",
            "treelite-2.1.0       | 1.4 MB    | ########## | 100%\n",
            "treelite-2.1.0       | 1.4 MB    | ########## | 100%\n",
            "\n",
            "async-timeout-4.0.2  | 9 KB      |            |   0%\n",
            "async-timeout-4.0.2  | 9 KB      | ########## | 100%\n",
            "\n",
            "libcugraph-21.12.00  | 259.4 MB  |            |   0%\n",
            "libcugraph-21.12.00  | 259.4 MB  |            |   0%\n",
            "libcugraph-21.12.00  | 259.4 MB  | 1          |   2%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | #          |  10%\n",
            "libcugraph-21.12.00  | 259.4 MB  | #2         |  12%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | #8         |  18%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ##         |  21%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ##2        |  23%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ##6        |  27%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ###        |  30%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ###2       |  32%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ###5       |  36%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ###6       |  37%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ###8       |  39%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ###9       |  40%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ####       |  41%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ####1      |  42%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ####9      |  50%\n",
            "libcugraph-21.12.00  | 259.4 MB  | #####2     |  53%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | #####9     |  60%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ######2    |  62%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ######5    |  65%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ######6    |  67%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ######7    |  68%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ########1  |  81%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ########2  |  82%\n",
            "libcugraph-21.12.00  | 259.4 MB  | ########4  |  84%\n",
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            "libcugraph-21.12.00  | 259.4 MB  | ########## | 100%\n",
            "\n",
            "xorg-recordproto-1.1 | 8 KB      |            |   0%\n",
            "xorg-recordproto-1.1 | 8 KB      | ########## | 100%\n",
            "\n",
            "scikit-learn-1.0.2   | 7.8 MB    |            |   0%\n",
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            "\n",
            "send2trash-1.8.0     | 17 KB     |            |   0%\n",
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            "\n",
            "xorg-libxau-1.0.9    | 13 KB     |            |   0%\n",
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            "\n",
            "libwebp-base-1.2.2   | 824 KB    |            |   0%\n",
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            "\n",
            "networkx-2.7.1       | 1.5 MB    |            |   0%\n",
            "networkx-2.7.1       | 1.5 MB    | ########## | 100%\n",
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            "\n",
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            "\n",
            "libopenblas-0.3.18   | 9.6 MB    |            |   0%\n",
            "libopenblas-0.3.18   | 9.6 MB    | #####6     |  56%\n",
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            "\n",
            "mapclassify-2.4.3    | 36 KB     |            |   0%\n",
            "mapclassify-2.4.3    | 36 KB     | ########## | 100%\n",
            "\n",
            "nbconvert-core-6.4.5 | 427 KB    |            |   0%\n",
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            "\n",
            "backports.zoneinfo-0 | 47 KB     |            |   0%\n",
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            "\n",
            "numba-0.55.1         | 3.8 MB    |            |   0%\n",
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            "\n",
            "pywavelets-1.3.0     | 4.4 MB    |            |   0%\n",
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            "\n",
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            "\n",
            "giflib-5.2.1         | 77 KB     |            |   0%\n",
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            "\n",
            "pyppeteer-1.0.2      | 63 KB     |            |   0%\n",
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            "\n",
            "backports-1.0        | 4 KB      |            |   0%\n",
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            "\n",
            "zstd-1.5.2           | 458 KB    |            |   0%\n",
            "zstd-1.5.2           | 458 KB    | ########## | 100%\n",
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            "\n",
            "gettext-0.19.8.1     | 3.6 MB    |            |   0%\n",
            "gettext-0.19.8.1     | 3.6 MB    | ########## | 100%\n",
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            "\n",
            "fontconfig-2.13.96   | 306 KB    |            |   0%\n",
            "fontconfig-2.13.96   | 306 KB    | ########## | 100%\n",
            "\n",
            "notebook-6.4.10      | 6.3 MB    |            |   0%\n",
            "notebook-6.4.10      | 6.3 MB    | #########3 |  93%\n",
            "notebook-6.4.10      | 6.3 MB    | ########## | 100%\n",
            "\n",
            "jupyter_core-4.9.2   | 80 KB     |            |   0%\n",
            "jupyter_core-4.9.2   | 80 KB     | ########## | 100%\n",
            "\n",
            "libdeflate-1.8       | 67 KB     |            |   0%\n",
            "libdeflate-1.8       | 67 KB     | ########## | 100%\n",
            "\n",
            "openjpeg-2.4.0       | 444 KB    |            |   0%\n",
            "openjpeg-2.4.0       | 444 KB    | ########## | 100%\n",
            "openjpeg-2.4.0       | 444 KB    | ########## | 100%\n",
            "\n",
            "bokeh-2.4.0          | 13.5 MB   |            |   0%\n",
            "bokeh-2.4.0          | 13.5 MB   | ####       |  40%\n",
            "bokeh-2.4.0          | 13.5 MB   | ########## | 100%\n",
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            "\n",
            "testpath-0.6.0       | 85 KB     |            |   0%\n",
            "testpath-0.6.0       | 85 KB     | ########## | 100%\n",
            "\n",
            "xorg-libxdmcp-1.1.3  | 19 KB     |            |   0%\n",
            "xorg-libxdmcp-1.1.3  | 19 KB     | ########## | 100%\n",
            "\n",
            "libxcb-1.13          | 391 KB    |            |   0%\n",
            "libxcb-1.13          | 391 KB    | ########## | 100%\n",
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            "\n",
            "tblib-1.7.0          | 15 KB     |            |   0%\n",
            "tblib-1.7.0          | 15 KB     | ########## | 100%\n",
            "\n",
            "ipython-7.32.0       | 1.1 MB    |            |   0%\n",
            "ipython-7.32.0       | 1.1 MB    | ########## | 100%\n",
            "ipython-7.32.0       | 1.1 MB    | ########## | 100%\n",
            "\n",
            "cachetools-5.0.0     | 12 KB     |            |   0%\n",
            "cachetools-5.0.0     | 12 KB     | ########## | 100%\n",
            "\n",
            "tzcode-2022a         | 69 KB     |            |   0%\n",
            "tzcode-2022a         | 69 KB     | ########## | 100%\n",
            "\n",
            "harfbuzz-2.9.1       | 2.0 MB    |            |   0%\n",
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            "\n",
            "pandas-1.3.5         | 12.7 MB   |            |   0%\n",
            "pandas-1.3.5         | 12.7 MB   | ##         |  20%\n",
            "pandas-1.3.5         | 12.7 MB   | ###4       |  34%\n",
            "pandas-1.3.5         | 12.7 MB   | #######3   |  74%\n",
            "pandas-1.3.5         | 12.7 MB   | ########## | 100%\n",
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            "\n",
            "datashape-0.5.4      | 49 KB     |            |   0%\n",
            "datashape-0.5.4      | 49 KB     | ########## | 100%\n",
            "\n",
            "google-crc32c-1.1.2  | 24 KB     |            |   0%\n",
            "google-crc32c-1.1.2  | 24 KB     | ########## | 100%\n",
            "\n",
            "libspatialindex-1.9. | 4.6 MB    |            |   0%\n",
            "libspatialindex-1.9. | 4.6 MB    | ###6       |  37%\n",
            "libspatialindex-1.9. | 4.6 MB    | ########## | 100%\n",
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            "\n",
            "threadpoolctl-3.1.0  | 18 KB     |            |   0%\n",
            "threadpoolctl-3.1.0  | 18 KB     | ########## | 100%\n",
            "\n",
            "json-c-0.15          | 274 KB    |            |   0%\n",
            "json-c-0.15          | 274 KB    | 5          |   6%\n",
            "json-c-0.15          | 274 KB    | ########## | 100%\n",
            "\n",
            "alsa-lib-1.2.3       | 560 KB    |            |   0%\n",
            "alsa-lib-1.2.3       | 560 KB    | ########## | 100%\n",
            "alsa-lib-1.2.3       | 560 KB    | ########## | 100%\n",
            "\n",
            "gcsfs-2022.2.0       | 25 KB     |            |   0%\n",
            "gcsfs-2022.2.0       | 25 KB     | ########## | 100%\n",
            "\n",
            "poppler-21.09.0      | 16.8 MB   |            |   0%\n",
            "poppler-21.09.0      | 16.8 MB   | ####8      |  49%\n",
            "poppler-21.09.0      | 16.8 MB   | ########## | 100%\n",
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            "\n",
            "yarl-1.7.2           | 137 KB    |            |   0%\n",
            "yarl-1.7.2           | 137 KB    | ########## | 100%\n",
            "\n",
            "mistune-0.8.4        | 54 KB     |            |   0%\n",
            "mistune-0.8.4        | 54 KB     | ########## | 100%\n",
            "\n",
            "libgomp-11.2.0       | 429 KB    |            |   0%\n",
            "libgomp-11.2.0       | 429 KB    | ########## | 100%\n",
            "\n",
            "argon2-cffi-bindings | 34 KB     |            |   0%\n",
            "argon2-cffi-bindings | 34 KB     | ########## | 100%\n",
            "\n",
            "zlib-1.2.11          | 88 KB     |            |   0%\n",
            "zlib-1.2.11          | 88 KB     | ########## | 100%\n",
            "\n",
            "libcudf-21.12.02     | 288.2 MB  |            |   0%\n",
            "libcudf-21.12.02     | 288.2 MB  |            |   0%\n",
            "libcudf-21.12.02     | 288.2 MB  |            |   1%\n",
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            "libcudf-21.12.02     | 288.2 MB  | 9          |  10%\n",
            "libcudf-21.12.02     | 288.2 MB  | #1         |  12%\n",
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            "libcudf-21.12.02     | 288.2 MB  | #6         |  17%\n",
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            "libcudf-21.12.02     | 288.2 MB  | ###1       |  31%\n",
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            "libcudf-21.12.02     | 288.2 MB  | ###5       |  35%\n",
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            "libcudf-21.12.02     | 288.2 MB  | ####       |  41%\n",
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            "libcudf-21.12.02     | 288.2 MB  | #####3     |  53%\n",
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            "libcudf-21.12.02     | 288.2 MB  | #####8     |  59%\n",
            "libcudf-21.12.02     | 288.2 MB  | ######1    |  61%\n",
            "libcudf-21.12.02     | 288.2 MB  | ######3    |  63%\n",
            "libcudf-21.12.02     | 288.2 MB  | ######4    |  65%\n",
            "libcudf-21.12.02     | 288.2 MB  | ######6    |  67%\n",
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            "libcudf-21.12.02     | 288.2 MB  | #######    |  71%\n",
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            "libcudf-21.12.02     | 288.2 MB  | ########   |  80%\n",
            "libcudf-21.12.02     | 288.2 MB  | ########2  |  83%\n",
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            "libcudf-21.12.02     | 288.2 MB  | #########  |  90%\n",
            "libcudf-21.12.02     | 288.2 MB  | #########1 |  92%\n",
            "libcudf-21.12.02     | 288.2 MB  | #########3 |  94%\n",
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            "libcudf-21.12.02     | 288.2 MB  | #########7 |  97%\n",
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            "libcudf-21.12.02     | 288.2 MB  | ########## | 100%\n",
            "libcudf-21.12.02     | 288.2 MB  | ########## | 100%\n",
            "\n",
            "tzlocal-4.1          | 30 KB     |            |   0%\n",
            "tzlocal-4.1          | 30 KB     | ########## | 100%\n",
            "\n",
            "cfitsio-3.470        | 1.3 MB    |            |   0%\n",
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            "\n",
            "xorg-renderproto-0.1 | 9 KB      |            |   0%\n",
            "xorg-renderproto-0.1 | 9 KB      | ########## | 100%\n",
            "\n",
            "c-blosc2-2.0.4       | 183 KB    |            |   0%\n",
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            "\n",
            "fonts-conda-ecosyste | 4 KB      |            |   0%\n",
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            "\n",
            "protobuf-3.16.0      | 342 KB    |            |   0%\n",
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            "\n",
            "anyio-3.5.0          | 150 KB    |            |   0%\n",
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            "\n",
            "ucx-proc-1.0.0       | 9 KB      |            |   0%\n",
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            "\n",
            "geos-3.9.1           | 1.1 MB    |            |   0%\n",
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            "\n",
            "gdal-3.3.2           | 1.7 MB    |            |   0%\n",
            "gdal-3.3.2           | 1.7 MB    | ########## | 100%\n",
            "gdal-3.3.2           | 1.7 MB    | ########## | 100%\n",
            "\n",
            "librmm-21.12.00      | 688 KB    |            |   0%\n",
            "librmm-21.12.00      | 688 KB    | 2          |   2%\n",
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            "\n",
            "cuspatial-21.12.00   | 15.9 MB   |            |   0%\n",
            "cuspatial-21.12.00   | 15.9 MB   |            |   0%\n",
            "cuspatial-21.12.00   | 15.9 MB   | ####4      |  44%\n",
            "cuspatial-21.12.00   | 15.9 MB   | #########3 |  93%\n",
            "cuspatial-21.12.00   | 15.9 MB   | ########## | 100%\n",
            "\n",
            "typing_extensions-4. | 29 KB     |            |   0%\n",
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            "\n",
            "typing-extensions-4. | 8 KB      |            |   0%\n",
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            "\n",
            "libkml-1.3.0         | 591 KB    |            |   0%\n",
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            "\n",
            "libaec-1.0.6         | 45 KB     |            |   0%\n",
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            "\n",
            "pickleshare-0.7.5    | 9 KB      |            |   0%\n",
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            "\n",
            "pyct-0.4.6           | 3 KB      |            |   0%\n",
            "pyct-0.4.6           | 3 KB      | ########## | 100%\n",
            "\n",
            "pyviz_comms-2.2.0    | 31 KB     |            |   0%\n",
            "pyviz_comms-2.2.0    | 31 KB     | ########## | 100%\n",
            "\n",
            "ipython_genutils-0.2 | 21 KB     |            |   0%\n",
            "ipython_genutils-0.2 | 21 KB     | ########## | 100%\n",
            "\n",
            "uvicorn-0.17.6       | 78 KB     |            |   0%\n",
            "uvicorn-0.17.6       | 78 KB     | ########## | 100%\n",
            "\n",
            "libblas-3.9.0        | 12 KB     |            |   0%\n",
            "libblas-3.9.0        | 12 KB     | ########## | 100%\n",
            "\n",
            "rmm-21.12.00         | 943 KB    |            |   0%\n",
            "rmm-21.12.00         | 943 KB    | 1          |   2%\n",
            "rmm-21.12.00         | 943 KB    | ########## | 100%\n",
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            "\n",
            "liblapack-3.9.0      | 12 KB     |            |   0%\n",
            "liblapack-3.9.0      | 12 KB     | ########## | 100%\n",
            "\n",
            "lerc-3.0             | 216 KB    |            |   0%\n",
            "lerc-3.0             | 216 KB    | ########## | 100%\n",
            "\n",
            "pexpect-4.8.0        | 47 KB     |            |   0%\n",
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            "\n",
            "multipledispatch-0.6 | 12 KB     |            |   0%\n",
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            "\n",
            "frozenlist-1.3.0     | 43 KB     |            |   0%\n",
            "frozenlist-1.3.0     | 43 KB     | ########## | 100%\n",
            "\n",
            "fonts-conda-forge-1  | 4 KB      |            |   0%\n",
            "fonts-conda-forge-1  | 4 KB      | ########## | 100%\n",
            "\n",
            "libcucim-21.12.00    | 2.8 MB    |            |   0%\n",
            "libcucim-21.12.00    | 2.8 MB    |            |   1%\n",
            "libcucim-21.12.00    | 2.8 MB    | #######5   |  75%\n",
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            "\n",
            "libcuml-21.12.00     | 222.9 MB  |            |   0%\n",
            "libcuml-21.12.00     | 222.9 MB  |            |   0%\n",
            "libcuml-21.12.00     | 222.9 MB  | 3          |   4%\n",
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            "libcuml-21.12.00     | 222.9 MB  | #          |  10%\n",
            "libcuml-21.12.00     | 222.9 MB  | #3         |  13%\n",
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            "libcuml-21.12.00     | 222.9 MB  | #6         |  17%\n",
            "libcuml-21.12.00     | 222.9 MB  | #8         |  18%\n",
            "libcuml-21.12.00     | 222.9 MB  | #9         |  20%\n",
            "libcuml-21.12.00     | 222.9 MB  | ##2        |  23%\n",
            "libcuml-21.12.00     | 222.9 MB  | ##4        |  24%\n",
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            "libcuml-21.12.00     | 222.9 MB  | ##9        |  30%\n",
            "libcuml-21.12.00     | 222.9 MB  | ###        |  31%\n",
            "libcuml-21.12.00     | 222.9 MB  | ###1       |  32%\n",
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            "libcuml-21.12.00     | 222.9 MB  | ####       |  41%\n",
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            "libcuml-21.12.00     | 222.9 MB  | ####9      |  50%\n",
            "libcuml-21.12.00     | 222.9 MB  | #####2     |  52%\n",
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            "libcuml-21.12.00     | 222.9 MB  | #####6     |  57%\n",
            "libcuml-21.12.00     | 222.9 MB  | #####8     |  58%\n",
            "libcuml-21.12.00     | 222.9 MB  | ######     |  60%\n",
            "libcuml-21.12.00     | 222.9 MB  | ######1    |  62%\n",
            "libcuml-21.12.00     | 222.9 MB  | ######2    |  63%\n",
            "libcuml-21.12.00     | 222.9 MB  | ######6    |  66%\n",
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            "libcuml-21.12.00     | 222.9 MB  | #######    |  71%\n",
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            "libcuml-21.12.00     | 222.9 MB  | ########1  |  82%\n",
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            "libcuml-21.12.00     | 222.9 MB  | #########  |  90%\n",
            "libcuml-21.12.00     | 222.9 MB  | #########2 |  92%\n",
            "libcuml-21.12.00     | 222.9 MB  | #########4 |  94%\n",
            "libcuml-21.12.00     | 222.9 MB  | #########9 |  99%\n",
            "libcuml-21.12.00     | 222.9 MB  | ########## | 100%\n",
            "\n",
            "certifi-2021.10.8    | 145 KB    |            |   0%\n",
            "certifi-2021.10.8    | 145 KB    | ########## | 100%\n",
            "certifi-2021.10.8    | 145 KB    | ########## | 100%\n",
            "\n",
            "pyzmq-22.3.0         | 501 KB    |            |   0%\n",
            "pyzmq-22.3.0         | 501 KB    | ########## | 100%\n",
            "pyzmq-22.3.0         | 501 KB    | ########## | 100%\n",
            "\n",
            "backcall-0.2.0       | 13 KB     |            |   0%\n",
            "backcall-0.2.0       | 13 KB     | ########## | 100%\n",
            "\n",
            "aws-checksums-0.1.11 | 50 KB     |            |   0%\n",
            "aws-checksums-0.1.11 | 50 KB     | ########## | 100%\n",
            "\n",
            "asynctest-0.13.0     | 24 KB     |            |   0%\n",
            "asynctest-0.13.0     | 24 KB     | ########## | 100%\n",
            "\n",
            "libgfortran5-11.2.0  | 1.7 MB    |            |   0%\n",
            "libgfortran5-11.2.0  | 1.7 MB    | ########## | 100%\n",
            "libgfortran5-11.2.0  | 1.7 MB    | ########## | 100%\n",
            "\n",
            "libxml2-2.9.12       | 772 KB    |            |   0%\n",
            "libxml2-2.9.12       | 772 KB    | ########## | 100%\n",
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            "\n",
            "nbformat-5.2.0       | 105 KB    |            |   0%\n",
            "nbformat-5.2.0       | 105 KB    | ########## | 100%\n",
            "\n",
            "nodejs-14.17.4       | 15.8 MB   |            |   0%\n",
            "nodejs-14.17.4       | 15.8 MB   | #9         |  19%\n",
            "nodejs-14.17.4       | 15.8 MB   | #######7   |  77%\n",
            "nodejs-14.17.4       | 15.8 MB   | ########## | 100%\n",
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            "\n",
            "glog-0.5.0           | 104 KB    |            |   0%\n",
            "glog-0.5.0           | 104 KB    | ########## | 100%\n",
            "\n",
            "libgpg-error-1.44    | 294 KB    |            |   0%\n",
            "libgpg-error-1.44    | 294 KB    | ########## | 100%\n",
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            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\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",
            "Enabling notebook extension jupyter-js-widgets/extension...\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/jupyterlab-plotly.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/jupyterlab-plotly.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/jupyterlab-plotly.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.json\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",
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            "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": "code",
      "source": [
        "pip install -U sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wb-5ZfxSAJtw",
        "outputId": "4500fbe3-b411-4336-e23c-13d707070ab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.0.tar.gz (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 5.8 MB/s \n",
            "\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.17.0-py3-none-any.whl (3.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8 MB 16.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.63.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.10.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.11.1+cu111)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.21.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.4.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (3.2.5)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 67.2 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 6.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence-transformers) (3.10.0.2)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (4.11.3)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.49-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 69.3 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,>=0.11.1\n",
            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5 MB 77.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 99.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2019.12.20)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.7.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.10.8)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (7.1.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.0-py3-none-any.whl size=120747 sha256=3d4408382dbd9abb1f3465280102c1d408894dd586c87ba141958c0a4a2fca45\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/c0/df/b6873ab7aac3f2465aa9144b6b4c41c4391cfecc027c8b07e7\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece, sentence-transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.49 sentence-transformers-2.2.0 sentencepiece-0.1.96 tokenizers-0.11.6 transformers-4.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Asng85LpGd6T",
        "outputId": "0363a919-9046-4829-e9ba-4d669513da97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SentenceTransformer(\n",
              "  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel \n",
              "  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer \n",
        "model2 = SentenceTransformer('paraphrase-MiniLM-L6-v2').to('cuda')"
      ],
      "metadata": {
        "id": "8oZHifTZGOeN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer \n",
        "model_mpnet = SentenceTransformer('all-mpnet-base-v2').to('cuda')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
          "referenced_widgets": [
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "NoiVItdYALjg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayopiEOsANBy",
        "outputId": "be462747-76db-4338-cb62-78bab63cad07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/csvdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nmaCdvDdAOIl",
        "outputId": "99ed8a9f-b2b2-4fdd-9932-3fa86baf6e7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AskReddit.csv  explainlikeimfive.csv  Showerthoughts.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/csvdata/AskReddit.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "SGPW54ofAPTW",
        "outputId": "fc2bcbb1-50fe-4d1b-802f-78180245734c"
      },
      "execution_count": null,
      "outputs": [
        {
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        "df.rename(columns={\"Unnamed: 0\": \"Title\", \"Title\": \"Text\", \"Text\": \"Upvotes\", \"Upvotes\": \"Subreddit\", \"Subreddit\": \"Num_comments\", \n",
        "                   \"Num_comments\": \"nsfw\", \"nsfw\": \"Upvote_ratio\", \"Upvote_ratio\": \"Has_text\"}, inplace=True)\n",
        "df.head()"
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              "                                               Title       Text  Upvotes  \\\n",
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        "df"
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "df[\"Text_embedding\"] = \"\""
      ],
      "metadata": {
        "id": "tg21l31ZBGqc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def sentence_embedding(df):\n",
        "  df[\"Text_embedding\"] = df[\"Text\"].map(lambda text: model2.encode(str(text)))\n",
        "  return df"
      ],
      "metadata": {
        "id": "Bwc9F27pBIId"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_embedding(df.head(1000))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "2aD4NkAxBJU1",
        "outputId": "98b90472-4710-4386-b4ac-a59399db5ac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
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              "                                                 Title       Text  Upvotes  \\\n",
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              "4                              How I get karma points?  [removed]        1   \n",
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              "995  What’s the most underwhelming thing you’ve eve...        NaN        2   \n",
              "996  Married redditors, did you sign a prenuptial o...  [removed]        1   \n",
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              "998                What's something that no-one likes?        NaN        0   \n",
              "999  Do you agree with the statement that Florida i...        NaN        8   \n",
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            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import nltk\n",
        "import numpy as np\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "def concatenate(df):\n",
        "\tdf[\"Text\"].fillna(\" \",inplace = True)\n",
        "\tdf[\"Text\"] = df[\"Text\"].astype(str) + \" \"+ df[\"Title\"].astype(str)\n",
        "\treturn df\n",
        "\n",
        "# 2.1 (3 pts.) \n",
        "def lowerCase(df):\n",
        "\tdf = concatenate(df)\n",
        "\tdf[\"Text\"] = df[\"Text\"].str.lower()\n",
        "\treturn df\n",
        "\n",
        "def  clean_text(df):\n",
        "    df[\"Text\"] = df[\"Text\"].apply(lambda elem: re.sub(r\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \" \", str(elem) ))  \n",
        "    # remove numbers\n",
        "    df[\"Text\"] = df[\"Text\"].apply(lambda elem: re.sub(r\"\\d+\", \"\", elem))\n",
        "    return df\n",
        "\n",
        "# 2.2 (3 pts.) \n",
        "def tokenizeDF(df):\n",
        "\tdf[\"Text\"] = df[\"Text\"].map(lambda elem: word_tokenize(str(elem)))\n",
        "\treturn df\n",
        "\n",
        "def lemmatize(df):\n",
        "    def lemmatize_helper(text):\n",
        "        return [WordNetLemmatizer().lemmatize(i) for i in text]\n",
        "    df[\"Text\"] = df[\"Text\"].apply(lambda elem: lemmatize_helper(elem))\n",
        "    return df\n",
        "\n",
        "# 2.3 (4 pts.) \t\n",
        "def removeStop(df, stopeng):\n",
        "\tdf[\"Text\"] = df[\"Text\"].map(lambda x: [word for word in x if word not in (stopeng)])\n",
        "\treturn df\n",
        "\n",
        "\n",
        "# 2.4 (4 pts.) \n",
        "def keepAlpha(df):\n",
        "\talpahbets = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n",
        "\tdef keepAlpha_helper(a):\n",
        "\t\tdata_list=[]\n",
        "\t\tfor word in a:\n",
        "\t\t\tif len(word) == 0 or len(word) == 1:\n",
        "\t\t\t\tcontinue\n",
        "\t\t\thasChar = False\n",
        "\t\t\tfor character in word:\n",
        "\t\t\t\tif character in alpahbets:\n",
        "\t\t\t\t\thasChar = True\n",
        "\t\t\tif hasChar:\n",
        "\t\t\t\tdata_list.append(word)\n",
        "\t\treturn data_list\t\n",
        "\tdf[\"Text\"] = df[\"Text\"].map(lambda x: keepAlpha_helper(x))\n",
        "\treturn df\n",
        "\n",
        "# 2.5 (3 pts.) \n",
        "def joinText(df):\n",
        "\tdf[\"Text\"] = df[\"Text\"].map(lambda x: ' '.join(x))\n",
        "\treturn df"
      ],
      "metadata": {
        "id": "EwYqZak6BKhL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download(\"wordnet\")\n",
        "df = lowerCase(df)\n",
        "df = clean_text(df)\n",
        "df = tokenizeDF(df)\n",
        "df = lemmatize(df)\n",
        "stopeng = set(stopwords.words(\"english\"))\n",
        "df = removeStop(df,stopeng)\n",
        "df = keepAlpha(df)\n",
        "df = joinText(df)\n",
        "# df.to_csv(\"cleaned_post.csv\", index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQDGRD8TCk1F",
        "outputId": "1252af11-2646-41ea-d91e-a677a903c231"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"/content/drive/MyDrive/csvdata/cleaned_AskReddit.csv\", index=False)"
      ],
      "metadata": {
        "id": "m3FDqQg9Iq7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "Dr10sr_gIyu6",
        "outputId": "fa2a6211-4485-41f0-8cc6-efffd5aaf381"
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              "                                               Title  \\\n",
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              "      <td>Gen Z-ers, what do you like the least and most...</td>\n",
              "      <td>gen er like least millenials</td>\n",
              "      <td>2</td>\n",
              "      <td>AskReddit</td>\n",
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              "      <td>How I get karma points?</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = sentence_embedding(df)"
      ],
      "metadata": {
        "id": "3DlnrSabJrfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"/content/drive/MyDrive/csvdata/cleaned_AskReddit.csv\", index=False)"
      ],
      "metadata": {
        "id": "spGwusRCKDOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhIBD-rEh0w3",
        "outputId": "c0da96c6-92e8-47c4-9d0e-ccffb452fedc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "vU0xLP2miJfv"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_all = pd.read_csv(\"/content/drive/MyDrive/data/cleaned_berkeley.csv\")"
      ],
      "metadata": {
        "id": "YQ00gAGWhvPc"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_all"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7aRG1ETXfuOA",
        "outputId": "7d20e684-bc2e-43af-cb18-1b9b0cbd3a80"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       Unnamed: 0                                              Title  \\\n",
              "0               0                   Looking for: Beach House Tickets   \n",
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              "83241       83241      UC considers changes to guaranteed admissions   \n",
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              "83237  http://www.dailycal.org/article/104399/for_the...   \n",
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              "83239  https://www.reddit.com/r/berkeley/comments/7oh...   \n",
              "83240                                        http://test   \n",
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              "                                                 Content  \\\n",
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              "3                                                  doggo   \n",
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              "...                                                  ...   \n",
              "83237                                love code hackathon   \n",
              "83238         berkeley cop warn skirt lifting sex attack   \n",
              "83239         deleted trying track name ex ucb professor   \n",
              "83240                                       deleted test   \n",
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              "    <tr>\n",
              "      <th>83241</th>\n",
              "      <td>83241</td>\n",
              "      <td>UC considers changes to guaranteed admissions</td>\n",
              "      <td></td>\n",
              "      <td>1.214520e+09</td>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>berkeley</td>\n",
              "      <td>http://www.sfgate.com/cgi-bin/article.cgi?f=/c...</td>\n",
              "      <td>uc considers change guaranteed admission</td>\n",
              "      <td>[ 0.34827298 -0.16433766  0.30172607  0.533017...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>83242 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-526a33c2-f946-4c52-bb98-ed403bb12d4d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-526a33c2-f946-4c52-bb98-ed403bb12d4d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-526a33c2-f946-4c52-bb98-ed403bb12d4d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def string2list(stri):\n",
        "  return list(map(float, stri[1:-1].split()))"
      ],
      "metadata": {
        "id": "ZK6kO0K-l0E1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_all"
      ],
      "metadata": {
        "id": "b4h9kuuvIaLr"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSo7wlMDIVoi",
        "outputId": "718b418a-7a2f-4242-8b49-4a2ad493fa01"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83242"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_all[\"Upvotes\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYsKvCxKgFfJ",
        "outputId": "cfa2d017-f355-4870-c68a-0b4aba32f4e3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       17320\n",
              "0       15383\n",
              "2        7191\n",
              "3        5442\n",
              "4        4061\n",
              "        ...  \n",
              "566         1\n",
              "403         1\n",
              "510         1\n",
              "517         1\n",
              "7165        1\n",
              "Name: Upvotes, Length: 624, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLi5-pm0SEJk",
        "outputId": "3dd0f6c1-3ec7-4c84-dc91-19d4b02d496a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83242"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sentence_embedding(df):\n",
        "  df[\"Text_embedding\"] = df[\"Text\"].map(lambda text: model_mpnet.encode(str(text)))\n",
        "  return df"
      ],
      "metadata": {
        "id": "m7rNCeGmRxPm"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = sentence_embedding(df)"
      ],
      "metadata": {
        "id": "DCaOCQ3ORzXO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "21d94265-0876-4abc-9d7d-89a717524743"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-11d717219ba9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msentence_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-11-c2b5f9168bf8>\u001b[0m in \u001b[0;36msentence_embedding\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msentence_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Text_embedding\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Text\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel_mpnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   4159\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4160\u001b[0m         \"\"\"\n\u001b[0;32m-> 4161\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4162\u001b[0m         return self._constructor(new_values, index=self.index).__finalize__(\n\u001b[1;32m   4163\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-11-c2b5f9168bf8>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msentence_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Text_embedding\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Text\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel_mpnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model_mpnet' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hot = df_all[df_all[\"Upvotes\"] >= 5]\n",
        "df_cool = df_all[df_all[\"Upvotes\"] <= 1]\n",
        "\n",
        "if len(df_hot) <= len(df_cool):\n",
        "  df_cool = df_cool[:len(df_hot)]\n",
        "else:\n",
        "  df_hot = df_hot[:len(df_cool)]"
      ],
      "metadata": {
        "id": "VkjCPMmEEa5-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df = pd.concat([df_hot, df_cool], ignore_index=True)\n",
        "df = df_hot.append(df_cool, ignore_index=True)"
      ],
      "metadata": {
        "id": "zlgUlWfAIHy1"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwRdAh1xIJOX",
        "outputId": "bfd00971-46e6-4b6a-bd77-b5f3581cc640"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65406"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Text_embedding'] = df['Text_embedding'].apply(string2list)"
      ],
      "metadata": {
        "id": "YA1LLKp3ijI8"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Upvotes2Cates(upvote):\n",
        "  return 1 if upvote >= 5 else 0"
      ],
      "metadata": {
        "id": "4QV0SXGEqWZ8"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Upvotes_category\"] = df[\"Upvotes\"].apply(Upvotes2Cates)"
      ],
      "metadata": {
        "id": "gifIxvnHqUaj"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "JESpQO5sI-Ry",
        "outputId": "b435a53e-cb02-4a1b-b311-4e57e3cfa88b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                              Title  \\\n",
              "0           1          Dropped a CS interview at the last minute   \n",
              "1           3                                              Doggo   \n",
              "2           4  Contemplating not committing to UC Berkeley ov...   \n",
              "3           5  GSIs: have you ever passed a student for perso...   \n",
              "4          11                                 Grinnell is gone:(   \n",
              "\n",
              "                                                Text     Timestamp  Upvotes  \\\n",
              "0  I had been selected to interview this morning ...  1.648798e+09       68   \n",
              "1                                                     1.648797e+09       38   \n",
              "2  I have some other options which are arguably n...  1.648797e+09       11   \n",
              "3                                                     1.648796e+09        6   \n",
              "4  They found grinnells body in dt berk and I am ...  1.648792e+09       72   \n",
              "\n",
              "   Upvote_ratio  Num_comments   Nsfw  Text_Only Subreddit  \\\n",
              "0          0.97            15  False       True  berkeley   \n",
              "1          0.90             6  False       True  berkeley   \n",
              "2          0.70            27  False       True  berkeley   \n",
              "3          0.75             2  False       True  berkeley   \n",
              "4          0.98             9  False       True  berkeley   \n",
              "\n",
              "                                                 Url  \\\n",
              "0  https://www.reddit.com/r/berkeley/comments/ttl...   \n",
              "1  https://www.reddit.com/r/berkeley/comments/ttk...   \n",
              "2  https://www.reddit.com/r/berkeley/comments/ttk...   \n",
              "3  https://www.reddit.com/r/berkeley/comments/ttk...   \n",
              "4  https://www.reddit.com/r/berkeley/comments/ttj...   \n",
              "\n",
              "                                             Content  \\\n",
              "0  selected interview morning summer camp instruc...   \n",
              "1                                              doggo   \n",
              "2  option arguably good thing pass sure even wort...   \n",
              "3           gsis ever passed student personal reason   \n",
              "4  found grinnells body dt berk honestly sad grin...   \n",
              "\n",
              "                                      Text_embedding  Upvotes_category  \n",
              "0  [0.04479124, 0.08691485, 0.22561271, 0.0892584...                 1  \n",
              "1  [0.12569244, -0.21759918, 0.01353921, -0.04427...                 1  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Upvotes_category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmqseZSoHQSu",
        "outputId": "52e73851-f3e9-465c-cb96-6d745c4ce90e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    32703\n",
              "0    32703\n",
              "Name: Upvotes_category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(df[\"Text_embedding\"], df[\"Upvotes_category\"], test_size=0.2)"
      ],
      "metadata": {
        "id": "iblM23G035hQ"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60Ty1xC2IvsQ",
        "outputId": "6e13d714-8c5f-4854-f432-89fe3fc14613"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       list([-0.195715278, 0.0247195046, 0.132729635, -0.105140008, 0.45099169, 0.285913378, 0.216740534, -0.246333122, -0.230787426, -0.0855159909, -0.416238457, -0.302165091, -0.249352381, -0.346024334, 0.111330278, 0.059386041, 0.329511821, -0.207695305, 0.284058213, -0.0983137563, -0.601486444, -0.0862674415, -0.218225971, 0.0970295817, -0.457269162, 0.0555565171, -0.369902909, 0.11271552, 0.65710187, 0.532942832, 0.113441825, 0.537874818, 0.248064384, -0.00825876463, 0.00833794102, 0.192787319, -0.11924006, 0.0951629728, -0.211102933, -0.168203801, -0.0366911069, -0.135289267, 0.0937619731, 0.0548684746, -0.249109969, -0.104909115, -0.21842204, -0.277404189, -0.0162906367, 0.139234766, -0.52103436, -0.0758456364, 0.317723095, 0.0664064065, -0.0109338509, 0.0888117179, 0.0584656857, 0.1789141, -0.0804872215, 0.133478031, -0.187675789, -0.472405136, -0.799959064, 0.212296158, 0.647533059, 0.420671165, -0.170462027, 0.381701976, -0.475992709, -0.100339122, 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0.66088748, -0.216649756, -0.00116144237, -0.102226645, 0.338065982, 0.184401482, 0.024974823, 0.304753691, 0.270107955, 0.199199945, -0.0808608085, 0.2870076, -0.11244227, 0.402214199, -0.244209915, -0.26779905, -0.304424196, -0.123785861, -0.148447916, 0.235753521, -0.0123634106, 0.0940763503, 0.72503674, 0.304301381, -0.194736883, -0.217957467, -0.251270115, -0.241759926, 0.255560607, 0.414807618, -0.113110468, 0.242692575, 0.421694398, 0.102814965, -0.18704389, 0.228164777, 0.180509269, 0.172544688, -0.67971921, -0.327385008, -0.403778732, -0.157181963, 0.367355764, -0.176387176, -0.153703451, 0.0783032104, 0.205395564, -0.0665629134, -0.448193282, 0.111737125, -0.584860742, -0.536846101, 0.0159497261, -0.446326733, 0.555760503, 0.375943989, -0.180488452, 0.395788074, 0.14915809, 0.19364056, -0.026268363, 0.000808472803, -0.146902964, -0.227521762, -0.164428562, -0.355642021, 0.279897988, 0.435739875, 0.197545275, -0.301837385, 0.301323563, 0.0980792195, -0.406534731, -0.204751492, 0.171411544, 0.217925146, 0.380356431, 0.162793249, -0.153766453, 0.00348345144, 0.00286173122, -0.798022866, -0.347621918, -0.404351652, 0.250183582, 0.0677327216, 0.376828998, 0.0374717489, 0.287903279, 0.464894503, 0.230163947, 0.140859693, 0.0841268003, 0.0251173358, -0.778694153, 0.503863871])],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.vstack(X_train).astype(np.float32)\n",
        "X_test = np.vstack(X_test).astype(np.float32)"
      ],
      "metadata": {
        "id": "lu7xezDJ5GX7"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYYjjSGz55Kw",
        "outputId": "ec79a350-1135-4a79-f775-cf36a04bf7bb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(52324, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os, time, random\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import metrics\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import torch.utils.data as data_utils"
      ],
      "metadata": {
        "id": "NmceNQqCrEKR"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128"
      ],
      "metadata": {
        "id": "_b07NdRPsC_C"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_target = torch.tensor(y_train.values.astype(np.float32))\n",
        "train = torch.tensor(X_train) \n",
        "train_tensor = data_utils.TensorDataset(train, train_target) \n",
        "train_loader = data_utils.DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)"
      ],
      "metadata": {
        "id": "ZtI_uGTP4EdO"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_target = torch.tensor(y_test.values.astype(np.float32))\n",
        "test = torch.tensor(X_test) \n",
        "test_tensor = data_utils.TensorDataset(test, test_target) \n",
        "test_loader = data_utils.DataLoader(dataset = test_tensor, batch_size = batch_size, shuffle = True)"
      ],
      "metadata": {
        "id": "JY7qm7cC7PrY"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "class Model(nn.Module):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__()\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(in_features=input_size, out_features=256, bias=True),\n",
        "            # nn.BatchNorm1d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), \n",
        "            nn.ReLU(inplace=True), \n",
        "            nn.Dropout(p=0.3, inplace=False),\n",
        "            nn.Linear(in_features=256, out_features=128, bias=True),\n",
        "            # nn.BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),  \n",
        "            nn.ReLU(inplace=True), \n",
        "            nn.Dropout(p=0.2, inplace=False), \n",
        "            nn.Linear(in_features=128, out_features=10, bias=True),\n",
        "            # nn.BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), \n",
        "            nn.ReLU(inplace=True), \n",
        "            nn.Dropout(p=0.1, inplace=False), \n",
        "            nn.Linear(in_features=10, out_features=1, bias=True)\n",
        "        )\n",
        "    \n",
        "    def forward(self, x):\n",
        "        z1 = self.classifier(x)\n",
        "        z = torch.sigmoid(z1)\n",
        "        return z\n"
      ],
      "metadata": {
        "id": "A5dFcsvij5n7"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def _train_epoch(data_loader, model, criterion, optimizer):\n",
        "    \"\"\"\n",
        "    Train the `model` for one epoch of data from `data_loader`\n",
        "    Use `optimizer` to optimize the specified `criterion`\n",
        "    \"\"\"\n",
        "    model.train()\n",
        "    for i, (X, y) in enumerate(data_loader):\n",
        "        # clear parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        output = model(X)\n",
        "        loss = criterion(output, y.reshape(-1, 1))\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "def _evaluate_epoch(tr_loader, va_loader, model, criterion):\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        # Evaluate on train\n",
        "        y_true, y_score = [], []\n",
        "        running_loss = []\n",
        "        for X, y in tr_loader:\n",
        "            output = model(X)\n",
        "            y_true.append(y.numpy())\n",
        "            y_score.append(output)\n",
        "            # y_pred = (output > 0.5).float().reshape(1, -1)[0]\n",
        "            # y_pred = y_pred.tolist()\n",
        "            # print(y_true)\n",
        "            # print(y_pred)\n",
        "            running_loss.append(criterion(output, y.reshape(-1, 1)).item())\n",
        "\n",
        "        y_true, y_score = np.concatenate(y_true), np.concatenate(y_score)\n",
        "        train_loss = np.mean(running_loss)\n",
        "        y_pred = (y_score > 0.5).astype(int).reshape(1, -1)[0]\n",
        "        # print(y_pred)\n",
        "        # print(y_true)\n",
        "        train_acc = np.sum(y_true == y_pred) / len(y_true)\n",
        "        # train_score = metrics.roc_auc_score(y_true, y_score)\n",
        "        print('tr loss', train_loss)\n",
        "        print('tr acc', train_acc)\n",
        "\n",
        "        # Evaluate on validation\n",
        "        y_true, y_score, y_pred = [], [], []\n",
        "        running_loss = []\n",
        "        for X, y in va_loader:\n",
        "            output = model(X)\n",
        "            y_true.append(y.numpy())\n",
        "            y_score.append(output)\n",
        "            running_loss.append(criterion(output, y.reshape(-1, 1)).item())\n",
        "\n",
        "        y_true, y_score = np.concatenate(y_true), np.concatenate(y_score)\n",
        "        val_loss = np.mean(running_loss)\n",
        "        # val_score = metrics.roc_auc_score(y_true, y_score)\n",
        "        print('va loss', val_loss)\n",
        "        y_pred = (y_score > 0.5).astype(int).reshape(1, -1)[0]\n",
        "        # print(y_pred)\n",
        "        # print(y_true)\n",
        "        test_acc = np.sum(y_true == y_pred) / len(y_true)\n",
        "        # train_score = metrics.roc_auc_score(y_true, y_score)\n",
        "        print('va acc', test_acc)\n",
        "    return train_loss, val_loss, train_acc, test_acc\n",
        "\n",
        "def save_checkpoint(model, epoch, checkpoint_dir):\n",
        "    state = {\n",
        "        'epoch': epoch,\n",
        "        'state_dict': model.state_dict(),\n",
        "    }\n",
        "\n",
        "    filename = os.path.join(checkpoint_dir, 'epoch={}.checkpoint.pth.tar'.format(epoch))\n",
        "    torch.save(state, filename)"
      ],
      "metadata": {
        "id": "VYpqpIPI7LaT"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os, time, random\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import metrics\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "device = torch.device(\"cuda:0\")\n",
        "n_epochs = 20\n",
        "learning_rate = 1e-4\n",
        "\n",
        "model = Model(384, 1)\n",
        "# print('Number of float-valued parameters:', count_parameters(model))\n",
        "\n",
        "# criterion = torch.nn.MSELoss()\n",
        "criterion = torch.nn.BCELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "outputs = []\n",
        "\n",
        "# model = model.cuda()\n",
        "\n",
        "print('Epoch', 0)\n",
        "out = _evaluate_epoch(train_loader, test_loader, model, criterion)\n",
        "outputs.append(out)\n",
        "\n",
        "for epoch in range(0, n_epochs):\n",
        "    print('Epoch', epoch+1)\n",
        "    # Train model\n",
        "    _train_epoch(train_loader, model, criterion, optimizer)\n",
        "\n",
        "    # Evaluate model\n",
        "    out = _evaluate_epoch(train_loader, test_loader, model, criterion)\n",
        "    outputs.append(out)\n",
        "    \n",
        "    # Save model parameters\n",
        "    # save_checkpoint(model, epoch+1, 'checkpoint/')\n",
        "\n",
        "train_losses, val_losses, train_acces, val_acces = zip(*outputs)\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n",
        "ax[0].plot(range(n_epochs + 1), train_acces, '--o', label='Train')\n",
        "ax[0].plot(range(n_epochs + 1), val_acces, '--o', label='Validation')\n",
        "ax[0].set_xlabel('epoch')\n",
        "ax[0].set_ylabel('Accuracy')\n",
        "ax[0].legend()\n",
        "\n",
        "ax[1].plot(range(n_epochs + 1), train_losses, '--o', label='Train')\n",
        "ax[1].plot(range(n_epochs + 1), val_losses, '--o', label='Validation')\n",
        "ax[1].set_xlabel('epoch')\n",
        "ax[1].set_ylabel('Loss (binary cross entropy)')\n",
        "ax[1].legend()\n",
        "\n",
        "plt.savefig('train_plot.png', dpi=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7jek8OcGvLr5",
        "outputId": "bbb1c7d2-9867-4f7c-d755-47fbccab4953"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "tr loss 0.69514548283043\n",
            "tr acc 0.4993119792064827\n",
            "va loss 0.6946928217573073\n",
            "va acc 0.5027518728023238\n",
            "Epoch 1\n",
            "tr loss 0.5950114098912638\n",
            "tr acc 0.6946716611879826\n",
            "va loss 0.5912156463826744\n",
            "va acc 0.7001987463690568\n",
            "Epoch 2\n",
            "tr loss 0.5726040296420492\n",
            "tr acc 0.7044377341181867\n",
            "va loss 0.5714963911806495\n",
            "va acc 0.705090964684299\n",
            "Epoch 3\n",
            "tr loss 0.5607684067379874\n",
            "tr acc 0.7105725861937161\n",
            "va loss 0.5640125072118148\n",
            "va acc 0.7096009784436631\n",
            "Epoch 4\n",
            "tr loss 0.5514726410722383\n",
            "tr acc 0.7160194174757282\n",
            "va loss 0.5580062608695725\n",
            "va acc 0.710059623910717\n",
            "Epoch 5\n",
            "tr loss 0.5443935758618679\n",
            "tr acc 0.7203959941900466\n",
            "va loss 0.55435378516762\n",
            "va acc 0.7119706466901086\n",
            "Epoch 6\n",
            "tr loss 0.5395861035599977\n",
            "tr acc 0.7241418851769742\n",
            "va loss 0.5535282391367606\n",
            "va acc 0.7129643785353922\n",
            "Epoch 7\n",
            "tr loss 0.5326464059591877\n",
            "tr acc 0.7299136151670361\n",
            "va loss 0.5512557142567867\n",
            "va acc 0.7121235285124599\n",
            "Epoch 8\n",
            "tr loss 0.5279001840606586\n",
            "tr acc 0.7333919425120403\n",
            "va loss 0.5488837552302092\n",
            "va acc 0.7148754013147837\n",
            "Epoch 9\n",
            "tr loss 0.5215630683601631\n",
            "tr acc 0.7373098386973473\n",
            "va loss 0.5479498711025831\n",
            "va acc 0.7163277786271213\n",
            "Epoch 10\n",
            "tr loss 0.516907479914593\n",
            "tr acc 0.7426802232245241\n",
            "va loss 0.5481320399682499\n",
            "va acc 0.7170157468277022\n",
            "Epoch 11\n",
            "tr loss 0.5104806504068864\n",
            "tr acc 0.7469421298065897\n",
            "va loss 0.5486494704936314\n",
            "va acc 0.7175508332059318\n",
            "Epoch 12\n",
            "tr loss 0.5052397949188437\n",
            "tr acc 0.7513760415870346\n",
            "va loss 0.5472496154817562\n",
            "va acc 0.7190796514294451\n",
            "Epoch 13\n",
            "tr loss 0.5003278462402978\n",
            "tr acc 0.7540516780062686\n",
            "va loss 0.5480097043282778\n",
            "va acc 0.7182388014065128\n",
            "Epoch 14\n",
            "tr loss 0.49444035693893806\n",
            "tr acc 0.7596896261753688\n",
            "va loss 0.5485768645133787\n",
            "va acc 0.720684910564134\n",
            "Epoch 15\n",
            "tr loss 0.4890938145461467\n",
            "tr acc 0.7637030808042199\n",
            "va loss 0.5486685280660981\n",
            "va acc 0.7173215104724048\n",
            "Epoch 16\n",
            "tr loss 0.48314225914134373\n",
            "tr acc 0.7662449354024922\n",
            "va loss 0.5489445270843876\n",
            "va acc 0.7167864240941753\n",
            "Epoch 17\n",
            "tr loss 0.47729767833478876\n",
            "tr acc 0.7729913615167037\n",
            "va loss 0.5490314413621588\n",
            "va acc 0.7159455740712429\n",
            "Epoch 18\n",
            "tr loss 0.47250679783832766\n",
            "tr acc 0.7755141044262671\n",
            "va loss 0.5523538661813273\n",
            "va acc 0.7168628650053509\n",
            "Epoch 19\n",
            "tr loss 0.46692109567028967\n",
            "tr acc 0.7807507071324822\n",
            "va loss 0.5543164104512595\n",
            "va acc 0.7125821739795138\n",
            "Epoch 20\n",
            "tr loss 0.46149870563835854\n",
            "tr acc 0.7860637565935326\n",
            "va loss 0.5548054295257457\n",
            "va acc 0.714340314936554\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IePFQYQUzW8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XW5YiUutNpg-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}